library(tidyverse)
library(tidyr)
library(dplyr)
library(readr)
library(purrr)
library(ggplot2)
library(e1071)
library(emmeans)
library(lme4)
library(lmerTest)
library(patchwork)
library(brms)
library(bayesplot)
library(car)
library(effects)
library(glue)
library(scales)
library(data.table)
library(effects)
# Disable emmeans computation limits for large models
emmeans::emm_options(
lmerTest.limit = Inf,
pbkrtest.limit = Inf
)Data Analysis complete
#A: Behvioural analysis
# --- Load E-Prime file ---
RT <- read.csv("/Users/can/Documents/Uni/Thesis/Data/E-Prime/all_excluded2.csv", sep = ";")
# --- Filter, convert, exclude S12 ---
RTR <- RT %>%
dplyr::filter(procedure == "responsprocedure") %>%
dplyr::mutate(
feedback.ACC = as.numeric(feedback.ACC),
feedback.RT = as.numeric(feedback.RT)
) %>%
dplyr::filter(subject != 12)
# --- Trial numbering within subject × session ---
RTR <- RTR %>%
dplyr::group_by(subject, session) %>%
dplyr::mutate(trial = cumsum(sub.trial.number == 1)) %>%
dplyr::ungroup()
# --- Compute per-trial accuracy & mean RT ---
df <- RTR %>%
dplyr::group_by(subject, session, trial) %>%
dplyr::mutate(
trial.acc = sum(feedback.ACC, na.rm = TRUE) / dplyr::n(),
trial.RT = mean(feedback.RT, na.rm = TRUE)
) %>%
dplyr::ungroup()
# --- keep all trials once, tag correctness ---
df_acc_base <- df %>%
dplyr::mutate(
subject = as.factor(subject),
sub.trial.number = as.factor(sub.trial.number),
# keep session numeric here; convert to labels locally in plots when needed
is_correct = trial.acc == 1,
trial_acc_cat = factor(dplyr::if_else(trial.acc == 1, "correct", "wrong"),
levels = c("correct", "wrong"))
)
# helper to label sessions as "Block 1".."Block 5" on demand
as_block_factor <- function(x) factor(x, levels = 1:5, labels = paste("Block", 1:5))safe_fit <- function(formula, data) {
# Try lmer first; fall back to lm if random effects not identifiable
out <- tryCatch(lme4::lmer(formula, data = data), error = function(e) NULL)
if (is.null(out)) {
message("⚠️ Falling back to lm (random effect not identifiable).")
out <- stats::lm(update(formula, . ~ . - (1 | subject)), data = data)
}
out
}#A1 RT per block
# ===== RT per block (correct vs wrong) — one-stop function =====
# - Fits RT ~ trial_acc_cat * Block + (1|subject)
# Helper (safe lmer with fallback)
if (!exists("safe_fit")) {
safe_fit <- function(formula, data) {
m <- tryCatch(lme4::lmer(formula, data = data), error = function(e) NULL)
if (is.null(m)) {
message("⚠️ Falling back to lm (random effect not identifiable).")
m <- stats::lm(update(formula, . ~ . - (1 | subject)), data = data)
}
m
}
}
# Helper (block labels)
if (!exists("as_block_factor")) {
as_block_factor <- function(x) factor(x, levels = 1:5, labels = paste("Block", 1:5))
}
rt_per_block_with_correctness <- function(df_acc_all, make_plot = TRUE) {
# 1) Build trial-level dataset
df_trial_all <- df_acc_all %>%
dplyr::distinct(subject, session, trial, .keep_all = TRUE) %>%
dplyr::transmute(subject, session_f = as_block_factor(session),
rt = trial.RT, trial_acc_cat)
# 2) Fit combined model with correctness factor
cat("\n\n========== RT per block — COMBINED (correctness as factor) ==========\n")
mdl_all <- safe_fit(rt ~ trial_acc_cat * session_f + (1 | subject), data = df_trial_all)
cat("\nModel summary:\n"); print(summary(mdl_all))
cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl_all, type = 2))
# 3) EMMs by block and correctness
emms_all <- emmeans::emmeans(mdl_all, ~ session_f | trial_acc_cat)
cat("\nEMMs (session | correctness):\n"); print(summary(emms_all))
cat("\nPairwise session (within correctness):\n")
print(emmeans::emmeans(mdl_all, pairwise ~ session_f | trial_acc_cat)$contrasts)
# 4) % faster (correct vs wrong) — overall and per block
overall_subtitle <- NULL
em_overall <- tryCatch(emmeans::emmeans(mdl_all, ~ trial_acc_cat) %>% as.data.frame(),
error = function(e) NULL)
if (!is.null(em_overall) && all(c("correct","wrong") %in% em_overall$trial_acc_cat)) {
mean_correct <- em_overall$emmean[em_overall$trial_acc_cat == "correct"]
mean_wrong <- em_overall$emmean[em_overall$trial_acc_cat == "wrong"]
pct_faster_overall <- 100 * (1 - mean_correct / mean_wrong)
cat(sprintf("\nOverall %% faster (correct vs wrong): %.2f%%\n", pct_faster_overall))
overall_subtitle <- sprintf("Overall %% faster (correct vs wrong): %.2f%%", pct_faster_overall)
} else {
cat("\nOverall %% faster: not available (only one correctness level present).\n")
}
em_per_block <- emmeans::emmeans(mdl_all, ~ trial_acc_cat | session_f) %>% as.data.frame()
if (all(c("correct","wrong") %in% em_per_block$trial_acc_cat)) {
pct_tbl <- em_per_block %>%
dplyr::select(session_f, trial_acc_cat, emmean) %>%
tidyr::pivot_wider(names_from = trial_acc_cat, values_from = emmean) %>%
dplyr::mutate(`% faster (correct vs wrong)` = 100 * (1 - correct / wrong)) %>%
dplyr::select(session_f, `% faster (correct vs wrong)`)
cat("\nPer-block %% faster (correct vs wrong):\n"); print(pct_tbl)
} else {
cat("\nPer-block %% faster: not available (only one correctness level present within blocks).\n")
}
# 5) Fitted EMM plot (always printed)
p <- as.data.frame(emms_all) %>%
ggplot2::ggplot(ggplot2::aes(x = session_f, y = emmean,
group = trial_acc_cat, color = trial_acc_cat)) +
ggplot2::geom_point() +
ggplot2::geom_errorbar(ggplot2::aes(ymin = emmean - SE, ymax = emmean + SE), width = .15) +
ggplot2::labs(title = "RT per block — fitted EMMs (correct vs wrong)",
subtitle = overall_subtitle,
x = "Block", y = "Estimated RT", color = "Correctness") +
ggplot2::theme_minimal(base_size = 12) +
ggplot2::theme(panel.grid = ggplot2::element_blank())
if (make_plot) print(p)
# 6) Split (correct-only / wrong-only) models — diagnostics in console (no extra plots)
for (lab in c("correct", "wrong")) {
df_trial <- df_trial_all %>% dplyr::filter(trial_acc_cat == lab)
if (nrow(df_trial) == 0) { cat("\n⚠️ No data for", lab, "\n"); next }
cat("\n\n========== RT per block —", toupper(lab), "==========\n")
mdl <- safe_fit(rt ~ session_f + (1 | subject), data = df_trial)
cat("\nModel summary:\n"); print(summary(mdl))
cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl, type = 2))
emms <- emmeans::emmeans(mdl, ~ session_f)
cat("\nEMMs (session):\n"); print(summary(emms))
cat("\nPairwise (Tukey):\n"); print(emmeans::emmeans(mdl, pairwise ~ session_f)$contrasts)
}
invisible(list(model = mdl_all, emms = emms_all, plot = p))
}
rt_per_block_with_correctness(df_acc_base)
========== RT per block — COMBINED (correctness as factor) ==========
Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ trial_acc_cat * session_f + (1 | subject)
Data: data
REML criterion at convergence: 59183.5
Scaled residuals:
Min 1Q Median 3Q Max
-4.5546 -0.4805 -0.0988 0.3305 17.6628
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 44156 210.1
Residual 51999 228.0
Number of obs: 4320, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 498.49 50.27 9.916
trial_acc_catwrong 362.46 20.05 18.077
session_fBlock 2 -16.31 12.95 -1.260
session_fBlock 3 21.83 13.64 1.600
session_fBlock 4 -36.97 12.41 -2.978
session_fBlock 5 68.17 13.71 4.972
trial_acc_catwrong:session_fBlock 2 -145.39 25.90 -5.613
trial_acc_catwrong:session_fBlock 3 -230.63 25.52 -9.037
trial_acc_catwrong:session_fBlock 4 -210.33 27.09 -7.765
trial_acc_catwrong:session_fBlock 5 -150.08 25.56 -5.872
Correlation of Fixed Effects:
(Intr) trl_c_ sss_B2 sss_B3 sss_B4 sss_B5 t__:_2 t__:_3 t__:_4
trl_cc_ctwr -0.074
sssn_fBlck2 -0.114 0.288
sssn_fBlck3 -0.108 0.271 0.424
sssn_fBlck4 -0.119 0.300 0.462 0.440
sssn_fBlck5 -0.108 0.274 0.419 0.397 0.436
trl_cc_:_B2 0.057 -0.773 -0.506 -0.217 -0.234 -0.213
trl_cc_:_B3 0.058 -0.782 -0.232 -0.541 -0.239 -0.215 0.614
trl_cc_:_B4 0.055 -0.739 -0.215 -0.207 -0.464 -0.202 0.575 0.586
trl_cc_:_B5 0.058 -0.786 -0.227 -0.214 -0.236 -0.545 0.609 0.616 0.580
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
trial_acc_cat 715.960 1 < 2.2e-16 ***
session_f 141.487 4 < 2.2e-16 ***
trial_acc_cat:session_f 92.146 4 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs (session | correctness):
trial_acc_cat = correct:
session_f emmean SE df lower.CL upper.CL
Block 1 498 50.3 17.9 393 604
Block 2 482 50.5 18.1 376 588
Block 3 520 50.6 18.4 414 627
Block 4 462 50.3 17.9 356 567
Block 5 567 50.7 18.4 460 673
trial_acc_cat = wrong:
session_f emmean SE df lower.CL upper.CL
Block 1 861 52.7 21.6 751 970
Block 2 699 51.3 19.3 592 806
Block 3 652 50.9 18.8 546 759
Block 4 614 52.0 20.4 505 722
Block 5 779 50.8 18.7 672 886
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise session (within correctness):
trial_acc_cat = correct:
contrast estimate SE df t.ratio p.value
Block 1 - Block 2 16.3 12.9 4293 1.260 0.7159
Block 1 - Block 3 -21.8 13.6 4293 -1.600 0.4971
Block 1 - Block 4 37.0 12.4 4293 2.978 0.0243
Block 1 - Block 5 -68.2 13.7 4293 -4.972 <.0001
Block 2 - Block 3 -38.1 14.3 4293 -2.672 0.0584
Block 2 - Block 4 20.7 13.2 4293 1.570 0.5172
Block 2 - Block 5 -84.5 14.4 4293 -5.872 <.0001
Block 3 - Block 4 58.8 13.8 4293 4.254 0.0002
Block 3 - Block 5 -46.3 15.0 4294 -3.084 0.0175
Block 4 - Block 5 -105.1 13.9 4293 -7.552 <.0001
trial_acc_cat = wrong:
contrast estimate SE df t.ratio p.value
Block 1 - Block 2 161.7 22.3 4293 7.236 <.0001
Block 1 - Block 3 208.8 21.5 4293 9.731 <.0001
Block 1 - Block 4 247.3 24.0 4293 10.305 <.0001
Block 1 - Block 5 81.9 21.4 4294 3.822 0.0013
Block 2 - Block 3 47.1 17.5 4293 2.697 0.0545
Block 2 - Block 4 85.6 20.5 4293 4.169 0.0003
Block 2 - Block 5 -79.8 17.5 4294 -4.568 <.0001
Block 3 - Block 4 38.5 19.5 4293 1.971 0.2803
Block 3 - Block 5 -126.9 16.4 4294 -7.758 <.0001
Block 4 - Block 5 -165.4 19.6 4294 -8.454 <.0001
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 5 estimates NOTE: Results may be misleading due to involvement in interactions
Overall % faster (correct vs wrong): 29.84%
Per-block %% faster (correct vs wrong):
# A tibble: 5 × 2
session_f `% faster (correct vs wrong)`
<fct> <dbl>
1 Block 1 42.1
2 Block 2 31.0
3 Block 3 20.2
4 Block 4 24.8
5 Block 5 27.3
========== RT per block — CORRECT ==========
Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ session_f + (1 | subject)
Data: data
REML criterion at convergence: 37124.9
Scaled residuals:
Min 1Q Median 3Q Max
-2.8886 -0.5512 -0.1382 0.3723 10.4716
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 37404 193.4
Residual 25812 160.7
Number of obs: 2852, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 499.092 45.988 10.853
session_fBlock 2 -18.479 9.135 -2.023
session_fBlock 3 17.916 9.637 1.859
session_fBlock 4 -38.771 8.756 -4.428
session_fBlock 5 64.359 9.691 6.641
Correlation of Fixed Effects:
(Intr) sss_B2 sss_B3 sss_B4
sssn_fBlck2 -0.088
sssn_fBlck3 -0.083 0.425
sssn_fBlck4 -0.092 0.462 0.441
sssn_fBlck5 -0.083 0.418 0.395 0.435
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
session_f 123.77 4 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs (session):
session_f emmean SE df lower.CL upper.CL
Block 1 499 46.0 17.4 402 596
Block 2 481 46.1 17.6 384 578
Block 3 517 46.2 17.8 420 614
Block 4 460 46.0 17.5 363 557
Block 5 563 46.2 17.8 466 661
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise (Tukey):
contrast estimate SE df t.ratio p.value
Block 1 - Block 2 18.5 9.14 2830 2.023 0.2552
Block 1 - Block 3 -17.9 9.64 2830 -1.859 0.3399
Block 1 - Block 4 38.8 8.76 2830 4.428 0.0001
Block 1 - Block 5 -64.4 9.69 2830 -6.641 <.0001
Block 2 - Block 3 -36.4 10.10 2830 -3.613 0.0028
Block 2 - Block 4 20.3 9.28 2830 2.186 0.1854
Block 2 - Block 5 -82.8 10.20 2830 -8.146 <.0001
Block 3 - Block 4 56.7 9.76 2830 5.811 <.0001
Block 3 - Block 5 -46.4 10.60 2831 -4.368 0.0001
Block 4 - Block 5 -103.1 9.84 2830 -10.480 <.0001
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 5 estimates
========== RT per block — WRONG ==========
Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ session_f + (1 | subject)
Data: data
REML criterion at convergence: 21091.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.3493 -0.4884 -0.0940 0.3321 12.5268
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 58984 242.9
Residual 100214 316.6
Number of obs: 1468, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 862.51 62.58 13.783
session_fBlock 2 -165.20 31.34 -5.271
session_fBlock 3 -214.20 30.07 -7.124
session_fBlock 4 -252.45 33.64 -7.505
session_fBlock 5 -88.35 30.07 -2.938
Correlation of Fixed Effects:
(Intr) sss_B2 sss_B3 sss_B4
sssn_fBlck2 -0.325
sssn_fBlck3 -0.337 0.687
sssn_fBlck4 -0.302 0.611 0.638
sssn_fBlck5 -0.340 0.682 0.706 0.631
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
session_f 86.063 4 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs (session):
session_f emmean SE df lower.CL upper.CL
Block 1 863 62.6 23.1 733 992
Block 2 697 60.2 19.8 572 823
Block 3 648 59.6 19.1 524 773
Block 4 610 61.4 21.5 482 738
Block 5 774 59.5 18.9 650 899
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise (Tukey):
contrast estimate SE df t.ratio p.value
Block 1 - Block 2 165.2 31.3 1448 5.270 <.0001
Block 1 - Block 3 214.2 30.1 1448 7.123 <.0001
Block 1 - Block 4 252.5 33.6 1448 7.504 <.0001
Block 1 - Block 5 88.3 30.1 1448 2.938 0.0277
Block 2 - Block 3 49.0 24.3 1447 2.015 0.2593
Block 2 - Block 4 87.3 28.7 1448 3.036 0.0206
Block 2 - Block 5 -76.8 24.5 1448 -3.134 0.0151
Block 3 - Block 4 38.3 27.3 1447 1.402 0.6263
Block 3 - Block 5 -125.8 23.1 1449 -5.454 <.0001
Block 4 - Block 5 -164.1 27.5 1449 -5.957 <.0001
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 5 estimates rt_block_full_report <- function(df, file = NULL, make_plot = TRUE, open = TRUE, width = 200) {
if (is.null(file)) {
file <- file.path(getwd(), sprintf("rt_block_output_%s.txt",
format(Sys.time(), "%Y-%m-%d_%H-%M-%S")))
}
old_width <- getOption("width")
options(width = width) # wider lines so nothing wraps oddly
con <- file(file, open = "wt")
# capture both stdout and messages
sink(con); sink(con, type = "message")
# ALWAYS restore sinks & width even if an error happens
on.exit({
try(sink(NULL), silent = TRUE)
try(sink(NULL, type = "message"), silent = TRUE)
try(close(con), silent = TRUE)
options(width = old_width)
}, add = TRUE)
# >>> run your existing function (prints go to the file)
res <- rt_per_block_with_correctness(df, make_plot = make_plot)
# restore and open
sink(NULL); sink(NULL, type = "message"); close(con)
options(width = old_width)
if (open) file.show(file)
invisible(list(result = res, path = file))
}
# ===== RUN (this will create & open a scrollable .txt with the full output) =====
rt_block_full_report(df_acc_base, make_plot = TRUE)NOTE: Results may be misleading due to involvement in interactions
#A2.1 Correct-(or Wrong-)trials per block (counts, model & plot)
# ===== Combined plot: RT EMMs + % correct on x-axis =====
if (!exists("safe_fit")) {
safe_fit <- function(formula, data) {
m <- tryCatch(lme4::lmer(formula, data = data), error = function(e) NULL)
if (is.null(m)) {
message("⚠️ Falling back to lm (random effect not identifiable).")
m <- stats::lm(update(formula, . ~ . - (1 | subject)), data = data)
}
m
}
}
if (!exists("as_block_factor")) {
as_block_factor <- function(x) factor(x, levels = 1:5, labels = paste("Block", 1:5))
}
plot_rt_with_percent_correct <- function(df_acc_all, total_trials_per_block = 48, show_plot = TRUE, digits = 0) {
# ---------- RT model & EMMs (correct vs wrong by block) ----------
df_trial_all <- df_acc_all %>%
dplyr::distinct(subject, session, trial, .keep_all = TRUE) %>%
dplyr::transmute(subject,
session_f = as_block_factor(session),
rt = trial.RT,
trial_acc_cat)
mdl_rt <- safe_fit(rt ~ trial_acc_cat * session_f + (1 | subject), data = df_trial_all)
em_rt <- emmeans::emmeans(mdl_rt, ~ session_f | trial_acc_cat)
df_rt <- as.data.frame(em_rt)
# Overall % faster (subtitle)
overall_subtitle <- NULL
em_overall <- tryCatch(emmeans::emmeans(mdl_rt, ~ trial_acc_cat) %>% as.data.frame(), error = function(e) NULL)
if (!is.null(em_overall) && all(c("correct","wrong") %in% em_overall$trial_acc_cat)) {
mean_correct <- em_overall$emmean[em_overall$trial_acc_cat == "correct"]
mean_wrong <- em_overall$emmean[em_overall$trial_acc_cat == "wrong"]
pct_faster_overall <- 100 * (1 - mean_correct / mean_wrong)
overall_subtitle <- sprintf("Overall %% faster (correct vs wrong): %.2f%%", pct_faster_overall)
}
# ---------- % correct per block from GLMM (for x-axis labels) ----------
correct_counts <- df_acc_all %>%
dplyr::filter(trial_acc_cat == "correct") %>%
dplyr::distinct(subject, session, trial) %>%
dplyr::count(subject, session, name = "correct_trials") %>%
dplyr::mutate(session_f = as_block_factor(session),
fail_trials = total_trials_per_block - correct_trials)
label_map <- setNames(levels(df_rt$session_f), levels(df_rt$session_f)) # default
base_names <- c("Block 1" = "6 Steps",
"Block 2" = "12 Steps",
"Block 3" = "18 Steps",
"Block 4" = "Familiar",
"Block 5" = "Unfamiliar")
label_map <- base_names[names(label_map)]
if (nrow(correct_counts) > 0) {
mdl_pc <- tryCatch(
lme4::glmer(cbind(correct_trials, fail_trials) ~ session_f + (1 | subject),
data = correct_counts, family = binomial),
error = function(e) NULL
)
if (!is.null(mdl_pc)) {
em_pc <- emmeans::emmeans(mdl_pc, ~ session_f, type = "response")
df_pc <- as.data.frame(em_pc)
val_col <- if ("prob" %in% names(df_pc)) "prob" else if ("response" %in% names(df_pc)) "response" else "emmean"
df_pc <- df_pc %>% dplyr::transmute(session_f, pct = round(.data[[val_col]] * 100, digits))
} else {
# fallback: descriptive mean % correct out of 48
df_pc <- correct_counts %>%
dplyr::mutate(pct = 100 * correct_trials / total_trials_per_block) %>%
dplyr::group_by(session_f) %>%
dplyr::summarise(pct = round(mean(pct, na.rm = TRUE), digits), .groups = "drop")
}
add_map <- setNames(paste0(label_map[df_pc$session_f], " (", df_pc$pct, "%)"),
df_pc$session_f)
label_map[names(add_map)] <- add_map
}
# ---------- Combined plot (clean) ----------
p <- ggplot2::ggplot(df_rt, ggplot2::aes(x = session_f, y = emmean,
group = trial_acc_cat, color = trial_acc_cat)) +
# dotted divider between Block 3 and Block 4 (i.e., after the 3rd discrete position)
ggplot2::geom_vline(xintercept = 3.5, linetype = "dotted") +
ggplot2::geom_point() +
ggplot2::geom_errorbar(ggplot2::aes(ymin = emmean - SE, ymax = emmean + SE), width = 0.15) +
ggplot2::scale_x_discrete(labels = label_map) +
ggplot2::labs(title = "RT across difficulty levels",
subtitle = overall_subtitle,
x = "Difficulty",
y = "Estimated RT (in ms)",
color = "Correctness") +
ggplot2::theme_minimal(base_size = 12) +
ggplot2::theme(panel.grid = ggplot2::element_blank())
if (show_plot) print(p)
invisible(list(m_rt = mdl_rt, em_rt = em_rt,
m_pc = if (exists("mdl_pc")) mdl_pc else NULL,
xaxis_labels = label_map, plot = p))
}
# RUN
plot_rt_with_percent_correct(df_acc_base)NOTE: Results may be misleading due to involvement in interactions
# === Training-only plot: Blocks 1–3 ===
# RT EMMs (correct vs wrong) + % correct on x-axis
if (!exists("safe_fit")) {
safe_fit <- function(formula, data) {
m <- tryCatch(lme4::lmer(formula, data = data), error = function(e) NULL)
if (is.null(m)) {
message("⚠️ Falling back to lm (random effect not identifiable).")
m <- stats::lm(update(formula, . ~ . - (1 | subject)), data = data)
}
m
}
}
if (!exists("as_block_factor")) {
as_block_factor <- function(x) factor(x, levels = 1:5, labels = paste("Block", 1:5))
}
plot_rt_training_blocks <- function(df_acc_all, total_trials_per_block = 48, show_plot = TRUE, digits = 0) {
df_trial_all <- df_acc_all %>%
dplyr::distinct(subject, session, trial, .keep_all = TRUE) %>%
dplyr::transmute(subject,
session_f = as_block_factor(session),
rt = trial.RT,
trial_acc_cat) %>%
dplyr::filter(session_f %in% c("Block 1","Block 2","Block 3")) %>%
dplyr::mutate(session_f = factor(session_f, levels = c("Block 1","Block 2","Block 3")))
# Model & EMMs (training only)
mdl_rt <- safe_fit(rt ~ trial_acc_cat * session_f + (1 | subject), data = df_trial_all)
em_rt <- emmeans::emmeans(mdl_rt, ~ session_f | trial_acc_cat)
df_rt <- as.data.frame(em_rt)
# Overall % faster from training only
overall_subtitle <- NULL
em_overall <- tryCatch(emmeans::emmeans(mdl_rt, ~ trial_acc_cat) %>% as.data.frame(), error = function(e) NULL)
if (!is.null(em_overall) && all(c("correct","wrong") %in% em_overall$trial_acc_cat)) {
mc <- em_overall$emmean[em_overall$trial_acc_cat == "correct"]
mw <- em_overall$emmean[em_overall$trial_acc_cat == "wrong"]
pct_faster_overall <- 100 * (1 - mc / mw)
overall_subtitle <- sprintf("Overall %% faster (correct vs wrong): %.2f%%", pct_faster_overall)
}
# ---------- % correct per training block for x-axis labels ----------
correct_counts <- df_acc_all %>%
dplyr::filter(trial_acc_cat == "correct", session %in% 1:3) %>%
dplyr::distinct(subject, session, trial) %>%
dplyr::count(subject, session, name = "correct_trials") %>%
dplyr::mutate(session_f = factor(as_block_factor(session),
levels = c("Block 1","Block 2","Block 3")),
fail_trials = total_trials_per_block - correct_trials)
label_map <- c("Block 1" = "6 Steps",
"Block 2" = "12 Steps",
"Block 3" = "18 Steps")
if (nrow(correct_counts) > 0) {
mdl_pc <- tryCatch(
lme4::glmer(cbind(correct_trials, fail_trials) ~ session_f + (1 | subject),
data = correct_counts, family = binomial),
error = function(e) NULL
)
if (!is.null(mdl_pc)) {
em_pc <- emmeans::emmeans(mdl_pc, ~ session_f, type = "response")
df_pc <- as.data.frame(em_pc)
val_col <- if ("prob" %in% names(df_pc)) "prob" else if ("response" %in% names(df_pc)) "response" else "emmean"
df_pc <- df_pc %>% dplyr::transmute(session_f, pct = round(.data[[val_col]] * 100, digits))
} else {
df_pc <- correct_counts %>%
dplyr::mutate(pct = 100 * correct_trials / total_trials_per_block) %>%
dplyr::group_by(session_f) %>%
dplyr::summarise(pct = round(mean(pct, na.rm = TRUE), digits), .groups = "drop")
}
label_map[df_pc$session_f] <- paste0(label_map[df_pc$session_f], " (", df_pc$pct, "%)")
} else {
label_map <- paste0(label_map, " (n/a)")
}
pd <- ggplot2::position_dodge(width = 0.35)
p <- ggplot2::ggplot(df_rt, ggplot2::aes(x = session_f, y = emmean,
color = trial_acc_cat, group = trial_acc_cat)) +
ggplot2::geom_point(position = pd) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = emmean - SE, ymax = emmean + SE),
width = 0.15, position = pd) +
ggplot2::scale_x_discrete(labels = label_map) +
ggplot2::labs(title = "RT across difficulty levels",
subtitle = overall_subtitle,
x = "difficulty",
y = "Estimated RT (in ms)",
color = "Correctness") +
ggplot2::theme_minimal(base_size = 12) +
ggplot2::theme(panel.grid = ggplot2::element_blank())
if (show_plot) print(p)
invisible(list(m_rt = mdl_rt, em_rt = em_rt,
xaxis_labels = label_map, plot = p))
}
# RUN (training-only figure)
plot_rt_training_blocks(df_acc_base)NOTE: Results may be misleading due to involvement in interactions
# === Training-only plots: Blocks 1–3 ===
# 1) Correct vs wrong (dodged points) with overall % faster computed on Blocks 1–3
# 2) Correct-only plot (EMMs by block)
# Needs: dplyr, tidyr, ggplot2, lme4, emmeans, car
if (!exists("safe_fit")) {
safe_fit <- function(formula, data) {
m <- tryCatch(lme4::lmer(formula, data = data), error = function(e) NULL)
if (is.null(m)) {
message("⚠️ Falling back to lm (random effect not identifiable).")
m <- stats::lm(update(formula, . ~ . - (1 | subject)), data = data)
}
m
}
}
if (!exists("as_block_factor")) {
as_block_factor <- function(x) factor(x, levels = 1:5, labels = paste("Block", 1:5))
}
plot_rt_training_blocks <- function(df_acc_all, total_trials_per_block = 48, show_plots = TRUE, digits = 0) {
df_trial_all <- df_acc_all %>%
dplyr::distinct(subject, session, trial, .keep_all = TRUE) %>%
dplyr::transmute(subject,
session_f = as_block_factor(session),
rt = trial.RT,
trial_acc_cat) %>%
dplyr::filter(session_f %in% c("Block 1","Block 2","Block 3")) %>%
dplyr::mutate(session_f = factor(session_f, levels = c("Block 1","Block 2","Block 3")))
# Model & EMMs (training only, correct vs wrong)
mdl_rt <- safe_fit(rt ~ trial_acc_cat * session_f + (1 | subject), data = df_trial_all)
em_rt <- emmeans::emmeans(mdl_rt, ~ session_f | trial_acc_cat)
df_rt <- as.data.frame(em_rt)
# Overall % faster from training only
overall_subtitle <- NULL
em_overall <- tryCatch(emmeans::emmeans(mdl_rt, ~ trial_acc_cat) %>% as.data.frame(), error = function(e) NULL)
if (!is.null(em_overall) && all(c("correct","wrong") %in% em_overall$trial_acc_cat)) {
mc <- em_overall$emmean[em_overall$trial_acc_cat == "correct"]
mw <- em_overall$emmean[em_overall$trial_acc_cat == "wrong"]
pct_faster_overall <- 100 * (1 - mc / mw)
overall_subtitle <- sprintf("Overall %% faster (correct vs wrong): %.2f%%", pct_faster_overall)
}
# ---------- % correct per training block for x-axis labels ----------
correct_counts <- df_acc_all %>%
dplyr::filter(trial_acc_cat == "correct", session %in% 1:3) %>%
dplyr::distinct(subject, session, trial) %>%
dplyr::count(subject, session, name = "correct_trials") %>%
dplyr::mutate(session_f = factor(as_block_factor(session),
levels = c("Block 1","Block 2","Block 3")),
fail_trials = total_trials_per_block - correct_trials)
label_map <- c("Block 1" = "6 Steps",
"Block 2" = "12 Steps",
"Block 3" = "18 Steps")
if (nrow(correct_counts) > 0) {
mdl_pc <- tryCatch(
lme4::glmer(cbind(correct_trials, fail_trials) ~ session_f + (1 | subject),
data = correct_counts, family = binomial),
error = function(e) NULL
)
if (!is.null(mdl_pc)) {
em_pc <- emmeans::emmeans(mdl_pc, ~ session_f, type = "response")
df_pc <- as.data.frame(em_pc)
val_col <- if ("prob" %in% names(df_pc)) "prob" else if ("response" %in% names(df_pc)) "response" else "emmean"
df_pc <- df_pc %>% dplyr::transmute(session_f, pct = round(.data[[val_col]] * 100, digits))
} else {
df_pc <- correct_counts %>%
dplyr::mutate(pct = 100 * correct_trials / total_trials_per_block) %>%
dplyr::group_by(session_f) %>%
dplyr::summarise(pct = round(mean(pct, na.rm = TRUE), digits), .groups = "drop")
}
label_map[df_pc$session_f] <- paste0(label_map[df_pc$session_f], " (", df_pc$pct, "%)")
} else {
label_map <- paste0(label_map, " (n/a)")
}
# ---------- Plot 1: correct vs wrong ----------
pd <- ggplot2::position_dodge(width = 0.35)
p_both <- ggplot2::ggplot(df_rt, ggplot2::aes(x = session_f, y = emmean,
color = trial_acc_cat, group = trial_acc_cat)) +
ggplot2::geom_point(position = pd) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = emmean - SE, ymax = emmean + SE),
width = 0.15, position = pd) +
ggplot2::scale_x_discrete(labels = label_map) +
ggplot2::labs(title = "RT across difficulty levels",
subtitle = overall_subtitle,
x = "Difficulty",
y = "Estimated RT (in ms)",
color = "Correctness") +
ggplot2::theme_classic(base_size = 12) + # APA7: visible axes
ggplot2::theme(
panel.grid = ggplot2::element_blank(),
axis.line = ggplot2::element_line(),
axis.ticks = ggplot2::element_line()
)
if (show_plots) print(p_both)
# ---------- Plot 2: correct-only ----------
df_correct <- df_trial_all %>% dplyr::filter(trial_acc_cat == "correct")
p_correct <- NULL
if (nrow(df_correct) > 0) {
mdl_corr <- safe_fit(rt ~ session_f + (1 | subject), data = df_correct)
em_corr <- emmeans::emmeans(mdl_corr, ~ session_f)
df_corr <- as.data.frame(em_corr)
p_correct <- ggplot2::ggplot(df_corr, ggplot2::aes(x = session_f, y = emmean, group = 1)) +
ggplot2::geom_point() +
ggplot2::geom_errorbar(ggplot2::aes(ymin = emmean - SE, ymax = emmean + SE), width = 0.15) +
ggplot2::scale_x_discrete(labels = label_map) +
ggplot2::labs(title = "RT across difficulty levels — Correct Trials",
x = "Difficulty (correct trials)",
y = "Estimated RT (in ms)") +
ggplot2::theme_classic(base_size = 12) + # APA7: visible axes
ggplot2::theme(
panel.grid = ggplot2::element_blank(),
axis.line = ggplot2::element_line(),
axis.ticks = ggplot2::element_line()
)
if (show_plots) print(p_correct)
} else {
message("No correct trials found for Blocks 1–3.")
}
invisible(list(
m_rt = mdl_rt, em_rt = em_rt, plot_both = p_both,
plot_correct = p_correct,
xaxis_labels = label_map
))
}
# RUN (training-only figure + correct-only figure)
plot_rt_training_blocks(df_acc_base)NOTE: Results may be misleading due to involvement in interactions
# 2.1 — Statistical output
suppressPackageStartupMessages({
library(dplyr); library(tidyr); library(lme4); library(car); library(emmeans)
})
# Training blocks (1–3): trial-level data
df_train <- df_acc_base %>%
dplyr::distinct(subject, session, trial, .keep_all = TRUE) %>%
dplyr::filter(session %in% 1:3) %>%
dplyr::transmute(
subject,
session_f = factor(session, levels = 1:3, labels = paste("Block", 1:3)),
rt = trial.RT,
trial_acc_cat
)
# --- 2.1A: RT ~ correctness × block (Blocks 1–3) ---
mdl_rt_all <- tryCatch(
lme4::lmer(rt ~ trial_acc_cat * session_f + (1 | subject), data = df_train),
error = function(e) stats::lm(rt ~ trial_acc_cat * session_f, data = df_train)
)
cat("\n## 2.1A RT model (correct vs wrong × block)\n")
## 2.1A RT model (correct vs wrong × block)print(summary(mdl_rt_all))Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ trial_acc_cat * session_f + (1 | subject)
Data: df_train
REML criterion at convergence: 35859.1
Scaled residuals:
Min 1Q Median 3Q Max
-4.8327 -0.4429 -0.0856 0.2722 16.7623
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 57688 240.2
Residual 58826 242.5
Number of obs: 2592, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 497.02 57.35 8.667
trial_acc_catwrong 370.33 21.42 17.290
session_fBlock 2 -11.00 13.80 -0.797
session_fBlock 3 25.18 14.56 1.729
trial_acc_catwrong:session_fBlock 2 -164.19 27.70 -5.928
trial_acc_catwrong:session_fBlock 3 -242.64 27.31 -8.884
Correlation of Fixed Effects:
(Intr) trl_c_ sss_B2 sss_B3 t__:_2
trl_cc_ctwr -0.070
sssn_fBlck2 -0.106 0.288
sssn_fBlck3 -0.100 0.269 0.426
trl_cc_:_B2 0.054 -0.772 -0.508 -0.221
trl_cc_:_B3 0.054 -0.778 -0.235 -0.545 0.616cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl_rt_all, type = 2))
Type II Chi-square ANOVA:Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
trial_acc_cat 402.407 1 < 2.2e-16 ***
session_f 17.823 2 0.0001348 ***
trial_acc_cat:session_f 79.253 2 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1em_rt_all <- emmeans::emmeans(mdl_rt_all, ~ session_f | trial_acc_cat)
cat("\nEMMs by block within correctness:\n"); print(summary(em_rt_all))
EMMs by block within correctness:trial_acc_cat = correct:
session_f emmean SE df lower.CL upper.CL
Block 1 497 57.3 17.6 376 618
Block 2 486 57.5 17.9 365 607
Block 3 522 57.7 18.1 401 643
trial_acc_cat = wrong:
session_f emmean SE df lower.CL upper.CL
Block 1 867 59.8 20.9 743 992
Block 2 692 58.3 18.9 570 814
Block 3 650 58.0 18.4 528 771
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 cat("\nPairwise (Tukey) for blocks within correctness:\n"); print(pairs(em_rt_all, adjust = "tukey"))
Pairwise (Tukey) for blocks within correctness:trial_acc_cat = correct:
contrast estimate SE df t.ratio p.value
Block 1 - Block 2 11.0 13.8 2569 0.797 0.7047
Block 1 - Block 3 -25.2 14.6 2570 -1.729 0.1945
Block 2 - Block 3 -36.2 15.2 2569 -2.379 0.0459
trial_acc_cat = wrong:
contrast estimate SE df t.ratio p.value
Block 1 - Block 2 175.2 23.9 2570 7.343 <.0001
Block 1 - Block 3 217.5 22.9 2570 9.494 <.0001
Block 2 - Block 3 42.3 18.6 2569 2.273 0.0598
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates em_acc <- emmeans::emmeans(mdl_rt_all, ~ trial_acc_cat)NOTE: Results may be misleading due to involvement in interactionscat("\nEMMs (correct vs wrong):\n"); print(summary(em_acc))
EMMs (correct vs wrong): trial_acc_cat emmean SE df lower.CL upper.CL
correct 502 56.9 17.1 382 622
wrong 736 57.3 17.6 616 857
Results are averaged over the levels of: session_f
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 cat("\nPairwise (correct vs wrong):\n"); print(pairs(em_acc))
Pairwise (correct vs wrong): contrast estimate SE df t.ratio p.value
correct - wrong -235 11 2572 -21.274 <.0001
Results are averaged over the levels of: session_f
Degrees-of-freedom method: kenward-roger # --- 2.1B: RT ~ block (correct trials only, Blocks 1–3) ---
df_train_corr <- df_train %>% dplyr::filter(trial_acc_cat == "correct")
if (nrow(df_train_corr) > 0 && dplyr::n_distinct(df_train_corr$session_f) > 1) {
mdl_rt_corr <- tryCatch(
lme4::lmer(rt ~ session_f + (1 | subject), data = df_train_corr),
error = function(e) stats::lm(rt ~ session_f, data = df_train_corr)
)
cat("\n## 2.1B RT model (correct-only)\n")
print(summary(mdl_rt_corr))
cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl_rt_corr, type = 2))
em_rt_corr <- emmeans::emmeans(mdl_rt_corr, ~ session_f)
cat("\nEMMs by block (correct-only):\n"); print(summary(em_rt_corr))
cat("\nPairwise (Tukey) between blocks (correct-only):\n"); print(pairs(em_rt_corr, adjust = "tukey"))
} else {
cat("\n[Correct-only model skipped: insufficient data]\n")
}
## 2.1B RT model (correct-only)
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ session_f + (1 | subject)
Data: df_train_corr
REML criterion at convergence: 22560
Scaled residuals:
Min 1Q Median 3Q Max
-3.0473 -0.5630 -0.1433 0.3475 10.7922
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 51782 227.6
Residual 24952 158.0
Number of obs: 1735, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 497.596 53.968 9.220
session_fBlock 2 -13.996 9.010 -1.553
session_fBlock 3 20.260 9.531 2.126
Correlation of Fixed Effects:
(Intr) sss_B2
sssn_fBlck2 -0.073
sssn_fBlck3 -0.069 0.428
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
session_f 11.947 2 0.002546 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs by block (correct-only):
session_f emmean SE df lower.CL upper.CL
Block 1 498 54.0 17.2 384 611
Block 2 484 54.1 17.4 370 597
Block 3 518 54.1 17.5 404 632
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise (Tukey) between blocks (correct-only):
contrast estimate SE df t.ratio p.value
Block 1 - Block 2 14.0 9.01 1715 1.553 0.2664
Block 1 - Block 3 -20.3 9.53 1716 -2.126 0.0850
Block 2 - Block 3 -34.3 9.93 1715 -3.451 0.0017
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates # --- 2.1C: Accuracy per block (binomial GLMM, Blocks 1–3) ---
total_trials_per_block <- 48L
counts_train <- df_acc_base %>%
dplyr::distinct(subject, session, trial, .keep_all = TRUE) %>%
dplyr::filter(session %in% 1:3) %>%
dplyr::filter(trial_acc_cat == "correct") %>%
dplyr::count(subject, session, name = "correct_trials") %>%
dplyr::mutate(
session_f = factor(session, levels = 1:3, labels = paste("Block", 1:3)),
fail_trials = total_trials_per_block - correct_trials
)
if (nrow(counts_train) > 0) {
mdl_acc <- lme4::glmer(cbind(correct_trials, fail_trials) ~ session_f + (1 | subject),
data = counts_train, family = binomial)
cat("\n## 2.1C Accuracy model (binomial, Blocks 1–3)\n")
print(summary(mdl_acc))
cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl_acc, type = 2))
em_acc_blk <- emmeans::emmeans(mdl_acc, ~ session_f, type = "response")
cat("\nEMMs (prob correct) by block:\n"); print(summary(em_acc_blk))
cat("\nPairwise (Tukey) between blocks (prob correct):\n"); print(pairs(em_acc_blk, adjust = "tukey"))
} else {
cat("\n[Accuracy model skipped: no counts available]\n")
}
## 2.1C Accuracy model (binomial, Blocks 1–3)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: cbind(correct_trials, fail_trials) ~ session_f + (1 | subject)
Data: counts_train
AIC BIC logLik deviance df.resid
401.8 409.7 -196.9 393.8 50
Scaled residuals:
Min 1Q Median 3Q Max
-2.9722 -1.1243 0.2848 1.0188 3.4491
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.4233 0.6506
Number of obs: 54, groups: subject, 18
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 1.5991 0.1787 8.951 < 2e-16 ***
session_fBlock 2 -0.9251 0.1168 -7.924 2.31e-15 ***
session_fBlock 3 -1.3892 0.1156 -12.014 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) sss_B2
sssn_fBlck2 -0.393
sssn_fBlck3 -0.402 0.606
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: cbind(correct_trials, fail_trials)
Chisq Df Pr(>Chisq)
session_f 144.98 2 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs (prob correct) by block:
session_f prob SE df asymp.LCL asymp.UCL
Block 1 0.832 0.0250 Inf 0.777 0.875
Block 2 0.662 0.0382 Inf 0.584 0.733
Block 3 0.552 0.0419 Inf 0.470 0.632
Confidence level used: 0.95
Intervals are back-transformed from the logit scale
Pairwise (Tukey) between blocks (prob correct):
contrast odds.ratio SE df null z.ratio p.value
Block 1 / Block 2 2.52 0.294 Inf 1 7.924 <.0001
Block 1 / Block 3 4.01 0.464 Inf 1 12.014 <.0001
Block 2 / Block 3 1.59 0.164 Inf 1 4.499 <.0001
P value adjustment: tukey method for comparing a family of 3 estimates
Tests are performed on the log odds ratio scale # === NEW ANALYSIS (single model): Overall RT per block, ALL trials (Blocks 1–3) ===
# Fixed effects: rt ~ session_f
# Random effects: (1 | subject) + (1 + trial_acc_cat || subject:session_f)
# -> correctness (wrong – correct) varies randomly per participant × block
# -> no fixed correctness effect is included
suppressPackageStartupMessages({
library(dplyr); library(tidyr); library(lme4); library(car); library(emmeans); library(ggplot2)
})
# --- Data (Blocks 1–3; one row per trial) ---
df_overall_train <- df_acc_base %>%
dplyr::distinct(subject, session, trial, .keep_all = TRUE) %>%
dplyr::filter(session %in% 1:3) %>%
dplyr::transmute(
subject,
session_f = factor(session, levels = 1:3, labels = paste("Block", 1:3)),
rt = trial.RT,
trial_acc_cat = factor(trial_acc_cat, levels = c("correct","wrong"))
)
# --- Single, explicit model ---
form_model <- rt ~ session_f + (1 | subject) + (1 + trial_acc_cat | subject:session_f)
mdl_overall <- lme4::lmer(
formula = form_model,
data = df_overall_train,
REML = TRUE,
control = lme4::lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e5))
)
cat("\n=== NEW ANALYSIS: Overall RT per block (ALL trials) ===\n")
=== NEW ANALYSIS: Overall RT per block (ALL trials) ===cat("Formula:\nrt ~ session_f + (1 | subject) + (1 + trial_acc_cat | subject:session_f)\n\n")Formula:
rt ~ session_f + (1 | subject) + (1 + trial_acc_cat | subject:session_f)# (Optional) Note if the fit is singular; we still proceed because this is the chosen model
if (lme4::isSingular(mdl_overall, tol = 1e-6)) {
cat("⚠️ Note: Fit is singular (some variance components ~ 0). Proceeding with the specified model.\n\n")
}
# --- Inference on overall block means (all trials) ---
cat("Model summary:\n"); print(summary(mdl_overall))Model summary:Linear mixed model fit by REML ['lmerMod']
Formula:
rt ~ session_f + (1 | subject) + (1 + trial_acc_cat | subject:session_f)
Data: df_overall_train
Control:
lme4::lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e+05))
REML criterion at convergence: 35686.3
Scaled residuals:
Min 1Q Median 3Q Max
-5.5914 -0.3965 -0.1017 0.2285 15.5099
Random effects:
Groups Name Variance Std.Dev. Corr
subject:session_f (Intercept) 5865 76.58
trial_acc_catwrong 91590 302.64 -0.03
subject (Intercept) 48880 221.09
Residual 50080 223.79
Number of obs: 2592, groups: subject:session_f, 54; subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 506.06 55.78 9.073
session_fBlock 2 -13.14 28.51 -0.461
session_fBlock 3 23.49 29.03 0.809
Correlation of Fixed Effects:
(Intr) sss_B2
sssn_fBlck2 -0.249
sssn_fBlck3 -0.244 0.479cat("\nType II Chi-square ANOVA for session_f:\n"); print(car::Anova(mdl_overall, type = 2))
Type II Chi-square ANOVA for session_f:Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
session_f 1.5901 2 0.4516em_overall <- emmeans::emmeans(mdl_overall, ~ session_f)
cat("\nEstimated marginal means (overall RT by block):\n"); print(summary(em_overall))
Estimated marginal means (overall RT by block): session_f emmean SE df lower.CL upper.CL
Block 1 506 56.0 20.1 389 623
Block 2 493 56.2 20.4 376 610
Block 3 530 56.5 20.8 412 647
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 cat("\nPairwise (Tukey) between blocks:\n"); print(pairs(em_overall, adjust = "tukey"))
Pairwise (Tukey) between blocks: contrast estimate SE df t.ratio p.value
Block 1 - Block 2 13.1 29.3 32.1 0.448 0.8956
Block 1 - Block 3 -23.5 29.9 34.0 -0.787 0.7137
Block 2 - Block 3 -36.6 30.2 35.5 -1.213 0.4535
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates # --- Plot: Overall RT per block (all trials) ---
df_em <- as.data.frame(em_overall)
p_overall_train <- ggplot(df_em, aes(x = session_f, y = emmean, group = 1)) +
geom_point() +
geom_errorbar(aes(ymin = emmean - SE, ymax = emmean + SE), width = 0.15) +
labs(title = "Overall RT across difficulty levels (Blocks 1–3, ALL trials)",
subtitle = "No fixed correctness; subject×block random correctness slope ",
x = "Difficulty", y = "Estimated RT (ms)") +
theme_minimal(base_size = 12) +
theme(panel.grid = element_blank())
print(p_overall_train)
#A2.2 Correct Trials per block × sequence length — Test phase (Blocks 4–5)
# Test phase (Blocks 4–5)
if (!exists("safe_fit")) {
safe_fit <- function(formula, data) {
m <- tryCatch(lme4::lmer(formula, data = data), error = function(e) NULL)
if (is.null(m)) { message("⚠️ Falling back to lm."); m <- stats::lm(update(formula, . ~ . - (1 | subject)), data = data) }
m
}
}
plot_test_RT_by_length <- function(df_acc_all, total_trials_per_block = 48, digits = 0, show_plots = TRUE) {
trials_per_length <- total_trials_per_block / 3 # 48/3 = 16
df_len <- df_acc_all %>%
dplyr::group_by(subject, session, trial) %>%
dplyr::mutate(seq_length_trial = dplyr::n_distinct(sub.trial.number)) %>%
dplyr::ungroup() %>%
dplyr::filter(session %in% c(4, 5)) %>%
dplyr::mutate(
session_f = factor(session, levels = c(4, 5), labels = c("Familiar", "Unfamiliar")),
seq_length_trial = factor(seq_length_trial, levels = c(6, 12, 18))
)
if (nrow(df_len) == 0) { message("No test-phase data (Blocks 4–5)."); return(invisible(NULL)) }
# % correct per block (denom = 16 per length) -> facet strip labels
corr_len <- df_len %>%
dplyr::filter(trial_acc_cat == "correct") %>%
dplyr::distinct(subject, session_f, trial, seq_length_trial) %>%
dplyr::count(subject, session_f, seq_length_trial, name = "n_correct_len") %>%
dplyr::mutate(n_fail_len = trials_per_length - n_correct_len)
strip_labels <- setNames(levels(df_len$session_f), levels(df_len$session_f))
if (nrow(corr_len) > 0) {
mdl_pc <- tryCatch(
lme4::glmer(cbind(n_correct_len, n_fail_len) ~ session_f + (1 | subject) + (1 | seq_length_trial),
data = corr_len, family = binomial),
error = function(e) NULL
)
pc_tbl <- if (!is.null(mdl_pc)) {
em <- emmeans::emmeans(mdl_pc, ~ session_f, type = "response") %>% as.data.frame()
val <- if ("prob" %in% names(em)) "prob" else if ("response" %in% names(em)) "response" else "emmean"
dplyr::transmute(em, session_f, pct = round(.data[[val]] * 100, digits))
} else {
corr_len %>%
dplyr::mutate(pct_len = 100 * n_correct_len / trials_per_length) %>%
dplyr::group_by(session_f) %>%
dplyr::summarise(pct = round(mean(pct_len, na.rm = TRUE), digits), .groups = "drop")
}
strip_labels[pc_tbl$session_f] <- paste0(pc_tbl$session_f, " (", pc_tbl$pct, "%)")
}
strip_labeller <- ggplot2::as_labeller(strip_labels)
# helper: draw ONE dotted line exactly at the right edge of the left facet
add_separator <- function() {
ggplot2::geom_segment(
data = data.frame(session_f = "Familiar"),
ggplot2::aes(x = Inf, xend = Inf, y = -Inf, yend = Inf),
inherit.aes = FALSE, linetype = "dotted"
)
}
# ===== 1) Combined (correct vs wrong) =====
df_rt_trials <- df_len %>%
dplyr::distinct(subject, session_f, trial, seq_length_trial, .keep_all = TRUE) %>%
dplyr::transmute(subject, session_f, seq_length_trial, trial_acc_cat, rt = trial.RT)
mdl_rt <- safe_fit(rt ~ trial_acc_cat * session_f * seq_length_trial + (1 | subject), data = df_rt_trials)
em_rt <- emmeans::emmeans(mdl_rt, ~ seq_length_trial | session_f * trial_acc_cat)
df_em <- as.data.frame(em_rt)
pd <- ggplot2::position_dodge(width = 0.5)
p_combined <- ggplot2::ggplot(
df_em,
ggplot2::aes(x = seq_length_trial, y = emmean,
color = trial_acc_cat, shape = trial_acc_cat, group = trial_acc_cat)
) +
add_separator() +
ggplot2::geom_point(position = pd) +
ggplot2::geom_errorbar(ggplot2::aes(ymin = emmean - SE, ymax = emmean + SE), width = 0.2, position = pd) +
ggplot2::facet_wrap(~ session_f, nrow = 1, labeller = strip_labeller) +
ggplot2::labs(title = "RT across sequence lengths — test phase",
x = "Sequence length", y = "Estimated RT (in ms)",
color = "Correctness", shape = "Correctness") +
ggplot2::theme_classic(base_size = 12) +
ggplot2::theme(
panel.grid = ggplot2::element_blank(),
axis.line = ggplot2::element_line(),
axis.ticks = ggplot2::element_line()
)
if (show_plots) print(p_combined)
# ===== 2) Correct-only =====
p_correct <- NULL
df_rt_corr <- df_rt_trials %>% dplyr::filter(trial_acc_cat == "correct")
if (nrow(df_rt_corr) > 0) {
mdl_rt_corr <- safe_fit(rt ~ session_f * seq_length_trial + (1 | subject), data = df_rt_corr)
em_rt_corr <- emmeans::emmeans(mdl_rt_corr, ~ seq_length_trial | session_f)
df_rt_em <- as.data.frame(em_rt_corr)
# % correct per length (denom = 16) -> per-facet tick labels
mdl_pc_len <- tryCatch(
lme4::glmer(cbind(n_correct_len, n_fail_len) ~ session_f * seq_length_trial + (1 | subject),
data = corr_len, family = binomial),
error = function(e) NULL
)
lab_tbl <- if (!is.null(mdl_pc_len)) {
em <- emmeans::emmeans(mdl_pc_len, ~ seq_length_trial | session_f, type = "response") %>% as.data.frame()
val <- if ("prob" %in% names(em)) "prob" else if ("response" %in% names(em)) "response" else "emmean"
dplyr::transmute(em, session_f, seq_length_trial,
tick = paste0(seq_length_trial, " (", round(.data[[val]] * 100, digits), "%)"))
} else {
corr_len %>%
dplyr::mutate(pct = round(100 * n_correct_len / trials_per_length, digits)) %>%
dplyr::group_by(session_f, seq_length_trial) %>%
dplyr::summarise(pct = round(mean(pct, na.rm = TRUE), digits), .groups = "drop") %>%
dplyr::mutate(tick = paste0(seq_length_trial, " (", pct, "%)"))
}
df_rt_em <- df_rt_em %>%
dplyr::left_join(lab_tbl, by = c("session_f", "seq_length_trial")) %>%
dplyr::group_by(session_f) %>%
dplyr::mutate(seq_lab = factor(tick, levels = unique(tick))) %>%
dplyr::ungroup()
p_correct <- ggplot2::ggplot(df_rt_em, ggplot2::aes(x = seq_lab, y = emmean, group = 1)) +
add_separator() +
ggplot2::geom_point() +
ggplot2::geom_errorbar(ggplot2::aes(ymin = emmean - SE, ymax = emmean + SE), width = 0.2) +
ggplot2::facet_wrap(~ session_f, nrow = 1, scales = "free_x", labeller = strip_labeller) +
ggplot2::labs(title = "RT across Sequence Lengths — Correct Trials only (Test Phase)",
x = "Sequence length (Correct Trials)", y = "Estimated RT (in ms)") +
ggplot2::theme_classic(base_size = 12) +
ggplot2::theme(
panel.grid = ggplot2::element_blank(),
axis.line = ggplot2::element_line(),
axis.ticks = ggplot2::element_line()
)
if (show_plots) print(p_correct)
} else {
message("No correct trials available for the correct-only plot.")
}
invisible(list(m_rt = mdl_rt, em_rt = em_rt,
plot_combined = p_combined, plot_correct = p_correct,
facet_labels = strip_labels))
}
# RUN
plot_test_RT_by_length(df_acc_base)
# 2.2 — Statistical output (inline)
suppressPackageStartupMessages({
library(dplyr); library(tidyr); library(lme4); library(car); library(emmeans)
})
df_test <- df_acc_base %>%
dplyr::group_by(subject, session, trial) %>%
dplyr::mutate(seq_length_trial = dplyr::n_distinct(sub.trial.number)) %>% # TRUE length per trial
dplyr::ungroup() %>%
dplyr::distinct(subject, session, trial, .keep_all = TRUE) %>%
dplyr::filter(session %in% 4:5) %>%
dplyr::mutate(
session_f = factor(session, levels = c(4, 5), labels = paste("Block", 4:5)),
seq_length_trial = factor(seq_length_trial, levels = c(6, 12, 18)),
rt = trial.RT
)
# --- 2.2A: RT ~ block × length × correctness (Blocks 4–5) ---
mdl_rt_test <- tryCatch(
lme4::lmer(rt ~ session_f * seq_length_trial * trial_acc_cat + (1 | subject), data = df_test),
error = function(e) stats::lm(rt ~ session_f * seq_length_trial * trial_acc_cat, data = df_test)
)
cat("\n## 2.2A RT model (block × length × correctness)\n")
## 2.2A RT model (block × length × correctness)print(summary(mdl_rt_test))Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ session_f * seq_length_trial * trial_acc_cat + (1 | subject)
Data: df_test
REML criterion at convergence: 22875.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.9916 -0.5338 -0.0872 0.4120 10.1695
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 32914 181.4
Residual 33321 182.5
Number of obs: 1728, groups: subject, 18
Fixed effects:
Estimate Std. Error
(Intercept) 447.163 44.291
session_fBlock 5 103.205 16.960
seq_length_trial12 20.515 16.793
seq_length_trial18 25.478 17.939
trial_acc_catwrong 246.071 32.480
session_fBlock 5:seq_length_trial12 38.251 26.179
session_fBlock 5:seq_length_trial18 3.666 27.839
session_fBlock 5:trial_acc_catwrong 33.737 41.344
seq_length_trial12:trial_acc_catwrong -109.485 41.456
seq_length_trial18:trial_acc_catwrong -124.187 39.263
session_fBlock 5:seq_length_trial12:trial_acc_catwrong -27.321 53.138
session_fBlock 5:seq_length_trial18:trial_acc_catwrong 26.747 51.517
t value
(Intercept) 10.096
session_fBlock 5 6.085
seq_length_trial12 1.222
seq_length_trial18 1.420
trial_acc_catwrong 7.576
session_fBlock 5:seq_length_trial12 1.461
session_fBlock 5:seq_length_trial18 0.132
session_fBlock 5:trial_acc_catwrong 0.816
seq_length_trial12:trial_acc_catwrong -2.641
seq_length_trial18:trial_acc_catwrong -3.163
session_fBlock 5:seq_length_trial12:trial_acc_catwrong -0.514
session_fBlock 5:seq_length_trial18:trial_acc_catwrong 0.519
Correlation of Fixed Effects:
(Intr) sss_B5 sq__12 sq__18 trl_c_ ss_B5:__12 ss_B5:__18 ss_B5:__
sssn_fBlck5 -0.177
sq_lngth_12 -0.179 0.466
sq_lngth_18 -0.167 0.434 0.442
trl_cc_ctwr -0.094 0.245 0.244 0.226
sss_B5:__12 0.115 -0.643 -0.641 -0.283 -0.158
sss_B5:__18 0.107 -0.602 -0.284 -0.642 -0.142 0.394
sssn_fB5:__ 0.074 -0.421 -0.191 -0.174 -0.783 0.264 0.241
sq_ln_12:__ 0.073 -0.190 -0.408 -0.179 -0.776 0.262 0.114 0.608
sq_ln_18:__ 0.077 -0.198 -0.203 -0.460 -0.819 0.130 0.292 0.638
s_B5:__12:_ -0.057 0.322 0.318 0.138 0.605 -0.495 -0.193 -0.767
s_B5:__18:_ -0.058 0.330 0.154 0.347 0.620 -0.214 -0.540 -0.787
s__12: s__18: s_B5:__12:
sssn_fBlck5
sq_lngth_12
sq_lngth_18
trl_cc_ctwr
sss_B5:__12
sss_B5:__18
sssn_fB5:__
sq_ln_12:__
sq_ln_18:__ 0.641
s_B5:__12:_ -0.779 -0.498
s_B5:__18:_ -0.486 -0.756 0.612 cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl_rt_test, type = 2))
Type II Chi-square ANOVA:Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
session_f 203.5009 1 < 2.2e-16 ***
seq_length_trial 0.5873 2 0.74552
trial_acc_cat 296.8455 1 < 2.2e-16 ***
session_f:seq_length_trial 1.6037 2 0.44850
session_f:trial_acc_cat 2.8424 1 0.09181 .
seq_length_trial:trial_acc_cat 26.4726 2 1.785e-06 ***
session_f:seq_length_trial:trial_acc_cat 1.3761 2 0.50255
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# --- Overall correct vs wrong (averaged over block and length) ---
em_rt_acc_overall <- emmeans::emmeans(mdl_rt_test, ~ trial_acc_cat)NOTE: Results may be misleading due to involvement in interactionscat("\nEMMs (correct vs wrong), averaged over block and length:\n")
EMMs (correct vs wrong), averaged over block and length:print(summary(em_rt_acc_overall)) trial_acc_cat emmean SE df lower.CL upper.CL
correct 521 43.2 17.3 430 612
wrong 706 43.6 18.0 614 798
Results are averaged over the levels of: session_f, seq_length_trial
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 cat("\nPairwise (correct vs wrong):\n")
Pairwise (correct vs wrong):print(pairs(em_rt_acc_overall)) contrast estimate SE df t.ratio p.value
correct - wrong -185 10.6 1701 -17.488 <.0001
Results are averaged over the levels of: session_f, seq_length_trial
Degrees-of-freedom method: kenward-roger em_rt_acc_byblock <- emmeans::emmeans(mdl_rt_test, ~ trial_acc_cat | session_f)NOTE: Results may be misleading due to involvement in interactionscat("\nEMMs (correct vs wrong) within each block:\n")
EMMs (correct vs wrong) within each block:print(summary(em_rt_acc_byblock))session_f = Block 4:
trial_acc_cat emmean SE df lower.CL upper.CL
correct 462 43.4 17.6 371 554
wrong 631 45.0 20.4 537 724
session_f = Block 5:
trial_acc_cat emmean SE df lower.CL upper.CL
correct 580 43.7 18.1 488 671
wrong 781 44.0 18.6 689 874
Results are averaged over the levels of: seq_length_trial
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 cat("\nPairwise (correct vs wrong) within each block:\n")
Pairwise (correct vs wrong) within each block:print(pairs(em_rt_acc_byblock))session_f = Block 4:
contrast estimate SE df t.ratio p.value
correct - wrong -168 16.0 1701 -10.512 <.0001
session_f = Block 5:
contrast estimate SE df t.ratio p.value
correct - wrong -202 13.9 1702 -14.539 <.0001
Results are averaged over the levels of: seq_length_trial
Degrees-of-freedom method: kenward-roger # EMMs and within-block (length) pairwise
em_rt_len <- emmeans::emmeans(mdl_rt_test, ~ seq_length_trial | session_f * trial_acc_cat)
cat("\nEMMs by length within block × correctness:\n"); print(summary(em_rt_len))
EMMs by length within block × correctness:session_f = Block 4, trial_acc_cat = correct:
seq_length_trial emmean SE df lower.CL upper.CL
6 447 44.3 19.1 355 540
12 468 44.5 19.5 375 561
18 473 44.9 20.3 379 566
session_f = Block 5, trial_acc_cat = correct:
seq_length_trial emmean SE df lower.CL upper.CL
6 550 44.5 19.6 457 643
12 609 45.6 21.6 514 704
18 580 46.2 22.6 484 675
session_f = Block 4, trial_acc_cat = wrong:
seq_length_trial emmean SE df lower.CL upper.CL
6 693 52.4 37.4 587 799
12 604 48.6 27.6 505 704
18 595 46.2 22.8 499 690
session_f = Block 5, trial_acc_cat = wrong:
seq_length_trial emmean SE df lower.CL upper.CL
6 830 48.2 26.8 731 929
12 752 45.3 20.9 658 846
18 762 45.0 20.3 668 856
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 cat("\nPairwise (Tukey) across lengths within block × correctness:\n"); print(pairs(em_rt_len, adjust = "tukey"))
Pairwise (Tukey) across lengths within block × correctness:session_f = Block 4, trial_acc_cat = correct:
contrast estimate SE df t.ratio p.value
seq_length_trial6 - seq_length_trial12 -20.52 16.8 1699 -1.222 0.4405
seq_length_trial6 - seq_length_trial18 -25.48 17.9 1699 -1.420 0.3306
seq_length_trial12 - seq_length_trial18 -4.96 18.4 1699 -0.270 0.9606
session_f = Block 5, trial_acc_cat = correct:
contrast estimate SE df t.ratio p.value
seq_length_trial6 - seq_length_trial12 -58.77 20.1 1699 -2.926 0.0097
seq_length_trial6 - seq_length_trial18 -29.14 21.3 1699 -1.366 0.3591
seq_length_trial12 - seq_length_trial18 29.62 23.4 1699 1.263 0.4162
session_f = Block 4, trial_acc_cat = wrong:
contrast estimate SE df t.ratio p.value
seq_length_trial6 - seq_length_trial12 88.97 37.9 1699 2.351 0.0494
seq_length_trial6 - seq_length_trial18 98.71 34.9 1699 2.831 0.0130
seq_length_trial12 - seq_length_trial18 9.74 28.8 1699 0.338 0.9390
session_f = Block 5, trial_acc_cat = wrong:
contrast estimate SE df t.ratio p.value
seq_length_trial6 - seq_length_trial12 78.04 26.5 1700 2.945 0.0092
seq_length_trial6 - seq_length_trial18 68.30 26.0 1700 2.623 0.0239
seq_length_trial12 - seq_length_trial18 -9.74 20.2 1699 -0.483 0.8795
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates # Cross-block contrasts within each length × correctness (Block 4 vs Block 5)
em_rt_blk <- emmeans::emmeans(mdl_rt_test, ~ session_f | seq_length_trial * trial_acc_cat)
cat("\nBlock comparisons (RT) within each length × correctness (Block 4 vs Block 5):\n")
Block comparisons (RT) within each length × correctness (Block 4 vs Block 5):print(pairs(em_rt_blk))seq_length_trial = 6, trial_acc_cat = correct:
contrast estimate SE df t.ratio p.value
Block 4 - Block 5 -103 17.0 1699 -6.085 <.0001
seq_length_trial = 12, trial_acc_cat = correct:
contrast estimate SE df t.ratio p.value
Block 4 - Block 5 -141 20.1 1699 -7.054 <.0001
seq_length_trial = 18, trial_acc_cat = correct:
contrast estimate SE df t.ratio p.value
Block 4 - Block 5 -107 22.2 1700 -4.807 <.0001
seq_length_trial = 6, trial_acc_cat = wrong:
contrast estimate SE df t.ratio p.value
Block 4 - Block 5 -137 37.5 1700 -3.651 0.0003
seq_length_trial = 12, trial_acc_cat = wrong:
contrast estimate SE df t.ratio p.value
Block 4 - Block 5 -148 27.4 1700 -5.406 <.0001
seq_length_trial = 18, trial_acc_cat = wrong:
contrast estimate SE df t.ratio p.value
Block 4 - Block 5 -167 22.4 1700 -7.472 <.0001
Degrees-of-freedom method: kenward-roger cat("\nEMMs (RT) by block within length × correctness:\n")
EMMs (RT) by block within length × correctness:print(summary(em_rt_blk))seq_length_trial = 6, trial_acc_cat = correct:
session_f emmean SE df lower.CL upper.CL
Block 4 447 44.3 19.1 355 540
Block 5 550 44.5 19.6 457 643
seq_length_trial = 12, trial_acc_cat = correct:
session_f emmean SE df lower.CL upper.CL
Block 4 468 44.5 19.5 375 561
Block 5 609 45.6 21.6 514 704
seq_length_trial = 18, trial_acc_cat = correct:
session_f emmean SE df lower.CL upper.CL
Block 4 473 44.9 20.3 379 566
Block 5 580 46.2 22.6 484 675
seq_length_trial = 6, trial_acc_cat = wrong:
session_f emmean SE df lower.CL upper.CL
Block 4 693 52.4 37.4 587 799
Block 5 830 48.2 26.8 731 929
seq_length_trial = 12, trial_acc_cat = wrong:
session_f emmean SE df lower.CL upper.CL
Block 4 604 48.6 27.6 505 704
Block 5 752 45.3 20.9 658 846
seq_length_trial = 18, trial_acc_cat = wrong:
session_f emmean SE df lower.CL upper.CL
Block 4 595 46.2 22.8 499 690
Block 5 762 45.0 20.3 668 856
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 # --- 2.2B: RT ~ block × length (correct-only, Blocks 4–5) ---
df_test_corr <- df_test %>% dplyr::filter(trial_acc_cat == "correct")
if (nrow(df_test_corr) > 0) {
mdl_rt_test_corr <- tryCatch(
lme4::lmer(rt ~ session_f * seq_length_trial + (1 | subject), data = df_test_corr),
error = function(e) stats::lm(rt ~ session_f * seq_length_trial, data = df_test_corr)
)
cat("\n## 2.2B RT model (correct-only)\n")
print(summary(mdl_rt_test_corr))
cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl_rt_test_corr, type = 2))
em_rt_len_corr <- emmeans::emmeans(mdl_rt_test_corr, ~ seq_length_trial | session_f)
cat("\nEMMs by length within block (correct-only):\n"); print(summary(em_rt_len_corr))
cat("\nPairwise (Tukey) across lengths within block (correct-only):\n"); print(pairs(em_rt_len_corr, adjust = "tukey"))
# Cross-block contrasts within each length (correct-only)
em_rt_blk_corr <- emmeans::emmeans(mdl_rt_test_corr, ~ session_f | seq_length_trial)
cat("\nBlock comparisons (RT, correct-only) within each length (Block 4 vs Block 5):\n")
print(pairs(em_rt_blk_corr))
cat("\nEMMs (RT, correct-only) by block within length:\n")
print(summary(em_rt_blk_corr))
} else {
cat("\n[Correct-only RT model skipped: insufficient data]\n")
}
## 2.2B RT model (correct-only)
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ session_f * seq_length_trial + (1 | subject)
Data: df_test_corr
REML criterion at convergence: 14254.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.5853 -0.5408 -0.1110 0.4088 7.5602
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 29756 172.5
Residual 19832 140.8
Number of obs: 1117, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 447.2439 41.6235 10.745
session_fBlock 5 105.3359 13.1253 8.025
seq_length_trial12 21.1635 12.9629 1.633
seq_length_trial18 23.7453 13.8623 1.713
session_fBlock 5:seq_length_trial12 35.2801 20.2159 1.745
session_fBlock 5:seq_length_trial18 0.3142 21.5168 0.015
Correlation of Fixed Effects:
(Intr) sss_B5 sq__12 sq__18 s_B5:__12
sssn_fBlck5 -0.145
sq_lngth_12 -0.146 0.464
sq_lngth_18 -0.137 0.429 0.441
sss_B5:__12 0.094 -0.640 -0.641 -0.282
sss_B5:__18 0.088 -0.597 -0.284 -0.641 0.394
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
session_f 174.7094 1 < 2e-16 ***
seq_length_trial 13.5533 2 0.00114 **
session_f:seq_length_trial 3.5825 2 0.16675
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs by length within block (correct-only):
session_f = Block 4:
seq_length_trial emmean SE df lower.CL upper.CL
6 447 41.6 18.3 360 535
12 468 41.7 18.5 381 556
18 471 42.0 19.0 383 559
session_f = Block 5:
seq_length_trial emmean SE df lower.CL upper.CL
6 553 41.8 18.6 465 640
12 609 42.5 19.8 520 698
18 577 42.9 20.5 487 666
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise (Tukey) across lengths within block (correct-only):
session_f = Block 4:
contrast estimate SE df t.ratio p.value
seq_length_trial6 - seq_length_trial12 -21.16 13.0 1094 -1.633 0.2323
seq_length_trial6 - seq_length_trial18 -23.75 13.9 1094 -1.713 0.2008
seq_length_trial12 - seq_length_trial18 -2.58 14.2 1094 -0.182 0.9819
session_f = Block 5:
contrast estimate SE df t.ratio p.value
seq_length_trial6 - seq_length_trial12 -56.44 15.5 1094 -3.638 0.0008
seq_length_trial6 - seq_length_trial18 -24.06 16.5 1094 -1.457 0.3123
seq_length_trial12 - seq_length_trial18 32.38 18.1 1094 1.787 0.1744
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Block comparisons (RT, correct-only) within each length (Block 4 vs Block 5):
seq_length_trial = 6:
contrast estimate SE df t.ratio p.value
Block 4 - Block 5 -105 13.1 1094 -8.025 <.0001
seq_length_trial = 12:
contrast estimate SE df t.ratio p.value
Block 4 - Block 5 -141 15.5 1095 -9.051 <.0001
seq_length_trial = 18:
contrast estimate SE df t.ratio p.value
Block 4 - Block 5 -106 17.3 1095 -6.117 <.0001
Degrees-of-freedom method: kenward-roger
EMMs (RT, correct-only) by block within length:
seq_length_trial = 6:
session_f emmean SE df lower.CL upper.CL
Block 4 447 41.6 18.3 360 535
Block 5 553 41.8 18.6 465 640
seq_length_trial = 12:
session_f emmean SE df lower.CL upper.CL
Block 4 468 41.7 18.5 381 556
Block 5 609 42.5 19.8 520 698
seq_length_trial = 18:
session_f emmean SE df lower.CL upper.CL
Block 4 471 42.0 19.0 383 559
Block 5 577 42.9 20.5 487 666
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 # --- 2.2C: Accuracy per block × length (binomial GLMM, Blocks 4–5) ---
counts_test <- df_acc_base %>%
dplyr::group_by(subject, session, trial) %>%
dplyr::mutate(seq_length_trial = dplyr::n_distinct(sub.trial.number)) %>% # TRUE length
dplyr::ungroup() %>%
dplyr::distinct(subject, session, trial, .keep_all = TRUE) %>%
dplyr::filter(session %in% 4:5) %>%
dplyr::mutate(
session_f = factor(session, levels = c(4, 5), labels = paste("Block", 4:5)),
seq_length_trial = factor(seq_length_trial, levels = c(6, 12, 18))
) %>%
dplyr::group_by(subject, session_f, seq_length_trial) %>%
dplyr::summarise(
total_trials = dplyr::n(), # actual denominator after any exclusions
correct_trials = sum(trial_acc_cat == "correct", na.rm = TRUE),
.groups = "drop"
) %>%
dplyr::mutate(fail_trials = total_trials - correct_trials) %>%
dplyr::filter(!is.na(seq_length_trial))
if (nrow(counts_test) > 0) {
mdl_acc_test <- lme4::glmer(
cbind(correct_trials, fail_trials) ~ session_f * seq_length_trial + (1 | subject),
data = counts_test, family = binomial
)
cat("\n## 2.2C Accuracy model (binomial, block × length)\n")
print(summary(mdl_acc_test))
cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl_acc_test, type = 2))
# EMMs on probability (response) scale
em_acc_test <- emmeans::emmeans(mdl_acc_test, ~ seq_length_trial | session_f, type = "response")
cat("\nEMMs (prob correct) by length within block:\n"); print(summary(em_acc_test))
cat("\nPairwise (Tukey) across lengths within block (prob correct):\n"); print(pairs(em_acc_test, adjust = "tukey"))
# Cross-block contrasts within each length (prob correct; response scale)
em_acc_blk <- emmeans::emmeans(mdl_acc_test, ~ session_f | seq_length_trial, type = "response")
cat("\nBlock comparisons (prob correct) within each length (Block 4 vs Block 5):\n")
print(pairs(em_acc_blk))
cat("\nEMMs (prob correct) by block within length:\n")
print(summary(em_acc_blk))
} else {
cat("\n[Accuracy model skipped: no counts available]\n")
}
## 2.2C Accuracy model (binomial, block × length)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) [glmerMod]
Family: binomial ( logit )
Formula: cbind(correct_trials, fail_trials) ~ session_f * seq_length_trial +
(1 | subject)
Data: counts_test
AIC BIC logLik deviance df.resid
584.0 602.8 -285.0 570.0 101
Scaled residuals:
Min 1Q Median 3Q Max
-4.9843 -0.8720 0.1909 0.9885 4.0483
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.3185 0.5644
Number of obs: 108, groups: subject, 18
Fixed effects:
Estimate Std. Error z value Pr(>|z|)
(Intercept) 2.0307 0.2245 9.044 < 2e-16 ***
session_fBlock 5 -0.8171 0.2284 -3.577 0.000347 ***
seq_length_trial12 -0.6935 0.2310 -3.002 0.002682 **
seq_length_trial18 -1.5158 0.2196 -6.903 5.1e-12 ***
session_fBlock 5:seq_length_trial12 -0.6721 0.2973 -2.261 0.023789 *
session_fBlock 5:seq_length_trial18 -0.1854 0.2890 -0.641 0.521275
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) sss_B5 sq__12 sq__18 s_B5:__12
sssn_fBlck5 -0.631
sq_lngth_12 -0.623 0.610
sq_lngth_18 -0.660 0.644 0.636
sss_B5:__12 0.477 -0.766 -0.775 -0.489
sss_B5:__18 0.493 -0.788 -0.481 -0.753 0.607
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: cbind(correct_trials, fail_trials)
Chisq Df Pr(>Chisq)
session_f 98.2015 1 < 2e-16 ***
seq_length_trial 130.4703 2 < 2e-16 ***
session_f:seq_length_trial 5.9554 2 0.05091 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs (prob correct) by length within block:
session_f = Block 4:
seq_length_trial prob SE df asymp.LCL asymp.UCL
6 0.884 0.0230 Inf 0.831 0.922
12 0.792 0.0326 Inf 0.721 0.849
18 0.626 0.0429 Inf 0.539 0.706
session_f = Block 5:
seq_length_trial prob SE df asymp.LCL asymp.UCL
6 0.771 0.0344 Inf 0.697 0.831
12 0.462 0.0450 Inf 0.376 0.550
18 0.380 0.0431 Inf 0.300 0.468
Confidence level used: 0.95
Intervals are back-transformed from the logit scale
Pairwise (Tukey) across lengths within block (prob correct):
session_f = Block 4:
contrast odds.ratio SE df null z.ratio
seq_length_trial6 / seq_length_trial12 2.00 0.462 Inf 1 3.002
seq_length_trial6 / seq_length_trial18 4.55 1.000 Inf 1 6.903
seq_length_trial12 / seq_length_trial18 2.28 0.438 Inf 1 4.272
p.value
0.0076
<.0001
0.0001
session_f = Block 5:
contrast odds.ratio SE df null z.ratio
seq_length_trial6 / seq_length_trial12 3.92 0.736 Inf 1 7.268
seq_length_trial6 / seq_length_trial18 5.48 1.040 Inf 1 8.947
seq_length_trial12 / seq_length_trial18 1.40 0.245 Inf 1 1.917
p.value
<.0001
<.0001
0.1339
P value adjustment: tukey method for comparing a family of 3 estimates
Tests are performed on the log odds ratio scale
Block comparisons (prob correct) within each length (Block 4 vs Block 5):
seq_length_trial = 6:
contrast odds.ratio SE df null z.ratio p.value
Block 4 / Block 5 2.26 0.517 Inf 1 3.577 0.0003
seq_length_trial = 12:
contrast odds.ratio SE df null z.ratio p.value
Block 4 / Block 5 4.43 0.848 Inf 1 7.786 <.0001
seq_length_trial = 18:
contrast odds.ratio SE df null z.ratio p.value
Block 4 / Block 5 2.72 0.484 Inf 1 5.639 <.0001
Tests are performed on the log odds ratio scale
EMMs (prob correct) by block within length:
seq_length_trial = 6:
session_f prob SE df asymp.LCL asymp.UCL
Block 4 0.884 0.0230 Inf 0.831 0.922
Block 5 0.771 0.0344 Inf 0.697 0.831
seq_length_trial = 12:
session_f prob SE df asymp.LCL asymp.UCL
Block 4 0.792 0.0326 Inf 0.721 0.849
Block 5 0.462 0.0450 Inf 0.376 0.550
seq_length_trial = 18:
session_f prob SE df asymp.LCL asymp.UCL
Block 4 0.626 0.0429 Inf 0.539 0.706
Block 5 0.380 0.0431 Inf 0.300 0.468
Confidence level used: 0.95
Intervals are back-transformed from the logit scale #A3 Stepwise RT — Training blocks (1–3)
suppressPackageStartupMessages({
library(dplyr); library(lme4); library(car); library(emmeans)
})
# fallback in case safe_fit wasn't defined earlier
if (!exists("safe_fit")) {
safe_fit <- function(formula, data) {
tryCatch(lmer(formula, data = data),
error = function(e) { stop("Model failed: ", conditionMessage(e)) })
}
}
stepwise_rt_training_with_correctness <- function(df_acc_all) {
cat("\n\n========== Stepwise RT — Training (1–3) ==========\n")
for (blk in 1:3) {
cat(glue::glue("\n--- Block {blk} ---\n"))
# Per-step dataset (do not collapse steps)
df_blk_all <- df_acc_all %>%
filter(session == blk) %>%
group_by(subject, trial, step = as.integer(as.character(sub.trial.number)), trial_acc_cat) %>%
summarise(rt = mean(feedback.RT, na.rm = TRUE), .groups = "drop")
if (nrow(df_blk_all) == 0) { cat("No data\n"); next }
df_blk_all$step <- factor(df_blk_all$step, levels = sort(unique(df_blk_all$step)), ordered = TRUE)
# Combined model with correctness factor
has_two_steps <- n_distinct(df_blk_all$step) >= 2
has_two_corr <- n_distinct(df_blk_all$trial_acc_cat) >= 2
if (has_two_steps && has_two_corr) {
fmla <- rt ~ step * trial_acc_cat + (1 | subject)
} else if (has_two_steps) {
fmla <- rt ~ step + (1 | subject)
} else if (has_two_corr) {
fmla <- rt ~ trial_acc_cat + (1 | subject)
} else {
cat("⚠️ Not enough levels to model.\n"); next
}
cat("\n[Combined] Model summary:\n")
mdl_all <- safe_fit(fmla, data = df_blk_all)
print(summary(mdl_all))
cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl_all, type = 2))
# EMMs depending on formula
if (has_two_steps && has_two_corr) {
em <- emmeans(mdl_all, ~ step | trial_acc_cat)
cat("\nEMMs (step | correctness):\n"); print(summary(em))
# Pairwise (Tukey) across steps | correctness — print CORRECT only
pw_by <- pairs(em, by = "trial_acc_cat", adjust = "tukey")
if ("correct" %in% names(pw_by)) {
cat("\nPairwise (Tukey) across steps | correctness — correct only:\n")
print(pw_by[["correct"]])
}
# Adjacent-step (“consec”) | correctness — print CORRECT only
adj_by <- contrast(em, method = "consec", by = "trial_acc_cat", adjust = "holm")
if ("correct" %in% names(adj_by)) {
cat("\nAdjacent-step contrasts (consecutive) | correctness — correct only:\n")
print(adj_by[["correct"]])
}
} else if (has_two_steps) {
em <- emmeans(mdl_all, ~ step)
print(summary(em))
} else {
em <- emmeans(mdl_all, ~ trial_acc_cat); print(summary(em))
}
# Split outputs
for (lab in c("correct","wrong")) {
df_blk <- df_blk_all %>% filter(trial_acc_cat == lab)
if (nrow(df_blk) == 0) { cat("\n⚠️ No data for", lab, "\n"); next }
has_two_steps_lab <- n_distinct(df_blk$step) >= 2
if (!has_two_steps_lab) { cat("\n⚠️ Only one step level for", lab, "— skipping.\n"); next }
cat("\n[", toupper(lab), "] Model summary:\n", sep = "")
mdl <- safe_fit(rt ~ step + (1 | subject), data = df_blk)
print(summary(mdl))
cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl, type = 2))
em_lab <- emmeans(mdl, ~ step); print(summary(em_lab))
# >>> Pairwise comparisons ONLY for CORRECT trials <<<
if (lab == "correct") {
cat("\nPairwise (Tukey) across steps — correct:\n")
print(pairs(em_lab, adjust = "tukey"))
cat("\nAdjacent-step contrasts (consecutive) — correct:\n")
print(contrast(em_lab, "consec", adjust = "holm"))
}
}
}
}
stepwise_rt_training_with_correctness(df_acc_base)
========== Stepwise RT — Training (1–3) ==========
--- Block 1 ---
[Combined] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * trial_acc_cat + (1 | subject)
Data: data
REML criterion at convergence: 83308.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.053 -0.228 -0.068 0.118 33.226
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 72318 268.9
Residual 564412 751.3
Number of obs: 5184, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 495.49 64.44 7.689
step.L -186.72 28.33 -6.590
step.Q 170.62 28.33 6.022
step.C -120.67 28.33 -4.259
step^4 43.37 28.33 1.531
step^5 -12.27 28.33 -0.433
trial_acc_catwrong 378.53 27.67 13.680
step.L:trial_acc_catwrong 701.50 65.64 10.687
step.Q:trial_acc_catwrong 690.47 65.64 10.519
step.C:trial_acc_catwrong 349.61 65.64 5.326
step^4:trial_acc_catwrong 186.16 65.64 2.836
step^5:trial_acc_catwrong 37.65 65.64 0.574
Correlation of Fixed Effects:
(Intr) step.L step.Q step.C step^4 step^5 trl_c_ s.L:__ s.Q:__
step.L 0.000
step.Q 0.000 0.000
step.C 0.000 0.000 0.000
step^4 0.000 0.000 0.000 0.000
step^5 0.000 0.000 0.000 0.000 0.000
trl_cc_ctwr -0.080 0.000 0.000 0.000 0.000 0.000
stp.L:trl__ 0.000 -0.432 0.000 0.000 0.000 0.000 0.000
stp.Q:trl__ 0.000 0.000 -0.432 0.000 0.000 0.000 0.000 0.000
stp.C:trl__ 0.000 0.000 0.000 -0.432 0.000 0.000 0.000 0.000 0.000
stp^4:trl__ 0.000 0.000 0.000 0.000 -0.432 0.000 0.000 0.000 0.000
stp^5:trl__ 0.000 0.000 0.000 0.000 0.000 -0.432 0.000 0.000 0.000
s.C:__ s^4:__
step.L
step.Q
step.C
step^4
step^5
trl_cc_ctwr
stp.L:trl__
stp.Q:trl__
stp.C:trl__
stp^4:trl__ 0.000
stp^5:trl__ 0.000 0.000
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 156.01 5 < 2.2e-16 ***
trial_acc_cat 187.15 1 < 2.2e-16 ***
step:trial_acc_cat 261.61 5 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs (step | correctness):
trial_acc_cat = correct:
step emmean SE df lower.CL upper.CL
1 754 69.4 23.2 611 898
2 452 69.4 23.2 309 596
3 431 69.4 23.2 288 575
4 443 69.4 23.2 300 587
5 452 69.4 23.2 309 596
6 439 69.4 23.2 296 583
trial_acc_cat = wrong:
step emmean SE df lower.CL upper.CL
1 993 86.9 56.8 819 1167
2 593 86.9 56.8 419 767
3 576 86.9 56.8 402 750
4 594 86.9 56.8 420 768
5 707 86.9 56.8 533 881
6 1782 86.9 56.8 1608 1956
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
[CORRECT] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step + (1 | subject)
Data: data
REML criterion at convergence: 60818.2
Scaled residuals:
Min 1Q Median 3Q Max
-4.4444 -0.3995 -0.1412 0.2213 29.3410
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 66759 258.4
Residual 106031 325.6
Number of obs: 4218, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 498.01 61.11 8.149
step.L -186.72 12.28 -15.204
step.Q 170.62 12.28 13.893
step.C -120.67 12.28 -9.826
step^4 43.37 12.28 3.532
step^5 -12.27 12.28 -0.999
Correlation of Fixed Effects:
(Intr) step.L step.Q step.C step^4
step.L 0.000
step.Q 0.000 0.000
step.C 0.000 0.000 0.000
step^4 0.000 0.000 0.000 0.000
step^5 0.000 0.000 0.000 0.000 0.000
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 534.2 5 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
step emmean SE df lower.CL upper.CL
1 757 62.1 18.2 626 887
2 455 62.1 18.2 324 585
3 434 62.1 18.2 304 564
4 446 62.1 18.2 315 576
5 455 62.1 18.2 324 585
6 442 62.1 18.2 312 572
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise (Tukey) across steps — correct:
contrast estimate SE df t.ratio p.value
step1 - step2 301.691 17.4 4195 17.370 <.0001
step1 - step3 322.613 17.4 4195 18.575 <.0001
step1 - step4 310.754 17.4 4195 17.892 <.0001
step1 - step5 301.947 17.4 4195 17.385 <.0001
step1 - step6 314.669 17.4 4195 18.118 <.0001
step2 - step3 20.922 17.4 4195 1.205 0.8348
step2 - step4 9.063 17.4 4195 0.522 0.9953
step2 - step5 0.256 17.4 4195 0.015 1.0000
step2 - step6 12.977 17.4 4195 0.747 0.9759
step3 - step4 -11.859 17.4 4195 -0.683 0.9839
step3 - step5 -20.666 17.4 4195 -1.190 0.8419
step3 - step6 -7.944 17.4 4195 -0.457 0.9975
step4 - step5 -8.806 17.4 4195 -0.507 0.9959
step4 - step6 3.915 17.4 4195 0.225 0.9999
step5 - step6 12.721 17.4 4195 0.732 0.9780
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent-step contrasts (consecutive) — correct:
contrast estimate SE df t.ratio p.value
step2 - step1 -301.69 17.4 4195 -17.370 <.0001
step3 - step2 -20.92 17.4 4195 -1.205 0.9137
step4 - step3 11.86 17.4 4195 0.683 1.0000
step5 - step4 8.81 17.4 4195 0.507 1.0000
step6 - step5 -12.72 17.4 4195 -0.732 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 5 tests
[WRONG] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step + (1 | subject)
Data: data
REML criterion at convergence: 16938.6
Scaled residuals:
Min 1Q Median 3Q Max
-1.4700 -0.2600 -0.0791 0.0668 15.3544
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 129325 359.6
Residual 2548045 1596.3
Number of obs: 966, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 885.50 101.42 8.731
step.L 514.77 125.80 4.092
step.Q 861.09 125.80 6.845
step.C 228.93 125.80 1.820
step^4 229.54 125.80 1.825
step^5 25.38 125.80 0.202
Correlation of Fixed Effects:
(Intr) step.L step.Q step.C step^4
step.L 0.000
step.Q 0.000 0.000
step.C 0.000 0.000 0.000
step^4 0.000 0.000 0.000 0.000
step^5 0.000 0.000 0.000 0.000 0.000
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 70.275 5 8.981e-14 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
step emmean SE df lower.CL upper.CL
1 1004 153 84.2 699 1309
2 604 153 84.2 299 909
3 587 153 84.2 282 892
4 606 153 84.2 301 911
5 719 153 84.2 414 1023
6 1793 153 84.2 1488 2098
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
--- Block 2 ---
[Combined] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * trial_acc_cat + (1 | subject)
Data: data
REML criterion at convergence: 152133.8
Scaled residuals:
Min 1Q Median 3Q Max
-3.5910 -0.4328 -0.1421 0.1825 13.1480
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 73186 270.5
Residual 139488 373.5
Number of obs: 10368, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 491.831 63.934 7.693
step.L -134.063 15.782 -8.494
step.Q 78.716 15.782 4.988
step.C -183.800 15.782 -11.646
step^4 130.544 15.782 8.271
step^5 -59.893 15.782 -3.795
step^6 31.494 15.782 1.996
step^7 14.042 15.782 0.890
step^8 2.778 15.782 0.176
step^9 -32.254 15.782 -2.044
step^10 22.473 15.782 1.424
step^11 -44.771 15.782 -2.837
trial_acc_catwrong 189.621 8.128 23.329
step.L:trial_acc_catwrong 214.325 26.607 8.055
step.Q:trial_acc_catwrong 155.288 26.607 5.836
step.C:trial_acc_catwrong 51.468 26.607 1.934
step^4:trial_acc_catwrong 157.970 26.607 5.937
step^5:trial_acc_catwrong 24.774 26.607 0.931
step^6:trial_acc_catwrong 40.459 26.607 1.521
step^7:trial_acc_catwrong -20.228 26.607 -0.760
step^8:trial_acc_catwrong -18.810 26.607 -0.707
step^9:trial_acc_catwrong 66.831 26.607 2.512
step^10:trial_acc_catwrong 47.716 26.607 1.793
step^11:trial_acc_catwrong -60.857 26.607 -2.287
Correlation matrix not shown by default, as p = 24 > 12.
Use print(summary(mdl_all), correlation=TRUE) or
vcov(summary(mdl_all)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 582.81 11 < 2.2e-16 ***
trial_acc_cat 544.25 1 < 2.2e-16 ***
step:trial_acc_cat 156.96 11 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs (step | correctness):
trial_acc_cat = correct:
step emmean SE df lower.CL upper.CL
1 746 65.7 19.0 609 884
2 470 65.7 19.0 333 608
3 439 65.7 19.0 302 577
4 414 65.7 19.0 277 552
5 517 65.7 19.0 380 655
6 480 65.7 19.0 343 618
7 507 65.7 19.0 370 645
8 484 65.7 19.0 347 622
9 511 65.7 19.0 373 648
10 471 65.7 19.0 334 609
11 438 65.7 19.0 300 575
12 424 65.7 19.0 286 561
trial_acc_cat = wrong:
step emmean SE df lower.CL upper.CL
1 950 67.3 20.9 810 1090
2 564 67.3 20.9 424 704
3 524 67.3 20.9 384 664
4 547 67.3 20.9 407 687
5 703 67.3 20.9 563 843
6 573 67.3 20.9 433 713
7 737 67.3 20.9 597 877
8 701 67.3 20.9 561 841
9 664 67.3 20.9 524 804
10 666 67.3 20.9 526 806
11 666 67.3 20.9 526 806
12 884 67.3 20.9 744 1024
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
[CORRECT] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step + (1 | subject)
Data: data
REML criterion at convergence: 92865.7
Scaled residuals:
Min 1Q Median 3Q Max
-4.1593 -0.4831 -0.1817 0.2355 13.2133
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 60609 246.2
Residual 58656 242.2
Number of obs: 6720, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 489.472 58.108 8.423
step.L -134.063 10.234 -13.099
step.Q 78.716 10.234 7.691
step.C -183.800 10.234 -17.959
step^4 130.544 10.234 12.755
step^5 -59.893 10.234 -5.852
step^6 31.494 10.234 3.077
step^7 14.042 10.234 1.372
step^8 2.778 10.234 0.271
step^9 -32.254 10.234 -3.152
step^10 22.473 10.234 2.196
step^11 -44.771 10.234 -4.375
Correlation of Fixed Effects:
(Intr) step.L step.Q step.C step^4 step^5 step^6 step^7 step^8 step^9
step.L 0.000
step.Q 0.000 0.000
step.C 0.000 0.000 0.000
step^4 0.000 0.000 0.000 0.000
step^5 0.000 0.000 0.000 0.000 0.000
step^6 0.000 0.000 0.000 0.000 0.000 0.000
step^7 0.000 0.000 0.000 0.000 0.000 0.000 0.000
step^8 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
step^9 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
step^10 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
step^11 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
stp^10
step.L
step.Q
step.C
step^4
step^5
step^6
step^7
step^8
step^9
step^10
step^11 0.000
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 795.54 11 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
step emmean SE df lower.CL upper.CL
1 744 58.9 18 620 868
2 468 58.9 18 344 592
3 437 58.9 18 313 561
4 412 58.9 18 288 536
5 515 58.9 18 391 639
6 478 58.9 18 354 602
7 505 58.9 18 381 629
8 482 58.9 18 358 606
9 508 58.9 18 385 632
10 469 58.9 18 345 593
11 435 58.9 18 312 559
12 421 58.9 18 298 545
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise (Tukey) across steps — correct:
contrast estimate SE df t.ratio p.value
step1 - step2 275.707 14.5 6691 19.049 <.0001
step1 - step3 306.861 14.5 6691 21.201 <.0001
step1 - step4 331.834 14.5 6691 22.927 <.0001
step1 - step5 228.984 14.5 6691 15.821 <.0001
step1 - step6 265.964 14.5 6691 18.376 <.0001
step1 - step7 238.716 14.5 6691 16.493 <.0001
step1 - step8 261.957 14.5 6691 18.099 <.0001
step1 - step9 235.332 14.5 6691 16.259 <.0001
step1 - step10 274.986 14.5 6691 18.999 <.0001
step1 - step11 308.355 14.5 6691 21.305 <.0001
step1 - step12 322.405 14.5 6691 22.275 <.0001
step2 - step3 31.154 14.5 6691 2.152 0.5846
step2 - step4 56.127 14.5 6691 3.878 0.0060
step2 - step5 -46.723 14.5 6691 -3.228 0.0567
step2 - step6 -9.743 14.5 6691 -0.673 0.9999
step2 - step7 -36.991 14.5 6691 -2.556 0.3055
step2 - step8 -13.750 14.5 6691 -0.950 0.9986
step2 - step9 -40.375 14.5 6691 -2.790 0.1845
step2 - step10 -0.721 14.5 6691 -0.050 1.0000
step2 - step11 32.648 14.5 6691 2.256 0.5088
step2 - step12 46.698 14.5 6691 3.226 0.0570
step3 - step4 24.973 14.5 6691 1.725 0.8567
step3 - step5 -77.877 14.5 6691 -5.381 <.0001
step3 - step6 -40.896 14.5 6691 -2.826 0.1693
step3 - step7 -68.145 14.5 6691 -4.708 0.0002
step3 - step8 -44.904 14.5 6691 -3.102 0.0819
step3 - step9 -71.529 14.5 6691 -4.942 0.0001
step3 - step10 -31.875 14.5 6691 -2.202 0.5480
step3 - step11 1.495 14.5 6691 0.103 1.0000
step3 - step12 15.545 14.5 6691 1.074 0.9957
step4 - step5 -102.850 14.5 6691 -7.106 <.0001
step4 - step6 -65.870 14.5 6691 -4.551 0.0003
step4 - step7 -93.118 14.5 6691 -6.434 <.0001
step4 - step8 -69.877 14.5 6691 -4.828 0.0001
step4 - step9 -96.502 14.5 6691 -6.667 <.0001
step4 - step10 -56.848 14.5 6691 -3.928 0.0049
step4 - step11 -23.479 14.5 6691 -1.622 0.9013
step4 - step12 -9.429 14.5 6691 -0.651 1.0000
step5 - step6 36.980 14.5 6691 2.555 0.3059
step5 - step7 9.732 14.5 6691 0.672 0.9999
step5 - step8 32.973 14.5 6691 2.278 0.4924
step5 - step9 6.348 14.5 6691 0.439 1.0000
step5 - step10 46.002 14.5 6691 3.178 0.0657
step5 - step11 79.371 14.5 6691 5.484 <.0001
step5 - step12 93.421 14.5 6691 6.455 <.0001
step6 - step7 -27.248 14.5 6691 -1.883 0.7703
step6 - step8 -4.007 14.5 6691 -0.277 1.0000
step6 - step9 -30.632 14.5 6691 -2.116 0.6109
step6 - step10 9.021 14.5 6691 0.623 1.0000
step6 - step11 42.391 14.5 6691 2.929 0.1309
step6 - step12 56.441 14.5 6691 3.900 0.0055
step7 - step8 23.241 14.5 6691 1.606 0.9074
step7 - step9 -3.384 14.5 6691 -0.234 1.0000
step7 - step10 36.270 14.5 6691 2.506 0.3361
step7 - step11 69.639 14.5 6691 4.811 0.0001
step7 - step12 83.689 14.5 6691 5.782 <.0001
step8 - step9 -26.625 14.5 6691 -1.840 0.7961
step8 - step10 13.029 14.5 6691 0.900 0.9991
step8 - step11 46.398 14.5 6691 3.206 0.0606
step8 - step12 60.448 14.5 6691 4.176 0.0018
step9 - step10 39.654 14.5 6691 2.740 0.2070
step9 - step11 73.023 14.5 6691 5.045 <.0001
step9 - step12 87.073 14.5 6691 6.016 <.0001
step10 - step11 33.370 14.5 6691 2.306 0.4726
step10 - step12 47.420 14.5 6691 3.276 0.0489
step11 - step12 14.050 14.5 6691 0.971 0.9983
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent-step contrasts (consecutive) — correct:
contrast estimate SE df t.ratio p.value
step2 - step1 -275.7 14.5 6691 -19.049 <.0001
step3 - step2 -31.2 14.5 6691 -2.152 0.1884
step4 - step3 -25.0 14.5 6691 -1.725 0.2990
step5 - step4 102.8 14.5 6691 7.106 <.0001
step6 - step5 -37.0 14.5 6691 -2.555 0.0851
step7 - step6 27.2 14.5 6691 1.883 0.2990
step8 - step7 -23.2 14.5 6691 -1.606 0.2990
step9 - step8 26.6 14.5 6691 1.840 0.2990
step10 - step9 -39.7 14.5 6691 -2.740 0.0555
step11 - step10 -33.4 14.5 6691 -2.306 0.1482
step12 - step11 -14.1 14.5 6691 -0.971 0.3317
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 11 tests
[WRONG] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step + (1 | subject)
Data: data
REML criterion at convergence: 56105.2
Scaled residuals:
Min 1Q Median 3Q Max
-2.6066 -0.4986 -0.1921 0.1556 9.3032
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 90484 300.8
Residual 283253 532.2
Number of obs: 3648, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 683.972 71.647 9.546
step.L 80.262 30.525 2.629
step.Q 234.004 30.525 7.666
step.C -132.333 30.525 -4.335
step^4 288.514 30.525 9.452
step^5 -35.120 30.525 -1.151
step^6 71.953 30.525 2.357
step^7 -6.187 30.525 -0.203
step^8 -16.032 30.525 -0.525
step^9 34.576 30.525 1.133
step^10 70.189 30.525 2.299
step^11 -105.628 30.525 -3.460
Correlation of Fixed Effects:
(Intr) step.L step.Q step.C step^4 step^5 step^6 step^7 step^8 step^9
step.L 0.000
step.Q 0.000 0.000
step.C 0.000 0.000 0.000
step^4 0.000 0.000 0.000 0.000
step^5 0.000 0.000 0.000 0.000 0.000
step^6 0.000 0.000 0.000 0.000 0.000 0.000
step^7 0.000 0.000 0.000 0.000 0.000 0.000 0.000
step^8 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
step^9 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
step^10 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
step^11 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
stp^10
step.L
step.Q
step.C
step^4
step^5
step^6
step^7
step^8
step^9
step^10
step^11 0.000
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 199.56 11 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
step emmean SE df lower.CL upper.CL
1 953 77.4 23.1 793 1113
2 566 77.4 23.1 406 726
3 527 77.4 23.1 367 687
4 550 77.4 23.1 390 710
5 705 77.4 23.1 545 865
6 576 77.4 23.1 416 736
7 739 77.4 23.1 579 899
8 703 77.4 23.1 543 863
9 666 77.4 23.1 506 826
10 668 77.4 23.1 508 828
11 668 77.4 23.1 508 828
12 886 77.4 23.1 726 1046
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
--- Block 3 ---
[Combined] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * trial_acc_cat + (1 | subject)
Data: data
REML criterion at convergence: 233065.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.6317 -0.4232 -0.1656 0.1450 23.1355
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 45858 214.1
Residual 191532 437.6
Number of obs: 15552, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 534.1761 50.7214 10.532
step.L -74.1687 20.1442 -3.682
step.Q 52.8735 20.1442 2.625
step.C -212.6525 20.1442 -10.557
step^4 148.2090 20.1442 7.357
step^5 -72.2957 20.1442 -3.589
step^6 143.7340 20.1442 7.135
step^7 -39.4603 20.1442 -1.959
step^8 -30.7602 20.1442 -1.527
step^9 -68.5111 20.1442 -3.401
step^10 -65.2846 20.1442 -3.241
step^11 5.9738 20.1442 0.297
step^12 97.7373 20.1442 4.852
step^13 -3.5160 20.1442 -0.175
step^14 -61.4153 20.1442 -3.049
step^15 -91.9082 20.1442 -4.563
step^16 -45.7208 20.1442 -2.270
step^17 -24.5454 20.1442 -1.218
trial_acc_catwrong 101.2860 7.8532 12.897
step.L:trial_acc_catwrong 151.2608 29.9064 5.058
step.Q:trial_acc_catwrong 71.8196 29.9064 2.401
step.C:trial_acc_catwrong 6.5254 29.9064 0.218
step^4:trial_acc_catwrong 161.4595 29.9064 5.399
step^5:trial_acc_catwrong -53.6791 29.9064 -1.795
step^6:trial_acc_catwrong 69.2605 29.9064 2.316
step^7:trial_acc_catwrong 4.7810 29.9064 0.160
step^8:trial_acc_catwrong 116.4322 29.9064 3.893
step^9:trial_acc_catwrong 63.9203 29.9064 2.137
step^10:trial_acc_catwrong 29.9286 29.9064 1.001
step^11:trial_acc_catwrong -27.3549 29.9064 -0.915
step^12:trial_acc_catwrong -18.6902 29.9064 -0.625
step^13:trial_acc_catwrong -25.3792 29.9064 -0.849
step^14:trial_acc_catwrong -1.3827 29.9064 -0.046
step^15:trial_acc_catwrong 66.9411 29.9064 2.238
step^16:trial_acc_catwrong 11.6012 29.9064 0.388
step^17:trial_acc_catwrong 0.2499 29.9064 0.008
Correlation matrix not shown by default, as p = 36 > 12.
Use print(summary(mdl_all), correlation=TRUE) or
vcov(summary(mdl_all)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 742.457 17 < 2.2e-16 ***
trial_acc_cat 166.344 1 < 2.2e-16 ***
step:trial_acc_cat 96.992 17 3.201e-13 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs (step | correctness):
trial_acc_cat = correct:
step emmean SE df lower.CL upper.CL
1 809 54.4 22.7 696 922
2 501 54.4 22.7 388 613
3 463 54.4 22.7 350 575
4 451 54.4 22.7 339 564
5 529 54.4 22.7 416 641
6 525 54.4 22.7 413 638
7 505 54.4 22.7 392 618
8 493 54.4 22.7 381 606
9 544 54.4 22.7 432 657
10 570 54.4 22.7 457 682
11 514 54.4 22.7 401 626
12 480 54.4 22.7 367 592
13 707 54.4 22.7 595 820
14 565 54.4 22.7 453 678
15 513 54.4 22.7 400 626
16 449 54.4 22.7 337 562
17 508 54.4 22.7 396 621
18 489 54.4 22.7 376 602
trial_acc_cat = wrong:
step emmean SE df lower.CL upper.CL
1 992 55.1 24.0 878 1106
2 486 55.1 24.0 372 600
3 474 55.1 24.0 360 587
4 488 55.1 24.0 374 601
5 574 55.1 24.0 460 688
6 625 55.1 24.0 511 739
7 568 55.1 24.0 454 682
8 588 55.1 24.0 474 702
9 686 55.1 24.0 572 799
10 682 55.1 24.0 568 796
11 642 55.1 24.0 528 756
12 610 55.1 24.0 496 723
13 727 55.1 24.0 613 841
14 655 55.1 24.0 542 769
15 677 55.1 24.0 563 791
16 607 55.1 24.0 494 721
17 590 55.1 24.0 476 704
18 769 55.1 24.0 655 883
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
[CORRECT] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step + (1 | subject)
Data: data
REML criterion at convergence: 121086.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.579 -0.432 -0.161 0.192 33.518
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 40081 200.2
Residual 91189 302.0
Number of obs: 8496, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 530.575 47.335 11.209
step.L -74.169 13.900 -5.336
step.Q 52.873 13.900 3.804
step.C -212.652 13.900 -15.299
step^4 148.209 13.900 10.663
step^5 -72.296 13.900 -5.201
step^6 143.734 13.900 10.341
step^7 -39.460 13.900 -2.839
step^8 -30.760 13.900 -2.213
step^9 -68.511 13.900 -4.929
step^10 -65.285 13.900 -4.697
step^11 5.974 13.900 0.430
step^12 97.737 13.900 7.032
step^13 -3.516 13.900 -0.253
step^14 -61.415 13.900 -4.419
step^15 -91.908 13.900 -6.612
step^16 -45.721 13.900 -3.289
step^17 -24.545 13.900 -1.766
Correlation matrix not shown by default, as p = 18 > 12.
Use print(summary(mdl), correlation=TRUE) or
vcov(summary(mdl)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 710.88 17 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
step emmean SE df lower.CL upper.CL
1 805 49.2 19.9 703 908
2 497 49.2 19.9 395 600
3 459 49.2 19.9 356 562
4 448 49.2 19.9 345 551
5 525 49.2 19.9 422 628
6 522 49.2 19.9 419 624
7 501 49.2 19.9 399 604
8 490 49.2 19.9 387 592
9 541 49.2 19.9 438 643
10 566 49.2 19.9 463 669
11 510 49.2 19.9 407 613
12 476 49.2 19.9 373 579
13 704 49.2 19.9 601 807
14 562 49.2 19.9 459 664
15 509 49.2 19.9 407 612
16 446 49.2 19.9 343 548
17 505 49.2 19.9 402 607
18 485 49.2 19.9 383 588
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise (Tukey) across steps — correct:
contrast estimate SE df t.ratio p.value
step1 - step2 308.066 19.7 8461 15.672 <.0001
step1 - step3 346.254 19.7 8461 17.615 <.0001
step1 - step4 357.566 19.7 8461 18.190 <.0001
step1 - step5 280.229 19.7 8461 14.256 <.0001
step1 - step6 283.729 19.7 8461 14.434 <.0001
step1 - step7 303.913 19.7 8461 15.461 <.0001
step1 - step8 315.773 19.7 8461 16.064 <.0001
step1 - step9 264.585 19.7 8461 13.460 <.0001
step1 - step10 239.358 19.7 8461 12.177 <.0001
step1 - step11 295.263 19.7 8461 15.021 <.0001
step1 - step12 329.360 19.7 8461 16.755 <.0001
step1 - step13 101.496 19.7 8461 5.163 <.0001
step1 - step14 243.826 19.7 8461 12.404 <.0001
step1 - step15 296.002 19.7 8461 15.058 <.0001
step1 - step16 359.763 19.7 8461 18.302 <.0001
step1 - step17 300.820 19.7 8461 15.303 <.0001
step1 - step18 319.892 19.7 8461 16.274 <.0001
step2 - step3 38.189 19.7 8461 1.943 0.8954
step2 - step4 49.500 19.7 8461 2.518 0.5162
step2 - step5 -27.837 19.7 8461 -1.416 0.9951
step2 - step6 -24.337 19.7 8461 -1.238 0.9990
step2 - step7 -4.152 19.7 8461 -0.211 1.0000
step2 - step8 7.708 19.7 8461 0.392 1.0000
step2 - step9 -43.481 19.7 8461 -2.212 0.7459
step2 - step10 -68.708 19.7 8461 -3.495 0.0491
step2 - step11 -12.803 19.7 8461 -0.651 1.0000
step2 - step12 21.294 19.7 8461 1.083 0.9998
step2 - step13 -206.570 19.7 8461 -10.509 <.0001
step2 - step14 -64.239 19.7 8461 -3.268 0.0984
step2 - step15 -12.064 19.7 8461 -0.614 1.0000
step2 - step16 51.697 19.7 8461 2.630 0.4311
step2 - step17 -7.246 19.7 8461 -0.369 1.0000
step2 - step18 11.826 19.7 8461 0.602 1.0000
step3 - step4 11.311 19.7 8461 0.575 1.0000
step3 - step5 -66.025 19.7 8461 -3.359 0.0752
step3 - step6 -62.525 19.7 8461 -3.181 0.1256
step3 - step7 -42.341 19.7 8461 -2.154 0.7839
step3 - step8 -30.481 19.7 8461 -1.551 0.9868
step3 - step9 -81.669 19.7 8461 -4.155 0.0043
step3 - step10 -106.896 19.7 8461 -5.438 <.0001
step3 - step11 -50.992 19.7 8461 -2.594 0.4580
step3 - step12 -16.894 19.7 8461 -0.859 1.0000
step3 - step13 -244.758 19.7 8461 -12.451 <.0001
step3 - step14 -102.428 19.7 8461 -5.211 <.0001
step3 - step15 -50.252 19.7 8461 -2.556 0.4867
step3 - step16 13.508 19.7 8461 0.687 1.0000
step3 - step17 -45.434 19.7 8461 -2.311 0.6751
step3 - step18 -26.362 19.7 8461 -1.341 0.9974
step4 - step5 -77.337 19.7 8461 -3.934 0.0103
step4 - step6 -73.837 19.7 8461 -3.756 0.0201
step4 - step7 -53.653 19.7 8461 -2.729 0.3598
step4 - step8 -41.792 19.7 8461 -2.126 0.8012
step4 - step9 -92.981 19.7 8461 -4.730 0.0003
step4 - step10 -118.208 19.7 8461 -6.014 <.0001
step4 - step11 -62.303 19.7 8461 -3.170 0.1295
step4 - step12 -28.206 19.7 8461 -1.435 0.9944
step4 - step13 -256.070 19.7 8461 -13.027 <.0001
step4 - step14 -113.739 19.7 8461 -5.786 <.0001
step4 - step15 -61.564 19.7 8461 -3.132 0.1433
step4 - step16 2.197 19.7 8461 0.112 1.0000
step4 - step17 -56.746 19.7 8461 -2.887 0.2600
step4 - step18 -37.674 19.7 8461 -1.917 0.9059
step5 - step6 3.500 19.7 8461 0.178 1.0000
step5 - step7 23.684 19.7 8461 1.205 0.9993
step5 - step8 35.544 19.7 8461 1.808 0.9420
step5 - step9 -15.644 19.7 8461 -0.796 1.0000
step5 - step10 -40.871 19.7 8461 -2.079 0.8285
step5 - step11 15.034 19.7 8461 0.765 1.0000
step5 - step12 49.131 19.7 8461 2.499 0.5307
step5 - step13 -178.733 19.7 8461 -9.093 <.0001
step5 - step14 -36.403 19.7 8461 -1.852 0.9289
step5 - step15 15.773 19.7 8461 0.802 1.0000
step5 - step16 79.534 19.7 8461 4.046 0.0067
step5 - step17 20.591 19.7 8461 1.048 0.9999
step5 - step18 39.663 19.7 8461 2.018 0.8611
step6 - step7 20.184 19.7 8461 1.027 0.9999
step6 - step8 32.044 19.7 8461 1.630 0.9780
step6 - step9 -19.144 19.7 8461 -0.974 1.0000
step6 - step10 -44.371 19.7 8461 -2.257 0.7144
step6 - step11 11.534 19.7 8461 0.587 1.0000
step6 - step12 45.631 19.7 8461 2.321 0.6677
step6 - step13 -182.233 19.7 8461 -9.271 <.0001
step6 - step14 -39.903 19.7 8461 -2.030 0.8549
step6 - step15 12.273 19.7 8461 0.624 1.0000
step6 - step16 76.034 19.7 8461 3.868 0.0133
step6 - step17 17.091 19.7 8461 0.869 1.0000
step6 - step18 36.163 19.7 8461 1.840 0.9328
step7 - step8 11.860 19.7 8461 0.603 1.0000
step7 - step9 -39.328 19.7 8461 -2.001 0.8694
step7 - step10 -64.555 19.7 8461 -3.284 0.0939
step7 - step11 -8.650 19.7 8461 -0.440 1.0000
step7 - step12 25.447 19.7 8461 1.295 0.9983
step7 - step13 -202.417 19.7 8461 -10.297 <.0001
step7 - step14 -60.087 19.7 8461 -3.057 0.1739
step7 - step15 -7.911 19.7 8461 -0.402 1.0000
step7 - step16 55.850 19.7 8461 2.841 0.2871
step7 - step17 -3.093 19.7 8461 -0.157 1.0000
step7 - step18 15.979 19.7 8461 0.813 1.0000
step8 - step9 -51.189 19.7 8461 -2.604 0.4504
step8 - step10 -76.415 19.7 8461 -3.887 0.0124
step8 - step11 -20.511 19.7 8461 -1.043 0.9999
step8 - step12 13.587 19.7 8461 0.691 1.0000
step8 - step13 -214.278 19.7 8461 -10.901 <.0001
step8 - step14 -71.947 19.7 8461 -3.660 0.0283
step8 - step15 -19.771 19.7 8461 -1.006 0.9999
step8 - step16 43.989 19.7 8461 2.238 0.7281
step8 - step17 -14.953 19.7 8461 -0.761 1.0000
step8 - step18 4.119 19.7 8461 0.210 1.0000
step9 - step10 -25.227 19.7 8461 -1.283 0.9985
step9 - step11 30.678 19.7 8461 1.561 0.9859
step9 - step12 64.775 19.7 8461 3.295 0.0909
step9 - step13 -163.089 19.7 8461 -8.297 <.0001
step9 - step14 -20.759 19.7 8461 -1.056 0.9999
step9 - step15 31.417 19.7 8461 1.598 0.9820
step9 - step16 95.178 19.7 8461 4.842 0.0002
step9 - step17 36.235 19.7 8461 1.843 0.9316
step9 - step18 55.307 19.7 8461 2.814 0.3042
step10 - step11 55.905 19.7 8461 2.844 0.2854
step10 - step12 90.002 19.7 8461 4.579 0.0007
step10 - step13 -137.862 19.7 8461 -7.013 <.0001
step10 - step14 4.468 19.7 8461 0.227 1.0000
step10 - step15 56.644 19.7 8461 2.882 0.2630
step10 - step16 120.405 19.7 8461 6.125 <.0001
step10 - step17 61.462 19.7 8461 3.127 0.1452
step10 - step18 80.534 19.7 8461 4.097 0.0054
step11 - step12 34.097 19.7 8461 1.735 0.9601
step11 - step13 -193.767 19.7 8461 -9.857 <.0001
step11 - step14 -51.436 19.7 8461 -2.617 0.4410
step11 - step15 0.739 19.7 8461 0.038 1.0000
step11 - step16 64.500 19.7 8461 3.281 0.0947
step11 - step17 5.557 19.7 8461 0.283 1.0000
step11 - step18 24.629 19.7 8461 1.253 0.9989
step12 - step13 -227.864 19.7 8461 -11.592 <.0001
step12 - step14 -85.534 19.7 8461 -4.351 0.0019
step12 - step15 -33.358 19.7 8461 -1.697 0.9675
step12 - step16 30.402 19.7 8461 1.547 0.9872
step12 - step17 -28.540 19.7 8461 -1.452 0.9936
step12 - step18 -9.468 19.7 8461 -0.482 1.0000
step13 - step14 142.331 19.7 8461 7.241 <.0001
step13 - step15 194.506 19.7 8461 9.895 <.0001
step13 - step16 258.267 19.7 8461 13.139 <.0001
step13 - step17 199.324 19.7 8461 10.140 <.0001
step13 - step18 218.396 19.7 8461 11.110 <.0001
step14 - step15 52.176 19.7 8461 2.654 0.4132
step14 - step16 115.936 19.7 8461 5.898 <.0001
step14 - step17 56.994 19.7 8461 2.899 0.2529
step14 - step18 76.066 19.7 8461 3.870 0.0132
step15 - step16 63.761 19.7 8461 3.244 0.1054
step15 - step17 4.818 19.7 8461 0.245 1.0000
step15 - step18 23.890 19.7 8461 1.215 0.9992
step16 - step17 -58.943 19.7 8461 -2.999 0.2008
step16 - step18 -39.871 19.7 8461 -2.028 0.8558
step17 - step18 19.072 19.7 8461 0.970 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent-step contrasts (consecutive) — correct:
contrast estimate SE df t.ratio p.value
step2 - step1 -308.1 19.7 8461 -15.672 <.0001
step3 - step2 -38.2 19.7 8461 -1.943 0.4166
step4 - step3 -11.3 19.7 8461 -0.575 1.0000
step5 - step4 77.3 19.7 8461 3.934 0.0012
step6 - step5 -3.5 19.7 8461 -0.178 1.0000
step7 - step6 -20.2 19.7 8461 -1.027 1.0000
step8 - step7 -11.9 19.7 8461 -0.603 1.0000
step9 - step8 51.2 19.7 8461 2.604 0.0831
step10 - step9 25.2 19.7 8461 1.283 1.0000
step11 - step10 -55.9 19.7 8461 -2.844 0.0491
step12 - step11 -34.1 19.7 8461 -1.735 0.5799
step13 - step12 227.9 19.7 8461 11.592 <.0001
step14 - step13 -142.3 19.7 8461 -7.241 <.0001
step15 - step14 -52.2 19.7 8461 -2.654 0.0796
step16 - step15 -63.8 19.7 8461 -3.244 0.0154
step17 - step16 58.9 19.7 8461 2.999 0.0326
step18 - step17 -19.1 19.7 8461 -0.970 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 17 tests
[WRONG] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step + (1 | subject)
Data: data
REML criterion at convergence: 109180.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.1545 -0.4740 -0.2235 0.1305 18.0003
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 54101 232.6
Residual 311631 558.2
Number of obs: 7056, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 628.117 55.394 11.339
step.L 77.092 28.195 2.734
step.Q 124.693 28.195 4.422
step.C -206.127 28.195 -7.311
step^4 309.668 28.195 10.983
step^5 -125.975 28.195 -4.468
step^6 212.995 28.195 7.554
step^7 -34.679 28.195 -1.230
step^8 85.672 28.195 3.039
step^9 -4.591 28.195 -0.163
step^10 -35.356 28.195 -1.254
step^11 -21.381 28.195 -0.758
step^12 79.047 28.195 2.804
step^13 -28.895 28.195 -1.025
step^14 -62.798 28.195 -2.227
step^15 -24.967 28.195 -0.886
step^16 -34.120 28.195 -1.210
step^17 -24.296 28.195 -0.862
Correlation matrix not shown by default, as p = 18 > 12.
Use print(summary(mdl), correlation=TRUE) or
vcov(summary(mdl)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 307.92 17 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
step emmean SE df lower.CL upper.CL
1 985 61.8 26.3 858 1112
2 478 61.8 26.3 352 605
3 466 61.8 26.3 339 593
4 480 61.8 26.3 353 607
5 567 61.8 26.3 440 694
6 618 61.8 26.3 491 744
7 561 61.8 26.3 434 688
8 581 61.8 26.3 454 708
9 678 61.8 26.3 551 805
10 675 61.8 26.3 548 802
11 634 61.8 26.3 507 761
12 602 61.8 26.3 475 729
13 719 61.8 26.3 592 846
14 648 61.8 26.3 521 775
15 670 61.8 26.3 543 797
16 600 61.8 26.3 473 727
17 583 61.8 26.3 456 709
18 761 61.8 26.3 634 888
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 # === Stepwise RT — Training (Blocks 1–3), CORRECT trials only ===
suppressPackageStartupMessages({
library(dplyr); library(ggplot2); library(emmeans); library(lme4); library(stringr)
})
if (!exists("safe_fit")) {
safe_fit <- function(formula, data) {
m <- tryCatch(lmer(formula, data = data), error = function(e) NULL)
if (is.null(m)) { message("⚠️ Falling back to lm."); m <- stats::lm(update(formula, . ~ . - (1 | subject)), data = data) }
m
}
}
plot_stepwise_rt_training_correct_only <- function(df_acc_all, show_plot = TRUE, alpha = 0.05) {
emms_list <- list()
sig_list <- list()
for (blk in 1:3) {
df_blk <- df_acc_all %>%
filter(session == blk, trial_acc_cat == "correct") %>%
group_by(subject, trial, step = as.integer(as.character(sub.trial.number))) %>%
summarise(rt = mean(feedback.RT, na.rm = TRUE), .groups = "drop")
if (nrow(df_blk) == 0) next
df_blk <- df_blk %>% mutate(step = factor(step, levels = sort(unique(step)), ordered = TRUE))
mdl <- safe_fit(rt ~ step + (1 | subject), data = df_blk)
em_grid <- emmeans(mdl, ~ step)
# Store EMMs for plotting
em <- as.data.frame(em_grid)
em$block <- blk
em$step_num <- as.integer(as.character(em$step))
emms_list[[blk]] <- em
# Adjacent (consecutive) contrasts (Holm-adjusted)
consec_df <- as.data.frame(contrast(em_grid, method = "consec", adjust = "holm"))
nums <- stringr::str_extract_all(consec_df$contrast, "\\d+")
n1 <- vapply(nums, function(v) as.integer(v[1]), integer(1)) # first number in label
n2 <- vapply(nums, function(v) as.integer(v[2]), integer(1)) # second number in label
# Ensure estimate refers to (next - prev); if label is lower - higher
prev <- pmin(n1, n2)
nextv <- pmax(n1, n2)
sign_adj <- ifelse(nextv == n1, 1, -1)
est_adj <- consec_df$estimate * sign_adj
sig_df <- consec_df %>%
mutate(step_prev = prev,
step_next = nextv,
estimate_adj = est_adj,
sig = !is.na(p.value) & p.value < alpha) %>%
filter(sig) %>%
transmute(
block = blk,
step_num = step_prev, # put star at the previous step (the one followed by a change)
direction = ifelse(estimate_adj > 0, "up", "down"),
p.value = p.value
)
if (nrow(sig_df)) sig_list[[blk]] <- sig_df
}
if (!length(emms_list)) { message("No correct-trial data for Blocks 1–3."); return(invisible(NULL)) }
df_em <- bind_rows(emms_list) %>%
mutate(
seq_label = case_when(block == 1 ~ "6 Steps",
block == 2 ~ "12 Steps",
TRUE ~ "18 Steps"),
seq_label = factor(seq_label, levels = c("6 Steps","12 Steps","18 Steps"), ordered = TRUE)
)
# Star positions
df_stars <- if (length(sig_list)) bind_rows(sig_list) else
tibble(block = integer(), step_num = integer(), direction = character(), p.value = numeric())
if (nrow(df_stars)) {
df_stars <- df_stars %>%
left_join(df_em %>% select(block, step_num, emmean, SE, seq_label), by = c("block","step_num")) %>%
mutate(
y_star = emmean + pmax(SE * 1.2, 35)
)
}
df_stars_up <- df_stars %>% filter(direction == "up")
df_stars_down <- df_stars %>% filter(direction == "down")
# separators at right edge of the first two panels
sep_df <- data.frame(seq_label = factor(c("6 Steps","12 Steps"),
levels = levels(df_em$seq_label), ordered = TRUE))
pal <- c("6 Steps" = "#B22222", "12 Steps" = "#2E7D32", "18 Steps" = "#1E3A8A")
p <- ggplot(df_em, aes(x = step_num, y = emmean, color = seq_label, fill = seq_label, group = 1)) +
geom_segment(data = sep_df,
aes(x = Inf, xend = Inf, y = -Inf, yend = Inf),
inherit.aes = FALSE, linetype = "dotted") +
geom_ribbon(aes(ymin = emmean - SE, ymax = emmean + SE), alpha = 0.15, color = NA) +
geom_line(linewidth = 0.9) +
geom_point(size = 2) +
# star markers: red = following step ↑; blue = following step ↓
{ if (nrow(df_stars_up)) geom_text(data = df_stars_up,
aes(x = step_num, y = y_star, label = "*"),
inherit.aes = FALSE, size = 4.2, fontface = "bold",
color = "#D32F2F") } +
{ if (nrow(df_stars_down)) geom_text(data = df_stars_down,
aes(x = step_num, y = y_star, label = "*"),
inherit.aes = FALSE, size = 4.2, fontface = "bold",
color = "#1F6FEB") } +
facet_grid(cols = vars(seq_label), scales = "free_x", space = "free_x") +
scale_x_continuous(
breaks = function(lims) seq(floor(lims[1]), ceiling(lims[2]), by = 1),
expand = expansion(mult = c(0.02, 0.06))
) +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.10))) +
scale_color_manual(values = pal, breaks = names(pal)) +
scale_fill_manual(values = pal, breaks = names(pal)) +
labs(
title = "RT for each Step across Difficulty levels (Training phase)",
x = "Step", y = "Estimated RT (ms)",
caption = "* Following step is significantly different (red = \u2191, blue = \u2193)"
) +
theme_minimal(base_size = 12) +
theme(panel.grid = element_blank(),
legend.position = "none",
plot.caption = element_text(hjust = 0))
if (show_plot) print(p)
invisible(list(emms = df_em, stars = df_stars, plot = p, palette = pal))
}
# RUN
plot_stepwise_rt_training_correct_only(df_acc_base)Warning in geom_segment(data = sep_df, aes(x = Inf, xend = Inf, y = -Inf, : All aesthetics have length 1, but the data has 2 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
# === Stepwise RT — Training (Blocks 1–3), CORRECT trials only ===
suppressPackageStartupMessages({
library(dplyr); library(ggplot2); library(emmeans); library(lme4); library(stringr)
})
if (!exists("safe_fit")) {
safe_fit <- function(formula, data) {
m <- tryCatch(lmer(formula, data = data), error = function(e) NULL)
if (is.null(m)) { message("⚠️ Falling back to lm."); m <- stats::lm(update(formula, . ~ . - (1 | subject)), data = data) }
m
}
}
plot_stepwise_rt_training_correct_only <- function(df_acc_all, show_plot = TRUE, alpha = 0.05) {
emms_list <- list()
sig_list <- list()
for (blk in 1:3) {
df_blk <- df_acc_all %>%
filter(session == blk, trial_acc_cat == "correct") %>%
group_by(subject, trial, step = as.integer(as.character(sub.trial.number))) %>%
summarise(rt = mean(feedback.RT, na.rm = TRUE), .groups = "drop")
if (nrow(df_blk) == 0) next
df_blk <- df_blk %>% mutate(step = factor(step, levels = sort(unique(step)), ordered = TRUE))
mdl <- safe_fit(rt ~ step + (1 | subject), data = df_blk)
em_grid <- emmeans(mdl, ~ step)
# Store EMMs for plotting
em <- as.data.frame(em_grid)
em$block <- blk
em$step_num <- as.integer(as.character(em$step))
emms_list[[blk]] <- em
# Adjacent (consecutive) contrasts (Holm-adjusted)
consec_df <- as.data.frame(contrast(em_grid, method = "consec", adjust = "holm"))
nums <- stringr::str_extract_all(consec_df$contrast, "\\d+")
n1 <- vapply(nums, function(v) as.integer(v[1]), integer(1)) # first number in label
n2 <- vapply(nums, function(v) as.integer(v[2]), integer(1)) # second number in label
# Ensure estimate refers to (next - prev); if label is lower - higher
prev <- pmin(n1, n2)
nextv <- pmax(n1, n2)
sign_adj <- ifelse(nextv == n1, 1, -1)
est_adj <- consec_df$estimate * sign_adj
sig_df <- consec_df %>%
mutate(step_prev = prev,
step_next = nextv,
estimate_adj = est_adj,
sig = !is.na(p.value) & p.value < alpha) %>%
filter(sig) %>%
transmute(
block = blk,
step_num = step_prev, # put star at the previous step (the one followed by a change)
direction = ifelse(estimate_adj > 0, "up", "down"),
p.value = p.value
)
if (nrow(sig_df)) sig_list[[blk]] <- sig_df
}
if (!length(emms_list)) { message("No correct-trial data for Blocks 1–3."); return(invisible(NULL)) }
df_em <- bind_rows(emms_list) %>%
mutate(
seq_label = case_when(block == 1 ~ "6 Steps",
block == 2 ~ "12 Steps",
TRUE ~ "18 Steps"),
seq_label = factor(seq_label, levels = c("6 Steps","12 Steps","18 Steps"), ordered = TRUE)
)
# Star positions
df_stars <- if (length(sig_list)) bind_rows(sig_list) else
tibble(block = integer(), step_num = integer(), direction = character(), p.value = numeric())
if (nrow(df_stars)) {
df_stars <- df_stars %>%
left_join(df_em %>% select(block, step_num, emmean, SE, seq_label), by = c("block","step_num")) %>%
mutate(
y_star = emmean + pmax(SE * 1.2, 35)
)
}
df_stars_up <- df_stars %>% filter(direction == "up")
df_stars_down <- df_stars %>% filter(direction == "down")
# separators at right edge of the first two panels
sep_df <- data.frame(seq_label = factor(c("6 Steps","12 Steps"),
levels = levels(df_em$seq_label), ordered = TRUE))
pal <- c("6 Steps" = "#B22222", "12 Steps" = "#2E7D32", "18 Steps" = "#1E3A8A")
p <- ggplot(df_em, aes(x = step_num, y = emmean, color = seq_label, fill = seq_label, group = 1)) +
geom_segment(data = sep_df,
aes(x = Inf, xend = Inf, y = -Inf, yend = Inf),
inherit.aes = FALSE, linetype = "dotted") +
geom_ribbon(aes(ymin = emmean - SE, ymax = emmean + SE), alpha = 0.15, color = NA) +
geom_line(linewidth = 0.9) +
geom_point(size = 2) +
# star markers: red = following step ↑; blue = following step ↓
{ if (nrow(df_stars_up)) geom_text(data = df_stars_up,
aes(x = step_num, y = y_star, label = "*"),
inherit.aes = FALSE, size = 4.2, fontface = "bold",
color = "#D32F2F") } +
{ if (nrow(df_stars_down)) geom_text(data = df_stars_down,
aes(x = step_num, y = y_star, label = "*"),
inherit.aes = FALSE, size = 4.2, fontface = "bold",
color = "#1F6FEB") } +
facet_grid(cols = vars(seq_label), scales = "free_x", space = "free_x") +
scale_x_continuous(
breaks = function(lims) seq(floor(lims[1]), ceiling(lims[2]), by = 1),
expand = expansion(mult = c(0.02, 0.06))
) +
scale_y_continuous(expand = expansion(mult = c(0.05, 0.10))) +
scale_color_manual(values = pal, breaks = names(pal)) +
scale_fill_manual(values = pal, breaks = names(pal)) +
labs(
title = "RT for each Step across Difficulty levels (Training phase)",
x = "Step", y = "Estimated RT (ms)",
caption = "* Following step is significantly different (red = \u2191, blue = \u2193)"
) +
ggplot2::theme_classic(base_size = 12) + # APA7: visible axes
ggplot2::theme(
panel.grid = ggplot2::element_blank(),
legend.position = "none",
plot.caption = ggplot2::element_text(hjust = 0),
axis.line = ggplot2::element_line(),
axis.ticks = ggplot2::element_line()
)
if (show_plot) print(p)
invisible(list(emms = df_em, stars = df_stars, plot = p, palette = pal))
}
# RUN
plot_stepwise_rt_training_correct_only(df_acc_base)Warning in geom_segment(data = sep_df, aes(x = Inf, xend = Inf, y = -Inf, : All aesthetics have length 1, but the data has 2 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
#A4 Stepwise RT — Test blocks (4–5), split by sequence length
#4 Stepwise RT — Test blocks (4–5), split by sequence length
stepwise_rt_test_by_length_with_correctness <- function(df_acc_all) {
cat("\n\n====== Stepwise RT — Test (4–5) by length ======\n")
df_len <- df_acc_all %>%
dplyr::group_by(subject, session, trial) %>%
dplyr::mutate(seq_length_trial = dplyr::n_distinct(sub.trial.number)) %>%
dplyr::ungroup()
for (L in c(6,12,18)) {
cat(glue::glue("\n--- Sequence length {L} ---\n"))
df_seq_all <- df_len %>%
dplyr::filter(session %in% c(4,5), seq_length_trial == L) %>%
dplyr::group_by(subject, session, trial, step = as.integer(as.character(sub.trial.number)), trial_acc_cat) %>%
dplyr::summarise(rt = mean(feedback.RT, na.rm = TRUE), .groups = "drop")
if (nrow(df_seq_all) == 0) { cat("No data\n"); next }
df_seq_all$session <- factor(df_seq_all$session, levels = c(4,5), labels = c("Block 4","Block 5"))
df_seq_all$step <- factor(df_seq_all$step, levels = sort(unique(df_seq_all$step)))
has_two_sessions <- dplyr::n_distinct(df_seq_all$session) >= 2
has_two_steps <- dplyr::n_distinct(df_seq_all$step) >= 2
has_two_corr <- dplyr::n_distinct(df_seq_all$trial_acc_cat) >= 2
# Combined model with correctness
if (has_two_sessions && has_two_steps && has_two_corr) {
fmla <- rt ~ step * session * trial_acc_cat + (1 | subject)
} else if (has_two_sessions && has_two_steps) {
fmla <- rt ~ step * session + (1 | subject)
} else if (has_two_steps && has_two_corr) {
fmla <- rt ~ step * trial_acc_cat + (1 | subject)
} else if (has_two_sessions && has_two_corr) {
fmla <- rt ~ session * trial_acc_cat + (1 | subject)
} else if (has_two_steps) {
fmla <- rt ~ step + (1 | subject)
} else if (has_two_sessions) {
fmla <- rt ~ session + (1 | subject)
} else if (has_two_corr) {
fmla <- rt ~ trial_acc_cat + (1 | subject)
} else {
cat("⚠️ Not enough levels to model.\n"); next
}
cat("\n[Combined] Model summary:\n")
mdl_all <- safe_fit(fmla, data = df_seq_all)
print(summary(mdl_all))
cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl_all, type = 2))
# Sensible EMMs depending on fixed effects present
if (grepl("step.*session.*trial_acc_cat", deparse(fmla))) {
em <- emmeans::emmeans(mdl_all, ~ step | session * trial_acc_cat)
print(summary(em))
} else if (grepl("step.*session", deparse(fmla))) {
em <- emmeans::emmeans(mdl_all, ~ step | session); print(summary(em))
} else if (grepl("step.*trial_acc_cat", deparse(fmla))) {
em <- emmeans::emmeans(mdl_all, ~ step | trial_acc_cat); print(summary(em))
} else if (grepl("session.*trial_acc_cat", deparse(fmla))) {
em <- emmeans::emmeans(mdl_all, ~ session | trial_acc_cat); print(summary(em))
} else if (grepl("^rt ~ step", deparse(fmla))) {
em <- emmeans::emmeans(mdl_all, ~ step); print(summary(em))
} else if (grepl("^rt ~ session", deparse(fmla))) {
em <- emmeans::emmeans(mdl_all, ~ session); print(summary(em))
} else {
em <- emmeans::emmeans(mdl_all, ~ trial_acc_cat); print(summary(em))
}
# --- Split outputs: add pairwise ONLY for correct
for (lab in c("correct","wrong")) {
df_seq <- df_seq_all %>% dplyr::filter(trial_acc_cat == lab)
if (nrow(df_seq) == 0) { cat("\n⚠️ No data for", lab, "\n"); next }
has_two_sessions <- dplyr::n_distinct(df_seq$session) >= 2
has_two_steps <- dplyr::n_distinct(df_seq$step) >= 2
if (!has_two_steps) { cat("\n⚠️ Not enough step levels for", lab, "\n"); next }
fmla_s <- if (has_two_sessions) rt ~ step * session + (1 | subject) else rt ~ step + (1 | subject)
cat("\n[", toupper(lab), "] Model summary:\n", sep = "")
mdl <- safe_fit(fmla_s, data = df_seq)
print(summary(mdl))
cat("\nType II Chi-square ANOVA:\n"); print(car::Anova(mdl, type = 2))
# EMMs (always by step)
if (has_two_sessions) {
em_s <- emmeans::emmeans(mdl, ~ step | session)
} else {
em_s <- emmeans::emmeans(mdl, ~ step)
}
print(summary(em_s))
# Pairwise for CORRECT only
if (lab == "correct") {
if (has_two_sessions) {
cat("\nPairwise (Tukey) across steps — correct | session:\n")
} else {
cat("\nPairwise (Tukey) across steps — correct:\n")
}
print(pairs(em_s, adjust = "tukey"))
if (has_two_sessions) {
cat("\nAdjacent-step contrasts (consecutive) — correct | session:\n")
} else {
cat("\nAdjacent-step contrasts (consecutive) — correct:\n")
}
print(emmeans::contrast(em_s, method = "consec", adjust = "holm"))
}
}
}
}
stepwise_rt_test_by_length_with_correctness(df_acc_base)
====== Stepwise RT — Test (4–5) by length ======
--- Sequence length 6 ---
[Combined] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * session * trial_acc_cat + (1 | subject)
Data: data
REML criterion at convergence: 51434.3
Scaled residuals:
Min 1Q Median 3Q Max
-2.5669 -0.3601 -0.0877 0.1859 27.0864
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 33464 182.9
Residual 179548 423.7
Number of obs: 3456, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 764.911 50.749 15.073
step2 -348.940 37.824 -9.225
step3 -390.669 37.824 -10.329
step4 -402.183 37.824 -10.633
step5 -382.510 37.824 -10.113
step6 -376.163 37.824 -9.945
sessionBlock 5 71.983 39.302 1.832
trial_acc_catwrong 99.424 75.011 1.325
step2:sessionBlock 5 13.248 55.479 0.239
step3:sessionBlock 5 48.610 55.479 0.876
step4:sessionBlock 5 56.422 55.479 1.017
step5:sessionBlock 5 36.051 55.479 0.650
step6:sessionBlock 5 29.439 55.479 0.531
step2:trial_acc_catwrong 50.751 105.527 0.481
step3:trial_acc_catwrong 94.048 105.527 0.891
step4:trial_acc_catwrong -75.519 105.527 -0.716
step5:trial_acc_catwrong 63.591 105.527 0.603
step6:trial_acc_catwrong 700.163 105.527 6.635
sessionBlock 5:trial_acc_catwrong 330.744 95.319 3.470
step2:sessionBlock 5:trial_acc_catwrong -430.244 133.840 -3.215
step3:sessionBlock 5:trial_acc_catwrong -366.302 133.840 -2.737
step4:sessionBlock 5:trial_acc_catwrong -274.605 133.840 -2.052
step5:sessionBlock 5:trial_acc_catwrong 8.282 133.840 0.062
step6:sessionBlock 5:trial_acc_catwrong -682.424 133.840 -5.099
Correlation matrix not shown by default, as p = 24 > 12.
Use print(summary(mdl_all), correlation=TRUE) or
vcov(summary(mdl_all)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 394.1445 5 < 2.2e-16 ***
session 56.3310 1 6.124e-14 ***
trial_acc_cat 172.6516 1 < 2.2e-16 ***
step:session 8.5065 5 0.1304
step:trial_acc_cat 92.9202 5 < 2.2e-16 ***
session:trial_acc_cat 0.9797 1 0.3223
step:session:trial_acc_cat 39.3891 5 1.983e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
session = Block 4, trial_acc_cat = correct:
step emmean SE df lower.CL upper.CL
1 765 50.7 30.8 661 868
2 416 50.7 30.8 312 519
3 374 50.7 30.8 271 478
4 363 50.7 30.8 259 466
5 382 50.7 30.8 279 486
6 389 50.7 30.8 285 492
session = Block 5, trial_acc_cat = correct:
step emmean SE df lower.CL upper.CL
1 837 51.8 33.6 731 942
2 501 51.8 33.6 396 607
3 495 51.8 33.6 389 600
4 491 51.8 33.6 386 597
5 490 51.8 33.6 385 596
6 490 51.8 33.6 385 596
session = Block 4, trial_acc_cat = wrong:
step emmean SE df lower.CL upper.CL
1 864 82.2 205.5 702 1026
2 566 82.2 205.5 404 728
3 568 82.2 205.5 406 730
4 387 82.2 205.5 225 549
5 545 82.2 205.5 383 707
6 1188 82.2 205.5 1026 1350
session = Block 5, trial_acc_cat = wrong:
step emmean SE df lower.CL upper.CL
1 1267 66.9 92.2 1134 1400
2 552 66.9 92.2 419 685
3 653 66.9 92.2 520 786
4 571 66.9 92.2 438 704
5 992 66.9 92.2 860 1125
6 938 66.9 92.2 805 1071
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
[CORRECT] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * session + (1 | subject)
Data: data
REML criterion at convergence: 39822.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.7780 -0.4318 -0.1148 0.2646 20.8747
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 28464 168.7
Residual 83002 288.1
Number of obs: 2814, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 764.27 43.73 17.475
step2 -348.94 25.72 -13.568
step3 -390.67 25.72 -15.191
step4 -402.18 25.72 -15.639
step5 -382.51 25.72 -14.874
step6 -376.16 25.72 -14.627
sessionBlock 5 74.40 26.75 2.781
step2:sessionBlock 5 13.25 37.72 0.351
step3:sessionBlock 5 48.61 37.72 1.289
step4:sessionBlock 5 56.42 37.72 1.496
step5:sessionBlock 5 36.05 37.72 0.956
step6:sessionBlock 5 29.44 37.72 0.780
Correlation of Fixed Effects:
(Intr) step2 step3 step4 step5 step6 sssnB5 st2:B5 st3:B5
step2 -0.294
step3 -0.294 0.500
step4 -0.294 0.500 0.500
step5 -0.294 0.500 0.500 0.500
step6 -0.294 0.500 0.500 0.500 0.500
sessinBlck5 -0.283 0.481 0.481 0.481 0.481 0.481
stp2:sssnB5 0.200 -0.682 -0.341 -0.341 -0.341 -0.341 -0.705
stp3:sssnB5 0.200 -0.341 -0.682 -0.341 -0.341 -0.341 -0.705 0.500
stp4:sssnB5 0.200 -0.341 -0.341 -0.682 -0.341 -0.341 -0.705 0.500 0.500
stp5:sssnB5 0.200 -0.341 -0.341 -0.341 -0.682 -0.341 -0.705 0.500 0.500
stp6:sssnB5 0.200 -0.341 -0.341 -0.341 -0.341 -0.682 -0.705 0.500 0.500
st4:B5 st5:B5
step2
step3
step4
step5
step6
sessinBlck5
stp2:sssnB5
stp3:sssnB5
stp4:sssnB5
stp5:sssnB5 0.500
stp6:sssnB5 0.500 0.500
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 623.9439 5 <2e-16 ***
session 89.7160 1 <2e-16 ***
step:session 3.1762 5 0.6728
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
session = Block 4:
step emmean SE df lower.CL upper.CL
1 764 43.7 23.9 674 855
2 415 43.7 23.9 325 506
3 374 43.7 23.9 283 464
4 362 43.7 23.9 272 452
5 382 43.7 23.9 291 472
6 388 43.7 23.9 298 478
session = Block 5:
step emmean SE df lower.CL upper.CL
1 839 44.3 25.3 747 930
2 503 44.3 25.3 412 594
3 497 44.3 25.3 405 588
4 493 44.3 25.3 402 584
5 492 44.3 25.3 401 583
6 492 44.3 25.3 401 583
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise (Tukey) across steps — correct | session:
session = Block 4:
contrast estimate SE df t.ratio p.value
step1 - step2 348.940 25.7 2785 13.568 <.0001
step1 - step3 390.669 25.7 2785 15.191 <.0001
step1 - step4 402.183 25.7 2785 15.639 <.0001
step1 - step5 382.510 25.7 2785 14.874 <.0001
step1 - step6 376.163 25.7 2785 14.627 <.0001
step2 - step3 41.729 25.7 2785 1.623 0.5835
step2 - step4 53.243 25.7 2785 2.070 0.3032
step2 - step5 33.570 25.7 2785 1.305 0.7822
step2 - step6 27.223 25.7 2785 1.059 0.8976
step3 - step4 11.514 25.7 2785 0.448 0.9977
step3 - step5 -8.159 25.7 2785 -0.317 0.9996
step3 - step6 -14.506 25.7 2785 -0.564 0.9933
step4 - step5 -19.673 25.7 2785 -0.765 0.9733
step4 - step6 -26.020 25.7 2785 -1.012 0.9142
step5 - step6 -6.347 25.7 2785 -0.247 0.9999
session = Block 5:
contrast estimate SE df t.ratio p.value
step1 - step2 335.693 27.6 2785 12.165 <.0001
step1 - step3 342.060 27.6 2785 12.396 <.0001
step1 - step4 345.762 27.6 2785 12.530 <.0001
step1 - step5 346.459 27.6 2785 12.555 <.0001
step1 - step6 346.725 27.6 2785 12.565 <.0001
step2 - step3 6.367 27.6 2785 0.231 0.9999
step2 - step4 10.069 27.6 2785 0.365 0.9992
step2 - step5 10.766 27.6 2785 0.390 0.9988
step2 - step6 11.032 27.6 2785 0.400 0.9987
step3 - step4 3.702 27.6 2785 0.134 1.0000
step3 - step5 4.399 27.6 2785 0.159 1.0000
step3 - step6 4.665 27.6 2785 0.169 1.0000
step4 - step5 0.697 27.6 2785 0.025 1.0000
step4 - step6 0.963 27.6 2785 0.035 1.0000
step5 - step6 0.266 27.6 2785 0.010 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent-step contrasts (consecutive) — correct | session:
session = Block 4:
contrast estimate SE df t.ratio p.value
step2 - step1 -348.940 25.7 2785 -13.568 <.0001
step3 - step2 -41.729 25.7 2785 -1.623 0.4191
step4 - step3 -11.514 25.7 2785 -0.448 1.0000
step5 - step4 19.673 25.7 2785 0.765 1.0000
step6 - step5 6.347 25.7 2785 0.247 1.0000
session = Block 5:
contrast estimate SE df t.ratio p.value
step2 - step1 -335.693 27.6 2785 -12.165 <.0001
step3 - step2 -6.367 27.6 2785 -0.231 1.0000
step4 - step3 -3.702 27.6 2785 -0.134 1.0000
step5 - step4 -0.697 27.6 2785 -0.025 1.0000
step6 - step5 -0.266 27.6 2785 -0.010 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 5 tests
[WRONG] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * session + (1 | subject)
Data: data
REML criterion at convergence: 10242.4
Scaled residuals:
Min 1Q Median 3Q Max
-1.5788 -0.4499 -0.1617 0.1451 14.6358
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 68405 261.5
Residual 600745 775.1
Number of obs: 642, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 863.22 143.94 5.997
step2 -298.19 180.20 -1.655
step3 -296.62 180.20 -1.646
step4 -477.70 180.20 -2.651
step5 -318.92 180.20 -1.770
step6 324.00 180.20 1.798
sessionBlock 5 395.00 160.56 2.460
step2:sessionBlock 5 -417.00 222.79 -1.872
step3:sessionBlock 5 -317.69 222.79 -1.426
step4:sessionBlock 5 -218.18 222.79 -0.979
step5:sessionBlock 5 44.33 222.79 0.199
step6:sessionBlock 5 -652.99 222.79 -2.931
Correlation of Fixed Effects:
(Intr) step2 step3 step4 step5 step6 sssnB5 st2:B5 st3:B5
step2 -0.626
step3 -0.626 0.500
step4 -0.626 0.500 0.500
step5 -0.626 0.500 0.500 0.500
step6 -0.626 0.500 0.500 0.500 0.500
sessinBlck5 -0.726 0.561 0.561 0.561 0.561 0.561
stp2:sssnB5 0.506 -0.809 -0.404 -0.404 -0.404 -0.404 -0.694
stp3:sssnB5 0.506 -0.404 -0.809 -0.404 -0.404 -0.404 -0.694 0.500
stp4:sssnB5 0.506 -0.404 -0.404 -0.809 -0.404 -0.404 -0.694 0.500 0.500
stp5:sssnB5 0.506 -0.404 -0.404 -0.404 -0.809 -0.404 -0.694 0.500 0.500
stp6:sssnB5 0.506 -0.404 -0.404 -0.404 -0.404 -0.809 -0.694 0.500 0.500
st4:B5 st5:B5
step2
step3
step4
step5
step6
sessinBlck5
stp2:sssnB5
stp3:sssnB5
stp4:sssnB5
stp5:sssnB5 0.500
stp6:sssnB5 0.500 0.500
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 59.2825 5 1.71e-11 ***
session 3.5623 1 0.05911 .
step:session 13.8760 5 0.01642 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
session = Block 4:
step emmean SE df lower.CL upper.CL
1 863 144 206 579 1147
2 565 144 206 281 849
3 567 144 206 282 851
4 386 144 206 101 670
5 544 144 206 260 828
6 1187 144 206 903 1471
session = Block 5:
step emmean SE df lower.CL upper.CL
1 1258 114 97 1032 1485
2 543 114 97 317 769
3 644 114 97 418 870
4 562 114 97 336 789
5 984 114 97 757 1210
6 929 114 97 703 1156
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
--- Sequence length 12 ---
[Combined] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * session * trial_acc_cat + (1 | subject)
Data: data
REML criterion at convergence: 104004.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.2353 -0.4181 -0.1554 0.1457 19.2708
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 36507 191.1
Residual 213173 461.7
Number of obs: 6912, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 739.049 54.599 13.536
step2 -303.513 43.627 -6.957
step3 -343.759 43.627 -7.879
step4 -341.710 43.627 -7.833
step5 -300.915 43.627 -6.897
step6 -266.804 43.627 -6.116
step7 -154.647 43.627 -3.545
step8 -333.433 43.627 -7.643
step9 -247.138 43.627 -5.665
step10 -286.696 43.627 -6.572
step11 -317.045 43.627 -7.267
step12 -334.058 43.627 -7.657
sessionBlock 5 229.706 50.498 4.549
trial_acc_catwrong 21.904 65.621 0.334
step2:sessionBlock 5 -130.300 71.309 -1.827
step3:sessionBlock 5 -110.547 71.309 -1.550
step4:sessionBlock 5 -90.380 71.309 -1.267
step5:sessionBlock 5 -127.450 71.309 -1.787
step6:sessionBlock 5 -106.868 71.309 -1.499
step7:sessionBlock 5 -98.166 71.309 -1.377
step8:sessionBlock 5 -21.619 71.309 -0.303
step9:sessionBlock 5 45.623 71.309 0.640
step10:sessionBlock 5 -85.281 71.309 -1.196
step11:sessionBlock 5 -202.784 71.309 -2.844
step12:sessionBlock 5 -127.218 71.309 -1.784
step2:trial_acc_catwrong -14.940 92.547 -0.161
step3:trial_acc_catwrong 8.118 92.547 0.088
step4:trial_acc_catwrong 22.663 92.547 0.245
step5:trial_acc_catwrong 69.400 92.547 0.750
step6:trial_acc_catwrong -15.196 92.547 -0.164
step7:trial_acc_catwrong 43.491 92.547 0.470
step8:trial_acc_catwrong 273.339 92.547 2.954
step9:trial_acc_catwrong 53.717 92.547 0.580
step10:trial_acc_catwrong 90.493 92.547 0.978
step11:trial_acc_catwrong 316.967 92.547 3.425
step12:trial_acc_catwrong 407.855 92.547 4.407
sessionBlock 5:trial_acc_catwrong 209.009 85.445 2.446
step2:sessionBlock 5:trial_acc_catwrong -133.286 120.479 -1.106
step3:sessionBlock 5:trial_acc_catwrong -153.663 120.479 -1.275
step4:sessionBlock 5:trial_acc_catwrong -111.495 120.479 -0.925
step5:sessionBlock 5:trial_acc_catwrong -134.047 120.479 -1.113
step6:sessionBlock 5:trial_acc_catwrong -165.366 120.479 -1.373
step7:sessionBlock 5:trial_acc_catwrong -111.892 120.479 -0.929
step8:sessionBlock 5:trial_acc_catwrong -399.417 120.479 -3.315
step9:sessionBlock 5:trial_acc_catwrong -216.890 120.479 -1.800
step10:sessionBlock 5:trial_acc_catwrong -265.373 120.479 -2.203
step11:sessionBlock 5:trial_acc_catwrong -256.846 120.479 -2.132
step12:sessionBlock 5:trial_acc_catwrong -420.046 120.479 -3.486
Correlation matrix not shown by default, as p = 48 > 12.
Use print(summary(mdl_all), correlation=TRUE) or
vcov(summary(mdl_all)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 424.9073 11 < 2.2e-16 ***
session 153.1544 1 < 2.2e-16 ***
trial_acc_cat 111.2216 1 < 2.2e-16 ***
step:session 45.6047 11 3.797e-06 ***
step:trial_acc_cat 53.1721 11 1.670e-07 ***
session:trial_acc_cat 0.2094 1 0.64725
step:session:trial_acc_cat 22.5471 11 0.02046 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
session = Block 4, trial_acc_cat = correct:
step emmean SE df lower.CL upper.CL
1 739 54.6 35.6 628 850
2 436 54.6 35.6 325 546
3 395 54.6 35.6 285 506
4 397 54.6 35.6 287 508
5 438 54.6 35.6 327 549
6 472 54.6 35.6 361 583
7 584 54.6 35.6 474 695
8 406 54.6 35.6 295 516
9 492 54.6 35.6 381 603
10 452 54.6 35.6 342 563
11 422 54.6 35.6 311 533
12 405 54.6 35.6 294 516
session = Block 5, trial_acc_cat = correct:
step emmean SE df lower.CL upper.CL
1 969 60.2 52.6 848 1090
2 535 60.2 52.6 414 656
3 514 60.2 52.6 394 635
4 537 60.2 52.6 416 657
5 540 60.2 52.6 420 661
6 595 60.2 52.6 474 716
7 716 60.2 52.6 595 837
8 614 60.2 52.6 493 735
9 767 60.2 52.6 646 888
10 597 60.2 52.6 476 718
11 449 60.2 52.6 328 570
12 507 60.2 52.6 387 628
session = Block 4, trial_acc_cat = wrong:
step emmean SE df lower.CL upper.CL
1 761 73.3 115.0 616 906
2 442 73.3 115.0 297 588
3 425 73.3 115.0 280 571
4 442 73.3 115.0 297 587
5 529 73.3 115.0 384 675
6 479 73.3 115.0 334 624
7 650 73.3 115.0 505 795
8 701 73.3 115.0 556 846
9 568 73.3 115.0 422 713
10 565 73.3 115.0 420 710
11 761 73.3 115.0 616 906
12 835 73.3 115.0 690 980
session = Block 5, trial_acc_cat = wrong:
step emmean SE df lower.CL upper.CL
1 1200 58.5 46.8 1082 1317
2 618 58.5 46.8 500 735
3 600 58.5 46.8 482 717
4 679 58.5 46.8 561 796
5 707 58.5 46.8 589 824
6 645 58.5 46.8 528 763
7 878 58.5 46.8 761 996
8 719 58.5 46.8 601 836
9 835 58.5 46.8 717 953
10 653 58.5 46.8 535 770
11 740 58.5 46.8 622 858
12 726 58.5 46.8 609 844
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
[CORRECT] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * session + (1 | subject)
Data: data
REML criterion at convergence: 62279.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.3071 -0.3963 -0.1505 0.1613 25.8645
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 34623 186.1
Residual 119957 346.3
Number of obs: 4296, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 738.71 49.60 14.893
step2 -303.51 32.73 -9.274
step3 -343.76 32.73 -10.504
step4 -341.71 32.73 -10.441
step5 -300.92 32.73 -9.195
step6 -266.80 32.73 -8.152
step7 -154.65 32.73 -4.725
step8 -333.43 32.73 -10.188
step9 -247.14 32.73 -7.552
step10 -286.70 32.73 -8.760
step11 -317.04 32.73 -9.688
step12 -334.06 32.73 -10.207
sessionBlock 5 231.17 37.92 6.096
step2:sessionBlock 5 -130.30 53.49 -2.436
step3:sessionBlock 5 -110.55 53.49 -2.067
step4:sessionBlock 5 -90.38 53.49 -1.690
step5:sessionBlock 5 -127.45 53.49 -2.383
step6:sessionBlock 5 -106.87 53.49 -1.998
step7:sessionBlock 5 -98.17 53.49 -1.835
step8:sessionBlock 5 -21.62 53.49 -0.404
step9:sessionBlock 5 45.62 53.49 0.853
step10:sessionBlock 5 -85.28 53.49 -1.594
step11:sessionBlock 5 -202.78 53.49 -3.791
step12:sessionBlock 5 -127.22 53.49 -2.378
Correlation matrix not shown by default, as p = 24 > 12.
Use print(summary(mdl), correlation=TRUE) or
vcov(summary(mdl)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 424.13 11 < 2.2e-16 ***
session 162.02 1 < 2.2e-16 ***
step:session 34.28 11 0.0003255 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
session = Block 4:
step emmean SE df lower.CL upper.CL
1 739 49.6 27.0 637 840
2 435 49.6 27.0 333 537
3 395 49.6 27.0 293 497
4 397 49.6 27.0 295 499
5 438 49.6 27.0 336 540
6 472 49.6 27.0 370 574
7 584 49.6 27.0 482 686
8 405 49.6 27.0 303 507
9 492 49.6 27.0 390 593
10 452 49.6 27.0 350 554
11 422 49.6 27.0 320 523
12 405 49.6 27.0 303 506
session = Block 5:
step emmean SE df lower.CL upper.CL
1 970 53.2 35.5 862 1078
2 536 53.2 35.5 428 644
3 516 53.2 35.5 408 623
4 538 53.2 35.5 430 646
5 542 53.2 35.5 434 649
6 596 53.2 35.5 488 704
7 717 53.2 35.5 609 825
8 615 53.2 35.5 507 723
9 768 53.2 35.5 661 876
10 598 53.2 35.5 490 706
11 450 53.2 35.5 342 558
12 509 53.2 35.5 401 616
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise (Tukey) across steps — correct | session:
session = Block 4:
contrast estimate SE df t.ratio p.value
step1 - step2 303.513 32.7 4255 9.274 <.0001
step1 - step3 343.759 32.7 4255 10.504 <.0001
step1 - step4 341.710 32.7 4255 10.441 <.0001
step1 - step5 300.915 32.7 4255 9.195 <.0001
step1 - step6 266.804 32.7 4255 8.152 <.0001
step1 - step7 154.647 32.7 4255 4.725 0.0001
step1 - step8 333.433 32.7 4255 10.188 <.0001
step1 - step9 247.138 32.7 4255 7.552 <.0001
step1 - step10 286.696 32.7 4255 8.760 <.0001
step1 - step11 317.045 32.7 4255 9.688 <.0001
step1 - step12 334.058 32.7 4255 10.207 <.0001
step2 - step3 40.245 32.7 4255 1.230 0.9867
step2 - step4 38.196 32.7 4255 1.167 0.9913
step2 - step5 -2.598 32.7 4255 -0.079 1.0000
step2 - step6 -36.710 32.7 4255 -1.122 0.9938
step2 - step7 -148.866 32.7 4255 -4.549 0.0003
step2 - step8 29.920 32.7 4255 0.914 0.9990
step2 - step9 -56.375 32.7 4255 -1.723 0.8581
step2 - step10 -16.817 32.7 4255 -0.514 1.0000
step2 - step11 13.531 32.7 4255 0.413 1.0000
step2 - step12 30.545 32.7 4255 0.933 0.9988
step3 - step4 -2.049 32.7 4255 -0.063 1.0000
step3 - step5 -42.844 32.7 4255 -1.309 0.9781
step3 - step6 -76.955 32.7 4255 -2.351 0.4399
step3 - step7 -189.112 32.7 4255 -5.778 <.0001
step3 - step8 -10.326 32.7 4255 -0.316 1.0000
step3 - step9 -96.621 32.7 4255 -2.952 0.1234
step3 - step10 -57.062 32.7 4255 -1.744 0.8478
step3 - step11 -26.714 32.7 4255 -0.816 0.9997
step3 - step12 -9.701 32.7 4255 -0.296 1.0000
step4 - step5 -40.795 32.7 4255 -1.247 0.9851
step4 - step6 -74.906 32.7 4255 -2.289 0.4847
step4 - step7 -187.062 32.7 4255 -5.716 <.0001
step4 - step8 -8.277 32.7 4255 -0.253 1.0000
step4 - step9 -94.571 32.7 4255 -2.890 0.1448
step4 - step10 -55.013 32.7 4255 -1.681 0.8771
step4 - step11 -24.665 32.7 4255 -0.754 0.9998
step4 - step12 -7.652 32.7 4255 -0.234 1.0000
step5 - step6 -34.112 32.7 4255 -1.042 0.9967
step5 - step7 -146.268 32.7 4255 -4.469 0.0005
step5 - step8 32.518 32.7 4255 0.994 0.9979
step5 - step9 -53.777 32.7 4255 -1.643 0.8930
step5 - step10 -14.219 32.7 4255 -0.434 1.0000
step5 - step11 16.130 32.7 4255 0.493 1.0000
step5 - step12 33.143 32.7 4255 1.013 0.9975
step6 - step7 -112.156 32.7 4255 -3.427 0.0303
step6 - step8 66.629 32.7 4255 2.036 0.6686
step6 - step9 -19.665 32.7 4255 -0.601 1.0000
step6 - step10 19.893 32.7 4255 0.608 1.0000
step6 - step11 50.241 32.7 4255 1.535 0.9309
step6 - step12 67.254 32.7 4255 2.055 0.6551
step7 - step8 178.786 32.7 4255 5.463 <.0001
step7 - step9 92.491 32.7 4255 2.826 0.1693
step7 - step10 132.049 32.7 4255 4.035 0.0032
step7 - step11 162.397 32.7 4255 4.962 <.0001
step7 - step12 179.411 32.7 4255 5.482 <.0001
step8 - step9 -86.295 32.7 4255 -2.637 0.2593
step8 - step10 -46.737 32.7 4255 -1.428 0.9582
step8 - step11 -16.388 32.7 4255 -0.501 1.0000
step8 - step12 0.625 32.7 4255 0.019 1.0000
step9 - step10 39.558 32.7 4255 1.209 0.9884
step9 - step11 69.906 32.7 4255 2.136 0.5966
step9 - step12 86.920 32.7 4255 2.656 0.2490
step10 - step11 30.348 32.7 4255 0.927 0.9989
step10 - step12 47.362 32.7 4255 1.447 0.9540
step11 - step12 17.013 32.7 4255 0.520 1.0000
session = Block 5:
contrast estimate SE df t.ratio p.value
step1 - step2 433.813 42.3 4255 10.252 <.0001
step1 - step3 454.306 42.3 4255 10.737 <.0001
step1 - step4 432.090 42.3 4255 10.212 <.0001
step1 - step5 428.366 42.3 4255 10.124 <.0001
step1 - step6 373.672 42.3 4255 8.831 <.0001
step1 - step7 252.813 42.3 4255 5.975 <.0001
step1 - step8 355.052 42.3 4255 8.391 <.0001
step1 - step9 201.515 42.3 4255 4.762 0.0001
step1 - step10 371.978 42.3 4255 8.791 <.0001
step1 - step11 519.828 42.3 4255 12.285 <.0001
step1 - step12 461.276 42.3 4255 10.901 <.0001
step2 - step3 20.492 42.3 4255 0.484 1.0000
step2 - step4 -1.724 42.3 4255 -0.041 1.0000
step2 - step5 -5.448 42.3 4255 -0.129 1.0000
step2 - step6 -60.142 42.3 4255 -1.421 0.9596
step2 - step7 -181.000 42.3 4255 -4.278 0.0012
step2 - step8 -78.761 42.3 4255 -1.861 0.7832
step2 - step9 -232.298 42.3 4255 -5.490 <.0001
step2 - step10 -61.836 42.3 4255 -1.461 0.9507
step2 - step11 86.015 42.3 4255 2.033 0.6708
step2 - step12 27.463 42.3 4255 0.649 1.0000
step3 - step4 -22.216 42.3 4255 -0.525 1.0000
step3 - step5 -25.940 42.3 4255 -0.613 1.0000
step3 - step6 -80.634 42.3 4255 -1.906 0.7559
step3 - step7 -201.493 42.3 4255 -4.762 0.0001
step3 - step8 -99.254 42.3 4255 -2.346 0.4440
step3 - step9 -252.791 42.3 4255 -5.974 <.0001
step3 - step10 -82.328 42.3 4255 -1.946 0.7302
step3 - step11 65.522 42.3 4255 1.549 0.9268
step3 - step12 6.970 42.3 4255 0.165 1.0000
step4 - step5 -3.724 42.3 4255 -0.088 1.0000
step4 - step6 -58.418 42.3 4255 -1.381 0.9673
step4 - step7 -179.276 42.3 4255 -4.237 0.0014
step4 - step8 -77.037 42.3 4255 -1.821 0.8069
step4 - step9 -230.575 42.3 4255 -5.449 <.0001
step4 - step10 -60.112 42.3 4255 -1.421 0.9597
step4 - step11 87.739 42.3 4255 2.074 0.6419
step4 - step12 29.187 42.3 4255 0.690 0.9999
step5 - step6 -54.694 42.3 4255 -1.293 0.9802
step5 - step7 -175.552 42.3 4255 -4.149 0.0020
step5 - step8 -73.313 42.3 4255 -1.733 0.8532
step5 - step9 -226.851 42.3 4255 -5.361 <.0001
step5 - step10 -56.388 42.3 4255 -1.333 0.9749
step5 - step11 91.463 42.3 4255 2.162 0.5779
step5 - step12 32.910 42.3 4255 0.778 0.9998
step6 - step7 -120.858 42.3 4255 -2.856 0.1573
step6 - step8 -18.619 42.3 4255 -0.440 1.0000
step6 - step9 -172.157 42.3 4255 -4.069 0.0028
step6 - step10 -1.694 42.3 4255 -0.040 1.0000
step6 - step11 146.157 42.3 4255 3.454 0.0277
step6 - step12 87.605 42.3 4255 2.070 0.6442
step7 - step8 102.239 42.3 4255 2.416 0.3950
step7 - step9 -51.298 42.3 4255 -1.212 0.9881
step7 - step10 119.164 42.3 4255 2.816 0.1733
step7 - step11 267.015 42.3 4255 6.310 <.0001
step7 - step12 208.463 42.3 4255 4.927 0.0001
step8 - step9 -153.537 42.3 4255 -3.629 0.0152
step8 - step10 16.925 42.3 4255 0.400 1.0000
step8 - step11 164.776 42.3 4255 3.894 0.0056
step8 - step12 106.224 42.3 4255 2.510 0.3334
step9 - step10 170.463 42.3 4255 4.029 0.0033
step9 - step11 318.313 42.3 4255 7.523 <.0001
step9 - step12 259.761 42.3 4255 6.139 <.0001
step10 - step11 147.851 42.3 4255 3.494 0.0242
step10 - step12 89.299 42.3 4255 2.110 0.6153
step11 - step12 -58.552 42.3 4255 -1.384 0.9667
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent-step contrasts (consecutive) — correct | session:
session = Block 4:
contrast estimate SE df t.ratio p.value
step2 - step1 -303.51 32.7 4255 -9.274 <.0001
step3 - step2 -40.25 32.7 4255 -1.230 1.0000
step4 - step3 2.05 32.7 4255 0.063 1.0000
step5 - step4 40.79 32.7 4255 1.247 1.0000
step6 - step5 34.11 32.7 4255 1.042 1.0000
step7 - step6 112.16 32.7 4255 3.427 0.0055
step8 - step7 -178.79 32.7 4255 -5.463 <.0001
step9 - step8 86.29 32.7 4255 2.637 0.0672
step10 - step9 -39.56 32.7 4255 -1.209 1.0000
step11 - step10 -30.35 32.7 4255 -0.927 1.0000
step12 - step11 -17.01 32.7 4255 -0.520 1.0000
session = Block 5:
contrast estimate SE df t.ratio p.value
step2 - step1 -433.81 42.3 4255 -10.252 <.0001
step3 - step2 -20.49 42.3 4255 -0.484 1.0000
step4 - step3 22.22 42.3 4255 0.525 1.0000
step5 - step4 3.72 42.3 4255 0.088 1.0000
step6 - step5 54.69 42.3 4255 1.293 0.8325
step7 - step6 120.86 42.3 4255 2.856 0.0302
step8 - step7 -102.24 42.3 4255 -2.416 0.0943
step9 - step8 153.54 42.3 4255 3.629 0.0026
step10 - step9 -170.46 42.3 4255 -4.029 0.0006
step11 - step10 -147.85 42.3 4255 -3.494 0.0038
step12 - step11 58.55 42.3 4255 1.384 0.8325
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 11 tests
[WRONG] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * session + (1 | subject)
Data: data
REML criterion at convergence: 40704.9
Scaled residuals:
Min 1Q Median 3Q Max
-1.7587 -0.4847 -0.2199 0.1523 10.2604
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 44324 210.5
Residual 363999 603.3
Number of obs: 2616, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 759.62495 90.66198 8.379
step2 -318.45313 106.65348 -2.986
step3 -335.64063 106.65348 -3.147
step4 -319.04688 106.65348 -2.991
step5 -231.51563 106.65348 -2.171
step6 -282.00000 106.65348 -2.644
step7 -111.15625 106.65348 -1.042
step8 -60.09375 106.65348 -0.563
step9 -193.42188 106.65348 -1.814
step10 -196.20313 106.65348 -1.840
step11 -0.07813 106.65348 -0.001
step12 73.79687 106.65348 0.692
sessionBlock 5 436.34131 90.12844 4.841
step2:sessionBlock 5 -263.58584 126.89454 -2.077
step3:sessionBlock 5 -264.21002 126.89454 -2.082
step4:sessionBlock 5 -201.87520 126.89454 -1.591
step5:sessionBlock 5 -261.49736 126.89454 -2.061
step6:sessionBlock 5 -272.23377 126.89454 -2.145
step7:sessionBlock 5 -210.05804 126.89454 -1.655
step8:sessionBlock 5 -421.03612 126.89454 -3.318
step9:sessionBlock 5 -171.26644 126.89454 -1.350
step10:sessionBlock 5 -350.65402 126.89454 -2.763
step11:sessionBlock 5 -459.62967 126.89454 -3.622
step12:sessionBlock 5 -547.26441 126.89454 -4.313
Correlation matrix not shown by default, as p = 24 > 12.
Use print(summary(mdl), correlation=TRUE) or
vcov(summary(mdl)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 135.160 11 < 2.2e-16 ***
session 30.716 1 2.986e-08 ***
step:session 28.615 11 0.002603 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
session = Block 4:
step emmean SE df lower.CL upper.CL
1 760 90.7 160.7 581 939
2 441 90.7 160.7 262 620
3 424 90.7 160.7 245 603
4 441 90.7 160.7 262 620
5 528 90.7 160.7 349 707
6 478 90.7 160.7 299 657
7 648 90.7 160.7 469 828
8 700 90.7 160.7 520 879
9 566 90.7 160.7 387 745
10 563 90.7 160.7 384 742
11 760 90.7 160.7 580 939
12 833 90.7 160.7 654 1012
session = Block 5:
step emmean SE df lower.CL upper.CL
1 1196 69.7 57.6 1056 1335
2 614 69.7 57.6 474 753
3 596 69.7 57.6 457 736
4 675 69.7 57.6 536 815
5 703 69.7 57.6 563 842
6 642 69.7 57.6 502 781
7 875 69.7 57.6 735 1014
8 715 69.7 57.6 575 854
9 831 69.7 57.6 692 971
10 649 69.7 57.6 510 789
11 736 69.7 57.6 597 876
12 722 69.7 57.6 583 862
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
--- Sequence length 18 ---
[Combined] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * session * trial_acc_cat + (1 | subject)
Data: data
REML criterion at convergence: 160429.7
Scaled residuals:
Min 1Q Median 3Q Max
-2.048 -0.373 -0.145 0.102 41.159
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 29887 172.9
Residual 328654 573.3
Number of obs: 10368, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 811.48 59.25 13.695
step2 -365.53 60.77 -6.015
step3 -412.41 60.77 -6.787
step4 -420.02 60.77 -6.912
step5 -371.73 60.77 -6.117
step6 -354.89 60.77 -5.840
step7 -311.91 60.77 -5.133
step8 -365.18 60.77 -6.009
step9 -325.52 60.77 -5.357
step10 -329.51 60.77 -5.422
step11 -359.24 60.77 -5.912
step12 -394.91 60.77 -6.499
step13 -235.35 60.77 -3.873
step14 -261.72 60.77 -4.307
step15 -393.92 60.77 -6.482
step16 -415.43 60.77 -6.836
step17 -424.13 60.77 -6.980
step18 -354.54 60.77 -5.834
sessionBlock 5 37.15 69.26 0.536
trial_acc_catwrong -111.73 69.74 -1.602
step2:sessionBlock 5 9.68 97.78 0.099
step3:sessionBlock 5 70.97 97.78 0.726
step4:sessionBlock 5 109.66 97.78 1.121
step5:sessionBlock 5 78.91 97.78 0.807
step6:sessionBlock 5 64.38 97.78 0.658
step7:sessionBlock 5 88.42 97.78 0.904
step8:sessionBlock 5 29.70 97.78 0.304
step9:sessionBlock 5 222.83 97.78 2.279
step10:sessionBlock 5 192.98 97.78 1.974
step11:sessionBlock 5 -16.81 97.78 -0.172
step12:sessionBlock 5 34.57 97.78 0.354
step13:sessionBlock 5 257.23 97.78 2.631
step14:sessionBlock 5 36.57 97.78 0.374
step15:sessionBlock 5 -19.91 97.78 -0.204
step16:sessionBlock 5 38.46 97.78 0.393
step17:sessionBlock 5 93.07 97.78 0.952
step18:sessionBlock 5 -22.04 97.78 -0.225
step2:trial_acc_catwrong 80.78 98.33 0.822
step3:trial_acc_catwrong 157.29 98.33 1.600
step4:trial_acc_catwrong 241.25 98.33 2.454
step5:trial_acc_catwrong 226.18 98.33 2.300
step6:trial_acc_catwrong 154.77 98.33 1.574
step7:trial_acc_catwrong 247.11 98.33 2.513
step8:trial_acc_catwrong 313.71 98.33 3.190
step9:trial_acc_catwrong 389.47 98.33 3.961
step10:trial_acc_catwrong 184.42 98.33 1.876
step11:trial_acc_catwrong 215.97 98.33 2.196
step12:trial_acc_catwrong 232.25 98.33 2.362
step13:trial_acc_catwrong 109.75 98.33 1.116
step14:trial_acc_catwrong 110.25 98.33 1.121
step15:trial_acc_catwrong 212.77 98.33 2.164
step16:trial_acc_catwrong 305.60 98.33 3.108
step17:trial_acc_catwrong 373.30 98.33 3.796
step18:trial_acc_catwrong 642.13 98.33 6.531
sessionBlock 5:trial_acc_catwrong 477.49 98.47 4.849
step2:sessionBlock 5:trial_acc_catwrong -312.80 138.82 -2.253
step3:sessionBlock 5:trial_acc_catwrong -440.40 138.82 -3.172
step4:sessionBlock 5:trial_acc_catwrong -476.65 138.82 -3.434
step5:sessionBlock 5:trial_acc_catwrong -409.56 138.82 -2.950
step6:sessionBlock 5:trial_acc_catwrong -302.10 138.82 -2.176
step7:sessionBlock 5:trial_acc_catwrong -424.65 138.82 -3.059
step8:sessionBlock 5:trial_acc_catwrong -478.26 138.82 -3.445
step9:sessionBlock 5:trial_acc_catwrong -522.35 138.82 -3.763
step10:sessionBlock 5:trial_acc_catwrong -478.88 138.82 -3.450
step11:sessionBlock 5:trial_acc_catwrong -372.63 138.82 -2.684
step12:sessionBlock 5:trial_acc_catwrong -329.98 138.82 -2.377
step13:sessionBlock 5:trial_acc_catwrong -477.01 138.82 -3.436
step14:sessionBlock 5:trial_acc_catwrong -352.75 138.82 -2.541
step15:sessionBlock 5:trial_acc_catwrong -330.58 138.82 -2.381
step16:sessionBlock 5:trial_acc_catwrong -496.37 138.82 -3.576
step17:sessionBlock 5:trial_acc_catwrong -586.13 138.82 -4.222
step18:sessionBlock 5:trial_acc_catwrong -734.34 138.82 -5.290
Correlation matrix not shown by default, as p = 72 > 12.
Use print(summary(mdl_all), correlation=TRUE) or
vcov(summary(mdl_all)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 333.8643 17 < 2.2e-16 ***
session 139.6673 1 < 2.2e-16 ***
trial_acc_cat 152.2053 1 < 2.2e-16 ***
step:session 61.4012 17 6.159e-07 ***
step:trial_acc_cat 57.9133 17 2.313e-06 ***
session:trial_acc_cat 5.9238 1 0.014937 *
step:session:trial_acc_cat 39.5580 17 0.001493 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
session = Block 4, trial_acc_cat = correct:
step emmean SE df lower.CL upper.CL
1 811 59.3 73.0 693 930
2 446 59.3 73.0 328 564
3 399 59.3 73.0 281 517
4 391 59.3 73.0 273 510
5 440 59.3 73.0 322 558
6 457 59.3 73.0 338 575
7 500 59.3 73.0 381 618
8 446 59.3 73.0 328 564
9 486 59.3 73.0 368 604
10 482 59.3 73.0 364 600
11 452 59.3 73.0 334 570
12 417 59.3 73.0 298 535
13 576 59.3 73.0 458 694
14 550 59.3 73.0 432 668
15 418 59.3 73.0 299 536
16 396 59.3 73.0 278 514
17 387 59.3 73.0 269 505
18 457 59.3 73.0 339 575
session = Block 5, trial_acc_cat = correct:
step emmean SE df lower.CL upper.CL
1 849 67.9 125.3 714 983
2 493 67.9 125.3 358 627
3 507 67.9 125.3 373 642
4 538 67.9 125.3 404 673
5 556 67.9 125.3 421 690
6 558 67.9 125.3 424 692
7 625 67.9 125.3 491 759
8 513 67.9 125.3 379 647
9 746 67.9 125.3 612 880
10 712 67.9 125.3 578 846
11 473 67.9 125.3 338 607
12 488 67.9 125.3 354 623
13 870 67.9 125.3 736 1005
14 623 67.9 125.3 489 758
15 435 67.9 125.3 300 569
16 472 67.9 125.3 337 606
17 518 67.9 125.3 383 652
18 472 67.9 125.3 338 606
session = Block 4, trial_acc_cat = wrong:
step emmean SE df lower.CL upper.CL
1 700 68.3 128.2 565 835
2 415 68.3 128.2 280 550
3 445 68.3 128.2 310 580
4 521 68.3 128.2 386 656
5 554 68.3 128.2 419 689
6 500 68.3 128.2 365 635
7 635 68.3 128.2 500 770
8 648 68.3 128.2 513 783
9 764 68.3 128.2 629 899
10 555 68.3 128.2 420 690
11 556 68.3 128.2 421 692
12 537 68.3 128.2 402 672
13 574 68.3 128.2 439 709
14 548 68.3 128.2 413 683
15 519 68.3 128.2 384 654
16 590 68.3 128.2 455 725
17 649 68.3 128.2 514 784
18 987 68.3 128.2 852 1122
session = Block 5, trial_acc_cat = wrong:
step emmean SE df lower.CL upper.CL
1 1214 59.4 73.9 1096 1333
2 627 59.4 73.9 508 745
3 590 59.4 73.9 471 708
4 669 59.4 73.9 550 787
5 738 59.4 73.9 620 857
6 777 59.4 73.9 658 895
7 813 59.4 73.9 695 932
8 714 59.4 73.9 596 833
9 979 59.4 73.9 860 1097
10 783 59.4 73.9 665 902
11 682 59.4 73.9 563 800
12 756 59.4 73.9 638 875
13 869 59.4 73.9 751 987
14 747 59.4 73.9 628 865
15 683 59.4 73.9 564 801
16 647 59.4 73.9 528 765
17 670 59.4 73.9 552 789
18 746 59.4 73.9 627 864
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
[CORRECT] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * session + (1 | subject)
Data: data
REML criterion at convergence: 76476
Scaled residuals:
Min 1Q Median 3Q Max
-1.8632 -0.3708 -0.1344 0.1428 26.8933
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 31013 176.1
Residual 142434 377.4
Number of obs: 5220, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 814.17 50.27 16.195
step2 -365.53 40.01 -9.137
step3 -412.41 40.01 -10.309
step4 -420.02 40.01 -10.499
step5 -371.73 40.01 -9.292
step6 -354.89 40.01 -8.871
step7 -311.91 40.01 -7.797
step8 -365.18 40.01 -9.128
step9 -325.52 40.01 -8.137
step10 -329.51 40.01 -8.237
step11 -359.24 40.01 -8.980
step12 -394.91 40.01 -9.872
step13 -235.35 40.01 -5.883
step14 -261.72 40.01 -6.542
step15 -393.92 40.01 -9.847
step16 -415.43 40.01 -10.385
step17 -424.13 40.01 -10.602
step18 -354.54 40.01 -8.862
sessionBlock 5 35.90 45.69 0.786
step2:sessionBlock 5 9.68 64.37 0.150
step3:sessionBlock 5 70.97 64.37 1.103
step4:sessionBlock 5 109.66 64.37 1.703
step5:sessionBlock 5 78.91 64.37 1.226
step6:sessionBlock 5 64.38 64.37 1.000
step7:sessionBlock 5 88.42 64.37 1.374
step8:sessionBlock 5 29.70 64.37 0.461
step9:sessionBlock 5 222.83 64.37 3.461
step10:sessionBlock 5 192.98 64.37 2.998
step11:sessionBlock 5 -16.81 64.37 -0.261
step12:sessionBlock 5 34.57 64.37 0.537
step13:sessionBlock 5 257.23 64.37 3.996
step14:sessionBlock 5 36.57 64.37 0.568
step15:sessionBlock 5 -19.91 64.37 -0.309
step16:sessionBlock 5 38.46 64.37 0.597
step17:sessionBlock 5 93.07 64.37 1.446
step18:sessionBlock 5 -22.04 64.37 -0.342
Correlation matrix not shown by default, as p = 36 > 12.
Use print(summary(mdl), correlation=TRUE) or
vcov(summary(mdl)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 385.211 17 < 2.2e-16 ***
session 86.629 1 < 2.2e-16 ***
step:session 54.881 17 7.161e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
session = Block 4:
step emmean SE df lower.CL upper.CL
1 814 50.3 35.3 712 916
2 449 50.3 35.3 347 551
3 402 50.3 35.3 300 504
4 394 50.3 35.3 292 496
5 442 50.3 35.3 340 544
6 459 50.3 35.3 357 561
7 502 50.3 35.3 400 604
8 449 50.3 35.3 347 551
9 489 50.3 35.3 387 591
10 485 50.3 35.3 383 587
11 455 50.3 35.3 353 557
12 419 50.3 35.3 317 521
13 579 50.3 35.3 477 681
14 552 50.3 35.3 450 654
15 420 50.3 35.3 318 522
16 399 50.3 35.3 297 501
17 390 50.3 35.3 288 492
18 460 50.3 35.3 358 562
session = Block 5:
step emmean SE df lower.CL upper.CL
1 850 54.8 49.9 740 960
2 494 54.8 49.9 384 604
3 509 54.8 49.9 398 619
4 540 54.8 49.9 430 650
5 557 54.8 49.9 447 667
6 560 54.8 49.9 449 670
7 627 54.8 49.9 516 737
8 515 54.8 49.9 404 625
9 747 54.8 49.9 637 858
10 714 54.8 49.9 603 824
11 474 54.8 49.9 364 584
12 490 54.8 49.9 380 600
13 872 54.8 49.9 762 982
14 625 54.8 49.9 515 735
15 436 54.8 49.9 326 546
16 473 54.8 49.9 363 583
17 519 54.8 49.9 409 629
18 473 54.8 49.9 363 584
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95
Pairwise (Tukey) across steps — correct | session:
session = Block 4:
contrast estimate SE df t.ratio p.value
step1 - step2 365.528 40.0 5167 9.137 <.0001
step1 - step3 412.410 40.0 5167 10.309 <.0001
step1 - step4 420.022 40.0 5167 10.499 <.0001
step1 - step5 371.730 40.0 5167 9.292 <.0001
step1 - step6 354.888 40.0 5167 8.871 <.0001
step1 - step7 311.910 40.0 5167 7.797 <.0001
step1 - step8 365.180 40.0 5167 9.128 <.0001
step1 - step9 325.522 40.0 5167 8.137 <.0001
step1 - step10 329.511 40.0 5167 8.237 <.0001
step1 - step11 359.242 40.0 5167 8.980 <.0001
step1 - step12 394.910 40.0 5167 9.872 <.0001
step1 - step13 235.354 40.0 5167 5.883 <.0001
step1 - step14 261.719 40.0 5167 6.542 <.0001
step1 - step15 393.916 40.0 5167 9.847 <.0001
step1 - step16 415.433 40.0 5167 10.385 <.0001
step1 - step17 424.135 40.0 5167 10.602 <.0001
step1 - step18 354.539 40.0 5167 8.862 <.0001
step2 - step3 46.882 40.0 5167 1.172 0.9995
step2 - step4 54.494 40.0 5167 1.362 0.9969
step2 - step5 6.202 40.0 5167 0.155 1.0000
step2 - step6 -10.640 40.0 5167 -0.266 1.0000
step2 - step7 -53.618 40.0 5167 -1.340 0.9974
step2 - step8 -0.348 40.0 5167 -0.009 1.0000
step2 - step9 -40.006 40.0 5167 -1.000 0.9999
step2 - step10 -36.017 40.0 5167 -0.900 1.0000
step2 - step11 -6.287 40.0 5167 -0.157 1.0000
step2 - step12 29.382 40.0 5167 0.734 1.0000
step2 - step13 -130.174 40.0 5167 -3.254 0.1026
step2 - step14 -103.809 40.0 5167 -2.595 0.4574
step2 - step15 28.388 40.0 5167 0.710 1.0000
step2 - step16 49.904 40.0 5167 1.247 0.9989
step2 - step17 58.607 40.0 5167 1.465 0.9929
step2 - step18 -10.989 40.0 5167 -0.275 1.0000
step3 - step4 7.612 40.0 5167 0.190 1.0000
step3 - step5 -40.680 40.0 5167 -1.017 0.9999
step3 - step6 -57.523 40.0 5167 -1.438 0.9942
step3 - step7 -100.500 40.0 5167 -2.512 0.5209
step3 - step8 -47.230 40.0 5167 -1.181 0.9995
step3 - step9 -86.888 40.0 5167 -2.172 0.7724
step3 - step10 -82.899 40.0 5167 -2.072 0.8324
step3 - step11 -53.169 40.0 5167 -1.329 0.9977
step3 - step12 -17.500 40.0 5167 -0.437 1.0000
step3 - step13 -177.056 40.0 5167 -4.426 0.0013
step3 - step14 -150.691 40.0 5167 -3.767 0.0194
step3 - step15 -18.494 40.0 5167 -0.462 1.0000
step3 - step16 3.022 40.0 5167 0.076 1.0000
step3 - step17 11.725 40.0 5167 0.293 1.0000
step3 - step18 -57.871 40.0 5167 -1.447 0.9938
step4 - step5 -48.292 40.0 5167 -1.207 0.9993
step4 - step6 -65.135 40.0 5167 -1.628 0.9782
step4 - step7 -108.112 40.0 5167 -2.702 0.3787
step4 - step8 -54.843 40.0 5167 -1.371 0.9967
step4 - step9 -94.500 40.0 5167 -2.362 0.6369
step4 - step10 -90.511 40.0 5167 -2.263 0.7106
step4 - step11 -60.781 40.0 5167 -1.519 0.9894
step4 - step12 -25.112 40.0 5167 -0.628 1.0000
step4 - step13 -184.668 40.0 5167 -4.616 0.0006
step4 - step14 -158.303 40.0 5167 -3.957 0.0095
step4 - step15 -26.107 40.0 5167 -0.653 1.0000
step4 - step16 -4.590 40.0 5167 -0.115 1.0000
step4 - step17 4.112 40.0 5167 0.103 1.0000
step4 - step18 -65.483 40.0 5167 -1.637 0.9771
step5 - step6 -16.843 40.0 5167 -0.421 1.0000
step5 - step7 -59.820 40.0 5167 -1.495 0.9911
step5 - step8 -6.551 40.0 5167 -0.164 1.0000
step5 - step9 -46.208 40.0 5167 -1.155 0.9996
step5 - step10 -42.219 40.0 5167 -1.055 0.9999
step5 - step11 -12.489 40.0 5167 -0.312 1.0000
step5 - step12 23.180 40.0 5167 0.579 1.0000
step5 - step13 -136.376 40.0 5167 -3.409 0.0647
step5 - step14 -110.011 40.0 5167 -2.750 0.3459
step5 - step15 22.185 40.0 5167 0.555 1.0000
step5 - step16 43.702 40.0 5167 1.092 0.9998
step5 - step17 52.404 40.0 5167 1.310 0.9981
step5 - step18 -17.191 40.0 5167 -0.430 1.0000
step6 - step7 -42.977 40.0 5167 -1.074 0.9999
step6 - step8 10.292 40.0 5167 0.257 1.0000
step6 - step9 -29.365 40.0 5167 -0.734 1.0000
step6 - step10 -25.376 40.0 5167 -0.634 1.0000
step6 - step11 4.354 40.0 5167 0.109 1.0000
step6 - step12 40.023 40.0 5167 1.000 0.9999
step6 - step13 -119.534 40.0 5167 -2.988 0.2061
step6 - step14 -93.168 40.0 5167 -2.329 0.6620
step6 - step15 39.028 40.0 5167 0.976 1.0000
step6 - step16 60.545 40.0 5167 1.513 0.9898
step6 - step17 69.247 40.0 5167 1.731 0.9608
step6 - step18 -0.348 40.0 5167 -0.009 1.0000
step7 - step8 53.270 40.0 5167 1.332 0.9976
step7 - step9 13.612 40.0 5167 0.340 1.0000
step7 - step10 17.601 40.0 5167 0.440 1.0000
step7 - step11 47.331 40.0 5167 1.183 0.9995
step7 - step12 83.000 40.0 5167 2.075 0.8310
step7 - step13 -76.556 40.0 5167 -1.914 0.9070
step7 - step14 -50.191 40.0 5167 -1.255 0.9989
step7 - step15 82.006 40.0 5167 2.050 0.8445
step7 - step16 103.522 40.0 5167 2.588 0.4629
step7 - step17 112.225 40.0 5167 2.805 0.3096
step7 - step18 42.629 40.0 5167 1.066 0.9999
step8 - step9 -39.657 40.0 5167 -0.991 1.0000
step8 - step10 -35.669 40.0 5167 -0.892 1.0000
step8 - step11 -5.938 40.0 5167 -0.148 1.0000
step8 - step12 29.730 40.0 5167 0.743 1.0000
step8 - step13 -129.826 40.0 5167 -3.245 0.1051
step8 - step14 -103.461 40.0 5167 -2.586 0.4640
step8 - step15 28.736 40.0 5167 0.718 1.0000
step8 - step16 50.253 40.0 5167 1.256 0.9988
step8 - step17 58.955 40.0 5167 1.474 0.9924
step8 - step18 -10.640 40.0 5167 -0.266 1.0000
step9 - step10 3.989 40.0 5167 0.100 1.0000
step9 - step11 33.719 40.0 5167 0.843 1.0000
step9 - step12 69.388 40.0 5167 1.734 0.9601
step9 - step13 -90.168 40.0 5167 -2.254 0.7167
step9 - step14 -63.803 40.0 5167 -1.595 0.9823
step9 - step15 68.393 40.0 5167 1.710 0.9651
step9 - step16 89.910 40.0 5167 2.247 0.7213
step9 - step17 98.612 40.0 5167 2.465 0.5575
step9 - step18 29.017 40.0 5167 0.725 1.0000
step10 - step11 29.730 40.0 5167 0.743 1.0000
step10 - step12 65.399 40.0 5167 1.635 0.9773
step10 - step13 -94.157 40.0 5167 -2.354 0.6434
step10 - step14 -67.792 40.0 5167 -1.695 0.9679
step10 - step15 64.404 40.0 5167 1.610 0.9806
step10 - step16 85.921 40.0 5167 2.148 0.7878
step10 - step17 94.624 40.0 5167 2.365 0.6346
step10 - step18 25.028 40.0 5167 0.626 1.0000
step11 - step12 35.669 40.0 5167 0.892 1.0000
step11 - step13 -123.888 40.0 5167 -3.097 0.1572
step11 - step14 -97.522 40.0 5167 -2.438 0.5787
step11 - step15 34.674 40.0 5167 0.867 1.0000
step11 - step16 56.191 40.0 5167 1.405 0.9956
step11 - step17 64.893 40.0 5167 1.622 0.9790
step11 - step18 -4.702 40.0 5167 -0.118 1.0000
step12 - step13 -159.556 40.0 5167 -3.988 0.0084
step12 - step14 -133.191 40.0 5167 -3.329 0.0823
step12 - step15 -0.994 40.0 5167 -0.025 1.0000
step12 - step16 20.523 40.0 5167 0.513 1.0000
step12 - step17 29.225 40.0 5167 0.731 1.0000
step12 - step18 -40.371 40.0 5167 -1.009 0.9999
step13 - step14 26.365 40.0 5167 0.659 1.0000
step13 - step15 158.562 40.0 5167 3.964 0.0093
step13 - step16 180.079 40.0 5167 4.501 0.0010
step13 - step17 188.781 40.0 5167 4.719 0.0003
step13 - step18 119.185 40.0 5167 2.979 0.2105
step14 - step15 132.197 40.0 5167 3.305 0.0886
step14 - step16 153.714 40.0 5167 3.842 0.0147
step14 - step17 162.416 40.0 5167 4.060 0.0063
step14 - step18 92.820 40.0 5167 2.320 0.6685
step15 - step16 21.517 40.0 5167 0.538 1.0000
step15 - step17 30.219 40.0 5167 0.755 1.0000
step15 - step18 -39.376 40.0 5167 -0.984 1.0000
step16 - step17 8.702 40.0 5167 0.218 1.0000
step16 - step18 -60.893 40.0 5167 -1.522 0.9892
step17 - step18 -69.596 40.0 5167 -1.740 0.9589
session = Block 5:
contrast estimate SE df t.ratio p.value
step1 - step2 355.848 50.4 5167 7.056 <.0001
step1 - step3 341.438 50.4 5167 6.770 <.0001
step1 - step4 310.366 50.4 5167 6.154 <.0001
step1 - step5 292.821 50.4 5167 5.806 <.0001
step1 - step6 290.509 50.4 5167 5.760 <.0001
step1 - step7 223.491 50.4 5167 4.431 0.0013
step1 - step8 335.482 50.4 5167 6.652 <.0001
step1 - step9 102.696 50.4 5167 2.036 0.8516
step1 - step10 136.536 50.4 5167 2.707 0.3753
step1 - step11 376.054 50.4 5167 7.457 <.0001
step1 - step12 360.339 50.4 5167 7.145 <.0001
step1 - step13 -21.875 50.4 5167 -0.434 1.0000
step1 - step14 225.152 50.4 5167 4.464 0.0011
step1 - step15 413.830 50.4 5167 8.206 <.0001
step1 - step16 376.973 50.4 5167 7.475 <.0001
step1 - step17 331.062 50.4 5167 6.564 <.0001
step1 - step18 376.580 50.4 5167 7.467 <.0001
step2 - step3 -14.411 50.4 5167 -0.286 1.0000
step2 - step4 -45.482 50.4 5167 -0.902 1.0000
step2 - step5 -63.027 50.4 5167 -1.250 0.9989
step2 - step6 -65.339 50.4 5167 -1.296 0.9983
step2 - step7 -132.357 50.4 5167 -2.624 0.4353
step2 - step8 -20.366 50.4 5167 -0.404 1.0000
step2 - step9 -253.152 50.4 5167 -5.020 0.0001
step2 - step10 -219.312 50.4 5167 -4.349 0.0019
step2 - step11 20.205 50.4 5167 0.401 1.0000
step2 - step12 4.491 50.4 5167 0.089 1.0000
step2 - step13 -377.723 50.4 5167 -7.490 <.0001
step2 - step14 -130.696 50.4 5167 -2.591 0.4600
step2 - step15 57.982 50.4 5167 1.150 0.9996
step2 - step16 21.125 50.4 5167 0.419 1.0000
step2 - step17 -24.786 50.4 5167 -0.491 1.0000
step2 - step18 20.732 50.4 5167 0.411 1.0000
step3 - step4 -31.071 50.4 5167 -0.616 1.0000
step3 - step5 -48.616 50.4 5167 -0.964 1.0000
step3 - step6 -50.929 50.4 5167 -1.010 0.9999
step3 - step7 -117.946 50.4 5167 -2.339 0.6547
step3 - step8 -5.955 50.4 5167 -0.118 1.0000
step3 - step9 -238.741 50.4 5167 -4.734 0.0003
step3 - step10 -204.902 50.4 5167 -4.063 0.0063
step3 - step11 34.616 50.4 5167 0.686 1.0000
step3 - step12 18.902 50.4 5167 0.375 1.0000
step3 - step13 -363.312 50.4 5167 -7.204 <.0001
step3 - step14 -116.286 50.4 5167 -2.306 0.6793
step3 - step15 72.393 50.4 5167 1.435 0.9943
step3 - step16 35.536 50.4 5167 0.705 1.0000
step3 - step17 -10.375 50.4 5167 -0.206 1.0000
step3 - step18 35.143 50.4 5167 0.697 1.0000
step4 - step5 -17.545 50.4 5167 -0.348 1.0000
step4 - step6 -19.857 50.4 5167 -0.394 1.0000
step4 - step7 -86.875 50.4 5167 -1.723 0.9625
step4 - step8 25.116 50.4 5167 0.498 1.0000
step4 - step9 -207.670 50.4 5167 -4.118 0.0050
step4 - step10 -173.830 50.4 5167 -3.447 0.0575
step4 - step11 65.688 50.4 5167 1.302 0.9982
step4 - step12 49.973 50.4 5167 0.991 1.0000
step4 - step13 -332.241 50.4 5167 -6.588 <.0001
step4 - step14 -85.214 50.4 5167 -1.690 0.9688
step4 - step15 103.464 50.4 5167 2.052 0.8436
step4 - step16 66.607 50.4 5167 1.321 0.9979
step4 - step17 20.696 50.4 5167 0.410 1.0000
step4 - step18 66.214 50.4 5167 1.313 0.9980
step5 - step6 -2.312 50.4 5167 -0.046 1.0000
step5 - step7 -69.330 50.4 5167 -1.375 0.9966
step5 - step8 42.661 50.4 5167 0.846 1.0000
step5 - step9 -190.125 50.4 5167 -3.770 0.0192
step5 - step10 -156.286 50.4 5167 -3.099 0.1564
step5 - step11 83.232 50.4 5167 1.650 0.9751
step5 - step12 67.518 50.4 5167 1.339 0.9975
step5 - step13 -314.696 50.4 5167 -6.240 <.0001
step5 - step14 -67.670 50.4 5167 -1.342 0.9974
step5 - step15 121.009 50.4 5167 2.399 0.6084
step5 - step16 84.152 50.4 5167 1.669 0.9723
step5 - step17 38.241 50.4 5167 0.758 1.0000
step5 - step18 83.759 50.4 5167 1.661 0.9735
step6 - step7 -67.018 50.4 5167 -1.329 0.9977
step6 - step8 44.973 50.4 5167 0.892 1.0000
step6 - step9 -187.812 50.4 5167 -3.724 0.0226
step6 - step10 -153.973 50.4 5167 -3.053 0.1758
step6 - step11 85.545 50.4 5167 1.696 0.9676
step6 - step12 69.830 50.4 5167 1.385 0.9962
step6 - step13 -312.384 50.4 5167 -6.194 <.0001
step6 - step14 -65.357 50.4 5167 -1.296 0.9983
step6 - step15 123.321 50.4 5167 2.445 0.5729
step6 - step16 86.464 50.4 5167 1.714 0.9641
step6 - step17 40.554 50.4 5167 0.804 1.0000
step6 - step18 86.071 50.4 5167 1.707 0.9656
step7 - step8 111.991 50.4 5167 2.221 0.7400
step7 - step9 -120.795 50.4 5167 -2.395 0.6117
step7 - step10 -86.955 50.4 5167 -1.724 0.9622
step7 - step11 152.562 50.4 5167 3.025 0.1884
step7 - step12 136.848 50.4 5167 2.713 0.3710
step7 - step13 -245.366 50.4 5167 -4.865 0.0002
step7 - step14 1.661 50.4 5167 0.033 1.0000
step7 - step15 190.339 50.4 5167 3.774 0.0189
step7 - step16 153.482 50.4 5167 3.043 0.1801
step7 - step17 107.571 50.4 5167 2.133 0.7969
step7 - step18 153.089 50.4 5167 3.036 0.1836
step8 - step9 -232.786 50.4 5167 -4.616 0.0006
step8 - step10 -198.946 50.4 5167 -3.945 0.0100
step8 - step11 40.571 50.4 5167 0.804 1.0000
step8 - step12 24.857 50.4 5167 0.493 1.0000
step8 - step13 -357.357 50.4 5167 -7.086 <.0001
step8 - step14 -110.330 50.4 5167 -2.188 0.7621
step8 - step15 78.348 50.4 5167 1.554 0.9865
step8 - step16 41.491 50.4 5167 0.823 1.0000
step8 - step17 -4.420 50.4 5167 -0.088 1.0000
step8 - step18 41.098 50.4 5167 0.815 1.0000
step9 - step10 33.839 50.4 5167 0.671 1.0000
step9 - step11 273.357 50.4 5167 5.420 <.0001
step9 - step12 257.643 50.4 5167 5.109 <.0001
step9 - step13 -124.571 50.4 5167 -2.470 0.5536
step9 - step14 122.455 50.4 5167 2.428 0.5862
step9 - step15 311.134 50.4 5167 6.169 <.0001
step9 - step16 274.277 50.4 5167 5.438 <.0001
step9 - step17 228.366 50.4 5167 4.528 0.0009
step9 - step18 273.884 50.4 5167 5.431 <.0001
step10 - step11 239.518 50.4 5167 4.749 0.0003
step10 - step12 223.804 50.4 5167 4.438 0.0013
step10 - step13 -158.411 50.4 5167 -3.141 0.1400
step10 - step14 88.616 50.4 5167 1.757 0.9550
step10 - step15 277.295 50.4 5167 5.498 <.0001
step10 - step16 240.438 50.4 5167 4.767 0.0003
step10 - step17 194.527 50.4 5167 3.857 0.0139
step10 - step18 240.045 50.4 5167 4.760 0.0003
step11 - step12 -15.714 50.4 5167 -0.312 1.0000
step11 - step13 -397.929 50.4 5167 -7.890 <.0001
step11 - step14 -150.902 50.4 5167 -2.992 0.2041
step11 - step15 37.777 50.4 5167 0.749 1.0000
step11 - step16 0.920 50.4 5167 0.018 1.0000
step11 - step17 -44.991 50.4 5167 -0.892 1.0000
step11 - step18 0.527 50.4 5167 0.010 1.0000
step12 - step13 -382.214 50.4 5167 -7.579 <.0001
step12 - step14 -135.188 50.4 5167 -2.681 0.3943
step12 - step15 53.491 50.4 5167 1.061 0.9999
step12 - step16 16.634 50.4 5167 0.330 1.0000
step12 - step17 -29.277 50.4 5167 -0.581 1.0000
step12 - step18 16.241 50.4 5167 0.322 1.0000
step13 - step14 247.027 50.4 5167 4.898 0.0001
step13 - step15 435.705 50.4 5167 8.639 <.0001
step13 - step16 398.848 50.4 5167 7.909 <.0001
step13 - step17 352.938 50.4 5167 6.998 <.0001
step13 - step18 398.455 50.4 5167 7.901 <.0001
step14 - step15 188.679 50.4 5167 3.741 0.0213
step14 - step16 151.821 50.4 5167 3.010 0.1953
step14 - step17 105.911 50.4 5167 2.100 0.8166
step14 - step18 151.429 50.4 5167 3.003 0.1990
step15 - step16 -36.857 50.4 5167 -0.731 1.0000
step15 - step17 -82.768 50.4 5167 -1.641 0.9765
step15 - step18 -37.250 50.4 5167 -0.739 1.0000
step16 - step17 -45.911 50.4 5167 -0.910 1.0000
step16 - step18 -0.393 50.4 5167 -0.008 1.0000
step17 - step18 45.518 50.4 5167 0.903 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent-step contrasts (consecutive) — correct | session:
session = Block 4:
contrast estimate SE df t.ratio p.value
step2 - step1 -365.53 40.0 5167 -9.137 <.0001
step3 - step2 -46.88 40.0 5167 -1.172 1.0000
step4 - step3 -7.61 40.0 5167 -0.190 1.0000
step5 - step4 48.29 40.0 5167 1.207 1.0000
step6 - step5 16.84 40.0 5167 0.421 1.0000
step7 - step6 42.98 40.0 5167 1.074 1.0000
step8 - step7 -53.27 40.0 5167 -1.332 1.0000
step9 - step8 39.66 40.0 5167 0.991 1.0000
step10 - step9 -3.99 40.0 5167 -0.100 1.0000
step11 - step10 -29.73 40.0 5167 -0.743 1.0000
step12 - step11 -35.67 40.0 5167 -0.892 1.0000
step13 - step12 159.56 40.0 5167 3.988 0.0011
step14 - step13 -26.37 40.0 5167 -0.659 1.0000
step15 - step14 -132.20 40.0 5167 -3.305 0.0144
step16 - step15 -21.52 40.0 5167 -0.538 1.0000
step17 - step16 -8.70 40.0 5167 -0.218 1.0000
step18 - step17 69.60 40.0 5167 1.740 1.0000
session = Block 5:
contrast estimate SE df t.ratio p.value
step2 - step1 -355.85 50.4 5167 -7.056 <.0001
step3 - step2 14.41 50.4 5167 0.286 1.0000
step4 - step3 31.07 50.4 5167 0.616 1.0000
step5 - step4 17.54 50.4 5167 0.348 1.0000
step6 - step5 2.31 50.4 5167 0.046 1.0000
step7 - step6 67.02 50.4 5167 1.329 1.0000
step8 - step7 -111.99 50.4 5167 -2.221 0.2906
step9 - step8 232.79 50.4 5167 4.616 0.0001
step10 - step9 -33.84 50.4 5167 -0.671 1.0000
step11 - step10 -239.52 50.4 5167 -4.749 <.0001
step12 - step11 15.71 50.4 5167 0.312 1.0000
step13 - step12 382.21 50.4 5167 7.579 <.0001
step14 - step13 -247.03 50.4 5167 -4.898 <.0001
step15 - step14 -188.68 50.4 5167 -3.741 0.0022
step16 - step15 36.86 50.4 5167 0.731 1.0000
step17 - step16 45.91 50.4 5167 0.910 1.0000
step18 - step17 -45.52 50.4 5167 -0.903 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: holm method for 17 tests
[WRONG] Model summary:
Linear mixed model fit by REML ['lmerMod']
Formula: rt ~ step * session + (1 | subject)
Data: data
REML criterion at convergence: 81979.1
Scaled residuals:
Min 1Q Median 3Q Max
-1.605 -0.411 -0.196 0.089 32.890
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 33915 184.2
Residual 516197 718.5
Number of obs: 5148, groups: subject, 18
Fixed effects:
Estimate Std. Error t value
(Intercept) 705.95 81.32 8.682
step2 -284.75 96.88 -2.939
step3 -255.12 96.88 -2.633
step4 -178.77 96.88 -1.845
step5 -145.55 96.88 -1.502
step6 -200.12 96.88 -2.066
step7 -64.80 96.88 -0.669
step8 -51.47 96.88 -0.531
step9 63.95 96.88 0.660
step10 -145.09 96.88 -1.498
step11 -143.27 96.88 -1.479
step12 -162.66 96.88 -1.679
step13 -125.60 96.88 -1.296
step14 -151.47 96.88 -1.564
step15 -181.15 96.88 -1.870
step16 -109.84 96.88 -1.134
step17 -50.84 96.88 -0.525
step18 287.59 96.88 2.969
sessionBlock 5 507.34 87.60 5.791
step2:sessionBlock 5 -303.12 123.50 -2.455
step3:sessionBlock 5 -369.43 123.50 -2.991
step4:sessionBlock 5 -366.99 123.50 -2.972
step5:sessionBlock 5 -330.66 123.50 -2.677
step6:sessionBlock 5 -237.72 123.50 -1.925
step7:sessionBlock 5 -336.23 123.50 -2.723
step8:sessionBlock 5 -448.56 123.50 -3.632
step9:sessionBlock 5 -299.52 123.50 -2.425
step10:sessionBlock 5 -285.91 123.50 -2.315
step11:sessionBlock 5 -389.44 123.50 -3.153
step12:sessionBlock 5 -295.40 123.50 -2.392
step13:sessionBlock 5 -219.79 123.50 -1.780
step14:sessionBlock 5 -316.18 123.50 -2.560
step15:sessionBlock 5 -350.49 123.50 -2.838
step16:sessionBlock 5 -457.91 123.50 -3.708
step17:sessionBlock 5 -493.06 123.50 -3.993
step18:sessionBlock 5 -756.38 123.50 -6.125
Correlation matrix not shown by default, as p = 36 > 12.
Use print(summary(mdl), correlation=TRUE) or
vcov(summary(mdl)) if you need it
Type II Chi-square ANOVA:
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rt
Chisq Df Pr(>Chisq)
step 135.053 17 < 2.2e-16 ***
session 54.066 1 1.939e-13 ***
step:session 49.136 17 5.747e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
session = Block 4:
step emmean SE df lower.CL upper.CL
1 706 81.3 182.7 546 866
2 421 81.3 182.7 261 582
3 451 81.3 182.7 290 611
4 527 81.3 182.7 367 688
5 560 81.3 182.7 400 721
6 506 81.3 182.7 345 666
7 641 81.3 182.7 481 802
8 654 81.3 182.7 494 815
9 770 81.3 182.7 609 930
10 561 81.3 182.7 400 721
11 563 81.3 182.7 402 723
12 543 81.3 182.7 383 704
13 580 81.3 182.7 420 741
14 554 81.3 182.7 394 715
15 525 81.3 182.7 364 685
16 596 81.3 182.7 436 757
17 655 81.3 182.7 495 816
18 994 81.3 182.7 833 1154
session = Block 5:
step emmean SE df lower.CL upper.CL
1 1213 69.5 98.8 1075 1351
2 625 69.5 98.8 487 763
3 589 69.5 98.8 451 727
4 668 69.5 98.8 530 805
5 737 69.5 98.8 599 875
6 775 69.5 98.8 637 913
7 812 69.5 98.8 674 950
8 713 69.5 98.8 575 851
9 978 69.5 98.8 840 1116
10 782 69.5 98.8 644 920
11 681 69.5 98.8 543 819
12 755 69.5 98.8 617 893
13 868 69.5 98.8 730 1006
14 746 69.5 98.8 608 884
15 682 69.5 98.8 544 820
16 646 69.5 98.8 508 783
17 669 69.5 98.8 531 807
18 744 69.5 98.8 607 882
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 # helper (kept)
if (!exists("safe_fit")) {
safe_fit <- function(formula, data) {
m <- tryCatch(lme4::lmer(formula, data = data), error = function(e) NULL)
if (is.null(m)) { message("⚠️ Falling back to lm."); m <- stats::lm(update(formula, . ~ . - (1 | subject)), data = data) }
m
}
}
# === Test phase (Blocks 4–5): Stepwise RT, CORRECT trials only ===
# Facets: rows = Familiar/Unfamiliar, cols = 6/12/18 (with adjacent-step stars)
plot_stepwise_rt_test_correct_only <- function(df_acc_all, show_plot = TRUE, alpha = 0.05) {
df_len0 <- df_acc_all %>%
dplyr::group_by(subject, session, trial) %>%
dplyr::mutate(seq_length_trial = dplyr::n_distinct(sub.trial.number)) %>%
dplyr::ungroup()
df_len <- df_len0 %>%
dplyr::filter(session %in% c(4, 5), trial_acc_cat == "correct") %>%
dplyr::mutate(
session_f = factor(session, levels = c(4, 5), labels = c("Familiar", "Unfamiliar")),
seq_length_trial = factor(seq_length_trial, levels = c(6, 12, 18))
)
if (nrow(df_len) == 0) { message("No correct-trial data for test phase (4–5)."); return(invisible(NULL)) }
lengths <- sort(unique(as.character(df_len$seq_length_trial)))
emms_list <- list(); stars_list <- list()
for (L in lengths) {
df_seq <- df_len %>%
dplyr::filter(as.character(seq_length_trial) == L) %>%
dplyr::group_by(subject, session_f, trial,
step = as.integer(as.character(sub.trial.number))) %>%
dplyr::summarise(rt = mean(feedback.RT, na.rm = TRUE), .groups = "drop") %>%
dplyr::mutate(step = factor(step, levels = sort(unique(step))))
if (nrow(df_seq) == 0) next
has_two_cond <- dplyr::n_distinct(df_seq$session_f) >= 2
has_two_step <- dplyr::n_distinct(df_seq$step) >= 2
if (!has_two_step) next
fmla <- if (has_two_cond) rt ~ step * session_f + (1 | subject) else rt ~ step + (1 | subject)
mdl <- safe_fit(fmla, data = df_seq)
# EMMs
if (has_two_cond) {
em <- emmeans::emmeans(mdl, ~ step | session_f) %>% as.data.frame()
} else {
em <- emmeans::emmeans(mdl, ~ step) %>% as.data.frame()
one_lab <- as.character(unique(df_seq$session_f))
em$session_f <- factor(rep(one_lab, nrow(em)), levels = c("Familiar","Unfamiliar"))
}
em$len_fac <- factor(paste0(L, "-step"), levels = c("6-step","12-step","18-step"))
em$step_num <- as.integer(as.character(em$step))
emms_list[[L]] <- em
# Adjacent-step tests (Holm) -> stars
em_grid <- if (has_two_cond) emmeans::emmeans(mdl, ~ step | session_f) else emmeans::emmeans(mdl, ~ step)
consec <- as.data.frame(emmeans::contrast(em_grid, method = "consec", adjust = "holm"))
nums <- stringr::str_extract_all(consec$contrast, "\\d+")
n1 <- vapply(nums, function(v) as.integer(v[1]), integer(1))
n2 <- vapply(nums, function(v) as.integer(v[2]), integer(1))
prev <- pmin(n1, n2); nextv <- pmax(n1, n2)
sign_adj <- ifelse(nextv == n1, 1, -1)
est_adj <- consec$estimate * sign_adj
st <- consec %>%
dplyr::mutate(step_prev = prev, step_next = nextv,
estimate_adj = est_adj,
sig = !is.na(p.value) & p.value < alpha) %>%
dplyr::filter(sig) %>%
dplyr::transmute(
len_fac = factor(paste0(L,"-step"), levels = c("6-step","12-step","18-step")),
step_num = step_prev,
session_f = if ("session_f" %in% names(.))
as.character(session_f)
else
rep(as.character(unique(df_seq$session_f)), dplyr::n()),
direction = ifelse(estimate_adj > 0, "up", "down"),
p.value = p.value
) %>%
dplyr::mutate(session_f = factor(session_f, levels = c("Familiar","Unfamiliar")))
if (nrow(st)) stars_list[[length(stars_list)+1]] <- st
}
if (!length(emms_list)) { message("No estimable EMMs for test-phase correct trials."); return(invisible(NULL)) }
df_em <- dplyr::bind_rows(emms_list) %>%
dplyr::mutate(
session_f = factor(session_f, levels = c("Familiar","Unfamiliar")),
len_fac = factor(len_fac, levels = c("6-step","12-step","18-step"))
)
df_stars <- if (length(stars_list)) dplyr::bind_rows(stars_list) else
dplyr::tibble(
len_fac = factor(character(), levels = levels(df_em$len_fac)),
step_num = integer(),
session_f = factor(character(), levels = levels(df_em$session_f)),
direction = character(),
p.value = numeric()
)
# y-positions for stars
if (nrow(df_stars)) {
df_stars <- df_stars %>%
dplyr::left_join(df_em %>% dplyr::select(len_fac, session_f, step_num, emmean, SE),
by = c("len_fac","session_f","step_num")) %>%
dplyr::mutate(y_star = emmean + pmax(SE * 1.2, 35))
}
df_stars_up <- df_stars %>% dplyr::filter(direction == "up")
df_stars_down <- df_stars %>% dplyr::filter(direction == "down")
pal <- c("6-step"="#B22222","12-step"="#2E7D32","18-step"="#1E3A8A")
# vertical separators
sep_df <- expand.grid(
session_f = levels(df_em$session_f),
len_fac = c("6-step","12-step"),
KEEP.OUT.ATTRS = FALSE, stringsAsFactors = FALSE
) %>%
dplyr::mutate(
session_f = factor(session_f, levels = levels(df_em$session_f)),
len_fac = factor(len_fac, levels = levels(df_em$len_fac))
)
p <- ggplot2::ggplot(df_em, ggplot2::aes(x = step_num, y = emmean)) +
ggplot2::geom_segment(
data = sep_df,
ggplot2::aes(x = Inf, xend = Inf, y = -Inf, yend = Inf),
inherit.aes = FALSE, linetype = "dotted"
) +
ggplot2::geom_ribbon(
ggplot2::aes(ymin = emmean - SE, ymax = emmean + SE, fill = len_fac),
alpha = 0.15, color = NA
) +
ggplot2::geom_line(ggplot2::aes(color = len_fac), linewidth = 0.9) +
ggplot2::geom_point(ggplot2::aes(fill = len_fac), shape = 21, color = "black", size = 2, stroke = 0.5) +
ggplot2::geom_text(data = df_stars_up,
ggplot2::aes(x = step_num, y = y_star, label = "*"),
inherit.aes = FALSE, size = 4.2, fontface = "bold", color = "#D32F2F") +
ggplot2::geom_text(data = df_stars_down,
ggplot2::aes(x = step_num, y = y_star, label = "*"),
inherit.aes = FALSE, size = 4.2, fontface = "bold", color = "#1F6FEB") +
ggplot2::facet_grid(rows = ggplot2::vars(session_f), cols = ggplot2::vars(len_fac),
scales = "free_x", space = "free_x", drop = FALSE) +
ggplot2::scale_x_continuous(
breaks = function(lims) seq(floor(lims[1]), ceiling(lims[2]), by = 1),
expand = ggplot2::expansion(mult = c(0.02, 0.06))
) +
ggplot2::scale_y_continuous(expand = ggplot2::expansion(mult = c(0.05, 0.12))) +
ggplot2::scale_color_manual(values = pal) +
ggplot2::scale_fill_manual(values = pal) +
ggplot2::labs(
title = "RT Comparison for each step across Difficulty levels (Test phase)",
x = "Step", y = "Estimated RT (ms)",
caption = "* Following step is significantly different (red = ↑, blue = ↓); Holm-adjusted adjacent tests."
) +
ggplot2::guides(color = "none", fill = "none") +
ggplot2::theme_classic(base_size = 12) +
ggplot2::theme(
panel.grid = ggplot2::element_blank(),
plot.caption = ggplot2::element_text(hjust = 0),
axis.line = ggplot2::element_line(),
axis.ticks = ggplot2::element_line()
)
if (show_plot) print(p)
invisible(list(emms = df_em, stars = df_stars, plot = p, palette = pal))
}
# === Test phase (Blocks 4–5): Stepwise RT — Familiar & Unfamiliar overlaid ===
# Facets: cols = 6/12/18 (with adjacent-step stars)
plot_stepwise_rt_test_correct_combined <- function(df_acc_all, show_plot = TRUE, alpha = 0.05) {
df_len0 <- df_acc_all %>%
dplyr::group_by(subject, session, trial) %>%
dplyr::mutate(seq_length_trial = dplyr::n_distinct(sub.trial.number)) %>%
dplyr::ungroup()
df_len <- df_len0 %>%
dplyr::filter(session %in% c(4, 5), trial_acc_cat == "correct") %>%
dplyr::mutate(
session_f = factor(session, levels = c(4, 5), labels = c("Familiar", "Unfamiliar")),
seq_length_trial = factor(seq_length_trial, levels = c(6, 12, 18))
)
if (nrow(df_len) == 0) { message("No correct-trial data for test phase (4–5)."); return(invisible(NULL)) }
lengths <- sort(unique(as.character(df_len$seq_length_trial)))
emms_list <- list(); stars_list <- list()
for (L in lengths) {
df_seq <- df_len %>%
dplyr::filter(as.character(seq_length_trial) == L) %>%
dplyr::group_by(
subject, session_f, trial,
step = as.integer(as.character(sub.trial.number))
) %>%
dplyr::summarise(rt = mean(feedback.RT, na.rm = TRUE), .groups = "drop") %>%
dplyr::mutate(step = factor(step, levels = sort(unique(step))))
if (nrow(df_seq) == 0) next
has_two_cond <- dplyr::n_distinct(df_seq$session_f) >= 2
has_two_step <- dplyr::n_distinct(df_seq$step) >= 2
if (!has_two_step) next
fmla <- if (has_two_cond) rt ~ step * session_f + (1 | subject) else rt ~ step + (1 | subject)
mdl <- safe_fit(fmla, data = df_seq)
if (has_two_cond) {
em <- emmeans::emmeans(mdl, ~ step | session_f) %>% as.data.frame()
} else {
em <- emmeans::emmeans(mdl, ~ step) %>% as.data.frame()
one_lab <- as.character(unique(df_seq$session_f))
em$session_f <- factor(rep(one_lab, nrow(em)), levels = c("Familiar","Unfamiliar"))
}
em$len_fac <- factor(paste0(L, "-step"), levels = c("6-step","12-step","18-step"))
em$step_num <- as.integer(as.character(em$step))
emms_list[[L]] <- em
em_grid <- if (has_two_cond) emmeans::emmeans(mdl, ~ step | session_f) else emmeans::emmeans(mdl, ~ step)
consec <- as.data.frame(emmeans::contrast(em_grid, method = "consec", adjust = "holm"))
nums <- stringr::str_extract_all(consec$contrast, "\\d+")
n1 <- vapply(nums, function(v) as.integer(v[1]), integer(1))
n2 <- vapply(nums, function(v) as.integer(v[2]), integer(1))
prev <- pmin(n1, n2); nextv <- pmax(n1, n2)
sign_adj <- ifelse(nextv == n1, 1, -1)
est_adj <- consec$estimate * sign_adj
st <- consec %>%
dplyr::mutate(step_prev = prev, step_next = nextv,
estimate_adj = est_adj,
sig = !is.na(p.value) & p.value < alpha) %>%
dplyr::filter(sig) %>%
dplyr::transmute(
len_fac = factor(paste0(L,"-step"), levels = c("6-step","12-step","18-step")),
step_num = step_prev,
session_f = if ("session_f" %in% names(.))
as.character(session_f)
else
rep(as.character(unique(df_seq$session_f)), dplyr::n()),
direction = ifelse(estimate_adj > 0, "up", "down"),
p.value = p.value
) %>%
dplyr::mutate(session_f = factor(session_f, levels = c("Familiar","Unfamiliar")))
if (nrow(st)) stars_list[[length(stars_list)+1]] <- st
}
df_em <- dplyr::bind_rows(emms_list) %>%
dplyr::mutate(
len_fac = factor(len_fac, levels = c("6-step","12-step","18-step")),
session_f = factor(session_f, levels = c("Familiar","Unfamiliar"))
)
df_stars <- if (length(stars_list)) dplyr::bind_rows(stars_list) else
dplyr::tibble(
len_fac = factor(character(), levels = levels(df_em$len_fac)),
step_num = integer(),
session_f = factor(character(), levels = levels(df_em$session_f)),
direction = character(),
p.value = numeric()
)
if (nrow(df_stars)) {
df_stars <- df_stars %>%
dplyr::left_join(df_em %>% dplyr::select(len_fac, session_f, step_num, emmean, SE),
by = c("len_fac","session_f","step_num")) %>%
dplyr::mutate(y_star = emmean + pmax(SE * 1.2, 35))
}
df_stars_up <- df_stars %>% dplyr::filter(direction == "up")
df_stars_down <- df_stars %>% dplyr::filter(direction == "down")
pal_cond <- c("Familiar"="#1E3A8A", "Unfamiliar"="#C2410C")
sep_df <- data.frame(len_fac = factor(c("6-step","12-step"),
levels = c("6-step","12-step","18-step")))
p <- ggplot2::ggplot(df_em, ggplot2::aes(x = step_num, y = emmean)) +
ggplot2::geom_segment(
data = sep_df,
ggplot2::aes(x = Inf, xend = Inf, y = -Inf, yend = Inf),
inherit.aes = FALSE, linetype = "dotted"
) +
ggplot2::geom_ribbon(
ggplot2::aes(ymin = emmean - SE, ymax = emmean + SE, fill = session_f, group = session_f),
alpha = 0.15, color = NA
) +
ggplot2::geom_line(ggplot2::aes(color = session_f, group = session_f), linewidth = 0.9) +
ggplot2::geom_point(ggplot2::aes(fill = session_f), shape = 21, color = "black", size = 2, stroke = 0.5) +
ggplot2::geom_text(data = df_stars_up,
ggplot2::aes(x = step_num, y = y_star, label = "*"),
inherit.aes = FALSE, size = 4.2, fontface = "bold", color = "#D32F2F") +
ggplot2::geom_text(data = df_stars_down,
ggplot2::aes(x = step_num, y = y_star, label = "*"),
inherit.aes = FALSE, size = 4.2, fontface = "bold", color = "#1F6FEB") +
ggplot2::facet_grid(cols = ggplot2::vars(len_fac), scales = "free_x", space = "free_x", drop = FALSE) +
ggplot2::scale_x_continuous(
breaks = function(lims) seq(floor(lims[1]), ceiling(lims[2]), by = 1),
expand = ggplot2::expansion(mult = c(0.02, 0.06))
) +
ggplot2::scale_y_continuous(expand = ggplot2::expansion(mult = c(0.05, 0.12))) +
ggplot2::scale_color_manual(values = pal_cond, name = NULL) +
ggplot2::scale_fill_manual(values = pal_cond, name = NULL) +
ggplot2::labs(
title = "RT for each Step across Difficulty levels (Test phase)",
x = "Steps", y = "Estimated RT (ms)",
caption = "* Following step is significantly different (red = ↑, blue = ↓)"
) +
ggplot2::theme_classic(base_size = 12) +
ggplot2::theme(
panel.grid = ggplot2::element_blank(),
legend.position = "bottom",
legend.box = "horizontal",
plot.caption = ggplot2::element_text(hjust = 0),
axis.line = ggplot2::element_line(),
axis.ticks = ggplot2::element_line()
)
if (show_plot) print(p)
invisible(list(emms = df_em, stars = df_stars, plot = p, palette = pal_cond))
}
plot_stepwise_rt_test_correct_only(df_acc_base)Warning in ggplot2::geom_segment(data = sep_df, ggplot2::aes(x = Inf, xend = Inf, : All aesthetics have length 1, but the data has 4 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
plot_stepwise_rt_test_correct_combined(df_acc_base)Warning in ggplot2::geom_segment(data = sep_df, ggplot2::aes(x = Inf, xend = Inf, : All aesthetics have length 1, but the data has 2 rows.
ℹ Please consider using `annotate()` or provide this layer with data containing
a single row.
#B: Movement variability analysis
suppressPackageStartupMessages({
library(tidyverse)
library(readr)
library(lme4)
library(lmerTest)
library(emmeans)
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
library(car)
})
# Lighter-weight df method for emmeans on lmer (avoids huge memory from KR/Satt)
emm_options(lmer.df = "asymptotic")
options(
dplyr.summarise.inform = FALSE,
contrasts = c("contr.sum", "contr.poly")
)
# Axis labels helper
axis_labels <- c(x = "X", y = "Y", z = "Z")
data_dir <- "/Users/can/Documents/Uni/Thesis/Data/...v/merged/Cleaned"
# Step counts per block (nominal)
step_counts <- tibble(
Block = c(1, 2, 3, 4, 5),
Steps = c(6, 12, 18, 18, 18)
)
# binary Accuracy factor from trial.acc (1 -> 1, else -> 0)
mk_accuracy <- function(df) {
df %>% mutate(Accuracy = factor(if_else(trial.acc == 1, 1L, 0L), levels = c(0, 1)))
}
normalize_ids <- function(df) {
df %>%
mutate(
subject = if ("subject" %in% names(.)) subject else Subject,
Block = as.integer(Block),
trial = if ("trial" %in% names(.)) trial else Trial
) %>%
mutate(trial_id = interaction(subject, Block, trial, drop = TRUE))
}
assign_steps_by_block <- function(df, steps_df = step_counts) {
df %>%
inner_join(steps_df, by = "Block") %>%
group_by(subject, Block, trial) %>%
mutate(Step = cut_number(row_number(), n = unique(Steps), labels = FALSE)) %>%
ungroup()
}
# Tag trial phases using Marker.Text: start (27), end (26 or 25). Preparation = 1500 ms pre-start
# Requires columns: subject, Block, trial, ms, Marker.Text
tag_trial_phases <- function(df) {
df %>%
group_by(subject, Block, trial) %>%
mutate(
start_ms = ms[which(Marker.Text == 27)[1]],
end_ms = {
end_candidates <- which(Marker.Text %in% c(26, 25))
if (length(end_candidates) > 0) ms[end_candidates[1]] else NA_real_
},
phase = case_when(
!is.na(start_ms) & !is.na(end_ms) & ms >= start_ms & ms <= end_ms ~ "Execution",
!is.na(start_ms) & ms >= (start_ms - 1500) & ms < start_ms ~ "Preparation",
TRUE ~ NA_character_
)
) %>%
ungroup() %>%
filter(!is.na(phase))
}
# Compute RMS per phase (Preparation / Execution) per subject × Block × trial
compute_phase_rms <- function(df) {
df %>%
group_by(subject, Block, trial, phase) %>%
summarise(
rms_x = sqrt(mean(CoM.acc.x^2, na.rm = TRUE)),
rms_y = sqrt(mean(CoM.acc.y^2, na.rm = TRUE)),
rms_z = sqrt(mean(CoM.acc.z^2, na.rm = TRUE)),
.groups = "drop"
)
}
# Detect per-step onsets (Marker.Text in {14,15,16,17}) and compute 7-sample window RMS (±3)
# Returns one row per detected step onset per trial with RMS per axis
compute_stepwise_rms <- function(tagged_exec_df, max_steps_keep = 18) {
step_markers <- c(14L, 15L, 16L, 17L)
# Keep only execution rows
exec <- tagged_exec_df %>% filter(phase == "Execution") %>% arrange(subject, Block, trial, ms)
# Detect step onset rows within each trial
exec_mark <- exec %>%
group_by(subject, Block, trial) %>%
mutate(
in_step = Marker.Text %in% step_markers,
onset = in_step & (is.na(lag(in_step)) | !lag(in_step) | (Marker.Text != lag(Marker.Text)))
) %>%
filter(onset) %>%
mutate(step_index = row_number()) %>%
ungroup()
# If there are no onsets, return empty tibble
if (nrow(exec_mark) == 0) return(tibble())
# Per-trial total step count
step_counts_lookup <- exec_mark %>%
group_by(subject, Block, trial) %>%
summarise(step_count = max(step_index), .groups = "drop")
# Join row indices to full exec to build windows
exec_with_row <- exec %>% group_by(subject, Block, trial) %>% mutate(row_id = row_number()) %>% ungroup()
onset_idx <- exec_mark %>% select(subject, Block, trial, ms, step_index) %>%
left_join(exec_with_row %>% select(subject, Block, trial, ms, row_id), by = c("subject", "Block", "trial", "ms"))
# Build ±3 row windows around each onset row and compute RMS
win <- purrr::map_dfr(seq_len(nrow(onset_idx)), function(i) {
r <- onset_idx$row_id[i]
s <- onset_idx$step_index[i]
grp <- onset_idx[i, c("subject", "Block", "trial")]
tmp <- exec_with_row %>%
dplyr::semi_join(grp, by = c("subject", "Block", "trial")) %>%
dplyr::filter(row_id >= (r - 3), row_id <= (r + 3))
if (nrow(tmp) == 0) return(NULL)
tibble(
subject = grp$subject, Block = grp$Block, trial = grp$trial,
Step = s,
RMS = c(
sqrt(mean(tmp$CoM.acc.x^2, na.rm = TRUE)),
sqrt(mean(tmp$CoM.acc.y^2, na.rm = TRUE)),
sqrt(mean(tmp$CoM.acc.z^2, na.rm = TRUE))
),
Axis = factor(c("x", "y", "z"), levels = c("x", "y", "z"))
)
})
# Keep first N steps per trial and add step_count
win %>%
group_by(subject, Block, trial) %>%
filter(Step <= max_steps_keep) %>%
ungroup() %>%
left_join(step_counts_lookup, by = c("subject", "Block", "trial"))
}
# Join trial-level Accuracy to a summary table (by subject × Block × trial)
add_accuracy_to <- function(df_core, df_lookup) {
# Build accuracy lookup with consistent key types (character) to avoid factor/int join issues
acc_tbl <- df_lookup %>%
distinct(subject, Block, trial, trial.acc) %>%
mk_accuracy() %>%
transmute(
subject = as.character(subject),
Block = as.character(Block),
trial = as.character(trial),
Accuracy
)
df_core %>%
mutate(
subject = as.character(subject),
Block = as.character(Block),
trial = as.character(trial)
) %>%
left_join(acc_tbl, by = c("subject", "Block", "trial")) %>%
# Convert Block/trial back to numeric where appropriate
mutate(
Block = type.convert(Block, as.is = TRUE),
trial = type.convert(trial, as.is = TRUE)
)
}
# Generic LMM runner for phase × block per axis
run_phase_block_models <- function(rms_combined) {
for (axis in c("x", "y", "z")) {
cat("
=============================
")
cat(paste("Axis:", toupper(axis), "
"))
cat("=============================
")
axis_col <- paste0("rms_", axis)
# Trim to needed columns to reduce memory footprint
df_axis <- rms_combined %>%
dplyr::select(subject, Block, Trial, phase, Accuracy, !!sym(axis_col)) %>%
droplevels()
fml <- as.formula(paste(axis_col, "~ Block * phase + Accuracy + (1 | subject) + (1 | Trial)"))
model <- lmer(fml, data = df_axis)
cat("
Model Summary:
"); print(summary(model))
emms <- emmeans(model, ~ Block * phase)
cat("
Estimated Marginal Means (df=asymptotic):
"); print(summary(emms))
cat("
Type II ANOVA (Chisq):
"); print(car::Anova(model, type = 2, test.statistic = "Chisq"))
cat("
Pairwise (within phase; Tukey):
")
pw <- contrast(emms, interaction = c("revpairwise"), by = "phase", adjust = "tukey")
print(pw)
rm(model, emms, df_axis); gc()
}
}
# Generic LMM runner for stepwise RMS per block/axis with Accuracy
run_step_models <- function(step_df, block, n_steps) {
d <- step_df %>% filter(Block == block, Step <= n_steps)
for (ax in c("x", "y", "z")) {
dd <- d %>% filter(Axis == ax)
if (nrow(dd) == 0) next
cat("\n\n--- Block", block, "Axis", toupper(ax), "---\n")
m <- lmer(RMS ~ Step + Accuracy + (1 | subject) + (1 | trial_id), data = dd)
print(car::Anova(m, type = 2, test.statistic = "Chisq"))
e <- emmeans(m, ~ Step)
cat("Estimated Marginal Means:\n"); print(summary(e))
cat("Pairwise (Tukey):\n"); print(contrast(e, method = "pairwise", adjust = "tukey"))
}
}
# Simple bar plot function: mean ± SE by Step × Block, faceted by Axis
plot_step_bars <- function(step_df, title_prefix = "") {
se <- function(x) sd(x, na.rm = TRUE) / sqrt(sum(!is.na(x)))
sum_df <- step_df %>% group_by(Block, Step, Axis) %>% summarise(mean_RMS = mean(RMS, na.rm = TRUE), se_RMS = se(RMS), .groups = "drop")
ggplot(sum_df, aes(x = Step, y = mean_RMS, fill = factor(Block))) +
geom_col(position = position_dodge(width = 0.8)) +
geom_errorbar(aes(ymin = mean_RMS - se_RMS, ymax = mean_RMS + se_RMS), width = 0.2, position = position_dodge(width = 0.8)) +
facet_wrap(~ Axis, nrow = 1, labeller = as_labeller(axis_labels)) +
labs(title = paste0(title_prefix, " Step-wise RMS (mean ± SE)"), x = "Step", y = "RMS") +
theme_minimal()
}# Gather mixed files
mixed_files <- list.files("/Users/can/Documents/Uni/Thesis/Data/Xsens/cleaned_csv/merged/Cleaned", pattern = "_final\\.csv$", full.names = TRUE)
all_data_mixed <- map_dfr(mixed_files, read_csv)Rows: 360534 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 379431 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 340045 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 342328 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 386760 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 355905 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 336982 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 434486 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 441207 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 368716 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 487396 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 435226 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 324591 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 372054 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 392294 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 412795 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 356869 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
Rows: 350365 Columns: 21
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
dbl (21): Frame, CoM.pos.x, CoM.pos.y, CoM.pos.z, CoM.vel.x, CoM.vel.y, CoM....
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.# Merge
all_data_mixed <- map_dfr(mixed_files, read_csv, show_col_types = FALSE) %>% normalize_ids() %>% mk_accuracy()
# Trial phases once
tagged_data <- tag_trial_phases(all_data_mixed) %>% mutate(DataType = "Mixed")
tagged_data2 <- tagged_data#Build step-wise table
# Helper: robust step-onset finder for Execution phase (uses Marker.Text)
.build_exec_step_onsets <- function(df) {
exec <- df %>% dplyr::filter(phase == "Execution")
exec_step <- exec %>%
dplyr::filter(
!is.na(Marker.Text),
suppressWarnings(!is.na(as.integer(Marker.Text))),
as.integer(Marker.Text) >= 1,
as.integer(Marker.Text) <= 18
) %>%
dplyr::mutate(
Step = as.integer(Marker.Text),
# create trial_id if missing
trial_id = if ("trial_id" %in% names(.)) trial_id else interaction(subject, Block, trial, drop = TRUE)
) %>%
# NOTE: do NOT select `Trial` (capital T); your data has `trial` (lowercase)
dplyr::select(subject, Block, trial, phase, ms, Step, trial_id)
exec_step
}
.assign_exec_steps_evenly <- function(df) {
df %>%
dplyr::filter(phase == "Execution") %>%
assign_steps_by_block() %>% # uses your existing helper + step_counts
dplyr::mutate(
trial_id = if ("trial_id" %in% names(.)) trial_id else interaction(subject, Block, trial, drop = TRUE)
) %>%
dplyr::select(subject, Block, trial, phase, ms, Step, trial_id)
}
# Try onsets first, else fallback
sw_all <- .build_exec_step_onsets(tagged_data)
if (nrow(sw_all) == 0) {
message("No explicit step-onset markers found; assigning steps evenly within trials.")
sw_all <- .assign_exec_steps_evenly(tagged_data)
}
# Per-trial step count (for mixed lengths in Blocks 4–5)
sw_all <- sw_all %>%
dplyr::group_by(subject, Block, trial) %>%
dplyr::mutate(step_count = max(Step, na.rm = TRUE)) %>%
dplyr::ungroup()
sw_all <- sw_all %>%
dplyr::select(subject, Block, trial, phase, Step, step_count, trial_id)data preprocessing and creating models
trial_acc_summary <- tagged_data %>%
select(subject, Block, trial, trial.acc) %>%
distinct() %>%
filter(trial.acc == 1) %>%
group_by(subject, Block) %>%
summarise(n_trials_with_acc1 = n(), .groups = "drop") %>%
group_by(Block) %>%
summarise(
mean_trials_with_acc1 = mean(n_trials_with_acc1),
sd_trials_with_acc1 = sd(n_trials_with_acc1),
n_subjects = n()
)
print(trial_acc_summary)# A tibble: 5 × 4
Block mean_trials_with_acc1 sd_trials_with_acc1 n_subjects
<int> <dbl> <dbl> <int>
1 1 40.2 3.13 18
2 2 34.2 7.19 18
3 3 27.8 9.85 18
4 4 39.2 4.51 18
5 5 28.9 10.0 18# Phase RMS
rms_data <- compute_phase_rms(tagged_data)
exec_data <- rms_data %>% filter(phase == "Execution")
for (axis in c("x", "y", "z")) {
axis_col <- paste0("rms_", axis)
print(
ggplot(exec_data, aes(x = factor(Block), y = .data[[axis_col]], fill = phase)) +
geom_boxplot(alpha = 0.7, outlier.shape = NA) +
geom_jitter(width = 0.2, alpha = 0.4, size = 0.6) +
geom_vline(xintercept = 3.5, linetype = "dashed") +
coord_cartesian(ylim = c(0, 2.5)) +
labs(title = paste("Execution Phase:", toupper(axis), "Axis"), x = "Block", y = "RMS") +
theme_minimal()
)
}
prep_data <- tagged_data %>% filter(phase == "Preparation")
prep_rms <- compute_phase_rms(prep_data)
for (axis in c("x", "y", "z")) {
axis_col <- paste0("rms_", axis)
print(
ggplot(prep_rms, aes(x = factor(Block), y = .data[[axis_col]], fill = phase)) +
geom_boxplot(alpha = 0.7, outlier.shape = NA) +
geom_jitter(width = 0.2, alpha = 0.4, size = 0.6) +
geom_vline(xintercept = 3.5, linetype = "dashed") +
coord_cartesian(ylim = c(0, 0.5)) +
labs(title = paste("Preparation Phase:", toupper(axis), "Axis"), x = "Block", y = "RMS") +
theme_minimal()
)
}
# Combine prep + exec, join trial Accuracy, then model per axis
rms_combined <- bind_rows(prep_rms, exec_data) %>%
# Join Accuracy before converting key columns to factors
add_accuracy_to(all_data_mixed) %>%
mutate(
phase = factor(phase, levels = c("Preparation", "Execution")),
Block = factor(Block),
Trial = factor(trial)
)Warning in left_join(., acc_tbl, by = c("subject", "Block", "trial")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 3021 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.run_phase_block_models(rms_combined)
=============================
Axis: X
=============================
Model Summary:
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: fml
Data: df_axis
REML criterion at convergence: 1156.2
Scaled residuals:
Min 1Q Median 3Q Max
-4.2686 -0.5173 -0.0979 0.3264 8.9700
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.0002199 0.01483
subject (Intercept) 0.0170146 0.13044
Residual 0.0635587 0.25211
Number of obs: 11552, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.028e-01 3.091e-02 1.718e+01 13.030 2.47e-10 ***
Block1 3.041e-02 5.062e-03 1.152e+04 6.007 1.95e-09 ***
Block2 1.660e-02 4.709e-03 1.150e+04 3.525 0.000426 ***
Block3 -2.104e-02 4.733e-03 1.147e+04 -4.446 8.84e-06 ***
Block4 2.224e-02 4.743e-03 1.150e+04 4.689 2.77e-06 ***
phase1 -2.881e-01 2.357e-03 1.149e+04 -122.230 < 2e-16 ***
Accuracy1 -1.782e-02 2.499e-03 1.086e+04 -7.132 1.05e-12 ***
Block1:phase1 -9.185e-02 4.974e-03 1.148e+04 -18.467 < 2e-16 ***
Block2:phase1 -1.717e-02 4.703e-03 1.147e+04 -3.651 0.000262 ***
Block3:phase1 6.341e-02 4.685e-03 1.147e+04 13.535 < 2e-16 ***
Block4:phase1 -1.417e-02 4.719e-03 1.147e+04 -3.003 0.002681 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2 Block3 Block4 phase1 Accrc1 Blc1:1 Blc2:1
Block1 0.006
Block2 0.000 -0.268
Block3 -0.001 -0.285 -0.245
Block4 0.001 -0.254 -0.248 -0.255
phase1 -0.001 -0.013 0.002 0.003 0.003
Accuracy1 0.008 0.175 -0.001 -0.107 0.082 -0.005
Block1:phs1 -0.001 -0.033 0.009 0.009 0.008 0.072 -0.003
Block2:phs1 0.000 0.009 -0.015 0.002 0.002 -0.002 -0.001 -0.272
Block3:phs1 0.000 0.009 0.002 -0.014 0.002 -0.008 0.001 -0.271 -0.247
Block4:phs1 0.000 0.009 0.002 0.001 -0.012 0.002 0.003 -0.273 -0.250
Blc3:1
Block1
Block2
Block3
Block4
phase1
Accuracy1
Block1:phs1
Block2:phs1
Block3:phs1
Block4:phs1 -0.248
Estimated Marginal Means (df=asymptotic):
Block phase emmean SE df asymp.LCL asymp.UCL
1 Preparation 0.0533 0.0318 Inf -0.00911 0.116
2 Preparation 0.1141 0.0317 Inf 0.05201 0.176
3 Preparation 0.1571 0.0317 Inf 0.09496 0.219
4 Preparation 0.1228 0.0317 Inf 0.06062 0.185
5 Preparation 0.1263 0.0316 Inf 0.06436 0.188
1 Execution 0.8131 0.0319 Inf 0.75060 0.876
2 Execution 0.7246 0.0317 Inf 0.66248 0.787
3 Execution 0.6064 0.0317 Inf 0.54426 0.669
4 Execution 0.7273 0.0317 Inf 0.66510 0.789
5 Execution 0.5829 0.0316 Inf 0.52094 0.645
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Type II ANOVA (Chisq):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rms_x
Chisq Df Pr(>Chisq)
Block 148.880 4 < 2.2e-16 ***
phase 14600.810 1 < 2.2e-16 ***
Accuracy 50.864 1 9.897e-13 ***
Block:phase 574.253 4 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise (within phase; Tukey):
phase = Preparation:
Block_revpairwise estimate SE df z.ratio p.value
2 - 1 0.06087 0.0108 Inf 5.628 <.0001
3 - 1 0.10381 0.0109 Inf 9.564 <.0001
3 - 2 0.04295 0.0104 Inf 4.114 0.0004
4 - 1 0.06951 0.0108 Inf 6.423 <.0001
4 - 2 0.00865 0.0105 Inf 0.825 0.9231
4 - 3 -0.03430 0.0105 Inf -3.267 0.0096
5 - 1 0.07301 0.0106 Inf 6.905 <.0001
5 - 2 0.01214 0.0101 Inf 1.198 0.7524
5 - 3 -0.03081 0.0101 Inf -3.053 0.0192
5 - 4 0.00349 0.0102 Inf 0.343 0.9970
phase = Execution:
Block_revpairwise estimate SE df z.ratio p.value
2 - 1 -0.08848 0.0111 Inf -7.966 <.0001
3 - 1 -0.20671 0.0111 Inf -18.548 <.0001
3 - 2 -0.11822 0.0106 Inf -11.174 <.0001
4 - 1 -0.08584 0.0111 Inf -7.733 <.0001
4 - 2 0.00264 0.0106 Inf 0.249 0.9992
4 - 3 0.12087 0.0106 Inf 11.377 <.0001
5 - 1 -0.23025 0.0109 Inf -21.217 <.0001
5 - 2 -0.14176 0.0103 Inf -13.821 <.0001
5 - 3 -0.02354 0.0102 Inf -2.305 0.1431
5 - 4 -0.14441 0.0103 Inf -14.007 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 5 estimates
=============================
Axis: Y
=============================
Model Summary:
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: fml
Data: df_axis
REML criterion at convergence: 4871.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.7322 -0.5187 -0.0835 0.3331 23.4331
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.0001537 0.0124
subject (Intercept) 0.0214834 0.1466
Residual 0.0877958 0.2963
Number of obs: 11552, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.218e-01 3.471e-02 1.710e+01 12.152 7.70e-10 ***
Block1 3.571e-02 5.948e-03 1.152e+04 6.005 1.97e-09 ***
Block2 2.512e-02 5.534e-03 1.151e+04 4.539 5.70e-06 ***
Block3 -2.054e-02 5.556e-03 1.142e+04 -3.697 0.000219 ***
Block4 1.555e-02 5.574e-03 1.151e+04 2.789 0.005290 **
phase1 -3.046e-01 2.770e-03 1.150e+04 -109.975 < 2e-16 ***
Accuracy1 -1.736e-02 2.929e-03 1.044e+04 -5.925 3.22e-09 ***
Block1:phase1 -9.992e-02 5.845e-03 1.148e+04 -17.094 < 2e-16 ***
Block2:phase1 -1.923e-02 5.528e-03 1.147e+04 -3.480 0.000504 ***
Block3:phase1 7.315e-02 5.506e-03 1.148e+04 13.284 < 2e-16 ***
Block4:phase1 -1.343e-02 5.547e-03 1.147e+04 -2.422 0.015466 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2 Block3 Block4 phase1 Accrc1 Blc1:1 Blc2:1
Block1 0.007
Block2 0.000 -0.268
Block3 -0.001 -0.285 -0.245
Block4 0.001 -0.254 -0.248 -0.255
phase1 -0.002 -0.013 0.002 0.003 0.003
Accuracy1 0.008 0.175 -0.001 -0.108 0.082 -0.006
Block1:phs1 -0.001 -0.033 0.009 0.009 0.008 0.072 -0.004
Block2:phs1 0.000 0.009 -0.015 0.002 0.002 -0.002 -0.001 -0.272
Block3:phs1 0.000 0.009 0.002 -0.014 0.002 -0.008 0.001 -0.271 -0.247
Block4:phs1 0.000 0.009 0.002 0.001 -0.012 0.002 0.003 -0.273 -0.250
Blc3:1
Block1
Block2
Block3
Block4
phase1
Accuracy1
Block1:phs1
Block2:phs1
Block3:phs1
Block4:phs1 -0.248
Estimated Marginal Means (df=asymptotic):
Block phase emmean SE df asymp.LCL asymp.UCL
1 Preparation 0.053 0.0358 Inf -0.0173 0.123
2 Preparation 0.123 0.0357 Inf 0.0531 0.193
3 Preparation 0.170 0.0357 Inf 0.0999 0.240
4 Preparation 0.119 0.0357 Inf 0.0493 0.189
5 Preparation 0.121 0.0355 Inf 0.0511 0.190
1 Execution 0.862 0.0359 Inf 0.7916 0.932
2 Execution 0.771 0.0357 Inf 0.7007 0.841
3 Execution 0.633 0.0357 Inf 0.5627 0.703
4 Execution 0.755 0.0357 Inf 0.6853 0.825
5 Execution 0.611 0.0356 Inf 0.5414 0.681
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Type II ANOVA (Chisq):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rms_y
Chisq Df Pr(>Chisq)
Block 138.607 4 < 2.2e-16 ***
phase 11822.323 1 < 2.2e-16 ***
Accuracy 35.108 1 3.12e-09 ***
Block:phase 485.928 4 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise (within phase; Tukey):
phase = Preparation:
Block_revpairwise estimate SE df z.ratio p.value
2 - 1 0.07009 0.0127 Inf 5.515 <.0001
3 - 1 0.11681 0.0128 Inf 9.161 <.0001
3 - 2 0.04672 0.0123 Inf 3.810 0.0013
4 - 1 0.06632 0.0127 Inf 5.215 <.0001
4 - 2 -0.00377 0.0123 Inf -0.306 0.9981
4 - 3 -0.05049 0.0123 Inf -4.093 0.0004
5 - 1 0.06780 0.0124 Inf 5.457 <.0001
5 - 2 -0.00229 0.0119 Inf -0.192 0.9997
5 - 3 -0.04901 0.0119 Inf -4.134 0.0003
5 - 4 0.00148 0.0120 Inf 0.124 0.9999
phase = Execution:
Block_revpairwise estimate SE df z.ratio p.value
2 - 1 -0.09128 0.0131 Inf -6.992 <.0001
3 - 1 -0.22932 0.0131 Inf -17.516 <.0001
3 - 2 -0.13804 0.0124 Inf -11.105 <.0001
4 - 1 -0.10665 0.0130 Inf -8.176 <.0001
4 - 2 -0.01537 0.0125 Inf -1.233 0.7321
4 - 3 0.12266 0.0125 Inf 9.828 <.0001
5 - 1 -0.25091 0.0128 Inf -19.675 <.0001
5 - 2 -0.15964 0.0121 Inf -13.243 <.0001
5 - 3 -0.02160 0.0120 Inf -1.800 0.3734
5 - 4 -0.14426 0.0121 Inf -11.907 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 5 estimates
=============================
Axis: Z
=============================
Model Summary:
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: fml
Data: df_axis
REML criterion at convergence: 16049.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.8020 -0.5578 -0.1423 0.3881 11.5638
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.000956 0.03092
subject (Intercept) 0.061226 0.24744
Residual 0.230927 0.48055
Number of obs: 11552, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 6.666e-01 5.867e-02 1.721e+01 11.362 2.01e-09 ***
Block1 2.016e-02 9.651e-03 1.152e+04 2.089 0.03674 *
Block2 3.803e-02 8.977e-03 1.150e+04 4.237 2.29e-05 ***
Block3 -1.560e-02 9.024e-03 1.148e+04 -1.729 0.08389 .
Block4 3.704e-02 9.042e-03 1.150e+04 4.097 4.22e-05 ***
phase1 -5.173e-01 4.493e-03 1.149e+04 -115.147 < 2e-16 ***
Accuracy1 -3.789e-02 4.767e-03 1.099e+04 -7.947 2.09e-15 ***
Block1:phase1 -1.277e-01 9.481e-03 1.148e+04 -13.470 < 2e-16 ***
Block2:phase1 -2.512e-02 8.965e-03 1.147e+04 -2.802 0.00509 **
Block3:phase1 1.002e-01 8.930e-03 1.147e+04 11.217 < 2e-16 ***
Block4:phase1 -3.600e-02 8.996e-03 1.147e+04 -4.002 6.31e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2 Block3 Block4 phase1 Accrc1 Blc1:1 Blc2:1
Block1 0.006
Block2 0.000 -0.268
Block3 -0.001 -0.285 -0.245
Block4 0.001 -0.254 -0.248 -0.255
phase1 -0.001 -0.013 0.002 0.003 0.003
Accuracy1 0.008 0.175 -0.001 -0.107 0.081 -0.004
Block1:phs1 -0.001 -0.033 0.009 0.009 0.008 0.072 -0.003
Block2:phs1 0.000 0.009 -0.015 0.002 0.002 -0.002 -0.001 -0.272
Block3:phs1 0.000 0.009 0.002 -0.014 0.002 -0.008 0.001 -0.271 -0.247
Block4:phs1 0.000 0.009 0.002 0.001 -0.012 0.002 0.003 -0.273 -0.250
Blc3:1
Block1
Block2
Block3
Block4
phase1
Accuracy1
Block1:phs1
Block2:phs1
Block3:phs1
Block4:phs1 -0.248
Estimated Marginal Means (df=asymptotic):
Block phase emmean SE df asymp.LCL asymp.UCL
1 Preparation 0.0418 0.0604 Inf -0.0766 0.160
2 Preparation 0.1622 0.0602 Inf 0.0443 0.280
3 Preparation 0.2339 0.0602 Inf 0.1160 0.352
4 Preparation 0.1504 0.0602 Inf 0.0324 0.268
5 Preparation 0.1583 0.0600 Inf 0.0408 0.276
1 Execution 1.3318 0.0606 Inf 1.2131 1.451
2 Execution 1.2471 0.0602 Inf 1.1291 1.365
3 Execution 1.0682 0.0602 Inf 0.9502 1.186
4 Execution 1.2570 0.0602 Inf 1.1390 1.375
5 Execution 1.0157 0.0600 Inf 0.8981 1.133
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Type II ANOVA (Chisq):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: rms_z
Chisq Df Pr(>Chisq)
Block 97.924 4 < 2.2e-16 ***
phase 13038.956 1 < 2.2e-16 ***
Accuracy 63.158 1 1.908e-15 ***
Block:phase 348.635 4 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise (within phase; Tukey):
phase = Preparation:
Block_revpairwise estimate SE df z.ratio p.value
2 - 1 0.12046 0.0206 Inf 5.843 <.0001
3 - 1 0.19211 0.0207 Inf 9.284 <.0001
3 - 2 0.07166 0.0199 Inf 3.601 0.0029
4 - 1 0.10858 0.0206 Inf 5.263 <.0001
4 - 2 -0.01188 0.0200 Inf -0.594 0.9760
4 - 3 -0.08353 0.0200 Inf -4.173 0.0003
5 - 1 0.11655 0.0202 Inf 5.783 <.0001
5 - 2 -0.00391 0.0193 Inf -0.202 0.9996
5 - 3 -0.07556 0.0192 Inf -3.928 0.0008
5 - 4 0.00797 0.0195 Inf 0.410 0.9941
phase = Execution:
Block_revpairwise estimate SE df z.ratio p.value
2 - 1 -0.08471 0.0212 Inf -4.001 0.0006
3 - 1 -0.26363 0.0212 Inf -12.409 <.0001
3 - 2 -0.17892 0.0202 Inf -8.871 <.0001
4 - 1 -0.07481 0.0212 Inf -3.536 0.0037
4 - 2 0.00990 0.0202 Inf 0.489 0.9884
4 - 3 0.18882 0.0203 Inf 9.323 <.0001
5 - 1 -0.31614 0.0207 Inf -15.283 <.0001
5 - 2 -0.23143 0.0196 Inf -11.837 <.0001
5 - 3 -0.05251 0.0195 Inf -2.698 0.0543
5 - 4 -0.24133 0.0197 Inf -12.281 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 5 estimates # Descriptive means/SDs by phase × block
rms_summary_blockwise <- rms_combined %>%
group_by(phase, Block) %>%
summarise(
mean_rms_x = mean(rms_x, na.rm = TRUE), sd_rms_x = sd(rms_x, na.rm = TRUE),
mean_rms_y = mean(rms_y, na.rm = TRUE), sd_rms_y = sd(rms_y, na.rm = TRUE),
mean_rms_z = mean(rms_z, na.rm = TRUE), sd_rms_z = sd(rms_z, na.rm = TRUE),
.groups = "drop"
)
print(rbind(head(rms_summary_blockwise, 6)))# A tibble: 6 × 8
phase Block mean_rms_x sd_rms_x mean_rms_y sd_rms_y mean_rms_z sd_rms_z
<fct> <fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Preparation 1 0.0642 0.0635 0.0635 0.0634 0.0616 0.0918
2 Preparation 2 0.117 0.266 0.125 0.302 0.167 0.493
3 Preparation 3 0.150 0.241 0.161 0.269 0.220 0.456
4 Preparation 4 0.127 0.223 0.121 0.220 0.160 0.376
5 Preparation 5 0.119 0.172 0.113 0.163 0.145 0.302
6 Execution 1 0.822 0.399 0.870 0.502 1.35 0.770 # Collapsed across blocks (phase only)
rms_summary_phaseonly <- rms_combined %>%
group_by(phase) %>%
summarise(
mean_rms_x = mean(rms_x, na.rm = TRUE), sd_rms_x = sd(rms_x, na.rm = TRUE),
mean_rms_y = mean(rms_y, na.rm = TRUE), sd_rms_y = sd(rms_y, na.rm = TRUE),
mean_rms_z = mean(rms_z, na.rm = TRUE), sd_rms_z = sd(rms_z, na.rm = TRUE),
.groups = "drop"
)
print(rms_summary_phaseonly)# A tibble: 2 × 7
phase mean_rms_x sd_rms_x mean_rms_y sd_rms_y mean_rms_z sd_rms_z
<fct> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Preparation 0.117 0.209 0.118 0.223 0.153 0.377
2 Execution 0.684 0.357 0.718 0.425 1.17 0.683# Build a per-trial sequence length lookup from step-wise data if available; otherwise fall back to NA
if (exists("sw_all")) {
seq_lookup <- sw_all %>%
dplyr::filter(Block %in% c(4, 5) | Block %in% c("4","5")) %>%
dplyr::group_by(subject, Block, trial) %>%
dplyr::summarise(step_count = max(Step, na.rm = TRUE), .groups = "drop") %>%
dplyr::mutate(
SeqLen = dplyr::case_when(
step_count == 6 ~ "6-step",
step_count == 12 ~ "12-step",
step_count == 18 ~ "18-step",
TRUE ~ NA_character_
)
) %>%
dplyr::transmute(subject, Block, trial, SeqLen)
} else {
seq_lookup <- rms_data %>%
dplyr::filter(Block %in% c(4, 5) | Block %in% c("4","5")) %>%
dplyr::distinct(subject, Block, trial) %>%
dplyr::mutate(SeqLen = NA_character_)
}
prep_b45 <- rms_data %>%
dplyr::filter(phase == "Preparation", Block %in% c(4, 5) | Block %in% c("4","5")) %>%
dplyr::left_join(seq_lookup, by = c("subject","Block","trial")) %>%
tidyr::pivot_longer(dplyr::starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
dplyr::mutate(Axis = toupper(sub("^rms_", "", Axis)))
exec_b45 <- rms_data %>%
dplyr::filter(phase == "Execution", Block %in% c(4, 5) | Block %in% c("4","5")) %>%
dplyr::left_join(seq_lookup, by = c("subject","Block","trial")) %>%
tidyr::pivot_longer(dplyr::starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
dplyr::mutate(Axis = toupper(sub("^rms_", "", Axis)))#B1.1 Training phase comparison preparation and execution
# === TRAINING (Blocks 1–3) — Combined figure: Preparation (top) + Execution (bottom)
# Boxplots + jitter, no outliers, single X/Y/Z headers (top only), custom x labels
# Long-format data with Difficulty labels
exec_tr_long <- exec_data %>%
dplyr::filter(Block %in% 1:3) %>%
tidyr::pivot_longer(dplyr::starts_with("rms_"),
names_to = "Axis", values_to = "RMS") %>%
dplyr::mutate(
Axis = toupper(sub("^rms_", "", Axis)),
Difficulty = factor(Block, levels = c(1, 2, 3),
labels = c("6 steps", "12 steps", "18 steps"))
)
prep_tr_long <- prep_rms %>%
dplyr::filter(Block %in% 1:3) %>%
tidyr::pivot_longer(dplyr::starts_with("rms_"),
names_to = "Axis", values_to = "RMS") %>%
dplyr::mutate(
Axis = toupper(sub("^rms_", "", Axis)),
Difficulty = factor(Block, levels = c(1, 2, 3),
labels = c("6 steps", "12 steps", "18 steps"))
)
# Preparation (top)
p_train_prep_box <- ggplot(prep_tr_long, aes(x = Difficulty, y = RMS, fill = Difficulty)) +
geom_boxplot(width = 0.65, outlier.shape = NA) +
geom_jitter(aes(color = Difficulty),
width = 0.20, height = 0, size = 0.7, alpha = 0.35, shape = 16, stroke = 0) +
facet_wrap(~ Axis, nrow = 1, strip.position = "top") +
coord_cartesian(ylim = c(0, 0.4)) +
labs(title = "Preparation - Training Phase",
x = "Difficulty", y = "RMS") +
theme_classic() +
theme(strip.text.x = element_text(face = "bold")) +
guides(fill = "none", color = "none")
# Execution (bottom)
p_train_exec_box <- ggplot(exec_tr_long, aes(x = Difficulty, y = RMS, fill = Difficulty)) +
geom_boxplot(width = 0.65, outlier.shape = NA) +
geom_jitter(aes(color = Difficulty),
width = 0.20, height = 0, size = 0.7, alpha = 0.35, shape = 16, stroke = 0) +
facet_wrap(~ Axis, nrow = 1, strip.position = "top") +
coord_cartesian(ylim = c(0, 2.5)) +
labs(title = "Execution ",
x = "Difficulty", y = "RMS") +
theme_classic() +
theme(strip.text.x = element_blank()) +
guides(fill = "none", color = "none")
# Combine vertically
p_train_prep_box / p_train_exec_box
#B1.2 Test phase comparison preparation and execution
# === TEST BLOCKS (4–5)
# Preparation (top) + Execution (bottom), Condition = Familiar/Unfamiliar
library(dplyr)
library(tidyr)
library(ggplot2)
library(patchwork)
# Ensure Difficulty and Condition labels
prep_b45_plot <- prep_b45 %>%
mutate(
Difficulty = factor(SeqLen,
levels = c("6-step","12-step","18-step"),
labels = c("6 steps","12 steps","18 steps")),
Condition = factor(Block, levels = c("4","5"),
labels = c("Familiar","Unfamiliar"))
)
exec_b45_plot <- exec_b45 %>%
mutate(
Difficulty = factor(SeqLen,
levels = c("6-step","12-step","18-step"),
labels = c("6 steps","12 steps","18 steps")),
Condition = factor(Block, levels = c("4","5"),
labels = c("Familiar","Unfamiliar"))
)
dodge_w <- 0.65
jit_w <- 0.15
# --- Preparation (top) ---
p_test_prep_combined <- ggplot(prep_b45_plot, aes(x = Difficulty, y = RMS, fill = Condition)) +
geom_boxplot(outlier.shape = NA, width = 0.6,
position = position_dodge(width = dodge_w)) +
geom_point(aes(color = Condition),
position = position_jitterdodge(jitter.width = jit_w, dodge.width = dodge_w),
size = 0.6, alpha = 0.30, shape = 16, stroke = 0) +
facet_wrap(~ Axis, nrow = 1, strip.position = "top") +
coord_cartesian(ylim = c(0, 0.4)) +
labs(title = "Preparation - Test Blocks (4–5)",
x = "Difficulty", y = "RMS") +
theme_classic() +
theme(strip.text.x = element_text(face = "bold")) +
scale_fill_manual(name = "Condition",
values = c(Familiar = "#F8766D", Unfamiliar = "#00BFC4")) +
scale_color_manual(values = c(Familiar = "#F8766D", Unfamiliar = "#00BFC4"),
guide = "none") # show only one legend (fill)
# --- Execution (bottom) ---
p_test_exec_combined <- ggplot(exec_b45_plot, aes(x = Difficulty, y = RMS, fill = Condition)) +
geom_boxplot(outlier.shape = NA, width = 0.6,
position = position_dodge(width = dodge_w)) +
geom_point(aes(color = Condition),
position = position_jitterdodge(jitter.width = jit_w, dodge.width = dodge_w),
size = 0.6, alpha = 0.30, shape = 16, stroke = 0) +
facet_wrap(~ Axis, nrow = 1, strip.position = "top") +
coord_cartesian(ylim = c(0, 2.5)) +
labs(title = "Execution",
x = "Difficulty", y = "RMS") +
theme_classic() +
theme(strip.text.x = element_blank()) + # X/Y/Z only on top row
scale_fill_manual(name = "Condition",
values = c(Familiar = "#F8766D", Unfamiliar = "#00BFC4")) +
scale_color_manual(values = c(Familiar = "#F8766D", Unfamiliar = "#00BFC4"),
guide = "none") # keep one legend
# Combine vertically and show the legend at the bottom
(p_test_prep_combined / p_test_exec_combined) +
plot_layout(guides = "collect") & theme(legend.position = "bottom")
# ==== #1.3 Complement: Pairwise Block comparisons matching the plots ====
# - TRAINING (Blocks 1–3): within-phase (Prep/Exec), per axis — Tukey among 1,2,3
# - TEST (Blocks 4–5): within-phase AND within SeqLen (6/12/18), per axis — Block 4 vs 5
# Model used in each subset: RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial)
suppressPackageStartupMessages({
library(dplyr)
library(lme4)
library(emmeans)
})
emm_options(lmer.df = "asymptotic")
# -------- TRAINING: Blocks 1–3 (within-phase) --------
pairwise_training_blocks <- function(rms_combined) {
for (axis in c("x","y","z")) {
axis_col <- paste0("rms_", axis)
# long-ish subset per phase
for (ph in c("Preparation","Execution")) {
d <- rms_combined %>%
filter(phase == ph, Block %in% c("1","2","3")) %>%
transmute(
subject, Trial, phase,
Block = factor(Block, levels = c("1","2","3")),
Accuracy,
RMS = .data[[axis_col]]
) %>%
droplevels()
if (nrow(d) == 0) next
cat("\n\n====================\n",
"TRAINING | Axis ", toupper(axis), " | Phase: ", ph,
"\n====================\n", sep = "")
m <- lmer(RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial), data = d, REML = TRUE)
em_blk <- emmeans(m, ~ Block) # EMMs for Blocks 1–3 within this phase
cat("\nEstimated Marginal Means (Blocks 1–3):\n")
print(summary(em_blk))
cat("\nPairwise (Tukey) among Blocks 1–3:\n")
print(pairs(em_blk, adjust = "tukey"))
rm(m, em_blk); invisible(gc())
}
}
}
# -------- TEST: Blocks 4–5 (within-phase AND within SeqLen) --------
pairwise_test_blocks_by_length <- function(rms_combined) {
if (!exists("sw_all")) {
stop("Test-phase length-specific comparisons require `sw_all` (from step-wise prep) to be available.")
}
# Build sequence-length lookup from sw_all (actual detected step_count per trial)
seq_lookup <- sw_all %>%
distinct(subject, Block, trial, step_count) %>%
filter(Block %in% c(4,5)) %>%
mutate(
subject = as.character(subject),
Block_chr = as.character(Block),
trial_chr = as.character(trial),
SeqLen = factor(paste0(step_count, " steps"),
levels = c("6 steps","12 steps","18 steps"))
) %>%
select(subject, Block_chr, trial_chr, SeqLen)
# Attach SeqLen to phase-level RMS for blocks 4–5
rb45 <- rms_combined %>%
filter(Block %in% c("4","5")) %>%
mutate(
subject = as.character(subject),
Block_chr = as.character(Block),
trial_chr = as.character(trial)
) %>%
left_join(seq_lookup, by = c("subject","Block_chr","trial_chr")) %>%
mutate(
Block = factor(Block, levels = c("4","5")),
SeqLen = droplevels(SeqLen)
)
for (axis in c("x","y","z")) {
axis_col <- paste0("rms_", axis)
for (ph in c("Preparation","Execution")) {
for (sl in c("6 steps","12 steps","18 steps")) {
dd <- rb45 %>%
filter(phase == ph, SeqLen == sl) %>%
transmute(
subject, Trial, phase, SeqLen,
Block, Accuracy,
RMS = .data[[axis_col]]
) %>%
droplevels()
if (nrow(dd) == 0 || nlevels(dd$Block) < 2) next
cat("\n\n--------------------\n",
"TEST | Axis ", toupper(axis), " | Phase: ", ph, " | SeqLen: ", sl,
"\n--------------------\n", sep = "")
m <- lmer(RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial), data = dd, REML = TRUE)
em_blk <- emmeans(m, ~ Block) # Block 4 vs 5 within this phase × SeqLen
cat("\nEstimated Marginal Means (Block 4 vs 5):\n")
print(summary(em_blk))
cat("\nPairwise (Tukey) Block 4 vs 5:\n")
print(pairs(em_blk, adjust = "tukey"))
rm(m, em_blk); invisible(gc())
}
}
}
}
# ---- Run both analyses ----
pairwise_training_blocks(rms_combined)
====================
TRAINING | Axis X | Phase: Preparation
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 0.0599 0.0141 Inf 0.0323 0.0875
2 0.1208 0.0138 Inf 0.0938 0.1479
3 0.1686 0.0139 Inf 0.1414 0.1958
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.0609 0.00876 Inf -6.953 <.0001
Block1 - Block3 -0.1087 0.00897 Inf -12.120 <.0001
Block2 - Block3 -0.0477 0.00846 Inf -5.645 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis X | Phase: Execution
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 0.796 0.0657 Inf 0.667 0.925
2 0.713 0.0656 Inf 0.584 0.842
3 0.596 0.0656 Inf 0.467 0.724
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0829 0.0107 Inf 7.784 <.0001
Block1 - Block3 0.2004 0.0109 Inf 18.394 <.0001
Block2 - Block3 0.1175 0.0101 Inf 11.589 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Y | Phase: Preparation
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 0.0587 0.0150 Inf 0.0294 0.088
2 0.1290 0.0147 Inf 0.1003 0.158
3 0.1807 0.0147 Inf 0.1518 0.210
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.0703 0.00993 Inf -7.084 <.0001
Block1 - Block3 -0.1220 0.01020 Inf -12.008 <.0001
Block2 - Block3 -0.0516 0.00958 Inf -5.388 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Y | Phase: Execution
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 0.846 0.0755 Inf 0.698 0.994
2 0.758 0.0754 Inf 0.610 0.906
3 0.624 0.0754 Inf 0.476 0.772
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0881 0.0124 Inf 7.120 <.0001
Block1 - Block3 0.2222 0.0126 Inf 17.567 <.0001
Block2 - Block3 0.1341 0.0118 Inf 11.391 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Z | Phase: Preparation
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 0.0534 0.0257 Inf 0.00307 0.104
2 0.1728 0.0252 Inf 0.12348 0.222
3 0.2521 0.0253 Inf 0.20262 0.302
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.1194 0.0164 Inf -7.291 <.0001
Block1 - Block3 -0.1988 0.0168 Inf -11.864 <.0001
Block2 - Block3 -0.0794 0.0158 Inf -5.022 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Z | Phase: Execution
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 1.30 0.124 Inf 1.061 1.55
2 1.23 0.124 Inf 0.986 1.47
3 1.05 0.124 Inf 0.813 1.30
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0751 0.0210 Inf 3.583 0.0010
Block1 - Block3 0.2486 0.0214 Inf 11.615 <.0001
Block2 - Block3 0.1735 0.0199 Inf 8.710 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates pairwise_test_blocks_by_length(rms_combined)# ==== #1.3 Complement : Pairwise Block comparisons matching the plots ====
# - TRAINING (Blocks 1–3): within-phase (Prep/Exec), per axis — Tukey among 1,2,3
# - TEST (Blocks 4–5): within-phase AND within SeqLen (6/12/18), per axis — Block 4 vs 5
# Model in each subset: RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial)
suppressPackageStartupMessages({
library(dplyr)
library(lme4)
library(lmerTest) # for Satterthwaite tests in summary() and anova()
library(emmeans)
library(car) # for Type II / III Wald χ²
})
emm_options(lmer.df = "asymptotic")
# -------- TRAINING: Blocks 1–3 (within-phase) --------
pairwise_training_blocks <- function(rms_combined) {
for (axis in c("x","y","z")) {
axis_col <- paste0("rms_", axis)
for (ph in c("Preparation","Execution")) {
d <- rms_combined %>%
filter(phase == ph, Block %in% c("1","2","3")) %>%
transmute(
subject, Trial, phase,
Block = factor(Block, levels = c("1","2","3")),
Accuracy,
RMS = .data[[axis_col]]
) %>%
droplevels()
if (nrow(d) == 0) next
cat("\n\n====================\n",
"TRAINING | Axis ", toupper(axis), " | Phase: ", ph,
"\n====================\n", sep = "")
m <- lmer(RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial), data = d, REML = TRUE)
cat("\n--- Model Summary (lmerTest; Satterthwaite t-tests) ---\n")
print(summary(m))
cat("\n--- lmerTest ANOVA (F-tests; Satterthwaite) ---\n")
print(anova(m)) # Type I in order of terms; informative with full summary above
cat("\n--- Type II Wald χ² (car::Anova) ---\n")
print(car::Anova(m, type = 2, test.statistic = "Chisq"))
cat("\n--- Type III Wald χ² (car::Anova; sum contrasts) ---\n")
print(car::Anova(m, type = 3, test.statistic = "Chisq"))
em_blk <- emmeans(m, ~ Block)
cat("\n--- Estimated Marginal Means (Blocks 1–3) ---\n")
print(summary(em_blk))
cat("\n--- Pairwise (Tukey) among Blocks 1–3 ---\n")
print(pairs(em_blk, adjust = "tukey"))
rm(m, em_blk); invisible(gc())
}
}
}
# -------- TEST: Blocks 4–5 (within-phase AND within SeqLen) --------
pairwise_test_blocks_by_length <- function(rms_combined) {
if (!exists("sw_all")) {
stop("Test-phase length-specific comparisons require `sw_all` (from step-wise prep) to be available.")
}
# Sequence-length lookup (actual detected per trial)
seq_lookup <- sw_all %>%
distinct(subject, Block, trial, step_count) %>%
filter(Block %in% c(4,5)) %>%
mutate(
subject = as.character(subject),
Block_chr = as.character(Block),
trial_chr = as.character(trial),
SeqLen = factor(paste0(step_count, " steps"),
levels = c("6 steps","12 steps","18 steps"))
) %>%
select(subject, Block_chr, trial_chr, SeqLen)
# Attach SeqLen to phase-level RMS for Blocks 4–5
rb45 <- rms_combined %>%
filter(Block %in% c("4","5")) %>%
mutate(
subject = as.character(subject),
Block_chr = as.character(Block),
trial_chr = as.character(trial)
) %>%
left_join(seq_lookup, by = c("subject","Block_chr","trial_chr")) %>%
mutate(
Block = factor(Block, levels = c("4","5")),
SeqLen = droplevels(SeqLen)
)
for (axis in c("x","y","z")) {
axis_col <- paste0("rms_", axis)
for (ph in c("Preparation","Execution")) {
for (sl in c("6 steps","12 steps","18 steps")) {
dd <- rb45 %>%
filter(phase == ph, SeqLen == sl) %>%
transmute(
subject, Trial, phase, SeqLen,
Block, Accuracy,
RMS = .data[[axis_col]]
) %>%
droplevels()
if (nrow(dd) == 0 || nlevels(dd$Block) < 2) next
cat("\n\n--------------------\n",
"TEST | Axis ", toupper(axis), " | Phase: ", ph, " | SeqLen: ", sl,
"\n--------------------\n", sep = "")
m <- lmer(RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial), data = dd, REML = TRUE)
cat("\n--- Model Summary (lmerTest; Satterthwaite t-tests) ---\n")
print(summary(m))
cat("\n--- lmerTest ANOVA (F-tests; Satterthwaite) ---\n")
print(anova(m))
cat("\n--- Type II Wald χ² (car::Anova) ---\n")
print(car::Anova(m, type = 2, test.statistic = "Chisq"))
cat("\n--- Type III Wald χ² (car::Anova; sum contrasts) ---\n")
print(car::Anova(m, type = 3, test.statistic = "Chisq"))
em_blk <- emmeans(m, ~ Block) # Block 4 vs 5 within this phase × SeqLen
cat("\n--- Estimated Marginal Means (Block 4 vs 5) ---\n")
print(summary(em_blk))
cat("\n--- Pairwise (Tukey) Block 4 vs 5 ---\n")
print(pairs(em_blk, adjust = "tukey"))
rm(m, em_blk); invisible(gc())
}
}
}
}
# ---- Run both analyses ----
pairwise_training_blocks(rms_combined)
====================
TRAINING | Axis X | Phase: Preparation
====================
--- Model Summary (lmerTest; Satterthwaite t-tests) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial)
Data: d
REML criterion at convergence: -1053.8
Scaled residuals:
Min 1Q Median 3Q Max
-1.8161 -0.4221 -0.1614 0.1469 11.7183
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.0055881 0.07475
subject (Intercept) 0.0006911 0.02629
Residual 0.0407934 0.20197
Number of obs: 3378, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.164e-01 1.297e-02 5.473e+01 8.980 2.38e-12 ***
Block1 -5.654e-02 5.195e-03 3.325e+03 -10.883 < 2e-16 ***
Block2 4.397e-03 4.902e-03 3.321e+03 0.897 0.3698
Accuracy1 -7.152e-03 3.781e-03 3.217e+03 -1.892 0.0586 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2
Block1 0.021
Block2 -0.013 -0.506
Accuracy1 0.040 0.222 -0.041
--- lmerTest ANOVA (F-tests; Satterthwaite) ---
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 5.9995 2.9998 2 3327.5 73.535 < 2e-16 ***
Accuracy 0.1460 0.1460 1 3216.5 3.579 0.05861 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type II Wald χ² (car::Anova) ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Block 147.071 2 < 2e-16 ***
Accuracy 3.579 1 0.05852 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (car::Anova; sum contrasts) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 80.633 1 < 2e-16 ***
Block 147.071 2 < 2e-16 ***
Accuracy 3.579 1 0.05852 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Estimated Marginal Means (Blocks 1–3) ---
Block emmean SE df asymp.LCL asymp.UCL
1 0.0599 0.0141 Inf 0.0323 0.0875
2 0.1208 0.0138 Inf 0.0938 0.1479
3 0.1686 0.0139 Inf 0.1414 0.1958
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) among Blocks 1–3 ---
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.0609 0.00876 Inf -6.953 <.0001
Block1 - Block3 -0.1087 0.00897 Inf -12.120 <.0001
Block2 - Block3 -0.0477 0.00846 Inf -5.645 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis X | Phase: Execution
====================
--- Model Summary (lmerTest; Satterthwaite t-tests) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial)
Data: d
REML criterion at convergence: 120.9
Scaled residuals:
Min 1Q Median 3Q Max
-4.8704 -0.4840 -0.0071 0.4510 5.0806
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.003956 0.06289
subject (Intercept) 0.075076 0.27400
Residual 0.057055 0.23886
Number of obs: 3241, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 7.015e-01 6.537e-02 1.769e+01 10.732 3.58e-09 ***
Block1 9.445e-02 6.339e-03 3.183e+03 14.899 < 2e-16 ***
Block2 1.152e-02 5.905e-03 3.176e+03 1.951 0.0512 .
Accuracy1 -4.414e-02 4.608e-03 3.213e+03 -9.579 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2
Block1 0.007
Block2 -0.004 -0.514
Accuracy1 0.011 0.222 -0.042
--- lmerTest ANOVA (F-tests; Satterthwaite) ---
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 19.8101 9.9050 2 3188.5 173.604 < 2.2e-16 ***
Accuracy 5.2351 5.2351 1 3213.2 91.755 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type II Wald χ² (car::Anova) ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Block 347.208 2 < 2.2e-16 ***
Accuracy 91.755 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (car::Anova; sum contrasts) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 115.167 1 < 2.2e-16 ***
Block 347.208 2 < 2.2e-16 ***
Accuracy 91.755 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Estimated Marginal Means (Blocks 1–3) ---
Block emmean SE df asymp.LCL asymp.UCL
1 0.796 0.0657 Inf 0.667 0.925
2 0.713 0.0656 Inf 0.584 0.842
3 0.596 0.0656 Inf 0.467 0.724
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) among Blocks 1–3 ---
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0829 0.0107 Inf 7.784 <.0001
Block1 - Block3 0.2004 0.0109 Inf 18.394 <.0001
Block2 - Block3 0.1175 0.0101 Inf 11.589 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Y | Phase: Preparation
====================
--- Model Summary (lmerTest; Satterthwaite t-tests) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial)
Data: d
REML criterion at convergence: -218.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.5091 -0.4013 -0.1592 0.1394 24.5361
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.0062547 0.07909
subject (Intercept) 0.0006933 0.02633
Residual 0.0523860 0.22888
Number of obs: 3378, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.228e-01 1.363e-02 5.382e+01 9.007 2.5e-12 ***
Block1 -6.411e-02 5.886e-03 3.328e+03 -10.892 < 2e-16 ***
Block2 6.241e-03 5.554e-03 3.323e+03 1.124 0.2612
Accuracy1 -7.212e-03 4.274e-03 3.147e+03 -1.687 0.0917 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2
Block1 0.023
Block2 -0.014 -0.507
Accuracy1 0.043 0.222 -0.041
--- lmerTest ANOVA (F-tests; Satterthwaite) ---
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 7.5748 3.7874 2 3329.9 72.2978 < 2e-16 ***
Accuracy 0.1491 0.1491 1 3146.7 2.8468 0.09165 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type II Wald χ² (car::Anova) ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Block 144.5956 2 < 2e-16 ***
Accuracy 2.8468 1 0.09156 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (car::Anova; sum contrasts) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 81.1175 1 < 2e-16 ***
Block 144.5956 2 < 2e-16 ***
Accuracy 2.8468 1 0.09156 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Estimated Marginal Means (Blocks 1–3) ---
Block emmean SE df asymp.LCL asymp.UCL
1 0.0587 0.0150 Inf 0.0294 0.088
2 0.1290 0.0147 Inf 0.1003 0.158
3 0.1807 0.0147 Inf 0.1518 0.210
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) among Blocks 1–3 ---
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.0703 0.00993 Inf -7.084 <.0001
Block1 - Block3 -0.1220 0.01020 Inf -12.008 <.0001
Block2 - Block3 -0.0516 0.00958 Inf -5.388 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Y | Phase: Execution
====================
--- Model Summary (lmerTest; Satterthwaite t-tests) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial)
Data: d
REML criterion at convergence: 1078.6
Scaled residuals:
Min 1Q Median 3Q Max
-4.6672 -0.4816 0.0024 0.4511 6.6133
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.003739 0.06115
subject (Intercept) 0.099724 0.31579
Residual 0.077019 0.27752
Number of obs: 3241, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 7.429e-01 7.513e-02 1.750e+01 9.888 1.39e-08 ***
Block1 1.034e-01 7.362e-03 3.189e+03 14.050 < 2e-16 ***
Block2 1.532e-02 6.859e-03 3.181e+03 2.233 0.0256 *
Accuracy1 -4.498e-02 5.346e-03 3.219e+03 -8.413 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2
Block1 0.007
Block2 -0.004 -0.514
Accuracy1 0.011 0.222 -0.042
--- lmerTest ANOVA (F-tests; Satterthwaite) ---
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 24.5699 12.2849 2 3195.2 159.505 < 2.2e-16 ***
Accuracy 5.4517 5.4517 1 3219.5 70.784 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type II Wald χ² (car::Anova) ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Block 319.010 2 < 2.2e-16 ***
Accuracy 70.784 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (car::Anova; sum contrasts) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 97.776 1 < 2.2e-16 ***
Block 319.010 2 < 2.2e-16 ***
Accuracy 70.784 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Estimated Marginal Means (Blocks 1–3) ---
Block emmean SE df asymp.LCL asymp.UCL
1 0.846 0.0755 Inf 0.698 0.994
2 0.758 0.0754 Inf 0.610 0.906
3 0.624 0.0754 Inf 0.476 0.772
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) among Blocks 1–3 ---
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0881 0.0124 Inf 7.120 <.0001
Block1 - Block3 0.2222 0.0126 Inf 17.567 <.0001
Block2 - Block3 0.1341 0.0118 Inf 11.391 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Z | Phase: Preparation
====================
--- Model Summary (lmerTest; Satterthwaite t-tests) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial)
Data: d
REML criterion at convergence: 3162.3
Scaled residuals:
Min 1Q Median 3Q Max
-1.4689 -0.3958 -0.1519 0.1488 14.4245
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.017764 0.13328
subject (Intercept) 0.002477 0.04977
Residual 0.142476 0.37746
Number of obs: 3378, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.594e-01 2.354e-02 5.443e+01 6.772 9.34e-09 ***
Block1 -1.061e-01 9.708e-03 3.326e+03 -10.925 < 2e-16 ***
Block2 1.335e-02 9.161e-03 3.322e+03 1.458 0.1450
Accuracy1 -1.560e-02 7.065e-03 3.226e+03 -2.208 0.0273 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2
Block1 0.022
Block2 -0.013 -0.507
Accuracy1 0.042 0.222 -0.041
--- lmerTest ANOVA (F-tests; Satterthwaite) ---
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 20.1906 10.0953 2 3328.6 70.8559 < 2e-16 ***
Accuracy 0.6949 0.6949 1 3225.8 4.8771 0.02729 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type II Wald χ² (car::Anova) ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Block 141.7119 2 < 2e-16 ***
Accuracy 4.8771 1 0.02722 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (car::Anova; sum contrasts) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 45.8537 1 1.274e-11 ***
Block 141.7119 2 < 2.2e-16 ***
Accuracy 4.8771 1 0.02722 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Estimated Marginal Means (Blocks 1–3) ---
Block emmean SE df asymp.LCL asymp.UCL
1 0.0534 0.0257 Inf 0.00307 0.104
2 0.1728 0.0252 Inf 0.12348 0.222
3 0.2521 0.0253 Inf 0.20262 0.302
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) among Blocks 1–3 ---
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.1194 0.0164 Inf -7.291 <.0001
Block1 - Block3 -0.1988 0.0168 Inf -11.864 <.0001
Block2 - Block3 -0.0794 0.0158 Inf -5.022 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Z | Phase: Execution
====================
--- Model Summary (lmerTest; Satterthwaite t-tests) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial)
Data: d
REML criterion at convergence: 4479
Scaled residuals:
Min 1Q Median 3Q Max
-4.5636 -0.4699 -0.0113 0.4195 5.9733
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.008101 0.09001
subject (Intercept) 0.268090 0.51777
Residual 0.220928 0.47003
Number of obs: 3241, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.196e+00 1.230e-01 1.741e+01 9.717 1.89e-08 ***
Block1 1.079e-01 1.246e-02 3.191e+03 8.658 < 2e-16 ***
Block2 3.280e-02 1.161e-02 3.180e+03 2.824 0.00477 **
Accuracy1 -8.618e-02 9.043e-03 3.222e+03 -9.530 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2
Block1 0.007
Block2 -0.004 -0.515
Accuracy1 0.011 0.222 -0.042
--- lmerTest ANOVA (F-tests; Satterthwaite) ---
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 32.498 16.249 2 3197.0 73.549 < 2.2e-16 ***
Accuracy 20.065 20.065 1 3222.1 90.820 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type II Wald χ² (car::Anova) ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Block 147.10 2 < 2.2e-16 ***
Accuracy 90.82 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (car::Anova; sum contrasts) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 94.426 1 < 2.2e-16 ***
Block 147.098 2 < 2.2e-16 ***
Accuracy 90.820 1 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Estimated Marginal Means (Blocks 1–3) ---
Block emmean SE df asymp.LCL asymp.UCL
1 1.30 0.124 Inf 1.061 1.55
2 1.23 0.124 Inf 0.986 1.47
3 1.05 0.124 Inf 0.813 1.30
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) among Blocks 1–3 ---
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0751 0.0210 Inf 3.583 0.0010
Block1 - Block3 0.2486 0.0214 Inf 11.615 <.0001
Block2 - Block3 0.1735 0.0199 Inf 8.710 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates pairwise_test_blocks_by_length(rms_combined)#new
# ==== #1.3b All-axes models (collapsed reporting) ============================
suppressPackageStartupMessages({
library(dplyr); library(tidyr); library(lme4); library(lmerTest)
library(emmeans); library(car)
})
emm_options(lmer.df = "asymptotic")
# Helper: pivot to long with Axis factor
rms_long <- rms_combined %>%
tidyr::pivot_longer(
cols = starts_with("rms_"),
names_to = "Axis", names_prefix = "rms_",
values_to = "RMS"
) %>%
mutate(
Axis = factor(Axis, levels = c("x","y","z")),
Block = factor(Block),
Trial = if ("Trial" %in% names(.)) Trial else factor(trial)
) %>%
drop_na(RMS)
# Safe fitter with random-slope fallback (avoids convergence/singularity headaches)
.fit_all_axes <- function(form_full, form_simple, data) {
ctrl <- lmerControl(optimizer = "bobyqa", calc.derivs = TRUE,
check.conv.singular = "ignore")
fit <- try(suppressWarnings(lmer(form_full, data = data, REML = TRUE, control = ctrl)), silent = TRUE)
if (inherits(fit, "try-error") || isTRUE(isSingular(fit, tol = 1e-4))) {
fit <- lmer(form_simple, data = data, REML = TRUE, control = ctrl)
}
fit
}
# -------- TRAINING: Blocks 1–3 (within-phase), collapsed across axes ----------
pairwise_training_blocks_allaxes <- function(rms_long) {
d_train <- rms_long %>%
filter(Block %in% c("1","2","3")) %>%
mutate(Block = factor(Block, levels = c("1","2","3"))) %>%
droplevels()
for (ph in c("Preparation","Execution")) {
dd <- d_train %>% filter(phase == ph)
if (nrow(dd) == 0) next
cat("\n\n====================\n",
"TRAINING | Phase: ", ph, " | All Axes Together",
"\n====================\n", sep = "")
# Full model with subject-specific Axis slopes; fallback to simpler if needed
m <- .fit_all_axes(
form_full = RMS ~ Block * Axis + Accuracy + (1 + Axis | subject) + (1 | Trial),
form_simple = RMS ~ Block * Axis + Accuracy + (1 | subject) + (1 | Trial),
data = dd
)
cat("\n--- Model Summary (lmerTest; Satterthwaite) ---\n"); print(summary(m))
cat("\n--- Type II Wald χ² ---\n"); print(car::Anova(m, type = 2, test.statistic = "Chisq"))
cat("\n--- Type III Wald χ² (sum contrasts recommended) ---\n"); print(car::Anova(m, type = 3, test.statistic = "Chisq"))
# Primary report: Block effect averaged over axes (equal weight per axis)
em_blk <- emmeans(m, ~ Block, weights = "equal")
cat("\n--- EMMs for Block (collapsed across axes) ---\n"); print(summary(em_blk))
cat("\n--- Pairwise Tukey among Blocks 1–3 (collapsed across axes) ---\n"); print(pairs(em_blk, adjust = "tukey"))
# Optional: Only if Block × Axis matters, show simple effects by Axis
# (comment out to keep output minimal)
if (anova(m)["Block:Axis","Pr(>F)"] < 0.05 || car::Anova(m, type=3)$`Pr(>Chisq)`[rownames(car::Anova(m, type=3))=="Block:Axis"] < 0.05) {
cat("\n--- Simple Block effects within each Axis (only shown because Block×Axis significant) ---\n")
print(pairs(emmeans(m, ~ Block | Axis), adjust = "tukey"))
}
rm(m, em_blk); invisible(gc())
}
}
# -------- TEST: Blocks 4–5 (within-phase AND within SeqLen), collapsed --------
pairwise_test_blocks_by_length_allaxes <- function(rms_long) {
if (!exists("sw_all")) stop("Length-specific comparisons require `sw_all` (from stepwise prep).")
# Sequence-length lookup (actual detected per trial) for Blocks 4–5
seq_lookup <- sw_all %>%
distinct(subject, Block, trial, step_count) %>%
filter(Block %in% c(4,5)) %>%
transmute(
subject = as.character(subject),
Block = factor(as.character(Block), levels = c("4","5")),
Trial = factor(trial),
SeqLen = factor(paste0(step_count, " steps"),
levels = c("6 steps","12 steps","18 steps"))
)
d_test <- rms_long %>%
filter(Block %in% c("4","5")) %>%
mutate(
subject = as.character(subject),
Block = factor(Block, levels = c("4","5"))
) %>%
left_join(seq_lookup, by = c("subject","Block","Trial")) %>%
filter(!is.na(SeqLen)) %>%
droplevels()
for (ph in c("Preparation","Execution")) {
for (sl in levels(d_test$SeqLen)) {
dd <- d_test %>% filter(phase == ph, SeqLen == sl)
if (nrow(dd) == 0 || nlevels(dd$Block) < 2) next
cat("\n\n--------------------\n",
"TEST | Phase: ", ph, " | SeqLen: ", sl, " | All Axes Together",
"\n--------------------\n", sep = "")
m <- .fit_all_axes(
form_full = RMS ~ Block * Axis + Accuracy + (1 + Axis | subject) + (1 | Trial),
form_simple = RMS ~ Block * Axis + Accuracy + (1 | subject) + (1 | Trial),
data = dd
)
cat("\n--- Model Summary (lmerTest; Satterthwaite) ---\n"); print(summary(m))
cat("\n--- Type II Wald χ² ---\n"); print(car::Anova(m, type = 2, test.statistic = "Chisq"))
cat("\n--- Type III Wald χ² (sum contrasts recommended) ---\n"); print(car::Anova(m, type = 3, test.statistic = "Chisq"))
# Primary report: Block 4 vs 5 averaged over axes
em_blk <- emmeans(m, ~ Block, weights = "equal")
cat("\n--- EMMs for Block 4 vs 5 (collapsed across axes) ---\n"); print(summary(em_blk))
cat("\n--- Pairwise (Tukey) Block 4 vs 5 (collapsed across axes) ---\n"); print(pairs(em_blk, adjust = "tukey"))
# Optional: simple Block contrasts within each Axis if interaction is present
if (anova(m)["Block:Axis","Pr(>F)"] < 0.05 || car::Anova(m, type=3)$`Pr(>Chisq)`[rownames(car::Anova(m, type=3))=="Block:Axis"] < 0.05) {
cat("\n--- Block 4 vs 5 within Axis (only shown because Block×Axis significant) ---\n")
print(pairs(emmeans(m, ~ Block | Axis), adjust = "tukey"))
}
rm(m, em_blk); invisible(gc())
}
}
}
# ---- Run the collapsed-axes analyses ----
pairwise_training_blocks_allaxes(rms_long)
====================
TRAINING | Phase: Preparation | All Axes Together
====================
--- Model Summary (lmerTest; Satterthwaite) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: form_simple
Data: data
Control: ctrl
REML criterion at convergence: 3263.7
Scaled residuals:
Min 1Q Median 3Q Max
-1.7232 -0.4128 -0.1232 0.1647 19.9706
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.009948 0.09974
subject (Intercept) 0.001272 0.03566
Residual 0.078622 0.28040
Number of obs: 10134, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.338e-01 1.693e-02 6.047e+01 7.905 6.78e-11 ***
Block1 -7.621e-02 4.167e-03 1.008e+04 -18.289 < 2e-16 ***
Block2 7.525e-03 3.931e-03 1.008e+04 1.914 0.05565 .
Axis1 -1.538e-02 3.946e-03 1.006e+04 -3.898 9.75e-05 ***
Axis2 -8.977e-03 3.946e-03 1.006e+04 -2.275 0.02294 *
Accuracy1 -9.236e-03 3.053e-03 9.842e+03 -3.026 0.00249 **
Block1:Axis1 1.634e-02 5.700e-03 1.006e+04 2.867 0.00415 **
Block2:Axis1 -3.964e-03 5.527e-03 1.006e+04 -0.717 0.47326
Block1:Axis2 9.474e-03 5.700e-03 1.006e+04 1.662 0.09649 .
Block2:Axis2 -2.532e-03 5.527e-03 1.006e+04 -0.458 0.64691
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2 Axis1 Axis2 Accrc1 Bl1:A1 Bl2:A1 Bl1:A2
Block1 0.013
Block2 -0.008 -0.506
Axis1 0.000 0.000 0.000
Axis2 0.000 0.000 0.000 -0.500
Accuracy1 0.026 0.222 -0.041 0.000 0.000
Block1:Axs1 0.000 0.000 0.000 0.060 -0.030 0.000
Block2:Axs1 0.000 0.000 0.000 -0.028 0.014 0.000 -0.518
Block1:Axs2 0.000 0.000 0.000 -0.030 0.060 0.000 -0.500 0.259
Block2:Axs2 0.000 0.000 0.000 0.014 -0.028 0.000 0.259 -0.500 -0.518
--- Type II Wald χ² ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Block 406.7644 2 < 2.2e-16 ***
Axis 42.6958 2 5.354e-10 ***
Accuracy 9.1541 1 0.0024816 **
Block:Axis 22.9320 4 0.0001306 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (sum contrasts recommended) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 62.4866 1 2.683e-15 ***
Block 406.7644 2 < 2.2e-16 ***
Axis 38.9853 2 3.423e-09 ***
Accuracy 9.1541 1 0.0024816 **
Block:Axis 22.9320 4 0.0001306 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NOTE: Results may be misleading due to involvement in interactions
--- EMMs for Block (collapsed across axes) ---
Block emmean SE df asymp.LCL asymp.UCL
1 0.0576 0.0175 Inf 0.0233 0.0919
2 0.1414 0.0173 Inf 0.1074 0.1754
3 0.2025 0.0174 Inf 0.1685 0.2366
Results are averaged over the levels of: Axis, Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise Tukey among Blocks 1–3 (collapsed across axes) ---
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.0837 0.00703 Inf -11.915 <.0001
Block1 - Block3 -0.1449 0.00720 Inf -20.137 <.0001
Block2 - Block3 -0.0612 0.00679 Inf -9.013 <.0001
Results are averaged over the levels of: Axis, Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
--- Simple Block effects within each Axis (only shown because Block×Axis significant) ---
Axis = x:
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.0634 0.0120 Inf -5.267 <.0001
Block1 - Block3 -0.1162 0.0121 Inf -9.582 <.0001
Block2 - Block3 -0.0527 0.0116 Inf -4.532 <.0001
Axis = y:
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.0717 0.0120 Inf -5.956 <.0001
Block1 - Block3 -0.1285 0.0121 Inf -10.597 <.0001
Block2 - Block3 -0.0568 0.0116 Inf -4.876 <.0001
Axis = z:
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.1160 0.0120 Inf -9.636 <.0001
Block1 - Block3 -0.1900 0.0121 Inf -15.673 <.0001
Block2 - Block3 -0.0740 0.0116 Inf -6.357 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Phase: Execution | All Axes Together
====================
--- Model Summary (lmerTest; Satterthwaite) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: form_full
Data: data
Control: ctrl
REML criterion at convergence: 7170.1
Scaled residuals:
Min 1Q Median 3Q Max
-6.1734 -0.4417 -0.0102 0.4124 8.1143
Random effects:
Groups Name Variance Std.Dev. Corr
Trial (Intercept) 0.007736 0.08796
subject (Intercept) 0.131215 0.36224
Axis1 0.013740 0.11722 -0.82
Axis2 0.004497 0.06706 -0.74 0.86
Residual 0.117703 0.34308
Number of obs: 9723, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 8.759e-01 8.640e-02 1.777e+01 10.138 8.23e-09 ***
Block1 1.037e-01 5.262e-03 9.630e+03 19.711 < 2e-16 ***
Block2 2.085e-02 4.900e-03 9.623e+03 4.255 2.11e-05 ***
Axis1 -1.808e-01 2.807e-02 1.703e+01 -6.442 6.03e-06 ***
Axis2 -1.408e-01 1.657e-02 1.700e+01 -8.499 1.59e-07 ***
Accuracy1 -5.847e-02 3.832e-03 9.666e+03 -15.261 < 2e-16 ***
Block1:Axis1 -1.139e-02 7.201e-03 9.621e+03 -1.581 0.114
Block2:Axis1 -7.727e-03 6.895e-03 9.618e+03 -1.121 0.262
Block1:Axis2 -2.235e-03 7.200e-03 9.623e+03 -0.310 0.756
Block2:Axis2 -3.537e-03 6.894e-03 9.621e+03 -0.513 0.608
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block1 Block2 Axis1 Axis2 Accrc1 Bl1:A1 Bl2:A1 Bl1:A2
Block1 0.004
Block2 -0.002 -0.512
Axis1 -0.802 0.000 0.000
Axis2 -0.700 0.000 0.000 0.779
Accuracy1 0.007 0.221 -0.042 0.000 0.000
Block1:Axs1 0.000 0.000 0.000 0.014 -0.012 0.000
Block2:Axs1 0.000 0.000 0.000 -0.007 0.006 0.000 -0.525
Block1:Axs2 0.000 0.000 0.000 -0.007 0.024 0.000 -0.499 0.262
Block2:Axs2 0.000 0.000 0.000 0.003 -0.012 0.000 0.262 -0.500 -0.525
--- Type II Wald χ² ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Block 667.657 2 < 2.2e-16 ***
Axis 72.242 2 < 2.2e-16 ***
Accuracy 232.891 1 < 2.2e-16 ***
Block:Axis 14.384 4 0.006164 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (sum contrasts recommended) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 102.775 1 < 2.2e-16 ***
Block 667.658 2 < 2.2e-16 ***
Axis 72.310 2 < 2.2e-16 ***
Accuracy 232.891 1 < 2.2e-16 ***
Block:Axis 14.384 4 0.006164 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NOTE: Results may be misleading due to involvement in interactions
--- EMMs for Block (collapsed across axes) ---
Block emmean SE df asymp.LCL asymp.UCL
1 0.980 0.0866 Inf 0.810 1.149
2 0.897 0.0865 Inf 0.727 1.066
3 0.751 0.0865 Inf 0.582 0.921
Results are averaged over the levels of: Axis, Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise Tukey among Blocks 1–3 (collapsed across axes) ---
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0829 0.00884 Inf 9.376 <.0001
Block1 - Block3 0.2283 0.00905 Inf 25.219 <.0001
Block2 - Block3 0.1454 0.00842 Inf 17.269 <.0001
Results are averaged over the levels of: Axis, Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
--- Simple Block effects within each Axis (only shown because Block×Axis significant) ---
Axis = x:
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0792 0.0152 Inf 5.227 <.0001
Block1 - Block3 0.1978 0.0153 Inf 12.967 <.0001
Block2 - Block3 0.1186 0.0144 Inf 8.209 <.0001
Axis = y:
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0842 0.0152 Inf 5.555 <.0001
Block1 - Block3 0.2203 0.0152 Inf 14.444 <.0001
Block2 - Block3 0.1361 0.0144 Inf 9.423 <.0001
Axis = z:
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0852 0.0152 Inf 5.622 <.0001
Block1 - Block3 0.2668 0.0153 Inf 17.487 <.0001
Block2 - Block3 0.1816 0.0144 Inf 12.567 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates pairwise_test_blocks_by_length_allaxes(rms_long)#B2.1 comparison of sequence lengths training phase
# ==== TRAINING (Blocks 1–3): pairwise Block comparisons within each phase, per axis ====
# Uses the same family as plots: RMS ~ Block + Accuracy + (1|subject) + (1|Trial)
suppressPackageStartupMessages({
library(dplyr)
library(lme4)
library(emmeans)
})
emm_options(lmer.df = "asymptotic")
pairwise_training_blocks <- function(rms_combined) {
for (axis in c("x","y","z")) {
axis_col <- paste0("rms_", axis)
for (ph in c("Preparation","Execution")) {
d <- rms_combined %>%
filter(phase == ph, Block %in% c("1","2","3")) %>%
transmute(
subject, Trial, phase,
Block = factor(Block, levels = c("1","2","3")),
Accuracy,
RMS = .data[[axis_col]]
) %>%
droplevels()
if (nrow(d) == 0 || nlevels(d$Block) < 2) next
cat("\n\n====================\n",
"TRAINING | Axis ", toupper(axis), " | Phase: ", ph,
"\n====================\n", sep = "")
m <- lmer(RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial), data = d, REML = TRUE)
em_blk <- emmeans(m, ~ Block)
cat("\nEstimated Marginal Means (Blocks 1–3):\n"); print(summary(em_blk))
cat("\nPairwise (Tukey) among Blocks 1–3:\n"); print(pairs(em_blk, adjust = "tukey"))
rm(m, em_blk); invisible(gc())
}
}
}
# Run training comparisons
pairwise_training_blocks(rms_combined)
====================
TRAINING | Axis X | Phase: Preparation
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 0.0599 0.0141 Inf 0.0323 0.0875
2 0.1208 0.0138 Inf 0.0938 0.1479
3 0.1686 0.0139 Inf 0.1414 0.1958
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.0609 0.00876 Inf -6.953 <.0001
Block1 - Block3 -0.1087 0.00897 Inf -12.120 <.0001
Block2 - Block3 -0.0477 0.00846 Inf -5.645 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis X | Phase: Execution
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 0.796 0.0657 Inf 0.667 0.925
2 0.713 0.0656 Inf 0.584 0.842
3 0.596 0.0656 Inf 0.467 0.724
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0829 0.0107 Inf 7.784 <.0001
Block1 - Block3 0.2004 0.0109 Inf 18.394 <.0001
Block2 - Block3 0.1175 0.0101 Inf 11.589 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Y | Phase: Preparation
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 0.0587 0.0150 Inf 0.0294 0.088
2 0.1290 0.0147 Inf 0.1003 0.158
3 0.1807 0.0147 Inf 0.1518 0.210
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.0703 0.00993 Inf -7.084 <.0001
Block1 - Block3 -0.1220 0.01020 Inf -12.008 <.0001
Block2 - Block3 -0.0516 0.00958 Inf -5.388 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Y | Phase: Execution
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 0.846 0.0755 Inf 0.698 0.994
2 0.758 0.0754 Inf 0.610 0.906
3 0.624 0.0754 Inf 0.476 0.772
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0881 0.0124 Inf 7.120 <.0001
Block1 - Block3 0.2222 0.0126 Inf 17.567 <.0001
Block2 - Block3 0.1341 0.0118 Inf 11.391 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Z | Phase: Preparation
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 0.0534 0.0257 Inf 0.00307 0.104
2 0.1728 0.0252 Inf 0.12348 0.222
3 0.2521 0.0253 Inf 0.20262 0.302
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 -0.1194 0.0164 Inf -7.291 <.0001
Block1 - Block3 -0.1988 0.0168 Inf -11.864 <.0001
Block2 - Block3 -0.0794 0.0158 Inf -5.022 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
====================
TRAINING | Axis Z | Phase: Execution
====================
Estimated Marginal Means (Blocks 1–3):
Block emmean SE df asymp.LCL asymp.UCL
1 1.30 0.124 Inf 1.061 1.55
2 1.23 0.124 Inf 0.986 1.47
3 1.05 0.124 Inf 0.813 1.30
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
Pairwise (Tukey) among Blocks 1–3:
contrast estimate SE df z.ratio p.value
Block1 - Block2 0.0751 0.0210 Inf 3.583 0.0010
Block1 - Block3 0.2486 0.0214 Inf 11.615 <.0001
Block2 - Block3 0.1735 0.0199 Inf 8.710 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates #B2.1 comparison of sequence lengths test phase, and comparison of familiar vs unfamiliar
# ==== TEST (Blocks 4–5): block-vs-block within each length AND within-block among lengths ====
# Models:
# A) Block 4 vs 5 within phase & length: RMS ~ Block + Accuracy + (1|subject) + (1|Trial)
# B) Lengths (6/12/18) within block & phase: RMS ~ SeqLen + Accuracy + (1|subject) + (1|Trial)
suppressPackageStartupMessages({
library(dplyr)
library(lme4)
library(emmeans)
})
emm_options(lmer.df = "asymptotic")
pairwise_test_blocks_and_lengths <- function(rms_combined) {
if (!exists("sw_all")) {
stop("This analysis needs `sw_all` (step-wise table) to derive sequence lengths for Blocks 4–5.")
}
# Build a unique per-trial sequence length lookup (6/12/18 steps) from sw_all
seq_lookup <- sw_all %>%
group_by(subject, Block, trial) %>%
summarise(step_count = max(step_count, na.rm = TRUE), .groups = "drop") %>% # ensure unique per trial
filter(Block %in% c(4, 5)) %>%
transmute(
subject = as.character(subject),
Block_chr = as.character(Block),
trial_chr = as.character(trial),
SeqLen = factor(paste0(step_count, " steps"),
levels = c("6 steps","12 steps","18 steps"))
)
# Attach sequence length to the phase-level RMS rows for blocks 4–5
rb45 <- rms_combined %>%
filter(Block %in% c("4","5")) %>%
mutate(
subject = as.character(subject),
Block_chr = as.character(Block),
trial_chr = as.character(trial)
) %>%
left_join(seq_lookup, by = c("subject","Block_chr","trial_chr")) %>%
mutate(
Block = factor(Block, levels = c("4","5")),
SeqLen = droplevels(SeqLen)
)
# ---------- (A) Block 4 vs 5 within each length & phase ----------
for (axis in c("x","y","z")) {
axis_col <- paste0("rms_", axis)
for (ph in c("Preparation","Execution")) {
for (sl in c("6 steps","12 steps","18 steps")) {
dd <- rb45 %>%
filter(phase == ph, SeqLen == sl) %>%
transmute(
subject, Trial, phase, SeqLen,
Block, Accuracy,
RMS = .data[[axis_col]]
) %>% droplevels()
if (nrow(dd) == 0 || nlevels(dd$Block) < 2) next
cat("\n\n--------------------\n",
"TEST (Between blocks) | Axis ", toupper(axis),
" | Phase: ", ph, " | SeqLen: ", sl,
"\n--------------------\n", sep = "")
mA <- lmer(RMS ~ Block + Accuracy + (1 | subject) + (1 | Trial), data = dd, REML = TRUE)
em_blk <- emmeans(mA, ~ Block) # 4 vs 5
cat("\nEstimated Marginal Means (Block 4 vs 5):\n"); print(summary(em_blk))
cat("\nPairwise (Tukey) Block 4 vs 5:\n"); print(pairs(em_blk, adjust = "tukey"))
rm(mA, em_blk); invisible(gc())
}
}
}
# ---------- (B) Within each block: compare sequence lengths (6 vs 12 vs 18) per phase ----------
for (axis in c("x","y","z")) {
axis_col <- paste0("rms_", axis)
for (ph in c("Preparation","Execution")) {
for (blk in c("4","5")) {
dd <- rb45 %>%
filter(phase == ph, Block == blk) %>%
transmute(
subject, Trial, phase,
Block, SeqLen = factor(SeqLen, levels = c("6 steps","12 steps","18 steps")),
Accuracy,
RMS = .data[[axis_col]]
) %>% droplevels()
# Need at least 2 lengths present to compare
if (nrow(dd) == 0 || nlevels(dd$SeqLen) < 2) next
cat("\n\n====================\n",
"TEST (Within block) | Axis ", toupper(axis),
" | Phase: ", ph, " | Block: ", blk,
"\n====================\n", sep = "")
mB <- lmer(RMS ~ SeqLen + Accuracy + (1 | subject) + (1 | Trial), data = dd, REML = TRUE)
em_len <- emmeans(mB, ~ SeqLen)
cat("\nEstimated Marginal Means (SeqLen within Block ", blk, "):\n", sep = ""); print(summary(em_len))
cat("\nPairwise (Tukey) among 6/12/18 steps within Block ", blk, ":\n", sep = ""); print(pairs(em_len, adjust = "tukey"))
rm(mB, em_len); invisible(gc())
}
}
}
}
# Run test-phase comparisons
pairwise_test_blocks_and_lengths(rms_combined)# ==== TEST (Blocks 4–5): all-axes-together ====
# Block-vs-block within each sequence length, and within-block among lengths
# Collapses across axes (Axis factor), reports Block/SeqLen averaged over axes.
# Includes Wald χ² (Type II & III) and Likelihood-Ratio χ² via explicit ML refits (no update()).
suppressPackageStartupMessages({
library(dplyr)
library(tidyr)
library(lme4)
library(lmerTest) # optional: Satterthwaite tests in summary()/anova()
library(emmeans)
library(car) # Type II/III Wald χ²
})
emm_options(lmer.df = "asymptotic")
# If you interpret Type III, set sum contrasts globally (uncomment if needed):
# options(contrasts = c("contr.sum","contr.poly"))
pairwise_test_blocks_and_lengths_allaxes <- function(rms_combined) {
if (!exists("sw_all")) {
stop("This analysis needs `sw_all` (step-wise table) to derive sequence lengths for Blocks 4–5.")
}
# ----- Pivot RMS to long with Axis factor -----
rms_long <- rms_combined %>%
tidyr::pivot_longer(
cols = starts_with("rms_"),
names_to = "Axis", names_prefix = "rms_",
values_to = "RMS"
) %>%
mutate(
Axis = factor(Axis, levels = c("x","y","z")),
Block = factor(Block),
Trial = if ("Trial" %in% names(.)) Trial else factor(trial)
) %>%
drop_na(RMS)
# Helper: REML fitter with random-slope fallback (1 + Axis | subject) -> (1 | subject)
.fit_all_axes_REML <- function(form_full, form_simple, data) {
fit <- try(
suppressWarnings(
lmer(form_full, data = data, REML = TRUE,
control = lmerControl(optimizer = "bobyqa",
calc.derivs = TRUE,
check.conv.singular = "ignore"))
),
silent = TRUE
)
if (inherits(fit, "try-error") || isTRUE(isSingular(fit, tol = 1e-4))) {
fit <- lmer(form_simple, data = data, REML = TRUE,
control = lmerControl(optimizer = "bobyqa",
calc.derivs = TRUE,
check.conv.singular = "ignore"))
}
fit
}
# Helper: ML pair (full vs reduced) with *matched* random-effects structure.
# We try (1 + Axis | subject) first; if it fails/singular, we refit *both* models with (1 | subject).
.fit_ml_pair <- function(full_fixef_rhs, reduced_fixef_rhs, data) {
# Build formulas with rich random structure
full_re <- as.formula(paste0("RMS ~ ", full_fixef_rhs, " + (1 + Axis | subject) + (1 | Trial)"))
red_re <- as.formula(paste0("RMS ~ ", reduced_fixef_rhs," + (1 + Axis | subject) + (1 | Trial)"))
try_full <- try(
suppressWarnings(
lmer(full_re, data = data, REML = FALSE,
control = lmerControl(optimizer = "bobyqa",
calc.derivs = TRUE,
check.conv.singular = "ignore"))
),
silent = TRUE
)
if (!(inherits(try_full, "try-error") || isTRUE(isSingular(try_full, tol = 1e-4)))) {
m_full <- try_full
m_red <- lmer(red_re, data = data, REML = FALSE,
control = lmerControl(optimizer = "bobyqa",
calc.derivs = TRUE,
check.conv.singular = "ignore"))
return(list(full = m_full, reduced = m_red, slope = TRUE))
}
# Fallback: simple random structure for both models
full_simple <- as.formula(paste0("RMS ~ ", full_fixef_rhs, " + (1 | subject) + (1 | Trial)"))
red_simple <- as.formula(paste0("RMS ~ ", reduced_fixef_rhs," + (1 | subject) + (1 | Trial)"))
m_full <- lmer(full_simple, data = data, REML = FALSE,
control = lmerControl(optimizer = "bobyqa",
calc.derivs = TRUE,
check.conv.singular = "ignore"))
m_red <- lmer(red_simple, data = data, REML = FALSE,
control = lmerControl(optimizer = "bobyqa",
calc.derivs = TRUE,
check.conv.singular = "ignore"))
list(full = m_full, reduced = m_red, slope = FALSE)
}
# ----- Build unique per-trial sequence length lookup (6/12/18) from sw_all -----
seq_lookup <- sw_all %>%
group_by(subject, Block, trial) %>%
summarise(step_count = max(step_count, na.rm = TRUE), .groups = "drop") %>%
filter(Block %in% c(4, 5)) %>%
transmute(
subject = as.character(subject),
Block_chr = as.character(Block),
trial_chr = as.character(trial),
SeqLen = factor(paste0(step_count, " steps"),
levels = c("6 steps","12 steps","18 steps"))
)
# ----- Attach SeqLen to phase-level RMS (Blocks 4–5), keep Axis in long form -----
rb45_long <- rms_long %>%
filter(Block %in% c("4","5")) %>%
mutate(
subject = as.character(subject),
Block_chr = as.character(Block),
trial_chr = as.character(Trial)
) %>%
left_join(seq_lookup, by = c("subject","Block_chr","trial_chr")) %>%
mutate(
Block = factor(Block, levels = c("4","5")),
SeqLen = droplevels(SeqLen)
)
# ---------- (A) Block 4 vs 5 within each length & phase (all axes together) ----------
for (ph in c("Preparation","Execution")) {
for (sl in c("6 steps","12 steps","18 steps")) {
dd <- rb45_long %>%
filter(phase == ph, SeqLen == sl) %>%
transmute(
subject = factor(subject),
Trial = factor(Trial),
phase, SeqLen, Axis,
Block, Accuracy,
RMS
) %>%
filter(is.finite(RMS), !is.na(Accuracy), !is.na(Block), !is.na(Axis)) %>%
droplevels()
if (nrow(dd) == 0 || nlevels(dd$Block) < 2) next
cat("\n\n--------------------\n",
"TEST (Between blocks, all axes) | Phase: ", ph, " | SeqLen: ", sl,
"\n--------------------\n", sep = "")
# REML model for estimates/EMMs
mA <- .fit_all_axes_REML(
form_full = RMS ~ Block * Axis + Accuracy + (1 + Axis | subject) + (1 | Trial),
form_simple = RMS ~ Block * Axis + Accuracy + (1 | subject) + (1 | Trial),
data = dd
)
cat("\n--- Model Summary (lmerTest; Satterthwaite) ---\n"); print(summary(mA))
cat("\n--- Type II Wald χ² ---\n"); print(car::Anova(mA, type = 2, test.statistic = "Chisq"))
cat("\n--- Type III Wald χ² (sum contrasts recommended) ---\n"); print(car::Anova(mA, type = 3, test.statistic = "Chisq"))
# Primary: Block effect averaged over axes
em_blk <- emmeans(mA, ~ Block, weights = "equal")
cat("\n--- EMMs for Block (collapsed across axes) ---\n"); print(summary(em_blk))
cat("\n--- Pairwise (Tukey) Block 4 vs 5 (collapsed across axes) ---\n"); print(pairs(em_blk, adjust = "tukey"))
# ML LRT for overall Block (remove Block and Block:Axis)
cat("\n--- Likelihood-Ratio χ² (ML) for Block ---\n")
ml_pair <- .fit_ml_pair(
full_fixef_rhs = "Block * Axis + Accuracy",
reduced_fixef_rhs = "Axis + Accuracy",
data = dd
)
print(anova(ml_pair$reduced, ml_pair$full)) # Chi-square, df, p-value
# Optional: show Block within each Axis if Block×Axis significant
A3 <- car::Anova(mA, type = 3, test.statistic = "Chisq")
if ("Block:Axis" %in% rownames(A3) && is.finite(A3["Block:Axis","Pr(>Chisq)"]) &&
A3["Block:Axis","Pr(>Chisq)"] < 0.05) {
cat("\n--- Block 4 vs 5 within each Axis (shown because Block×Axis significant) ---\n")
print(pairs(emmeans(mA, ~ Block | Axis), adjust = "tukey"))
}
rm(mA, em_blk, ml_pair); invisible(gc())
}
}
# ---------- (B) Within each block: compare sequence lengths per phase (all axes) ----------
for (ph in c("Preparation","Execution")) {
for (blk in c("4","5")) {
dd <- rb45_long %>%
filter(phase == ph, Block == blk) %>%
transmute(
subject = factor(subject),
Trial = factor(Trial),
phase, Block,
SeqLen = factor(SeqLen, levels = c("6 steps","12 steps","18 steps")),
Axis, Accuracy, RMS
) %>%
filter(is.finite(RMS), !is.na(Accuracy), !is.na(SeqLen), !is.na(Axis)) %>%
droplevels()
if (nrow(dd) == 0 || nlevels(dd$SeqLen) < 2) next
cat("\n\n====================\n",
"TEST (Within block, all axes) | Phase: ", ph, " | Block: ", blk,
"\n====================\n", sep = "")
# REML model for estimates/EMMs
mB <- .fit_all_axes_REML(
form_full = RMS ~ SeqLen * Axis + Accuracy + (1 + Axis | subject) + (1 | Trial),
form_simple = RMS ~ SeqLen * Axis + Accuracy + (1 | subject) + (1 | Trial),
data = dd
)
cat("\n--- Model Summary (lmerTest; Satterthwaite) ---\n"); print(summary(mB))
cat("\n--- Type II Wald χ² ---\n"); print(car::Anova(mB, type = 2, test.statistic = "Chisq"))
cat("\n--- Type III Wald χ² (sum contrasts recommended) ---\n"); print(car::Anova(mB, type = 3, test.statistic = "Chisq"))
# ML LRT for overall SeqLen (remove SeqLen and SeqLen:Axis)
cat("\n--- Likelihood-Ratio χ² (ML) for SeqLen ---\n")
ml_pair <- .fit_ml_pair(
full_fixef_rhs = "SeqLen * Axis + Accuracy",
reduced_fixef_rhs = "Axis + Accuracy",
data = dd
)
print(anova(ml_pair$reduced, ml_pair$full)) # Chi-square, df, p-value
# EMMs & Tukey among sequence lengths, averaged equally over axes
em_len <- emmeans(mB, ~ SeqLen, weights = "equal")
cat("\n--- EMMs for SeqLen (collapsed across axes) ---\n"); print(summary(em_len))
cat("\n--- Pairwise (Tukey) among 6/12/18 within Block ", blk, " ---\n", sep = ""); print(pairs(em_len, adjust = "tukey"))
# Optional: if SeqLen×Axis significant, show SeqLen contrasts within each Axis
B3 <- car::Anova(mB, type = 3, test.statistic = "Chisq")
if ("SeqLen:Axis" %in% rownames(B3) && is.finite(B3["SeqLen:Axis","Pr(>Chisq)"]) &&
B3["SeqLen:Axis","Pr(>Chisq)"] < 0.05) {
cat("\n--- SeqLen contrasts within each Axis (shown because SeqLen×Axis significant) ---\n")
print(pairs(emmeans(mB, ~ SeqLen | Axis), adjust = "tukey"))
}
rm(mB, em_len, ml_pair); invisible(gc())
}
}
}
# Run the all-axes test-phase comparisons
pairwise_test_blocks_and_lengths_allaxes(rms_combined)# ==== #2.1c: Execution-phase % difference — Block 4 vs Block 5 (per axis & overall) ====
suppressPackageStartupMessages({
library(dplyr); library(tidyr); library(tibble)
library(lme4); library(lmerTest); library(emmeans)
})
emm_options(lmer.df = "asymptotic")
percent_table_exec_block4_vs5 <- function(rms_combined) {
if (!exists("sw_all")) {
stop("This analysis needs `sw_all` (step-wise table) to derive sequence lengths for Blocks 4–5).")
}
# Per-trial sequence length lookup (6/12/18)
seq_lookup <- sw_all %>%
group_by(subject, Block, trial) %>%
summarise(step_count = max(step_count, na.rm = TRUE), .groups = "drop") %>%
filter(Block %in% c(4, 5)) %>%
transmute(
subject = as.character(subject),
Block_chr = as.character(Block),
trial_chr = as.character(trial),
SeqLen = factor(
paste0(step_count, " steps"),
levels = c("6 steps","12 steps","18 steps")
)
)
# Execution-only rows for Blocks 4–5, joined with SeqLen
rb45_exec <- rms_combined %>%
filter(phase == "Execution", Block %in% c("4","5")) %>%
mutate(
subject = as.character(subject),
Block_chr = as.character(Block),
trial_chr = as.character(trial)
) %>%
left_join(seq_lookup, by = c("subject","Block_chr","trial_chr")) %>%
mutate(
Block = factor(Block, levels = c("4","5")),
SeqLen = droplevels(SeqLen),
Trial = factor(Trial)
)
# ----- Per-axis models (adjusted for SeqLen) -----
axis_rows <- lapply(c(x = "X", y = "Y", z = "Z"), function(ax_lab) {
col <- paste0("rms_", tolower(ax_lab))
dd <- rb45_exec %>%
transmute(subject, Trial, Block, Accuracy, SeqLen, RMS = .data[[col]]) %>%
tidyr::drop_na(subject, Trial, Block, Accuracy, SeqLen, RMS) %>%
droplevels()
if (nrow(dd) == 0 || nlevels(dd$Block) < 2) return(NULL)
m <- lmer(
RMS ~ Block + SeqLen + Accuracy + (1 | subject) + (1 | Trial),
data = dd, REML = TRUE
)
em <- summary(emmeans(m, ~ Block)) %>% as_tibble()
b4 <- em %>% filter(Block == "4") %>% pull(emmean)
b5 <- em %>% filter(Block == "5") %>% pull(emmean)
tibble(
Scope = "Per Axis",
Axis = ax_lab,
Block4_EMM = b4,
Block5_EMM = b5,
Diff_5_minus_4 = b5 - b4,
Percent_Faster_B4_vs_B5 = 100 * (b5 - b4) / b5
)
})
per_axis_tbl <- bind_rows(axis_rows)
# ----- Overall across axes (adjusted for Axis and SeqLen) -----
overall_dd <- rb45_exec %>%
select(subject, Trial, Block, Accuracy, SeqLen, rms_x, rms_y, rms_z) %>%
tidyr::drop_na(subject, Trial, Block, Accuracy, SeqLen, rms_x, rms_y, rms_z) %>%
pivot_longer(c(rms_x, rms_y, rms_z), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = dplyr::recode(Axis, "rms_x" = "X", "rms_y" = "Y", "rms_z" = "Z"),
Axis = factor(Axis, levels = c("X","Y","Z"))
) %>%
drop_na(RMS) %>%
droplevels()
overall_row <- {
if (nrow(overall_dd) > 0 && nlevels(overall_dd$Block) > 1) {
m_all <- lmer(
RMS ~ Block + Axis + SeqLen + Accuracy + (1 | subject) + (1 | Trial),
data = overall_dd, REML = TRUE
)
em_all <- summary(emmeans(m_all, ~ Block)) %>% as_tibble()
b4 <- em_all %>% filter(Block == "4") %>% pull(emmean)
b5 <- em_all %>% filter(Block == "5") %>% pull(emmean)
tibble(
Scope = "Overall (All Axes)",
Axis = "All",
Block4_EMM = b4,
Block5_EMM = b5,
Diff_5_minus_4 = b5 - b4,
Percent_Faster_B4_vs_B5 = 100 * (b5 - b4) / b5
)
} else {
NULL
}
}
# If both parts are empty, return a well-formed empty table (prevents mutate errors)
if ((is.null(per_axis_tbl) || nrow(per_axis_tbl) == 0) && is.null(overall_row)) {
message("No valid data for Block 4 vs 5 percentage table after filtering; returning empty table.")
return(tibble(
Scope = character(),
Axis = character(),
Block4_EMM = double(),
Block5_EMM = double(),
Diff_5_minus_4 = double(),
Percent_Faster_B4_vs_B5 = double()
))
}
out_tbl <- bind_rows(
per_axis_tbl,
if (is.null(overall_row)) tibble(
Scope = character(), Axis = character(),
Block4_EMM = double(), Block5_EMM = double(),
Diff_5_minus_4 = double(), Percent_Faster_B4_vs_B5 = double()
) else overall_row
)
if (nrow(out_tbl) == 0) {
message("No rows in Block 4 vs 5 percentage table; returning empty table.")
return(out_tbl)
}
out_tbl <- out_tbl %>%
mutate(
Block4_EMM = round(Block4_EMM, 3),
Block5_EMM = round(Block5_EMM, 3),
Diff_5_minus_4 = round(Diff_5_minus_4, 3),
Percent_Faster_B4_vs_B5 = round(Percent_Faster_B4_vs_B5, 1)
) %>%
arrange(match(Scope, c("Per Axis","Overall (All Axes)")), Axis)
cat("\n\n==============================================\n")
cat("Execution phase — % faster of Block 4 vs Block 5\n")
cat("Percent = 100 * (Block5_EMM - Block4_EMM) / Block5_EMM\n")
cat("EMMs are adjusted for SeqLen (per-axis) and for Axis + SeqLen (overall).\n")
cat("==============================================\n")
print(out_tbl)
invisible(out_tbl)
}
# ---- Run it ----
percent_table_exec_block4_vs5(rms_combined)No valid data for Block 4 vs 5 percentage table after filtering; returning empty table.# A tibble: 0 × 6
# ℹ 6 variables: Scope <chr>, Axis <chr>, Block4_EMM <dbl>, Block5_EMM <dbl>,
# Diff_5_minus_4 <dbl>, Percent_Faster_B4_vs_B5 <dbl>#B3.1 Concatenation analysis
# --- Build step-wise datasets for training (Blocks 1–3) and test (Blocks 4–5) ---
# Uses your earlier helper:
# compute_stepwise_rms(tagged_exec_df, max_steps_keep = 18)
# -> one row per detected step per trial: subject, Block, trial, Step, RMS (x/y/z), Axis ("x","y","z"), step_count
# And accuracy join helper:
# add_accuracy_to(df_core, df_lookup = all_data_mixed)
# 1) Compute all step-wise rows (up to 18) from tagged_data
stepwise_all <- compute_stepwise_rms(tagged_data, max_steps_keep = 18) %>%
# Add trial-level Accuracy
add_accuracy_to(., all_data_mixed) %>%
# Ensure IDs/Axis are present in the expected format
dplyr::mutate(
trial_id = if ("trial_id" %in% names(.)) trial_id else interaction(subject, Block, trial, drop = TRUE),
Axis = factor(as.character(Axis), levels = c("x","y","z"))
)Warning in left_join(., acc_tbl, by = c("subject", "Block", "trial")): Detected an unexpected many-to-many relationship between `x` and `y`.
ℹ Row 1 of `x` matches multiple rows in `y`.
ℹ Row 3021 of `y` matches multiple rows in `x`.
ℹ If a many-to-many relationship is expected, set `relationship =
"many-to-many"` to silence this warning.# 2) TRAINING subsets used by .report_step_block(...)
# Block 1 -> 6 steps nominally
stepwise_6 <- stepwise_all %>% dplyr::filter(Block == 1)
# Block 2 -> 12 steps nominally
stepwise_12 <- stepwise_all %>% dplyr::filter(Block == 2)
# Block 3 -> 18 steps nominally
stepwise_18 <- stepwise_all %>% dplyr::filter(Block == 3)
# 3) TEST subsets by actual per-trial step_count (Blocks 4–5, mixed sequence lengths)
# Block 4
sw_b4_6 <- stepwise_all %>% dplyr::filter(Block == 4, step_count == 6)
sw_b4_12 <- stepwise_all %>% dplyr::filter(Block == 4, step_count == 12)
sw_b4_18 <- stepwise_all %>% dplyr::filter(Block == 4, step_count == 18)
# Block 5
sw_b5_6 <- stepwise_all %>% dplyr::filter(Block == 5, step_count == 6)
sw_b5_12 <- stepwise_all %>% dplyr::filter(Block == 5, step_count == 12)
sw_b5_18 <- stepwise_all %>% dplyr::filter(Block == 5, step_count == 18)# ---- Option A (fixed): identical schema/names, faster & RAM-light ----
library(data.table)
library(dplyr)
# Helper: ensure step_count exists even if compute_stepwise_rms did not add it (safety net)
ensure_step_count <- function(df) {
if (!("step_count" %in% names(df))) {
df %>%
group_by(subject, Block, trial) %>%
mutate(step_count = max(Step, na.rm = TRUE)) %>% # or n_distinct(Step) if you prefer
ungroup()
} else df
}
# 1) Trial-level lookup ONLY for Accuracy (step_count is NOT in all_data_mixed)
acc_lookup <- all_data_mixed %>%
dplyr::distinct(subject, Block, trial, Accuracy) %>%
as.data.table()
setkey(acc_lookup, subject, Block, trial)
# 2) Compute step-wise (keep ALL columns; nothing dropped)
stepwise_all <- compute_stepwise_rms(tagged_data, max_steps_keep = 18) %>%
ensure_step_count() %>% # guarantees step_count exists
as.data.table()
# 3) Fast in-place join that adds only Accuracy (no step_count from lookup)
setkey(stepwise_all, subject, Block, trial)
stepwise_all[acc_lookup, Accuracy := i.Accuracy]
# 4) Ensure Axis levels and trial_id exactly like your slow version
if ("Axis" %in% names(stepwise_all)) {
stepwise_all[, Axis := factor(as.character(Axis), levels = c("x","y","z"))]
}
if (!("trial_id" %in% names(stepwise_all))) {
stepwise_all[, trial_id := interaction(subject, Block, trial, drop = TRUE)]
}
# 5) Back to tibble if downstream expects it
stepwise_all <- tibble::as_tibble(stepwise_all)
# --- TRAINING subsets (Blocks 1–3), names unchanged ---
stepwise_6 <- dplyr::filter(stepwise_all, Block == 1)
stepwise_12 <- dplyr::filter(stepwise_all, Block == 2)
stepwise_18 <- dplyr::filter(stepwise_all, Block == 3)
# --- TEST subsets (Blocks 4–5, mixed), names unchanged ---
sw_b4_6 <- dplyr::filter(stepwise_all, Block == 4, step_count == 6)
sw_b4_12 <- dplyr::filter(stepwise_all, Block == 4, step_count == 12)
sw_b4_18 <- dplyr::filter(stepwise_all, Block == 4, step_count == 18)
sw_b5_6 <- dplyr::filter(stepwise_all, Block == 5, step_count == 6)
sw_b5_12 <- dplyr::filter(stepwise_all, Block == 5, step_count == 12)
sw_b5_18 <- dplyr::filter(stepwise_all, Block == 5, step_count == 18)
# Optional sanity checks
stopifnot("Accuracy" %in% names(stepwise_all), "step_count" %in% names(stepwise_all))
if ("Axis" %in% names(stepwise_all)) stopifnot(identical(levels(stepwise_all$Axis), c("x","y","z")))# ==== TRAINING (Blocks 1–3): stepwise LMM + χ² + EMMs + all-pairs + adjacent (per block × axis) ====
suppressPackageStartupMessages({
library(dplyr); library(lme4); library(lmerTest); library(emmeans); library(car)
})
emm_options(lmer.df = "asymptotic")
.report_step_block <- function(df_block, block_label) {
for (ax in c("x","y","z")) {
dd <- df_block %>% filter(Axis == ax)
if (nrow(dd) == 0) next
dd <- dd %>%
mutate(
StepF = factor(Step, levels = sort(unique(Step))),
subject = factor(subject),
trial_id = factor(trial_id),
Accuracy = droplevels(Accuracy)
)
cat("\n\n==============================\n",
"TRAINING | Block ", block_label, " | Axis ", toupper(ax),
"\n==============================\n", sep = "")
m <- suppressWarnings(lmer(RMS ~ StepF + Accuracy + (1|subject) + (1|trial_id),
data = dd, REML = TRUE))
cat("\nType II Wald χ² (StepF & Accuracy):\n")
print(car::Anova(m, type = 2, test.statistic = "Chisq"))
if (nlevels(dd$Accuracy) >= 2) {
# EMMs by Accuracy
em <- emmeans(m, ~ StepF | Accuracy)
cat("\nEMMs per step | Accuracy:\n"); print(summary(em))
cat("\nAll-pairs (Tukey) among steps | Accuracy:\n")
print(pairs(em, adjust = "tukey"))
cat("\nAdjacent steps (consec; Holm) | Accuracy:\n")
print(contrast(em, method = "consec", by = "Accuracy", adjust = "holm"))
} else {
# Collapsed over Accuracy
em <- emmeans(m, ~ StepF)
cat("\nEMMs per step:\n"); print(summary(em))
cat("\nAll-pairs (Tukey) among steps:\n")
print(pairs(em, adjust = "tukey"))
cat("\nAdjacent steps (consec; Holm):\n")
print(contrast(em, method = "consec", adjust = "holm"))
}
rm(m, em); invisible(gc())
}
}
# Run training (Blocks 1,2,3)
.report_step_block(stepwise_6, "1 (6 steps)")
==============================
TRAINING | Block 1 (6 steps) | Axis X
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 179.588 5 < 2.2e-16 ***
Accuracy 10.277 1 0.001347 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.658 0.115 Inf 0.433 0.884
2 0.844 0.115 Inf 0.618 1.070
3 0.957 0.115 Inf 0.731 1.183
4 0.859 0.115 Inf 0.633 1.085
5 0.860 0.115 Inf 0.634 1.085
6 0.711 0.115 Inf 0.485 0.937
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.729 0.114 Inf 0.506 0.953
2 0.915 0.114 Inf 0.692 1.138
3 1.028 0.114 Inf 0.805 1.251
4 0.930 0.114 Inf 0.707 1.153
5 0.931 0.114 Inf 0.707 1.154
6 0.782 0.114 Inf 0.559 1.005
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.185713 0.0259 Inf -7.184 <.0001
StepF1 - StepF3 -0.298481 0.0259 Inf -11.546 <.0001
StepF1 - StepF4 -0.200559 0.0259 Inf -7.758 <.0001
StepF1 - StepF5 -0.201190 0.0259 Inf -7.771 <.0001
StepF1 - StepF6 -0.052615 0.0260 Inf -2.025 0.3279
StepF2 - StepF3 -0.112768 0.0259 Inf -4.362 0.0002
StepF2 - StepF4 -0.014845 0.0259 Inf -0.574 0.9927
StepF2 - StepF5 -0.015477 0.0259 Inf -0.598 0.9912
StepF2 - StepF6 0.133098 0.0260 Inf 5.123 <.0001
StepF3 - StepF4 0.097923 0.0259 Inf 3.788 0.0021
StepF3 - StepF5 0.097291 0.0259 Inf 3.758 0.0024
StepF3 - StepF6 0.245866 0.0260 Inf 9.463 <.0001
StepF4 - StepF5 -0.000631 0.0259 Inf -0.024 1.0000
StepF4 - StepF6 0.147944 0.0260 Inf 5.694 <.0001
StepF5 - StepF6 0.148575 0.0260 Inf 5.711 <.0001
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.185713 0.0259 Inf -7.184 <.0001
StepF1 - StepF3 -0.298481 0.0259 Inf -11.546 <.0001
StepF1 - StepF4 -0.200559 0.0259 Inf -7.758 <.0001
StepF1 - StepF5 -0.201190 0.0259 Inf -7.771 <.0001
StepF1 - StepF6 -0.052615 0.0260 Inf -2.025 0.3279
StepF2 - StepF3 -0.112768 0.0259 Inf -4.362 0.0002
StepF2 - StepF4 -0.014845 0.0259 Inf -0.574 0.9927
StepF2 - StepF5 -0.015477 0.0259 Inf -0.598 0.9912
StepF2 - StepF6 0.133098 0.0260 Inf 5.123 <.0001
StepF3 - StepF4 0.097923 0.0259 Inf 3.788 0.0021
StepF3 - StepF5 0.097291 0.0259 Inf 3.758 0.0024
StepF3 - StepF6 0.245866 0.0260 Inf 9.463 <.0001
StepF4 - StepF5 -0.000631 0.0259 Inf -0.024 1.0000
StepF4 - StepF6 0.147944 0.0260 Inf 5.694 <.0001
StepF5 - StepF6 0.148575 0.0260 Inf 5.711 <.0001
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.185713 0.0259 Inf 7.184 <.0001
StepF3 - StepF2 0.112768 0.0259 Inf 4.362 <.0001
StepF4 - StepF3 -0.097923 0.0259 Inf -3.788 0.0003
StepF5 - StepF4 0.000631 0.0259 Inf 0.024 0.9805
StepF6 - StepF5 -0.148575 0.0260 Inf -5.711 <.0001
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.185713 0.0259 Inf 7.184 <.0001
StepF3 - StepF2 0.112768 0.0259 Inf 4.362 <.0001
StepF4 - StepF3 -0.097923 0.0259 Inf -3.788 0.0003
StepF5 - StepF4 0.000631 0.0259 Inf 0.024 0.9805
StepF6 - StepF5 -0.148575 0.0260 Inf -5.711 <.0001
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests
==============================
TRAINING | Block 1 (6 steps) | Axis Y
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 73.3575 5 2.048e-14 ***
Accuracy 8.4768 1 0.003597 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.772 0.138 Inf 0.501 1.04
2 0.852 0.138 Inf 0.581 1.12
3 0.987 0.138 Inf 0.716 1.26
4 0.884 0.138 Inf 0.613 1.16
5 0.804 0.138 Inf 0.533 1.07
6 0.840 0.138 Inf 0.569 1.11
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.846 0.137 Inf 0.578 1.11
2 0.926 0.137 Inf 0.658 1.19
3 1.061 0.137 Inf 0.793 1.33
4 0.958 0.137 Inf 0.690 1.23
5 0.877 0.137 Inf 0.609 1.15
6 0.914 0.137 Inf 0.645 1.18
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.0804 0.0277 Inf -2.907 0.0424
StepF1 - StepF3 -0.2152 0.0277 Inf -7.781 <.0001
StepF1 - StepF4 -0.1121 0.0277 Inf -4.055 0.0007
StepF1 - StepF5 -0.0316 0.0277 Inf -1.142 0.8640
StepF1 - StepF6 -0.0678 0.0278 Inf -2.441 0.1423
StepF2 - StepF3 -0.1348 0.0277 Inf -4.874 <.0001
StepF2 - StepF4 -0.0317 0.0277 Inf -1.147 0.8615
StepF2 - StepF5 0.0488 0.0277 Inf 1.762 0.4909
StepF2 - StepF6 0.0126 0.0278 Inf 0.452 0.9977
StepF3 - StepF4 0.1030 0.0277 Inf 3.727 0.0027
StepF3 - StepF5 0.1836 0.0277 Inf 6.629 <.0001
StepF3 - StepF6 0.1473 0.0278 Inf 5.301 <.0001
StepF4 - StepF5 0.0805 0.0277 Inf 2.907 0.0424
StepF4 - StepF6 0.0443 0.0278 Inf 1.593 0.6031
StepF5 - StepF6 -0.0362 0.0278 Inf -1.302 0.7842
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.0804 0.0277 Inf -2.907 0.0424
StepF1 - StepF3 -0.2152 0.0277 Inf -7.781 <.0001
StepF1 - StepF4 -0.1121 0.0277 Inf -4.055 0.0007
StepF1 - StepF5 -0.0316 0.0277 Inf -1.142 0.8640
StepF1 - StepF6 -0.0678 0.0278 Inf -2.441 0.1423
StepF2 - StepF3 -0.1348 0.0277 Inf -4.874 <.0001
StepF2 - StepF4 -0.0317 0.0277 Inf -1.147 0.8615
StepF2 - StepF5 0.0488 0.0277 Inf 1.762 0.4909
StepF2 - StepF6 0.0126 0.0278 Inf 0.452 0.9977
StepF3 - StepF4 0.1030 0.0277 Inf 3.727 0.0027
StepF3 - StepF5 0.1836 0.0277 Inf 6.629 <.0001
StepF3 - StepF6 0.1473 0.0278 Inf 5.301 <.0001
StepF4 - StepF5 0.0805 0.0277 Inf 2.907 0.0424
StepF4 - StepF6 0.0443 0.0278 Inf 1.593 0.6031
StepF5 - StepF6 -0.0362 0.0278 Inf -1.302 0.7842
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.0804 0.0277 Inf 2.907 0.0109
StepF3 - StepF2 0.1348 0.0277 Inf 4.874 <.0001
StepF4 - StepF3 -0.1030 0.0277 Inf -3.727 0.0008
StepF5 - StepF4 -0.0805 0.0277 Inf -2.907 0.0109
StepF6 - StepF5 0.0362 0.0278 Inf 1.302 0.1929
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.0804 0.0277 Inf 2.907 0.0109
StepF3 - StepF2 0.1348 0.0277 Inf 4.874 <.0001
StepF4 - StepF3 -0.1030 0.0277 Inf -3.727 0.0008
StepF5 - StepF4 -0.0805 0.0277 Inf -2.907 0.0109
StepF6 - StepF5 0.0362 0.0278 Inf 1.302 0.1929
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests
==============================
TRAINING | Block 1 (6 steps) | Axis Z
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 112.9170 5 < 2.2e-16 ***
Accuracy 7.4374 1 0.006388 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.43 0.215 Inf 1.01 1.85
2 1.71 0.215 Inf 1.28 2.13
3 1.83 0.215 Inf 1.41 2.26
4 1.72 0.215 Inf 1.30 2.14
5 1.79 0.215 Inf 1.36 2.21
6 1.50 0.215 Inf 1.08 1.92
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.57 0.211 Inf 1.15 1.98
2 1.84 0.211 Inf 1.43 2.25
3 1.97 0.211 Inf 1.55 2.38
4 1.85 0.211 Inf 1.44 2.27
5 1.92 0.211 Inf 1.51 2.33
6 1.63 0.211 Inf 1.22 2.04
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.2742 0.0480 Inf -5.714 <.0001
StepF1 - StepF3 -0.4029 0.0480 Inf -8.397 <.0001
StepF1 - StepF4 -0.2867 0.0480 Inf -5.974 <.0001
StepF1 - StepF5 -0.3540 0.0481 Inf -7.367 <.0001
StepF1 - StepF6 -0.0648 0.0482 Inf -1.344 0.7602
StepF2 - StepF3 -0.1288 0.0480 Inf -2.684 0.0784
StepF2 - StepF4 -0.0125 0.0480 Inf -0.261 0.9998
StepF2 - StepF5 -0.0799 0.0481 Inf -1.662 0.5573
StepF2 - StepF6 0.2093 0.0482 Inf 4.340 0.0002
StepF3 - StepF4 0.1163 0.0480 Inf 2.423 0.1481
StepF3 - StepF5 0.0489 0.0481 Inf 1.018 0.9121
StepF3 - StepF6 0.3381 0.0482 Inf 7.009 <.0001
StepF4 - StepF5 -0.0674 0.0481 Inf -1.402 0.7261
StepF4 - StepF6 0.2218 0.0482 Inf 4.599 0.0001
StepF5 - StepF6 0.2892 0.0483 Inf 5.988 <.0001
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.2742 0.0480 Inf -5.714 <.0001
StepF1 - StepF3 -0.4029 0.0480 Inf -8.397 <.0001
StepF1 - StepF4 -0.2867 0.0480 Inf -5.974 <.0001
StepF1 - StepF5 -0.3540 0.0481 Inf -7.367 <.0001
StepF1 - StepF6 -0.0648 0.0482 Inf -1.344 0.7602
StepF2 - StepF3 -0.1288 0.0480 Inf -2.684 0.0784
StepF2 - StepF4 -0.0125 0.0480 Inf -0.261 0.9998
StepF2 - StepF5 -0.0799 0.0481 Inf -1.662 0.5573
StepF2 - StepF6 0.2093 0.0482 Inf 4.340 0.0002
StepF3 - StepF4 0.1163 0.0480 Inf 2.423 0.1481
StepF3 - StepF5 0.0489 0.0481 Inf 1.018 0.9121
StepF3 - StepF6 0.3381 0.0482 Inf 7.009 <.0001
StepF4 - StepF5 -0.0674 0.0481 Inf -1.402 0.7261
StepF4 - StepF6 0.2218 0.0482 Inf 4.599 0.0001
StepF5 - StepF6 0.2892 0.0483 Inf 5.988 <.0001
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2742 0.0480 Inf 5.714 <.0001
StepF3 - StepF2 0.1288 0.0480 Inf 2.684 0.0218
StepF4 - StepF3 -0.1163 0.0480 Inf -2.423 0.0308
StepF5 - StepF4 0.0674 0.0481 Inf 1.402 0.1610
StepF6 - StepF5 -0.2892 0.0483 Inf -5.988 <.0001
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2742 0.0480 Inf 5.714 <.0001
StepF3 - StepF2 0.1288 0.0480 Inf 2.684 0.0218
StepF4 - StepF3 -0.1163 0.0480 Inf -2.423 0.0308
StepF5 - StepF4 0.0674 0.0481 Inf 1.402 0.1610
StepF6 - StepF5 -0.2892 0.0483 Inf -5.988 <.0001
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests .report_step_block(stepwise_12, "2 (12 steps)")
==============================
TRAINING | Block 2 (12 steps) | Axis X
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 220.005 11 < 2e-16 ***
Accuracy 2.943 1 0.08625 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.616 0.0736 Inf 0.471 0.760
2 0.815 0.0736 Inf 0.671 0.960
3 0.795 0.0736 Inf 0.650 0.939
4 0.685 0.0736 Inf 0.540 0.829
5 0.688 0.0736 Inf 0.544 0.832
6 0.695 0.0736 Inf 0.551 0.840
7 0.628 0.0736 Inf 0.483 0.772
8 0.625 0.0736 Inf 0.481 0.769
9 0.643 0.0736 Inf 0.499 0.787
10 0.654 0.0736 Inf 0.510 0.799
11 0.608 0.0736 Inf 0.464 0.752
12 0.539 0.0736 Inf 0.395 0.683
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.655 0.0726 Inf 0.513 0.797
2 0.855 0.0726 Inf 0.713 0.997
3 0.834 0.0726 Inf 0.692 0.976
4 0.724 0.0726 Inf 0.582 0.866
5 0.727 0.0726 Inf 0.585 0.870
6 0.735 0.0726 Inf 0.592 0.877
7 0.667 0.0726 Inf 0.525 0.809
8 0.664 0.0726 Inf 0.522 0.807
9 0.682 0.0726 Inf 0.540 0.825
10 0.694 0.0726 Inf 0.551 0.836
11 0.647 0.0726 Inf 0.505 0.790
12 0.578 0.0726 Inf 0.436 0.721
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.19980 0.0246 Inf -8.136 <.0001
StepF1 - StepF3 -0.17892 0.0246 Inf -7.286 <.0001
StepF1 - StepF4 -0.06889 0.0246 Inf -2.805 0.1777
StepF1 - StepF5 -0.07242 0.0246 Inf -2.949 0.1241
StepF1 - StepF6 -0.07963 0.0246 Inf -3.242 0.0541
StepF1 - StepF7 -0.01188 0.0246 Inf -0.484 1.0000
StepF1 - StepF8 -0.00943 0.0246 Inf -0.384 1.0000
StepF1 - StepF9 -0.02731 0.0246 Inf -1.112 0.9942
StepF1 - StepF10 -0.03863 0.0246 Inf -1.573 0.9190
StepF1 - StepF11 0.00776 0.0246 Inf 0.316 1.0000
StepF1 - StepF12 0.07663 0.0246 Inf 3.117 0.0784
StepF2 - StepF3 0.02088 0.0246 Inf 0.850 0.9995
StepF2 - StepF4 0.13092 0.0246 Inf 5.331 <.0001
StepF2 - StepF5 0.12739 0.0246 Inf 5.187 <.0001
StepF2 - StepF6 0.12018 0.0246 Inf 4.893 0.0001
StepF2 - StepF7 0.18792 0.0246 Inf 7.652 <.0001
StepF2 - StepF8 0.19037 0.0246 Inf 7.752 <.0001
StepF2 - StepF9 0.17250 0.0246 Inf 7.024 <.0001
StepF2 - StepF10 0.16117 0.0246 Inf 6.563 <.0001
StepF2 - StepF11 0.20756 0.0246 Inf 8.452 <.0001
StepF2 - StepF12 0.27643 0.0246 Inf 11.244 <.0001
StepF3 - StepF4 0.11004 0.0246 Inf 4.481 0.0005
StepF3 - StepF5 0.10650 0.0246 Inf 4.337 0.0009
StepF3 - StepF6 0.09929 0.0246 Inf 4.043 0.0031
StepF3 - StepF7 0.16704 0.0246 Inf 6.802 <.0001
StepF3 - StepF8 0.16949 0.0246 Inf 6.901 <.0001
StepF3 - StepF9 0.15162 0.0246 Inf 6.174 <.0001
StepF3 - StepF10 0.14029 0.0246 Inf 5.713 <.0001
StepF3 - StepF11 0.18668 0.0246 Inf 7.601 <.0001
StepF3 - StepF12 0.25555 0.0246 Inf 10.395 <.0001
StepF4 - StepF5 -0.00353 0.0246 Inf -0.144 1.0000
StepF4 - StepF6 -0.01074 0.0246 Inf -0.437 1.0000
StepF4 - StepF7 0.05700 0.0246 Inf 2.321 0.4614
StepF4 - StepF8 0.05945 0.0246 Inf 2.421 0.3916
StepF4 - StepF9 0.04158 0.0246 Inf 1.693 0.8719
StepF4 - StepF10 0.03025 0.0246 Inf 1.232 0.9865
StepF4 - StepF11 0.07664 0.0246 Inf 3.121 0.0775
StepF4 - StepF12 0.14551 0.0246 Inf 5.919 <.0001
StepF5 - StepF6 -0.00721 0.0246 Inf -0.294 1.0000
StepF5 - StepF7 0.06053 0.0246 Inf 2.465 0.3623
StepF5 - StepF8 0.06299 0.0246 Inf 2.565 0.2999
StepF5 - StepF9 0.04511 0.0246 Inf 1.837 0.7977
StepF5 - StepF10 0.03379 0.0246 Inf 1.376 0.9682
StepF5 - StepF11 0.08017 0.0246 Inf 3.265 0.0505
StepF5 - StepF12 0.14905 0.0246 Inf 6.063 <.0001
StepF6 - StepF7 0.06774 0.0246 Inf 2.758 0.1981
StepF6 - StepF8 0.07020 0.0246 Inf 2.858 0.1561
StepF6 - StepF9 0.05232 0.0246 Inf 2.130 0.6006
StepF6 - StepF10 0.04100 0.0246 Inf 1.669 0.8822
StepF6 - StepF11 0.08738 0.0246 Inf 3.558 0.0193
StepF6 - StepF12 0.15626 0.0246 Inf 6.356 <.0001
StepF7 - StepF8 0.00245 0.0246 Inf 0.100 1.0000
StepF7 - StepF9 -0.01542 0.0246 Inf -0.628 1.0000
StepF7 - StepF10 -0.02675 0.0246 Inf -1.089 0.9952
StepF7 - StepF11 0.01964 0.0246 Inf 0.800 0.9997
StepF7 - StepF12 0.08851 0.0246 Inf 3.600 0.0166
StepF8 - StepF9 -0.01787 0.0246 Inf -0.728 0.9999
StepF8 - StepF10 -0.02920 0.0246 Inf -1.189 0.9899
StepF8 - StepF11 0.01719 0.0246 Inf 0.700 0.9999
StepF8 - StepF12 0.08606 0.0246 Inf 3.501 0.0235
StepF9 - StepF10 -0.01132 0.0246 Inf -0.461 1.0000
StepF9 - StepF11 0.03506 0.0246 Inf 1.428 0.9583
StepF9 - StepF12 0.10393 0.0246 Inf 4.228 0.0014
StepF10 - StepF11 0.04639 0.0246 Inf 1.889 0.7666
StepF10 - StepF12 0.11526 0.0246 Inf 4.688 0.0002
StepF11 - StepF12 0.06887 0.0246 Inf 2.801 0.1791
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.19980 0.0246 Inf -8.136 <.0001
StepF1 - StepF3 -0.17892 0.0246 Inf -7.286 <.0001
StepF1 - StepF4 -0.06889 0.0246 Inf -2.805 0.1777
StepF1 - StepF5 -0.07242 0.0246 Inf -2.949 0.1241
StepF1 - StepF6 -0.07963 0.0246 Inf -3.242 0.0541
StepF1 - StepF7 -0.01188 0.0246 Inf -0.484 1.0000
StepF1 - StepF8 -0.00943 0.0246 Inf -0.384 1.0000
StepF1 - StepF9 -0.02731 0.0246 Inf -1.112 0.9942
StepF1 - StepF10 -0.03863 0.0246 Inf -1.573 0.9190
StepF1 - StepF11 0.00776 0.0246 Inf 0.316 1.0000
StepF1 - StepF12 0.07663 0.0246 Inf 3.117 0.0784
StepF2 - StepF3 0.02088 0.0246 Inf 0.850 0.9995
StepF2 - StepF4 0.13092 0.0246 Inf 5.331 <.0001
StepF2 - StepF5 0.12739 0.0246 Inf 5.187 <.0001
StepF2 - StepF6 0.12018 0.0246 Inf 4.893 0.0001
StepF2 - StepF7 0.18792 0.0246 Inf 7.652 <.0001
StepF2 - StepF8 0.19037 0.0246 Inf 7.752 <.0001
StepF2 - StepF9 0.17250 0.0246 Inf 7.024 <.0001
StepF2 - StepF10 0.16117 0.0246 Inf 6.563 <.0001
StepF2 - StepF11 0.20756 0.0246 Inf 8.452 <.0001
StepF2 - StepF12 0.27643 0.0246 Inf 11.244 <.0001
StepF3 - StepF4 0.11004 0.0246 Inf 4.481 0.0005
StepF3 - StepF5 0.10650 0.0246 Inf 4.337 0.0009
StepF3 - StepF6 0.09929 0.0246 Inf 4.043 0.0031
StepF3 - StepF7 0.16704 0.0246 Inf 6.802 <.0001
StepF3 - StepF8 0.16949 0.0246 Inf 6.901 <.0001
StepF3 - StepF9 0.15162 0.0246 Inf 6.174 <.0001
StepF3 - StepF10 0.14029 0.0246 Inf 5.713 <.0001
StepF3 - StepF11 0.18668 0.0246 Inf 7.601 <.0001
StepF3 - StepF12 0.25555 0.0246 Inf 10.395 <.0001
StepF4 - StepF5 -0.00353 0.0246 Inf -0.144 1.0000
StepF4 - StepF6 -0.01074 0.0246 Inf -0.437 1.0000
StepF4 - StepF7 0.05700 0.0246 Inf 2.321 0.4614
StepF4 - StepF8 0.05945 0.0246 Inf 2.421 0.3916
StepF4 - StepF9 0.04158 0.0246 Inf 1.693 0.8719
StepF4 - StepF10 0.03025 0.0246 Inf 1.232 0.9865
StepF4 - StepF11 0.07664 0.0246 Inf 3.121 0.0775
StepF4 - StepF12 0.14551 0.0246 Inf 5.919 <.0001
StepF5 - StepF6 -0.00721 0.0246 Inf -0.294 1.0000
StepF5 - StepF7 0.06053 0.0246 Inf 2.465 0.3623
StepF5 - StepF8 0.06299 0.0246 Inf 2.565 0.2999
StepF5 - StepF9 0.04511 0.0246 Inf 1.837 0.7977
StepF5 - StepF10 0.03379 0.0246 Inf 1.376 0.9682
StepF5 - StepF11 0.08017 0.0246 Inf 3.265 0.0505
StepF5 - StepF12 0.14905 0.0246 Inf 6.063 <.0001
StepF6 - StepF7 0.06774 0.0246 Inf 2.758 0.1981
StepF6 - StepF8 0.07020 0.0246 Inf 2.858 0.1561
StepF6 - StepF9 0.05232 0.0246 Inf 2.130 0.6006
StepF6 - StepF10 0.04100 0.0246 Inf 1.669 0.8822
StepF6 - StepF11 0.08738 0.0246 Inf 3.558 0.0193
StepF6 - StepF12 0.15626 0.0246 Inf 6.356 <.0001
StepF7 - StepF8 0.00245 0.0246 Inf 0.100 1.0000
StepF7 - StepF9 -0.01542 0.0246 Inf -0.628 1.0000
StepF7 - StepF10 -0.02675 0.0246 Inf -1.089 0.9952
StepF7 - StepF11 0.01964 0.0246 Inf 0.800 0.9997
StepF7 - StepF12 0.08851 0.0246 Inf 3.600 0.0166
StepF8 - StepF9 -0.01787 0.0246 Inf -0.728 0.9999
StepF8 - StepF10 -0.02920 0.0246 Inf -1.189 0.9899
StepF8 - StepF11 0.01719 0.0246 Inf 0.700 0.9999
StepF8 - StepF12 0.08606 0.0246 Inf 3.501 0.0235
StepF9 - StepF10 -0.01132 0.0246 Inf -0.461 1.0000
StepF9 - StepF11 0.03506 0.0246 Inf 1.428 0.9583
StepF9 - StepF12 0.10393 0.0246 Inf 4.228 0.0014
StepF10 - StepF11 0.04639 0.0246 Inf 1.889 0.7666
StepF10 - StepF12 0.11526 0.0246 Inf 4.688 0.0002
StepF11 - StepF12 0.06887 0.0246 Inf 2.801 0.1791
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.19980 0.0246 Inf 8.136 <.0001
StepF3 - StepF2 -0.02088 0.0246 Inf -0.850 1.0000
StepF4 - StepF3 -0.11004 0.0246 Inf -4.481 0.0001
StepF5 - StepF4 0.00353 0.0246 Inf 0.144 1.0000
StepF6 - StepF5 0.00721 0.0246 Inf 0.294 1.0000
StepF7 - StepF6 -0.06774 0.0246 Inf -2.758 0.0465
StepF8 - StepF7 -0.00245 0.0246 Inf -0.100 1.0000
StepF9 - StepF8 0.01787 0.0246 Inf 0.728 1.0000
StepF10 - StepF9 0.01132 0.0246 Inf 0.461 1.0000
StepF11 - StepF10 -0.04639 0.0246 Inf -1.889 0.4124
StepF12 - StepF11 -0.06887 0.0246 Inf -2.801 0.0458
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.19980 0.0246 Inf 8.136 <.0001
StepF3 - StepF2 -0.02088 0.0246 Inf -0.850 1.0000
StepF4 - StepF3 -0.11004 0.0246 Inf -4.481 0.0001
StepF5 - StepF4 0.00353 0.0246 Inf 0.144 1.0000
StepF6 - StepF5 0.00721 0.0246 Inf 0.294 1.0000
StepF7 - StepF6 -0.06774 0.0246 Inf -2.758 0.0465
StepF8 - StepF7 -0.00245 0.0246 Inf -0.100 1.0000
StepF9 - StepF8 0.01787 0.0246 Inf 0.728 1.0000
StepF10 - StepF9 0.01132 0.0246 Inf 0.461 1.0000
StepF11 - StepF10 -0.04639 0.0246 Inf -1.889 0.4124
StepF12 - StepF11 -0.06887 0.0246 Inf -2.801 0.0458
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests
==============================
TRAINING | Block 2 (12 steps) | Axis Y
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 128.3750 11 < 2e-16 ***
Accuracy 4.9514 1 0.02607 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.776 0.0863 Inf 0.606 0.945
2 0.767 0.0863 Inf 0.598 0.936
3 0.862 0.0863 Inf 0.693 1.031
4 0.761 0.0863 Inf 0.592 0.930
5 0.706 0.0863 Inf 0.537 0.875
6 0.722 0.0863 Inf 0.553 0.891
7 0.801 0.0863 Inf 0.632 0.970
8 0.675 0.0863 Inf 0.506 0.844
9 0.698 0.0863 Inf 0.528 0.867
10 0.714 0.0863 Inf 0.545 0.883
11 0.675 0.0863 Inf 0.506 0.844
12 0.600 0.0863 Inf 0.431 0.770
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.830 0.0854 Inf 0.663 0.997
2 0.822 0.0854 Inf 0.654 0.989
3 0.916 0.0854 Inf 0.749 1.084
4 0.816 0.0854 Inf 0.648 0.983
5 0.761 0.0854 Inf 0.593 0.928
6 0.776 0.0854 Inf 0.609 0.943
7 0.855 0.0854 Inf 0.688 1.022
8 0.729 0.0854 Inf 0.562 0.896
9 0.752 0.0854 Inf 0.585 0.919
10 0.769 0.0854 Inf 0.601 0.936
11 0.729 0.0854 Inf 0.562 0.897
12 0.655 0.0854 Inf 0.488 0.822
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.008381 0.0283 Inf 0.296 1.0000
StepF1 - StepF3 -0.086446 0.0283 Inf -3.050 0.0947
StepF1 - StepF4 0.014324 0.0283 Inf 0.505 1.0000
StepF1 - StepF5 0.069302 0.0283 Inf 2.445 0.3755
StepF1 - StepF6 0.053860 0.0283 Inf 1.900 0.7595
StepF1 - StepF7 -0.025134 0.0283 Inf -0.887 0.9993
StepF1 - StepF8 0.100789 0.0283 Inf 3.556 0.0195
StepF1 - StepF9 0.078038 0.0283 Inf 2.753 0.2006
StepF1 - StepF10 0.061207 0.0283 Inf 2.159 0.5795
StepF1 - StepF11 0.100532 0.0283 Inf 3.547 0.0201
StepF1 - StepF12 0.175112 0.0284 Inf 6.171 <.0001
StepF2 - StepF3 -0.094827 0.0283 Inf -3.345 0.0392
StepF2 - StepF4 0.005944 0.0283 Inf 0.210 1.0000
StepF2 - StepF5 0.060922 0.0283 Inf 2.149 0.5869
StepF2 - StepF6 0.045479 0.0283 Inf 1.604 0.9080
StepF2 - StepF7 -0.033514 0.0283 Inf -1.182 0.9903
StepF2 - StepF8 0.092408 0.0283 Inf 3.260 0.0512
StepF2 - StepF9 0.069657 0.0283 Inf 2.457 0.3672
StepF2 - StepF10 0.052826 0.0283 Inf 1.864 0.7819
StepF2 - StepF11 0.092151 0.0283 Inf 3.251 0.0527
StepF2 - StepF12 0.166731 0.0284 Inf 5.876 <.0001
StepF3 - StepF4 0.100770 0.0283 Inf 3.555 0.0195
StepF3 - StepF5 0.155748 0.0283 Inf 5.495 <.0001
StepF3 - StepF6 0.140306 0.0283 Inf 4.950 <.0001
StepF3 - StepF7 0.061312 0.0283 Inf 2.163 0.5768
StepF3 - StepF8 0.187235 0.0283 Inf 6.605 <.0001
StepF3 - StepF9 0.164484 0.0283 Inf 5.803 <.0001
StepF3 - StepF10 0.147653 0.0283 Inf 5.209 <.0001
StepF3 - StepF11 0.186978 0.0283 Inf 6.596 <.0001
StepF3 - StepF12 0.261558 0.0284 Inf 9.218 <.0001
StepF4 - StepF5 0.054978 0.0283 Inf 1.940 0.7343
StepF4 - StepF6 0.039536 0.0283 Inf 1.395 0.9648
StepF4 - StepF7 -0.039458 0.0283 Inf -1.392 0.9653
StepF4 - StepF8 0.086464 0.0283 Inf 3.050 0.0945
StepF4 - StepF9 0.063714 0.0283 Inf 2.248 0.5145
StepF4 - StepF10 0.046883 0.0283 Inf 1.654 0.8887
StepF4 - StepF11 0.086208 0.0283 Inf 3.041 0.0969
StepF4 - StepF12 0.160788 0.0284 Inf 5.666 <.0001
StepF5 - StepF6 -0.015442 0.0283 Inf -0.545 1.0000
StepF5 - StepF7 -0.094436 0.0283 Inf -3.332 0.0410
StepF5 - StepF8 0.031486 0.0283 Inf 1.111 0.9943
StepF5 - StepF9 0.008736 0.0283 Inf 0.308 1.0000
StepF5 - StepF10 -0.008095 0.0283 Inf -0.286 1.0000
StepF5 - StepF11 0.031230 0.0283 Inf 1.102 0.9947
StepF5 - StepF12 0.105810 0.0284 Inf 3.729 0.0104
StepF6 - StepF7 -0.078994 0.0283 Inf -2.787 0.1855
StepF6 - StepF8 0.046929 0.0283 Inf 1.656 0.8881
StepF6 - StepF9 0.024178 0.0283 Inf 0.853 0.9995
StepF6 - StepF10 0.007347 0.0283 Inf 0.259 1.0000
StepF6 - StepF11 0.046672 0.0283 Inf 1.647 0.8918
StepF6 - StepF12 0.121252 0.0284 Inf 4.273 0.0012
StepF7 - StepF8 0.125922 0.0283 Inf 4.442 0.0005
StepF7 - StepF9 0.103172 0.0283 Inf 3.640 0.0145
StepF7 - StepF10 0.086340 0.0283 Inf 3.046 0.0956
StepF7 - StepF11 0.125666 0.0283 Inf 4.433 0.0006
StepF7 - StepF12 0.200245 0.0284 Inf 7.057 <.0001
StepF8 - StepF9 -0.022751 0.0283 Inf -0.803 0.9997
StepF8 - StepF10 -0.039582 0.0283 Inf -1.396 0.9645
StepF8 - StepF11 -0.000257 0.0283 Inf -0.009 1.0000
StepF8 - StepF12 0.074323 0.0284 Inf 2.619 0.2686
StepF9 - StepF10 -0.016831 0.0283 Inf -0.594 1.0000
StepF9 - StepF11 0.022494 0.0283 Inf 0.794 0.9997
StepF9 - StepF12 0.097074 0.0284 Inf 3.421 0.0307
StepF10 - StepF11 0.039325 0.0283 Inf 1.387 0.9662
StepF10 - StepF12 0.113905 0.0284 Inf 4.014 0.0035
StepF11 - StepF12 0.074580 0.0284 Inf 2.628 0.2636
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.008381 0.0283 Inf 0.296 1.0000
StepF1 - StepF3 -0.086446 0.0283 Inf -3.050 0.0947
StepF1 - StepF4 0.014324 0.0283 Inf 0.505 1.0000
StepF1 - StepF5 0.069302 0.0283 Inf 2.445 0.3755
StepF1 - StepF6 0.053860 0.0283 Inf 1.900 0.7595
StepF1 - StepF7 -0.025134 0.0283 Inf -0.887 0.9993
StepF1 - StepF8 0.100789 0.0283 Inf 3.556 0.0195
StepF1 - StepF9 0.078038 0.0283 Inf 2.753 0.2006
StepF1 - StepF10 0.061207 0.0283 Inf 2.159 0.5795
StepF1 - StepF11 0.100532 0.0283 Inf 3.547 0.0201
StepF1 - StepF12 0.175112 0.0284 Inf 6.171 <.0001
StepF2 - StepF3 -0.094827 0.0283 Inf -3.345 0.0392
StepF2 - StepF4 0.005944 0.0283 Inf 0.210 1.0000
StepF2 - StepF5 0.060922 0.0283 Inf 2.149 0.5869
StepF2 - StepF6 0.045479 0.0283 Inf 1.604 0.9080
StepF2 - StepF7 -0.033514 0.0283 Inf -1.182 0.9903
StepF2 - StepF8 0.092408 0.0283 Inf 3.260 0.0512
StepF2 - StepF9 0.069657 0.0283 Inf 2.457 0.3672
StepF2 - StepF10 0.052826 0.0283 Inf 1.864 0.7819
StepF2 - StepF11 0.092151 0.0283 Inf 3.251 0.0527
StepF2 - StepF12 0.166731 0.0284 Inf 5.876 <.0001
StepF3 - StepF4 0.100770 0.0283 Inf 3.555 0.0195
StepF3 - StepF5 0.155748 0.0283 Inf 5.495 <.0001
StepF3 - StepF6 0.140306 0.0283 Inf 4.950 <.0001
StepF3 - StepF7 0.061312 0.0283 Inf 2.163 0.5768
StepF3 - StepF8 0.187235 0.0283 Inf 6.605 <.0001
StepF3 - StepF9 0.164484 0.0283 Inf 5.803 <.0001
StepF3 - StepF10 0.147653 0.0283 Inf 5.209 <.0001
StepF3 - StepF11 0.186978 0.0283 Inf 6.596 <.0001
StepF3 - StepF12 0.261558 0.0284 Inf 9.218 <.0001
StepF4 - StepF5 0.054978 0.0283 Inf 1.940 0.7343
StepF4 - StepF6 0.039536 0.0283 Inf 1.395 0.9648
StepF4 - StepF7 -0.039458 0.0283 Inf -1.392 0.9653
StepF4 - StepF8 0.086464 0.0283 Inf 3.050 0.0945
StepF4 - StepF9 0.063714 0.0283 Inf 2.248 0.5145
StepF4 - StepF10 0.046883 0.0283 Inf 1.654 0.8887
StepF4 - StepF11 0.086208 0.0283 Inf 3.041 0.0969
StepF4 - StepF12 0.160788 0.0284 Inf 5.666 <.0001
StepF5 - StepF6 -0.015442 0.0283 Inf -0.545 1.0000
StepF5 - StepF7 -0.094436 0.0283 Inf -3.332 0.0410
StepF5 - StepF8 0.031486 0.0283 Inf 1.111 0.9943
StepF5 - StepF9 0.008736 0.0283 Inf 0.308 1.0000
StepF5 - StepF10 -0.008095 0.0283 Inf -0.286 1.0000
StepF5 - StepF11 0.031230 0.0283 Inf 1.102 0.9947
StepF5 - StepF12 0.105810 0.0284 Inf 3.729 0.0104
StepF6 - StepF7 -0.078994 0.0283 Inf -2.787 0.1855
StepF6 - StepF8 0.046929 0.0283 Inf 1.656 0.8881
StepF6 - StepF9 0.024178 0.0283 Inf 0.853 0.9995
StepF6 - StepF10 0.007347 0.0283 Inf 0.259 1.0000
StepF6 - StepF11 0.046672 0.0283 Inf 1.647 0.8918
StepF6 - StepF12 0.121252 0.0284 Inf 4.273 0.0012
StepF7 - StepF8 0.125922 0.0283 Inf 4.442 0.0005
StepF7 - StepF9 0.103172 0.0283 Inf 3.640 0.0145
StepF7 - StepF10 0.086340 0.0283 Inf 3.046 0.0956
StepF7 - StepF11 0.125666 0.0283 Inf 4.433 0.0006
StepF7 - StepF12 0.200245 0.0284 Inf 7.057 <.0001
StepF8 - StepF9 -0.022751 0.0283 Inf -0.803 0.9997
StepF8 - StepF10 -0.039582 0.0283 Inf -1.396 0.9645
StepF8 - StepF11 -0.000257 0.0283 Inf -0.009 1.0000
StepF8 - StepF12 0.074323 0.0284 Inf 2.619 0.2686
StepF9 - StepF10 -0.016831 0.0283 Inf -0.594 1.0000
StepF9 - StepF11 0.022494 0.0283 Inf 0.794 0.9997
StepF9 - StepF12 0.097074 0.0284 Inf 3.421 0.0307
StepF10 - StepF11 0.039325 0.0283 Inf 1.387 0.9662
StepF10 - StepF12 0.113905 0.0284 Inf 4.014 0.0035
StepF11 - StepF12 0.074580 0.0284 Inf 2.628 0.2636
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.00838 0.0283 Inf -0.296 1.0000
StepF3 - StepF2 0.09483 0.0283 Inf 3.345 0.0074
StepF4 - StepF3 -0.10077 0.0283 Inf -3.555 0.0038
StepF5 - StepF4 -0.05498 0.0283 Inf -1.940 0.3146
StepF6 - StepF5 0.01544 0.0283 Inf 0.545 1.0000
StepF7 - StepF6 0.07899 0.0283 Inf 2.787 0.0426
StepF8 - StepF7 -0.12592 0.0283 Inf -4.442 0.0001
StepF9 - StepF8 0.02275 0.0283 Inf 0.803 1.0000
StepF10 - StepF9 0.01683 0.0283 Inf 0.594 1.0000
StepF11 - StepF10 -0.03933 0.0283 Inf -1.387 0.8267
StepF12 - StepF11 -0.07458 0.0284 Inf -2.628 0.0601
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.00838 0.0283 Inf -0.296 1.0000
StepF3 - StepF2 0.09483 0.0283 Inf 3.345 0.0074
StepF4 - StepF3 -0.10077 0.0283 Inf -3.555 0.0038
StepF5 - StepF4 -0.05498 0.0283 Inf -1.940 0.3146
StepF6 - StepF5 0.01544 0.0283 Inf 0.545 1.0000
StepF7 - StepF6 0.07899 0.0283 Inf 2.787 0.0426
StepF8 - StepF7 -0.12592 0.0283 Inf -4.442 0.0001
StepF9 - StepF8 0.02275 0.0283 Inf 0.803 1.0000
StepF10 - StepF9 0.01683 0.0283 Inf 0.594 1.0000
StepF11 - StepF10 -0.03933 0.0283 Inf -1.387 0.8267
StepF12 - StepF11 -0.07458 0.0284 Inf -2.628 0.0601
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests
==============================
TRAINING | Block 2 (12 steps) | Axis Z
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 163.7656 11 < 2e-16 ***
Accuracy 5.4702 1 0.01934 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.36 0.143 Inf 1.077 1.64
2 1.61 0.143 Inf 1.329 1.89
3 1.58 0.143 Inf 1.299 1.86
4 1.46 0.143 Inf 1.175 1.74
5 1.47 0.143 Inf 1.188 1.75
6 1.34 0.143 Inf 1.061 1.62
7 1.40 0.143 Inf 1.123 1.68
8 1.32 0.143 Inf 1.037 1.60
9 1.32 0.143 Inf 1.042 1.60
10 1.34 0.143 Inf 1.059 1.62
11 1.31 0.143 Inf 1.025 1.59
12 1.11 0.143 Inf 0.833 1.39
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.47 0.141 Inf 1.198 1.75
2 1.73 0.141 Inf 1.449 2.00
3 1.70 0.141 Inf 1.420 1.97
4 1.57 0.141 Inf 1.296 1.85
5 1.58 0.141 Inf 1.309 1.86
6 1.46 0.141 Inf 1.181 1.73
7 1.52 0.141 Inf 1.243 1.80
8 1.43 0.141 Inf 1.158 1.71
9 1.44 0.141 Inf 1.163 1.71
10 1.46 0.141 Inf 1.180 1.73
11 1.42 0.141 Inf 1.146 1.70
12 1.23 0.141 Inf 0.953 1.51
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.25110 0.0486 Inf -5.167 <.0001
StepF1 - StepF3 -0.22154 0.0486 Inf -4.558 0.0003
StepF1 - StepF4 -0.09740 0.0486 Inf -2.004 0.6908
StepF1 - StepF5 -0.11037 0.0486 Inf -2.271 0.4976
StepF1 - StepF6 0.01692 0.0486 Inf 0.348 1.0000
StepF1 - StepF7 -0.04503 0.0486 Inf -0.927 0.9989
StepF1 - StepF8 0.04031 0.0486 Inf 0.829 0.9996
StepF1 - StepF9 0.03545 0.0486 Inf 0.729 0.9999
StepF1 - StepF10 0.01848 0.0486 Inf 0.380 1.0000
StepF1 - StepF11 0.05261 0.0486 Inf 1.083 0.9954
StepF1 - StepF12 0.24488 0.0486 Inf 5.033 <.0001
StepF2 - StepF3 0.02956 0.0486 Inf 0.608 1.0000
StepF2 - StepF4 0.15370 0.0486 Inf 3.163 0.0686
StepF2 - StepF5 0.14073 0.0486 Inf 2.896 0.1422
StepF2 - StepF6 0.26802 0.0486 Inf 5.515 <.0001
StepF2 - StepF7 0.20607 0.0486 Inf 4.240 0.0013
StepF2 - StepF8 0.29141 0.0486 Inf 5.996 <.0001
StepF2 - StepF9 0.28655 0.0486 Inf 5.896 <.0001
StepF2 - StepF10 0.26958 0.0486 Inf 5.547 <.0001
StepF2 - StepF11 0.30371 0.0486 Inf 6.249 <.0001
StepF2 - StepF12 0.49598 0.0486 Inf 10.195 <.0001
StepF3 - StepF4 0.12414 0.0486 Inf 2.554 0.3061
StepF3 - StepF5 0.11117 0.0486 Inf 2.288 0.4855
StepF3 - StepF6 0.23846 0.0486 Inf 4.907 0.0001
StepF3 - StepF7 0.17650 0.0486 Inf 3.632 0.0149
StepF3 - StepF8 0.26185 0.0486 Inf 5.388 <.0001
StepF3 - StepF9 0.25699 0.0486 Inf 5.288 <.0001
StepF3 - StepF10 0.24001 0.0486 Inf 4.939 0.0001
StepF3 - StepF11 0.27415 0.0486 Inf 5.641 <.0001
StepF3 - StepF12 0.46641 0.0486 Inf 9.587 <.0001
StepF4 - StepF5 -0.01297 0.0486 Inf -0.267 1.0000
StepF4 - StepF6 0.11432 0.0486 Inf 2.352 0.4391
StepF4 - StepF7 0.05236 0.0486 Inf 1.077 0.9956
StepF4 - StepF8 0.13771 0.0486 Inf 2.834 0.1659
StepF4 - StepF9 0.13284 0.0486 Inf 2.733 0.2097
StepF4 - StepF10 0.11587 0.0486 Inf 2.384 0.4167
StepF4 - StepF11 0.15001 0.0486 Inf 3.087 0.0854
StepF4 - StepF12 0.34227 0.0486 Inf 7.035 <.0001
StepF5 - StepF6 0.12729 0.0486 Inf 2.619 0.2687
StepF5 - StepF7 0.06533 0.0486 Inf 1.344 0.9733
StepF5 - StepF8 0.15067 0.0486 Inf 3.100 0.0821
StepF5 - StepF9 0.14581 0.0486 Inf 3.000 0.1083
StepF5 - StepF10 0.12884 0.0486 Inf 2.651 0.2512
StepF5 - StepF11 0.16297 0.0486 Inf 3.353 0.0382
StepF5 - StepF12 0.35524 0.0486 Inf 7.302 <.0001
StepF6 - StepF7 -0.06196 0.0486 Inf -1.275 0.9823
StepF6 - StepF8 0.02339 0.0486 Inf 0.481 1.0000
StepF6 - StepF9 0.01853 0.0486 Inf 0.381 1.0000
StepF6 - StepF10 0.00155 0.0486 Inf 0.032 1.0000
StepF6 - StepF11 0.03569 0.0486 Inf 0.734 0.9999
StepF6 - StepF12 0.22795 0.0486 Inf 4.686 0.0002
StepF7 - StepF8 0.08534 0.0486 Inf 1.756 0.8417
StepF7 - StepF9 0.08048 0.0486 Inf 1.656 0.8879
StepF7 - StepF10 0.06351 0.0486 Inf 1.307 0.9785
StepF7 - StepF11 0.09764 0.0486 Inf 2.009 0.6873
StepF7 - StepF12 0.28991 0.0486 Inf 5.959 <.0001
StepF8 - StepF9 -0.00486 0.0486 Inf -0.100 1.0000
StepF8 - StepF10 -0.02183 0.0486 Inf -0.449 1.0000
StepF8 - StepF11 0.01230 0.0486 Inf 0.253 1.0000
StepF8 - StepF12 0.20457 0.0486 Inf 4.205 0.0016
StepF9 - StepF10 -0.01697 0.0486 Inf -0.349 1.0000
StepF9 - StepF11 0.01716 0.0486 Inf 0.353 1.0000
StepF9 - StepF12 0.20943 0.0486 Inf 4.305 0.0010
StepF10 - StepF11 0.03413 0.0486 Inf 0.702 0.9999
StepF10 - StepF12 0.22640 0.0486 Inf 4.654 0.0002
StepF11 - StepF12 0.19227 0.0486 Inf 3.952 0.0044
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.25110 0.0486 Inf -5.167 <.0001
StepF1 - StepF3 -0.22154 0.0486 Inf -4.558 0.0003
StepF1 - StepF4 -0.09740 0.0486 Inf -2.004 0.6908
StepF1 - StepF5 -0.11037 0.0486 Inf -2.271 0.4976
StepF1 - StepF6 0.01692 0.0486 Inf 0.348 1.0000
StepF1 - StepF7 -0.04503 0.0486 Inf -0.927 0.9989
StepF1 - StepF8 0.04031 0.0486 Inf 0.829 0.9996
StepF1 - StepF9 0.03545 0.0486 Inf 0.729 0.9999
StepF1 - StepF10 0.01848 0.0486 Inf 0.380 1.0000
StepF1 - StepF11 0.05261 0.0486 Inf 1.083 0.9954
StepF1 - StepF12 0.24488 0.0486 Inf 5.033 <.0001
StepF2 - StepF3 0.02956 0.0486 Inf 0.608 1.0000
StepF2 - StepF4 0.15370 0.0486 Inf 3.163 0.0686
StepF2 - StepF5 0.14073 0.0486 Inf 2.896 0.1422
StepF2 - StepF6 0.26802 0.0486 Inf 5.515 <.0001
StepF2 - StepF7 0.20607 0.0486 Inf 4.240 0.0013
StepF2 - StepF8 0.29141 0.0486 Inf 5.996 <.0001
StepF2 - StepF9 0.28655 0.0486 Inf 5.896 <.0001
StepF2 - StepF10 0.26958 0.0486 Inf 5.547 <.0001
StepF2 - StepF11 0.30371 0.0486 Inf 6.249 <.0001
StepF2 - StepF12 0.49598 0.0486 Inf 10.195 <.0001
StepF3 - StepF4 0.12414 0.0486 Inf 2.554 0.3061
StepF3 - StepF5 0.11117 0.0486 Inf 2.288 0.4855
StepF3 - StepF6 0.23846 0.0486 Inf 4.907 0.0001
StepF3 - StepF7 0.17650 0.0486 Inf 3.632 0.0149
StepF3 - StepF8 0.26185 0.0486 Inf 5.388 <.0001
StepF3 - StepF9 0.25699 0.0486 Inf 5.288 <.0001
StepF3 - StepF10 0.24001 0.0486 Inf 4.939 0.0001
StepF3 - StepF11 0.27415 0.0486 Inf 5.641 <.0001
StepF3 - StepF12 0.46641 0.0486 Inf 9.587 <.0001
StepF4 - StepF5 -0.01297 0.0486 Inf -0.267 1.0000
StepF4 - StepF6 0.11432 0.0486 Inf 2.352 0.4391
StepF4 - StepF7 0.05236 0.0486 Inf 1.077 0.9956
StepF4 - StepF8 0.13771 0.0486 Inf 2.834 0.1659
StepF4 - StepF9 0.13284 0.0486 Inf 2.733 0.2097
StepF4 - StepF10 0.11587 0.0486 Inf 2.384 0.4167
StepF4 - StepF11 0.15001 0.0486 Inf 3.087 0.0854
StepF4 - StepF12 0.34227 0.0486 Inf 7.035 <.0001
StepF5 - StepF6 0.12729 0.0486 Inf 2.619 0.2687
StepF5 - StepF7 0.06533 0.0486 Inf 1.344 0.9733
StepF5 - StepF8 0.15067 0.0486 Inf 3.100 0.0821
StepF5 - StepF9 0.14581 0.0486 Inf 3.000 0.1083
StepF5 - StepF10 0.12884 0.0486 Inf 2.651 0.2512
StepF5 - StepF11 0.16297 0.0486 Inf 3.353 0.0382
StepF5 - StepF12 0.35524 0.0486 Inf 7.302 <.0001
StepF6 - StepF7 -0.06196 0.0486 Inf -1.275 0.9823
StepF6 - StepF8 0.02339 0.0486 Inf 0.481 1.0000
StepF6 - StepF9 0.01853 0.0486 Inf 0.381 1.0000
StepF6 - StepF10 0.00155 0.0486 Inf 0.032 1.0000
StepF6 - StepF11 0.03569 0.0486 Inf 0.734 0.9999
StepF6 - StepF12 0.22795 0.0486 Inf 4.686 0.0002
StepF7 - StepF8 0.08534 0.0486 Inf 1.756 0.8417
StepF7 - StepF9 0.08048 0.0486 Inf 1.656 0.8879
StepF7 - StepF10 0.06351 0.0486 Inf 1.307 0.9785
StepF7 - StepF11 0.09764 0.0486 Inf 2.009 0.6873
StepF7 - StepF12 0.28991 0.0486 Inf 5.959 <.0001
StepF8 - StepF9 -0.00486 0.0486 Inf -0.100 1.0000
StepF8 - StepF10 -0.02183 0.0486 Inf -0.449 1.0000
StepF8 - StepF11 0.01230 0.0486 Inf 0.253 1.0000
StepF8 - StepF12 0.20457 0.0486 Inf 4.205 0.0016
StepF9 - StepF10 -0.01697 0.0486 Inf -0.349 1.0000
StepF9 - StepF11 0.01716 0.0486 Inf 0.353 1.0000
StepF9 - StepF12 0.20943 0.0486 Inf 4.305 0.0010
StepF10 - StepF11 0.03413 0.0486 Inf 0.702 0.9999
StepF10 - StepF12 0.22640 0.0486 Inf 4.654 0.0002
StepF11 - StepF12 0.19227 0.0486 Inf 3.952 0.0044
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.25110 0.0486 Inf 5.167 <.0001
StepF3 - StepF2 -0.02956 0.0486 Inf -0.608 1.0000
StepF4 - StepF3 -0.12414 0.0486 Inf -2.554 0.0851
StepF5 - StepF4 0.01297 0.0486 Inf 0.267 1.0000
StepF6 - StepF5 -0.12729 0.0486 Inf -2.619 0.0793
StepF7 - StepF6 0.06196 0.0486 Inf 1.275 1.0000
StepF8 - StepF7 -0.08534 0.0486 Inf -1.756 0.5535
StepF9 - StepF8 0.00486 0.0486 Inf 0.100 1.0000
StepF10 - StepF9 0.01697 0.0486 Inf 0.349 1.0000
StepF11 - StepF10 -0.03413 0.0486 Inf -0.702 1.0000
StepF12 - StepF11 -0.19227 0.0486 Inf -3.952 0.0008
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.25110 0.0486 Inf 5.167 <.0001
StepF3 - StepF2 -0.02956 0.0486 Inf -0.608 1.0000
StepF4 - StepF3 -0.12414 0.0486 Inf -2.554 0.0851
StepF5 - StepF4 0.01297 0.0486 Inf 0.267 1.0000
StepF6 - StepF5 -0.12729 0.0486 Inf -2.619 0.0793
StepF7 - StepF6 0.06196 0.0486 Inf 1.275 1.0000
StepF8 - StepF7 -0.08534 0.0486 Inf -1.756 0.5535
StepF9 - StepF8 0.00486 0.0486 Inf 0.100 1.0000
StepF10 - StepF9 0.01697 0.0486 Inf 0.349 1.0000
StepF11 - StepF10 -0.03413 0.0486 Inf -0.702 1.0000
StepF12 - StepF11 -0.19227 0.0486 Inf -3.952 0.0008
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests .report_step_block(stepwise_18, "3 (18 steps)")
==============================
TRAINING | Block 3 (18 steps) | Axis X
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 95.7435 17 5.433e-13 ***
Accuracy 0.4092 1 0.5224
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.574 0.0499 Inf 0.477 0.672
2 0.609 0.0499 Inf 0.511 0.707
3 0.616 0.0499 Inf 0.518 0.714
4 0.585 0.0499 Inf 0.487 0.683
5 0.581 0.0499 Inf 0.483 0.678
6 0.596 0.0499 Inf 0.498 0.694
7 0.596 0.0499 Inf 0.498 0.694
8 0.565 0.0499 Inf 0.468 0.663
9 0.569 0.0499 Inf 0.471 0.667
10 0.569 0.0499 Inf 0.471 0.666
11 0.548 0.0499 Inf 0.450 0.646
12 0.501 0.0499 Inf 0.403 0.599
13 0.498 0.0499 Inf 0.400 0.596
14 0.552 0.0499 Inf 0.454 0.650
15 0.530 0.0499 Inf 0.433 0.628
16 0.538 0.0499 Inf 0.440 0.636
17 0.512 0.0499 Inf 0.415 0.610
18 0.523 0.0499 Inf 0.425 0.620
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.560 0.0496 Inf 0.463 0.658
2 0.595 0.0496 Inf 0.498 0.692
3 0.602 0.0496 Inf 0.505 0.699
4 0.571 0.0496 Inf 0.474 0.668
5 0.566 0.0496 Inf 0.469 0.664
6 0.582 0.0496 Inf 0.485 0.679
7 0.582 0.0496 Inf 0.485 0.679
8 0.551 0.0496 Inf 0.454 0.648
9 0.555 0.0496 Inf 0.458 0.652
10 0.555 0.0496 Inf 0.457 0.652
11 0.534 0.0496 Inf 0.437 0.631
12 0.487 0.0496 Inf 0.390 0.584
13 0.484 0.0496 Inf 0.387 0.581
14 0.538 0.0496 Inf 0.441 0.635
15 0.516 0.0496 Inf 0.419 0.614
16 0.524 0.0496 Inf 0.426 0.621
17 0.498 0.0496 Inf 0.401 0.596
18 0.508 0.0496 Inf 0.411 0.606
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.034792 0.0215 Inf -1.618 0.9797
StepF1 - StepF3 -0.041594 0.0215 Inf -1.934 0.8992
StepF1 - StepF4 -0.010482 0.0215 Inf -0.487 1.0000
StepF1 - StepF5 -0.006110 0.0215 Inf -0.284 1.0000
StepF1 - StepF6 -0.021781 0.0215 Inf -1.013 0.9999
StepF1 - StepF7 -0.021472 0.0215 Inf -0.998 0.9999
StepF1 - StepF8 0.009204 0.0215 Inf 0.428 1.0000
StepF1 - StepF9 0.005469 0.0215 Inf 0.254 1.0000
StepF1 - StepF10 0.005721 0.0215 Inf 0.266 1.0000
StepF1 - StepF11 0.026359 0.0215 Inf 1.226 0.9992
StepF1 - StepF12 0.073382 0.0215 Inf 3.412 0.0638
StepF1 - StepF13 0.076545 0.0215 Inf 3.559 0.0397
StepF1 - StepF14 0.022458 0.0215 Inf 1.044 0.9999
StepF1 - StepF15 0.043951 0.0215 Inf 2.043 0.8480
StepF1 - StepF16 0.036644 0.0215 Inf 1.704 0.9663
StepF1 - StepF17 0.061988 0.0215 Inf 2.882 0.2625
StepF1 - StepF18 0.051906 0.0215 Inf 2.411 0.5991
StepF2 - StepF3 -0.006801 0.0215 Inf -0.316 1.0000
StepF2 - StepF4 0.024311 0.0215 Inf 1.130 0.9997
StepF2 - StepF5 0.028682 0.0215 Inf 1.334 0.9976
StepF2 - StepF6 0.013011 0.0215 Inf 0.605 1.0000
StepF2 - StepF7 0.013321 0.0215 Inf 0.619 1.0000
StepF2 - StepF8 0.043997 0.0215 Inf 2.046 0.8469
StepF2 - StepF9 0.040262 0.0215 Inf 1.872 0.9224
StepF2 - StepF10 0.040514 0.0215 Inf 1.884 0.9183
StepF2 - StepF11 0.061151 0.0215 Inf 2.843 0.2857
StepF2 - StepF12 0.108175 0.0215 Inf 5.029 0.0001
StepF2 - StepF13 0.111338 0.0215 Inf 5.176 <.0001
StepF2 - StepF14 0.057250 0.0215 Inf 2.662 0.4076
StepF2 - StepF15 0.078744 0.0215 Inf 3.661 0.0280
StepF2 - StepF16 0.071437 0.0215 Inf 3.321 0.0840
StepF2 - StepF17 0.096781 0.0215 Inf 4.500 0.0009
StepF2 - StepF18 0.086699 0.0215 Inf 4.028 0.0071
StepF3 - StepF4 0.031112 0.0215 Inf 1.446 0.9938
StepF3 - StepF5 0.035483 0.0215 Inf 1.650 0.9753
StepF3 - StepF6 0.019812 0.0215 Inf 0.921 1.0000
StepF3 - StepF7 0.020122 0.0215 Inf 0.936 1.0000
StepF3 - StepF8 0.050798 0.0215 Inf 2.362 0.6373
StepF3 - StepF9 0.047063 0.0215 Inf 2.188 0.7619
StepF3 - StepF10 0.047315 0.0215 Inf 2.200 0.7541
StepF3 - StepF11 0.067953 0.0215 Inf 3.159 0.1329
StepF3 - StepF12 0.114976 0.0215 Inf 5.346 <.0001
StepF3 - StepF13 0.118139 0.0215 Inf 5.493 <.0001
StepF3 - StepF14 0.064052 0.0215 Inf 2.978 0.2107
StepF3 - StepF15 0.085545 0.0215 Inf 3.977 0.0087
StepF3 - StepF16 0.078238 0.0215 Inf 3.638 0.0304
StepF3 - StepF17 0.103582 0.0215 Inf 4.816 0.0002
StepF3 - StepF18 0.093500 0.0215 Inf 4.344 0.0019
StepF4 - StepF5 0.004372 0.0215 Inf 0.203 1.0000
StepF4 - StepF6 -0.011299 0.0215 Inf -0.525 1.0000
StepF4 - StepF7 -0.010990 0.0215 Inf -0.511 1.0000
StepF4 - StepF8 0.019686 0.0215 Inf 0.915 1.0000
StepF4 - StepF9 0.015951 0.0215 Inf 0.742 1.0000
StepF4 - StepF10 0.016203 0.0215 Inf 0.753 1.0000
StepF4 - StepF11 0.036841 0.0215 Inf 1.713 0.9646
StepF4 - StepF12 0.083864 0.0215 Inf 3.899 0.0118
StepF4 - StepF13 0.087027 0.0215 Inf 4.046 0.0066
StepF4 - StepF14 0.032940 0.0215 Inf 1.531 0.9885
StepF4 - StepF15 0.054433 0.0215 Inf 2.531 0.5064
StepF4 - StepF16 0.047126 0.0215 Inf 2.191 0.7600
StepF4 - StepF17 0.072470 0.0215 Inf 3.369 0.0727
StepF4 - StepF18 0.062388 0.0215 Inf 2.898 0.2531
StepF5 - StepF6 -0.015671 0.0215 Inf -0.729 1.0000
StepF5 - StepF7 -0.015361 0.0215 Inf -0.714 1.0000
StepF5 - StepF8 0.015315 0.0215 Inf 0.712 1.0000
StepF5 - StepF9 0.011580 0.0215 Inf 0.538 1.0000
StepF5 - StepF10 0.011832 0.0215 Inf 0.550 1.0000
StepF5 - StepF11 0.032469 0.0215 Inf 1.510 0.9901
StepF5 - StepF12 0.079493 0.0215 Inf 3.696 0.0248
StepF5 - StepF13 0.082656 0.0215 Inf 3.843 0.0145
StepF5 - StepF14 0.028568 0.0215 Inf 1.328 0.9977
StepF5 - StepF15 0.050062 0.0215 Inf 2.328 0.6631
StepF5 - StepF16 0.042755 0.0215 Inf 1.988 0.8756
StepF5 - StepF17 0.068099 0.0215 Inf 3.166 0.1305
StepF5 - StepF18 0.058016 0.0215 Inf 2.695 0.3834
StepF6 - StepF7 0.000310 0.0215 Inf 0.014 1.0000
StepF6 - StepF8 0.030985 0.0215 Inf 1.441 0.9941
StepF6 - StepF9 0.027250 0.0215 Inf 1.267 0.9987
StepF6 - StepF10 0.027502 0.0215 Inf 1.279 0.9986
StepF6 - StepF11 0.048140 0.0215 Inf 2.238 0.7279
StepF6 - StepF12 0.095163 0.0215 Inf 4.424 0.0013
StepF6 - StepF13 0.098326 0.0215 Inf 4.572 0.0007
StepF6 - StepF14 0.044239 0.0215 Inf 2.057 0.8409
StepF6 - StepF15 0.065732 0.0215 Inf 3.056 0.1740
StepF6 - StepF16 0.058426 0.0215 Inf 2.716 0.3687
StepF6 - StepF17 0.083770 0.0215 Inf 3.895 0.0120
StepF6 - StepF18 0.073687 0.0215 Inf 3.423 0.0615
StepF7 - StepF8 0.030676 0.0215 Inf 1.426 0.9947
StepF7 - StepF9 0.026941 0.0215 Inf 1.253 0.9989
StepF7 - StepF10 0.027193 0.0215 Inf 1.264 0.9988
StepF7 - StepF11 0.047831 0.0215 Inf 2.224 0.7379
StepF7 - StepF12 0.094854 0.0215 Inf 4.410 0.0014
StepF7 - StepF13 0.098017 0.0215 Inf 4.557 0.0007
StepF7 - StepF14 0.043930 0.0215 Inf 2.042 0.8486
StepF7 - StepF15 0.065423 0.0215 Inf 3.042 0.1804
StepF7 - StepF16 0.058116 0.0215 Inf 2.702 0.3787
StepF7 - StepF17 0.083460 0.0215 Inf 3.880 0.0126
StepF7 - StepF18 0.073378 0.0215 Inf 3.409 0.0643
StepF8 - StepF9 -0.003735 0.0215 Inf -0.174 1.0000
StepF8 - StepF10 -0.003483 0.0215 Inf -0.162 1.0000
StepF8 - StepF11 0.017155 0.0215 Inf 0.798 1.0000
StepF8 - StepF12 0.064178 0.0215 Inf 2.984 0.2078
StepF8 - StepF13 0.067341 0.0215 Inf 3.131 0.1434
StepF8 - StepF14 0.013254 0.0215 Inf 0.616 1.0000
StepF8 - StepF15 0.034747 0.0215 Inf 1.615 0.9799
StepF8 - StepF16 0.027440 0.0215 Inf 1.276 0.9986
StepF8 - StepF17 0.052784 0.0215 Inf 2.454 0.5660
StepF8 - StepF18 0.042702 0.0215 Inf 1.984 0.8774
StepF9 - StepF10 0.000252 0.0215 Inf 0.012 1.0000
StepF9 - StepF11 0.020890 0.0215 Inf 0.971 1.0000
StepF9 - StepF12 0.067913 0.0215 Inf 3.157 0.1336
StepF9 - StepF13 0.071076 0.0215 Inf 3.305 0.0882
StepF9 - StepF14 0.016989 0.0215 Inf 0.790 1.0000
StepF9 - StepF15 0.038482 0.0215 Inf 1.789 0.9473
StepF9 - StepF16 0.031175 0.0215 Inf 1.449 0.9937
StepF9 - StepF17 0.056519 0.0215 Inf 2.628 0.4326
StepF9 - StepF18 0.046437 0.0215 Inf 2.157 0.7818
StepF10 - StepF11 0.020638 0.0215 Inf 0.960 1.0000
StepF10 - StepF12 0.067661 0.0215 Inf 3.146 0.1378
StepF10 - StepF13 0.070824 0.0215 Inf 3.293 0.0913
StepF10 - StepF14 0.016737 0.0215 Inf 0.778 1.0000
StepF10 - StepF15 0.038230 0.0215 Inf 1.777 0.9503
StepF10 - StepF16 0.030923 0.0215 Inf 1.438 0.9942
StepF10 - StepF17 0.056267 0.0215 Inf 2.616 0.4413
StepF10 - StepF18 0.046185 0.0215 Inf 2.146 0.7892
StepF11 - StepF12 0.047023 0.0215 Inf 2.186 0.7632
StepF11 - StepF13 0.050186 0.0215 Inf 2.333 0.6588
StepF11 - StepF14 -0.003901 0.0215 Inf -0.181 1.0000
StepF11 - StepF15 0.017592 0.0215 Inf 0.818 1.0000
StepF11 - StepF16 0.010285 0.0215 Inf 0.478 1.0000
StepF11 - StepF17 0.035629 0.0215 Inf 1.657 0.9743
StepF11 - StepF18 0.025547 0.0215 Inf 1.187 0.9994
StepF12 - StepF13 0.003163 0.0215 Inf 0.147 1.0000
StepF12 - StepF14 -0.050924 0.0215 Inf -2.368 0.6328
StepF12 - StepF15 -0.029431 0.0215 Inf -1.368 0.9968
StepF12 - StepF16 -0.036738 0.0215 Inf -1.708 0.9655
StepF12 - StepF17 -0.011394 0.0215 Inf -0.530 1.0000
StepF12 - StepF18 -0.021476 0.0215 Inf -0.998 0.9999
StepF13 - StepF14 -0.054087 0.0215 Inf -2.515 0.5188
StepF13 - StepF15 -0.032594 0.0215 Inf -1.515 0.9897
StepF13 - StepF16 -0.039901 0.0215 Inf -1.855 0.9280
StepF13 - StepF17 -0.014557 0.0215 Inf -0.677 1.0000
StepF13 - StepF18 -0.024639 0.0215 Inf -1.145 0.9997
StepF14 - StepF15 0.021493 0.0215 Inf 0.999 0.9999
StepF14 - StepF16 0.014186 0.0215 Inf 0.660 1.0000
StepF14 - StepF17 0.039530 0.0215 Inf 1.838 0.9334
StepF14 - StepF18 0.029448 0.0215 Inf 1.368 0.9968
StepF15 - StepF16 -0.007307 0.0215 Inf -0.340 1.0000
StepF15 - StepF17 0.018037 0.0215 Inf 0.839 1.0000
StepF15 - StepF18 0.007955 0.0215 Inf 0.370 1.0000
StepF16 - StepF17 0.025344 0.0215 Inf 1.178 0.9995
StepF16 - StepF18 0.015262 0.0215 Inf 0.709 1.0000
StepF17 - StepF18 -0.010082 0.0215 Inf -0.468 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.034792 0.0215 Inf -1.618 0.9797
StepF1 - StepF3 -0.041594 0.0215 Inf -1.934 0.8992
StepF1 - StepF4 -0.010482 0.0215 Inf -0.487 1.0000
StepF1 - StepF5 -0.006110 0.0215 Inf -0.284 1.0000
StepF1 - StepF6 -0.021781 0.0215 Inf -1.013 0.9999
StepF1 - StepF7 -0.021472 0.0215 Inf -0.998 0.9999
StepF1 - StepF8 0.009204 0.0215 Inf 0.428 1.0000
StepF1 - StepF9 0.005469 0.0215 Inf 0.254 1.0000
StepF1 - StepF10 0.005721 0.0215 Inf 0.266 1.0000
StepF1 - StepF11 0.026359 0.0215 Inf 1.226 0.9992
StepF1 - StepF12 0.073382 0.0215 Inf 3.412 0.0638
StepF1 - StepF13 0.076545 0.0215 Inf 3.559 0.0397
StepF1 - StepF14 0.022458 0.0215 Inf 1.044 0.9999
StepF1 - StepF15 0.043951 0.0215 Inf 2.043 0.8480
StepF1 - StepF16 0.036644 0.0215 Inf 1.704 0.9663
StepF1 - StepF17 0.061988 0.0215 Inf 2.882 0.2625
StepF1 - StepF18 0.051906 0.0215 Inf 2.411 0.5991
StepF2 - StepF3 -0.006801 0.0215 Inf -0.316 1.0000
StepF2 - StepF4 0.024311 0.0215 Inf 1.130 0.9997
StepF2 - StepF5 0.028682 0.0215 Inf 1.334 0.9976
StepF2 - StepF6 0.013011 0.0215 Inf 0.605 1.0000
StepF2 - StepF7 0.013321 0.0215 Inf 0.619 1.0000
StepF2 - StepF8 0.043997 0.0215 Inf 2.046 0.8469
StepF2 - StepF9 0.040262 0.0215 Inf 1.872 0.9224
StepF2 - StepF10 0.040514 0.0215 Inf 1.884 0.9183
StepF2 - StepF11 0.061151 0.0215 Inf 2.843 0.2857
StepF2 - StepF12 0.108175 0.0215 Inf 5.029 0.0001
StepF2 - StepF13 0.111338 0.0215 Inf 5.176 <.0001
StepF2 - StepF14 0.057250 0.0215 Inf 2.662 0.4076
StepF2 - StepF15 0.078744 0.0215 Inf 3.661 0.0280
StepF2 - StepF16 0.071437 0.0215 Inf 3.321 0.0840
StepF2 - StepF17 0.096781 0.0215 Inf 4.500 0.0009
StepF2 - StepF18 0.086699 0.0215 Inf 4.028 0.0071
StepF3 - StepF4 0.031112 0.0215 Inf 1.446 0.9938
StepF3 - StepF5 0.035483 0.0215 Inf 1.650 0.9753
StepF3 - StepF6 0.019812 0.0215 Inf 0.921 1.0000
StepF3 - StepF7 0.020122 0.0215 Inf 0.936 1.0000
StepF3 - StepF8 0.050798 0.0215 Inf 2.362 0.6373
StepF3 - StepF9 0.047063 0.0215 Inf 2.188 0.7619
StepF3 - StepF10 0.047315 0.0215 Inf 2.200 0.7541
StepF3 - StepF11 0.067953 0.0215 Inf 3.159 0.1329
StepF3 - StepF12 0.114976 0.0215 Inf 5.346 <.0001
StepF3 - StepF13 0.118139 0.0215 Inf 5.493 <.0001
StepF3 - StepF14 0.064052 0.0215 Inf 2.978 0.2107
StepF3 - StepF15 0.085545 0.0215 Inf 3.977 0.0087
StepF3 - StepF16 0.078238 0.0215 Inf 3.638 0.0304
StepF3 - StepF17 0.103582 0.0215 Inf 4.816 0.0002
StepF3 - StepF18 0.093500 0.0215 Inf 4.344 0.0019
StepF4 - StepF5 0.004372 0.0215 Inf 0.203 1.0000
StepF4 - StepF6 -0.011299 0.0215 Inf -0.525 1.0000
StepF4 - StepF7 -0.010990 0.0215 Inf -0.511 1.0000
StepF4 - StepF8 0.019686 0.0215 Inf 0.915 1.0000
StepF4 - StepF9 0.015951 0.0215 Inf 0.742 1.0000
StepF4 - StepF10 0.016203 0.0215 Inf 0.753 1.0000
StepF4 - StepF11 0.036841 0.0215 Inf 1.713 0.9646
StepF4 - StepF12 0.083864 0.0215 Inf 3.899 0.0118
StepF4 - StepF13 0.087027 0.0215 Inf 4.046 0.0066
StepF4 - StepF14 0.032940 0.0215 Inf 1.531 0.9885
StepF4 - StepF15 0.054433 0.0215 Inf 2.531 0.5064
StepF4 - StepF16 0.047126 0.0215 Inf 2.191 0.7600
StepF4 - StepF17 0.072470 0.0215 Inf 3.369 0.0727
StepF4 - StepF18 0.062388 0.0215 Inf 2.898 0.2531
StepF5 - StepF6 -0.015671 0.0215 Inf -0.729 1.0000
StepF5 - StepF7 -0.015361 0.0215 Inf -0.714 1.0000
StepF5 - StepF8 0.015315 0.0215 Inf 0.712 1.0000
StepF5 - StepF9 0.011580 0.0215 Inf 0.538 1.0000
StepF5 - StepF10 0.011832 0.0215 Inf 0.550 1.0000
StepF5 - StepF11 0.032469 0.0215 Inf 1.510 0.9901
StepF5 - StepF12 0.079493 0.0215 Inf 3.696 0.0248
StepF5 - StepF13 0.082656 0.0215 Inf 3.843 0.0145
StepF5 - StepF14 0.028568 0.0215 Inf 1.328 0.9977
StepF5 - StepF15 0.050062 0.0215 Inf 2.328 0.6631
StepF5 - StepF16 0.042755 0.0215 Inf 1.988 0.8756
StepF5 - StepF17 0.068099 0.0215 Inf 3.166 0.1305
StepF5 - StepF18 0.058016 0.0215 Inf 2.695 0.3834
StepF6 - StepF7 0.000310 0.0215 Inf 0.014 1.0000
StepF6 - StepF8 0.030985 0.0215 Inf 1.441 0.9941
StepF6 - StepF9 0.027250 0.0215 Inf 1.267 0.9987
StepF6 - StepF10 0.027502 0.0215 Inf 1.279 0.9986
StepF6 - StepF11 0.048140 0.0215 Inf 2.238 0.7279
StepF6 - StepF12 0.095163 0.0215 Inf 4.424 0.0013
StepF6 - StepF13 0.098326 0.0215 Inf 4.572 0.0007
StepF6 - StepF14 0.044239 0.0215 Inf 2.057 0.8409
StepF6 - StepF15 0.065732 0.0215 Inf 3.056 0.1740
StepF6 - StepF16 0.058426 0.0215 Inf 2.716 0.3687
StepF6 - StepF17 0.083770 0.0215 Inf 3.895 0.0120
StepF6 - StepF18 0.073687 0.0215 Inf 3.423 0.0615
StepF7 - StepF8 0.030676 0.0215 Inf 1.426 0.9947
StepF7 - StepF9 0.026941 0.0215 Inf 1.253 0.9989
StepF7 - StepF10 0.027193 0.0215 Inf 1.264 0.9988
StepF7 - StepF11 0.047831 0.0215 Inf 2.224 0.7379
StepF7 - StepF12 0.094854 0.0215 Inf 4.410 0.0014
StepF7 - StepF13 0.098017 0.0215 Inf 4.557 0.0007
StepF7 - StepF14 0.043930 0.0215 Inf 2.042 0.8486
StepF7 - StepF15 0.065423 0.0215 Inf 3.042 0.1804
StepF7 - StepF16 0.058116 0.0215 Inf 2.702 0.3787
StepF7 - StepF17 0.083460 0.0215 Inf 3.880 0.0126
StepF7 - StepF18 0.073378 0.0215 Inf 3.409 0.0643
StepF8 - StepF9 -0.003735 0.0215 Inf -0.174 1.0000
StepF8 - StepF10 -0.003483 0.0215 Inf -0.162 1.0000
StepF8 - StepF11 0.017155 0.0215 Inf 0.798 1.0000
StepF8 - StepF12 0.064178 0.0215 Inf 2.984 0.2078
StepF8 - StepF13 0.067341 0.0215 Inf 3.131 0.1434
StepF8 - StepF14 0.013254 0.0215 Inf 0.616 1.0000
StepF8 - StepF15 0.034747 0.0215 Inf 1.615 0.9799
StepF8 - StepF16 0.027440 0.0215 Inf 1.276 0.9986
StepF8 - StepF17 0.052784 0.0215 Inf 2.454 0.5660
StepF8 - StepF18 0.042702 0.0215 Inf 1.984 0.8774
StepF9 - StepF10 0.000252 0.0215 Inf 0.012 1.0000
StepF9 - StepF11 0.020890 0.0215 Inf 0.971 1.0000
StepF9 - StepF12 0.067913 0.0215 Inf 3.157 0.1336
StepF9 - StepF13 0.071076 0.0215 Inf 3.305 0.0882
StepF9 - StepF14 0.016989 0.0215 Inf 0.790 1.0000
StepF9 - StepF15 0.038482 0.0215 Inf 1.789 0.9473
StepF9 - StepF16 0.031175 0.0215 Inf 1.449 0.9937
StepF9 - StepF17 0.056519 0.0215 Inf 2.628 0.4326
StepF9 - StepF18 0.046437 0.0215 Inf 2.157 0.7818
StepF10 - StepF11 0.020638 0.0215 Inf 0.960 1.0000
StepF10 - StepF12 0.067661 0.0215 Inf 3.146 0.1378
StepF10 - StepF13 0.070824 0.0215 Inf 3.293 0.0913
StepF10 - StepF14 0.016737 0.0215 Inf 0.778 1.0000
StepF10 - StepF15 0.038230 0.0215 Inf 1.777 0.9503
StepF10 - StepF16 0.030923 0.0215 Inf 1.438 0.9942
StepF10 - StepF17 0.056267 0.0215 Inf 2.616 0.4413
StepF10 - StepF18 0.046185 0.0215 Inf 2.146 0.7892
StepF11 - StepF12 0.047023 0.0215 Inf 2.186 0.7632
StepF11 - StepF13 0.050186 0.0215 Inf 2.333 0.6588
StepF11 - StepF14 -0.003901 0.0215 Inf -0.181 1.0000
StepF11 - StepF15 0.017592 0.0215 Inf 0.818 1.0000
StepF11 - StepF16 0.010285 0.0215 Inf 0.478 1.0000
StepF11 - StepF17 0.035629 0.0215 Inf 1.657 0.9743
StepF11 - StepF18 0.025547 0.0215 Inf 1.187 0.9994
StepF12 - StepF13 0.003163 0.0215 Inf 0.147 1.0000
StepF12 - StepF14 -0.050924 0.0215 Inf -2.368 0.6328
StepF12 - StepF15 -0.029431 0.0215 Inf -1.368 0.9968
StepF12 - StepF16 -0.036738 0.0215 Inf -1.708 0.9655
StepF12 - StepF17 -0.011394 0.0215 Inf -0.530 1.0000
StepF12 - StepF18 -0.021476 0.0215 Inf -0.998 0.9999
StepF13 - StepF14 -0.054087 0.0215 Inf -2.515 0.5188
StepF13 - StepF15 -0.032594 0.0215 Inf -1.515 0.9897
StepF13 - StepF16 -0.039901 0.0215 Inf -1.855 0.9280
StepF13 - StepF17 -0.014557 0.0215 Inf -0.677 1.0000
StepF13 - StepF18 -0.024639 0.0215 Inf -1.145 0.9997
StepF14 - StepF15 0.021493 0.0215 Inf 0.999 0.9999
StepF14 - StepF16 0.014186 0.0215 Inf 0.660 1.0000
StepF14 - StepF17 0.039530 0.0215 Inf 1.838 0.9334
StepF14 - StepF18 0.029448 0.0215 Inf 1.368 0.9968
StepF15 - StepF16 -0.007307 0.0215 Inf -0.340 1.0000
StepF15 - StepF17 0.018037 0.0215 Inf 0.839 1.0000
StepF15 - StepF18 0.007955 0.0215 Inf 0.370 1.0000
StepF16 - StepF17 0.025344 0.0215 Inf 1.178 0.9995
StepF16 - StepF18 0.015262 0.0215 Inf 0.709 1.0000
StepF17 - StepF18 -0.010082 0.0215 Inf -0.468 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.034792 0.0215 Inf 1.618 1.0000
StepF3 - StepF2 0.006801 0.0215 Inf 0.316 1.0000
StepF4 - StepF3 -0.031112 0.0215 Inf -1.446 1.0000
StepF5 - StepF4 -0.004372 0.0215 Inf -0.203 1.0000
StepF6 - StepF5 0.015671 0.0215 Inf 0.729 1.0000
StepF7 - StepF6 -0.000310 0.0215 Inf -0.014 1.0000
StepF8 - StepF7 -0.030676 0.0215 Inf -1.426 1.0000
StepF9 - StepF8 0.003735 0.0215 Inf 0.174 1.0000
StepF10 - StepF9 -0.000252 0.0215 Inf -0.012 1.0000
StepF11 - StepF10 -0.020638 0.0215 Inf -0.960 1.0000
StepF12 - StepF11 -0.047023 0.0215 Inf -2.186 0.4607
StepF13 - StepF12 -0.003163 0.0215 Inf -0.147 1.0000
StepF14 - StepF13 0.054087 0.0215 Inf 2.515 0.2025
StepF15 - StepF14 -0.021493 0.0215 Inf -0.999 1.0000
StepF16 - StepF15 0.007307 0.0215 Inf 0.340 1.0000
StepF17 - StepF16 -0.025344 0.0215 Inf -1.178 1.0000
StepF18 - StepF17 0.010082 0.0215 Inf 0.468 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.034792 0.0215 Inf 1.618 1.0000
StepF3 - StepF2 0.006801 0.0215 Inf 0.316 1.0000
StepF4 - StepF3 -0.031112 0.0215 Inf -1.446 1.0000
StepF5 - StepF4 -0.004372 0.0215 Inf -0.203 1.0000
StepF6 - StepF5 0.015671 0.0215 Inf 0.729 1.0000
StepF7 - StepF6 -0.000310 0.0215 Inf -0.014 1.0000
StepF8 - StepF7 -0.030676 0.0215 Inf -1.426 1.0000
StepF9 - StepF8 0.003735 0.0215 Inf 0.174 1.0000
StepF10 - StepF9 -0.000252 0.0215 Inf -0.012 1.0000
StepF11 - StepF10 -0.020638 0.0215 Inf -0.960 1.0000
StepF12 - StepF11 -0.047023 0.0215 Inf -2.186 0.4607
StepF13 - StepF12 -0.003163 0.0215 Inf -0.147 1.0000
StepF14 - StepF13 0.054087 0.0215 Inf 2.515 0.2025
StepF15 - StepF14 -0.021493 0.0215 Inf -0.999 1.0000
StepF16 - StepF15 0.007307 0.0215 Inf 0.340 1.0000
StepF17 - StepF16 -0.025344 0.0215 Inf -1.178 1.0000
StepF18 - StepF17 0.010082 0.0215 Inf 0.468 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests
==============================
TRAINING | Block 3 (18 steps) | Axis Y
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 165.1538 17 <2e-16 ***
Accuracy 0.5735 1 0.4489
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.591 0.0544 Inf 0.485 0.698
2 0.679 0.0544 Inf 0.573 0.786
3 0.698 0.0544 Inf 0.591 0.805
4 0.594 0.0544 Inf 0.487 0.701
5 0.603 0.0544 Inf 0.496 0.709
6 0.636 0.0544 Inf 0.530 0.743
7 0.660 0.0544 Inf 0.553 0.767
8 0.577 0.0544 Inf 0.470 0.684
9 0.602 0.0544 Inf 0.495 0.709
10 0.661 0.0544 Inf 0.554 0.768
11 0.653 0.0544 Inf 0.547 0.760
12 0.536 0.0544 Inf 0.429 0.642
13 0.550 0.0544 Inf 0.443 0.657
14 0.563 0.0544 Inf 0.456 0.669
15 0.600 0.0544 Inf 0.493 0.707
16 0.553 0.0544 Inf 0.447 0.660
17 0.569 0.0544 Inf 0.462 0.676
18 0.535 0.0544 Inf 0.428 0.642
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.573 0.0541 Inf 0.467 0.679
2 0.661 0.0541 Inf 0.555 0.767
3 0.680 0.0541 Inf 0.574 0.786
4 0.576 0.0541 Inf 0.469 0.682
5 0.585 0.0541 Inf 0.478 0.691
6 0.618 0.0541 Inf 0.512 0.724
7 0.642 0.0541 Inf 0.536 0.748
8 0.559 0.0541 Inf 0.453 0.665
9 0.584 0.0541 Inf 0.478 0.690
10 0.643 0.0541 Inf 0.537 0.749
11 0.635 0.0541 Inf 0.529 0.741
12 0.518 0.0541 Inf 0.411 0.624
13 0.532 0.0541 Inf 0.426 0.638
14 0.545 0.0541 Inf 0.438 0.651
15 0.582 0.0541 Inf 0.475 0.688
16 0.535 0.0541 Inf 0.429 0.641
17 0.551 0.0541 Inf 0.445 0.657
18 0.517 0.0541 Inf 0.411 0.623
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.087967 0.0229 Inf -3.846 0.0144
StepF1 - StepF3 -0.106572 0.0229 Inf -4.659 0.0005
StepF1 - StepF4 -0.002399 0.0229 Inf -0.105 1.0000
StepF1 - StepF5 -0.011316 0.0229 Inf -0.495 1.0000
StepF1 - StepF6 -0.044917 0.0229 Inf -1.964 0.8865
StepF1 - StepF7 -0.068425 0.0229 Inf -2.991 0.2041
StepF1 - StepF8 0.014541 0.0229 Inf 0.636 1.0000
StepF1 - StepF9 -0.010449 0.0229 Inf -0.457 1.0000
StepF1 - StepF10 -0.069550 0.0229 Inf -3.040 0.1810
StepF1 - StepF11 -0.061842 0.0229 Inf -2.703 0.3777
StepF1 - StepF12 0.055676 0.0229 Inf 2.434 0.5817
StepF1 - StepF13 0.041373 0.0229 Inf 1.809 0.9420
StepF1 - StepF14 0.028642 0.0229 Inf 1.252 0.9989
StepF1 - StepF15 -0.008389 0.0229 Inf -0.367 1.0000
StepF1 - StepF16 0.038291 0.0229 Inf 1.674 0.9715
StepF1 - StepF17 0.022498 0.0229 Inf 0.984 1.0000
StepF1 - StepF18 0.056477 0.0229 Inf 2.467 0.5558
StepF2 - StepF3 -0.018605 0.0229 Inf -0.813 1.0000
StepF2 - StepF4 0.085568 0.0229 Inf 3.741 0.0212
StepF2 - StepF5 0.076651 0.0229 Inf 3.351 0.0769
StepF2 - StepF6 0.043050 0.0229 Inf 1.882 0.9189
StepF2 - StepF7 0.019542 0.0229 Inf 0.854 1.0000
StepF2 - StepF8 0.102508 0.0229 Inf 4.481 0.0010
StepF2 - StepF9 0.077518 0.0229 Inf 3.389 0.0685
StepF2 - StepF10 0.018417 0.0229 Inf 0.805 1.0000
StepF2 - StepF11 0.026125 0.0229 Inf 1.142 0.9997
StepF2 - StepF12 0.143643 0.0229 Inf 6.279 <.0001
StepF2 - StepF13 0.129340 0.0229 Inf 5.654 <.0001
StepF2 - StepF14 0.116609 0.0229 Inf 5.098 0.0001
StepF2 - StepF15 0.079578 0.0229 Inf 3.479 0.0516
StepF2 - StepF16 0.126258 0.0229 Inf 5.519 <.0001
StepF2 - StepF17 0.110465 0.0229 Inf 4.829 0.0002
StepF2 - StepF18 0.144445 0.0229 Inf 6.310 <.0001
StepF3 - StepF4 0.104174 0.0229 Inf 4.554 0.0007
StepF3 - StepF5 0.095257 0.0229 Inf 4.164 0.0041
StepF3 - StepF6 0.061655 0.0229 Inf 2.695 0.3835
StepF3 - StepF7 0.038148 0.0229 Inf 1.668 0.9725
StepF3 - StepF8 0.121113 0.0229 Inf 5.295 <.0001
StepF3 - StepF9 0.096123 0.0229 Inf 4.202 0.0035
StepF3 - StepF10 0.037023 0.0229 Inf 1.618 0.9796
StepF3 - StepF11 0.044730 0.0229 Inf 1.955 0.8901
StepF3 - StepF12 0.162249 0.0229 Inf 7.093 <.0001
StepF3 - StepF13 0.147946 0.0229 Inf 6.468 <.0001
StepF3 - StepF14 0.135214 0.0229 Inf 5.911 <.0001
StepF3 - StepF15 0.098184 0.0229 Inf 4.292 0.0024
StepF3 - StepF16 0.144863 0.0229 Inf 6.333 <.0001
StepF3 - StepF17 0.129071 0.0229 Inf 5.642 <.0001
StepF3 - StepF18 0.163050 0.0229 Inf 7.123 <.0001
StepF4 - StepF5 -0.008917 0.0229 Inf -0.390 1.0000
StepF4 - StepF6 -0.042519 0.0229 Inf -1.859 0.9268
StepF4 - StepF7 -0.066026 0.0229 Inf -2.886 0.2600
StepF4 - StepF8 0.016939 0.0229 Inf 0.741 1.0000
StepF4 - StepF9 -0.008050 0.0229 Inf -0.352 1.0000
StepF4 - StepF10 -0.067151 0.0229 Inf -2.936 0.2327
StepF4 - StepF11 -0.059443 0.0229 Inf -2.599 0.4544
StepF4 - StepF12 0.058075 0.0229 Inf 2.539 0.5002
StepF4 - StepF13 0.043772 0.0229 Inf 1.914 0.9072
StepF4 - StepF14 0.031041 0.0229 Inf 1.357 0.9971
StepF4 - StepF15 -0.005990 0.0229 Inf -0.262 1.0000
StepF4 - StepF16 0.040689 0.0229 Inf 1.779 0.9499
StepF4 - StepF17 0.024897 0.0229 Inf 1.088 0.9998
StepF4 - StepF18 0.058876 0.0229 Inf 2.572 0.4747
StepF5 - StepF6 -0.033602 0.0229 Inf -1.469 0.9927
StepF5 - StepF7 -0.057109 0.0229 Inf -2.497 0.5329
StepF5 - StepF8 0.025857 0.0229 Inf 1.130 0.9997
StepF5 - StepF9 0.000867 0.0229 Inf 0.038 1.0000
StepF5 - StepF10 -0.058234 0.0229 Inf -2.546 0.4948
StepF5 - StepF11 -0.050526 0.0229 Inf -2.209 0.7481
StepF5 - StepF12 0.066992 0.0229 Inf 2.929 0.2365
StepF5 - StepF13 0.052689 0.0229 Inf 2.303 0.6811
StepF5 - StepF14 0.039958 0.0229 Inf 1.747 0.9575
StepF5 - StepF15 0.002927 0.0229 Inf 0.128 1.0000
StepF5 - StepF16 0.049607 0.0229 Inf 2.169 0.7747
StepF5 - StepF17 0.033814 0.0229 Inf 1.478 0.9922
StepF5 - StepF18 0.067793 0.0229 Inf 2.961 0.2191
StepF6 - StepF7 -0.023507 0.0229 Inf -1.028 0.9999
StepF6 - StepF8 0.059458 0.0229 Inf 2.599 0.4540
StepF6 - StepF9 0.034468 0.0229 Inf 1.507 0.9903
StepF6 - StepF10 -0.024632 0.0229 Inf -1.077 0.9998
StepF6 - StepF11 -0.016925 0.0229 Inf -0.740 1.0000
StepF6 - StepF12 0.100594 0.0229 Inf 4.398 0.0015
StepF6 - StepF13 0.086291 0.0229 Inf 3.772 0.0189
StepF6 - StepF14 0.073559 0.0229 Inf 3.216 0.1138
StepF6 - StepF15 0.036529 0.0229 Inf 1.597 0.9822
StepF6 - StepF16 0.083208 0.0229 Inf 3.638 0.0304
StepF6 - StepF17 0.067416 0.0229 Inf 2.947 0.2266
StepF6 - StepF18 0.101395 0.0229 Inf 4.429 0.0013
StepF7 - StepF8 0.082965 0.0229 Inf 3.627 0.0316
StepF7 - StepF9 0.057976 0.0229 Inf 2.534 0.5035
StepF7 - StepF10 -0.001125 0.0229 Inf -0.049 1.0000
StepF7 - StepF11 0.006583 0.0229 Inf 0.288 1.0000
StepF7 - StepF12 0.124101 0.0229 Inf 5.425 <.0001
StepF7 - StepF13 0.109798 0.0229 Inf 4.800 0.0002
StepF7 - StepF14 0.097067 0.0229 Inf 4.243 0.0029
StepF7 - StepF15 0.060036 0.0229 Inf 2.625 0.4350
StepF7 - StepF16 0.106715 0.0229 Inf 4.665 0.0004
StepF7 - StepF17 0.090923 0.0229 Inf 3.975 0.0088
StepF7 - StepF18 0.124902 0.0229 Inf 5.456 <.0001
StepF8 - StepF9 -0.024990 0.0229 Inf -1.092 0.9998
StepF8 - StepF10 -0.084090 0.0229 Inf -3.676 0.0266
StepF8 - StepF11 -0.076383 0.0229 Inf -3.339 0.0796
StepF8 - StepF12 0.041135 0.0229 Inf 1.798 0.9448
StepF8 - StepF13 0.026833 0.0229 Inf 1.173 0.9995
StepF8 - StepF14 0.014101 0.0229 Inf 0.616 1.0000
StepF8 - StepF15 -0.022930 0.0229 Inf -1.002 0.9999
StepF8 - StepF16 0.023750 0.0229 Inf 1.038 0.9999
StepF8 - StepF17 0.007958 0.0229 Inf 0.348 1.0000
StepF8 - StepF18 0.041937 0.0229 Inf 1.832 0.9352
StepF9 - StepF10 -0.059101 0.0229 Inf -2.584 0.4658
StepF9 - StepF11 -0.051393 0.0229 Inf -2.247 0.7219
StepF9 - StepF12 0.066125 0.0229 Inf 2.891 0.2576
StepF9 - StepF13 0.051822 0.0229 Inf 2.265 0.7086
StepF9 - StepF14 0.039091 0.0229 Inf 1.709 0.9653
StepF9 - StepF15 0.002060 0.0229 Inf 0.090 1.0000
StepF9 - StepF16 0.048740 0.0229 Inf 2.131 0.7985
StepF9 - StepF17 0.032947 0.0229 Inf 1.440 0.9941
StepF9 - StepF18 0.066926 0.0229 Inf 2.924 0.2392
StepF10 - StepF11 0.007708 0.0229 Inf 0.337 1.0000
StepF10 - StepF12 0.125226 0.0229 Inf 5.474 <.0001
StepF10 - StepF13 0.110923 0.0229 Inf 4.849 0.0002
StepF10 - StepF14 0.098192 0.0229 Inf 4.293 0.0024
StepF10 - StepF15 0.061161 0.0229 Inf 2.674 0.3990
StepF10 - StepF16 0.107841 0.0229 Inf 4.714 0.0003
StepF10 - StepF17 0.092048 0.0229 Inf 4.024 0.0072
StepF10 - StepF18 0.126027 0.0229 Inf 5.505 <.0001
StepF11 - StepF12 0.117518 0.0229 Inf 5.137 <.0001
StepF11 - StepF13 0.103215 0.0229 Inf 4.512 0.0009
StepF11 - StepF14 0.090484 0.0229 Inf 3.956 0.0095
StepF11 - StepF15 0.053453 0.0229 Inf 2.337 0.6562
StepF11 - StepF16 0.100133 0.0229 Inf 4.377 0.0016
StepF11 - StepF17 0.084340 0.0229 Inf 3.687 0.0256
StepF11 - StepF18 0.118319 0.0229 Inf 5.169 <.0001
StepF12 - StepF13 -0.014303 0.0229 Inf -0.625 1.0000
StepF12 - StepF14 -0.027034 0.0229 Inf -1.182 0.9995
StepF12 - StepF15 -0.064065 0.0229 Inf -2.801 0.3122
StepF12 - StepF16 -0.017385 0.0229 Inf -0.760 1.0000
StepF12 - StepF17 -0.033178 0.0229 Inf -1.450 0.9937
StepF12 - StepF18 0.000801 0.0229 Inf 0.035 1.0000
StepF13 - StepF14 -0.012731 0.0229 Inf -0.557 1.0000
StepF13 - StepF15 -0.049762 0.0229 Inf -2.175 0.7703
StepF13 - StepF16 -0.003083 0.0229 Inf -0.135 1.0000
StepF13 - StepF17 -0.018875 0.0229 Inf -0.825 1.0000
StepF13 - StepF18 0.015104 0.0229 Inf 0.660 1.0000
StepF14 - StepF15 -0.037031 0.0229 Inf -1.619 0.9795
StepF14 - StepF16 0.009649 0.0229 Inf 0.422 1.0000
StepF14 - StepF17 -0.006144 0.0229 Inf -0.269 1.0000
StepF14 - StepF18 0.027836 0.0229 Inf 1.216 0.9992
StepF15 - StepF16 0.046680 0.0229 Inf 2.041 0.8495
StepF15 - StepF17 0.030887 0.0229 Inf 1.350 0.9972
StepF15 - StepF18 0.064866 0.0229 Inf 2.834 0.2915
StepF16 - StepF17 -0.015792 0.0229 Inf -0.690 1.0000
StepF16 - StepF18 0.018187 0.0229 Inf 0.794 1.0000
StepF17 - StepF18 0.033979 0.0229 Inf 1.484 0.9918
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.087967 0.0229 Inf -3.846 0.0144
StepF1 - StepF3 -0.106572 0.0229 Inf -4.659 0.0005
StepF1 - StepF4 -0.002399 0.0229 Inf -0.105 1.0000
StepF1 - StepF5 -0.011316 0.0229 Inf -0.495 1.0000
StepF1 - StepF6 -0.044917 0.0229 Inf -1.964 0.8865
StepF1 - StepF7 -0.068425 0.0229 Inf -2.991 0.2041
StepF1 - StepF8 0.014541 0.0229 Inf 0.636 1.0000
StepF1 - StepF9 -0.010449 0.0229 Inf -0.457 1.0000
StepF1 - StepF10 -0.069550 0.0229 Inf -3.040 0.1810
StepF1 - StepF11 -0.061842 0.0229 Inf -2.703 0.3777
StepF1 - StepF12 0.055676 0.0229 Inf 2.434 0.5817
StepF1 - StepF13 0.041373 0.0229 Inf 1.809 0.9420
StepF1 - StepF14 0.028642 0.0229 Inf 1.252 0.9989
StepF1 - StepF15 -0.008389 0.0229 Inf -0.367 1.0000
StepF1 - StepF16 0.038291 0.0229 Inf 1.674 0.9715
StepF1 - StepF17 0.022498 0.0229 Inf 0.984 1.0000
StepF1 - StepF18 0.056477 0.0229 Inf 2.467 0.5558
StepF2 - StepF3 -0.018605 0.0229 Inf -0.813 1.0000
StepF2 - StepF4 0.085568 0.0229 Inf 3.741 0.0212
StepF2 - StepF5 0.076651 0.0229 Inf 3.351 0.0769
StepF2 - StepF6 0.043050 0.0229 Inf 1.882 0.9189
StepF2 - StepF7 0.019542 0.0229 Inf 0.854 1.0000
StepF2 - StepF8 0.102508 0.0229 Inf 4.481 0.0010
StepF2 - StepF9 0.077518 0.0229 Inf 3.389 0.0685
StepF2 - StepF10 0.018417 0.0229 Inf 0.805 1.0000
StepF2 - StepF11 0.026125 0.0229 Inf 1.142 0.9997
StepF2 - StepF12 0.143643 0.0229 Inf 6.279 <.0001
StepF2 - StepF13 0.129340 0.0229 Inf 5.654 <.0001
StepF2 - StepF14 0.116609 0.0229 Inf 5.098 0.0001
StepF2 - StepF15 0.079578 0.0229 Inf 3.479 0.0516
StepF2 - StepF16 0.126258 0.0229 Inf 5.519 <.0001
StepF2 - StepF17 0.110465 0.0229 Inf 4.829 0.0002
StepF2 - StepF18 0.144445 0.0229 Inf 6.310 <.0001
StepF3 - StepF4 0.104174 0.0229 Inf 4.554 0.0007
StepF3 - StepF5 0.095257 0.0229 Inf 4.164 0.0041
StepF3 - StepF6 0.061655 0.0229 Inf 2.695 0.3835
StepF3 - StepF7 0.038148 0.0229 Inf 1.668 0.9725
StepF3 - StepF8 0.121113 0.0229 Inf 5.295 <.0001
StepF3 - StepF9 0.096123 0.0229 Inf 4.202 0.0035
StepF3 - StepF10 0.037023 0.0229 Inf 1.618 0.9796
StepF3 - StepF11 0.044730 0.0229 Inf 1.955 0.8901
StepF3 - StepF12 0.162249 0.0229 Inf 7.093 <.0001
StepF3 - StepF13 0.147946 0.0229 Inf 6.468 <.0001
StepF3 - StepF14 0.135214 0.0229 Inf 5.911 <.0001
StepF3 - StepF15 0.098184 0.0229 Inf 4.292 0.0024
StepF3 - StepF16 0.144863 0.0229 Inf 6.333 <.0001
StepF3 - StepF17 0.129071 0.0229 Inf 5.642 <.0001
StepF3 - StepF18 0.163050 0.0229 Inf 7.123 <.0001
StepF4 - StepF5 -0.008917 0.0229 Inf -0.390 1.0000
StepF4 - StepF6 -0.042519 0.0229 Inf -1.859 0.9268
StepF4 - StepF7 -0.066026 0.0229 Inf -2.886 0.2600
StepF4 - StepF8 0.016939 0.0229 Inf 0.741 1.0000
StepF4 - StepF9 -0.008050 0.0229 Inf -0.352 1.0000
StepF4 - StepF10 -0.067151 0.0229 Inf -2.936 0.2327
StepF4 - StepF11 -0.059443 0.0229 Inf -2.599 0.4544
StepF4 - StepF12 0.058075 0.0229 Inf 2.539 0.5002
StepF4 - StepF13 0.043772 0.0229 Inf 1.914 0.9072
StepF4 - StepF14 0.031041 0.0229 Inf 1.357 0.9971
StepF4 - StepF15 -0.005990 0.0229 Inf -0.262 1.0000
StepF4 - StepF16 0.040689 0.0229 Inf 1.779 0.9499
StepF4 - StepF17 0.024897 0.0229 Inf 1.088 0.9998
StepF4 - StepF18 0.058876 0.0229 Inf 2.572 0.4747
StepF5 - StepF6 -0.033602 0.0229 Inf -1.469 0.9927
StepF5 - StepF7 -0.057109 0.0229 Inf -2.497 0.5329
StepF5 - StepF8 0.025857 0.0229 Inf 1.130 0.9997
StepF5 - StepF9 0.000867 0.0229 Inf 0.038 1.0000
StepF5 - StepF10 -0.058234 0.0229 Inf -2.546 0.4948
StepF5 - StepF11 -0.050526 0.0229 Inf -2.209 0.7481
StepF5 - StepF12 0.066992 0.0229 Inf 2.929 0.2365
StepF5 - StepF13 0.052689 0.0229 Inf 2.303 0.6811
StepF5 - StepF14 0.039958 0.0229 Inf 1.747 0.9575
StepF5 - StepF15 0.002927 0.0229 Inf 0.128 1.0000
StepF5 - StepF16 0.049607 0.0229 Inf 2.169 0.7747
StepF5 - StepF17 0.033814 0.0229 Inf 1.478 0.9922
StepF5 - StepF18 0.067793 0.0229 Inf 2.961 0.2191
StepF6 - StepF7 -0.023507 0.0229 Inf -1.028 0.9999
StepF6 - StepF8 0.059458 0.0229 Inf 2.599 0.4540
StepF6 - StepF9 0.034468 0.0229 Inf 1.507 0.9903
StepF6 - StepF10 -0.024632 0.0229 Inf -1.077 0.9998
StepF6 - StepF11 -0.016925 0.0229 Inf -0.740 1.0000
StepF6 - StepF12 0.100594 0.0229 Inf 4.398 0.0015
StepF6 - StepF13 0.086291 0.0229 Inf 3.772 0.0189
StepF6 - StepF14 0.073559 0.0229 Inf 3.216 0.1138
StepF6 - StepF15 0.036529 0.0229 Inf 1.597 0.9822
StepF6 - StepF16 0.083208 0.0229 Inf 3.638 0.0304
StepF6 - StepF17 0.067416 0.0229 Inf 2.947 0.2266
StepF6 - StepF18 0.101395 0.0229 Inf 4.429 0.0013
StepF7 - StepF8 0.082965 0.0229 Inf 3.627 0.0316
StepF7 - StepF9 0.057976 0.0229 Inf 2.534 0.5035
StepF7 - StepF10 -0.001125 0.0229 Inf -0.049 1.0000
StepF7 - StepF11 0.006583 0.0229 Inf 0.288 1.0000
StepF7 - StepF12 0.124101 0.0229 Inf 5.425 <.0001
StepF7 - StepF13 0.109798 0.0229 Inf 4.800 0.0002
StepF7 - StepF14 0.097067 0.0229 Inf 4.243 0.0029
StepF7 - StepF15 0.060036 0.0229 Inf 2.625 0.4350
StepF7 - StepF16 0.106715 0.0229 Inf 4.665 0.0004
StepF7 - StepF17 0.090923 0.0229 Inf 3.975 0.0088
StepF7 - StepF18 0.124902 0.0229 Inf 5.456 <.0001
StepF8 - StepF9 -0.024990 0.0229 Inf -1.092 0.9998
StepF8 - StepF10 -0.084090 0.0229 Inf -3.676 0.0266
StepF8 - StepF11 -0.076383 0.0229 Inf -3.339 0.0796
StepF8 - StepF12 0.041135 0.0229 Inf 1.798 0.9448
StepF8 - StepF13 0.026833 0.0229 Inf 1.173 0.9995
StepF8 - StepF14 0.014101 0.0229 Inf 0.616 1.0000
StepF8 - StepF15 -0.022930 0.0229 Inf -1.002 0.9999
StepF8 - StepF16 0.023750 0.0229 Inf 1.038 0.9999
StepF8 - StepF17 0.007958 0.0229 Inf 0.348 1.0000
StepF8 - StepF18 0.041937 0.0229 Inf 1.832 0.9352
StepF9 - StepF10 -0.059101 0.0229 Inf -2.584 0.4658
StepF9 - StepF11 -0.051393 0.0229 Inf -2.247 0.7219
StepF9 - StepF12 0.066125 0.0229 Inf 2.891 0.2576
StepF9 - StepF13 0.051822 0.0229 Inf 2.265 0.7086
StepF9 - StepF14 0.039091 0.0229 Inf 1.709 0.9653
StepF9 - StepF15 0.002060 0.0229 Inf 0.090 1.0000
StepF9 - StepF16 0.048740 0.0229 Inf 2.131 0.7985
StepF9 - StepF17 0.032947 0.0229 Inf 1.440 0.9941
StepF9 - StepF18 0.066926 0.0229 Inf 2.924 0.2392
StepF10 - StepF11 0.007708 0.0229 Inf 0.337 1.0000
StepF10 - StepF12 0.125226 0.0229 Inf 5.474 <.0001
StepF10 - StepF13 0.110923 0.0229 Inf 4.849 0.0002
StepF10 - StepF14 0.098192 0.0229 Inf 4.293 0.0024
StepF10 - StepF15 0.061161 0.0229 Inf 2.674 0.3990
StepF10 - StepF16 0.107841 0.0229 Inf 4.714 0.0003
StepF10 - StepF17 0.092048 0.0229 Inf 4.024 0.0072
StepF10 - StepF18 0.126027 0.0229 Inf 5.505 <.0001
StepF11 - StepF12 0.117518 0.0229 Inf 5.137 <.0001
StepF11 - StepF13 0.103215 0.0229 Inf 4.512 0.0009
StepF11 - StepF14 0.090484 0.0229 Inf 3.956 0.0095
StepF11 - StepF15 0.053453 0.0229 Inf 2.337 0.6562
StepF11 - StepF16 0.100133 0.0229 Inf 4.377 0.0016
StepF11 - StepF17 0.084340 0.0229 Inf 3.687 0.0256
StepF11 - StepF18 0.118319 0.0229 Inf 5.169 <.0001
StepF12 - StepF13 -0.014303 0.0229 Inf -0.625 1.0000
StepF12 - StepF14 -0.027034 0.0229 Inf -1.182 0.9995
StepF12 - StepF15 -0.064065 0.0229 Inf -2.801 0.3122
StepF12 - StepF16 -0.017385 0.0229 Inf -0.760 1.0000
StepF12 - StepF17 -0.033178 0.0229 Inf -1.450 0.9937
StepF12 - StepF18 0.000801 0.0229 Inf 0.035 1.0000
StepF13 - StepF14 -0.012731 0.0229 Inf -0.557 1.0000
StepF13 - StepF15 -0.049762 0.0229 Inf -2.175 0.7703
StepF13 - StepF16 -0.003083 0.0229 Inf -0.135 1.0000
StepF13 - StepF17 -0.018875 0.0229 Inf -0.825 1.0000
StepF13 - StepF18 0.015104 0.0229 Inf 0.660 1.0000
StepF14 - StepF15 -0.037031 0.0229 Inf -1.619 0.9795
StepF14 - StepF16 0.009649 0.0229 Inf 0.422 1.0000
StepF14 - StepF17 -0.006144 0.0229 Inf -0.269 1.0000
StepF14 - StepF18 0.027836 0.0229 Inf 1.216 0.9992
StepF15 - StepF16 0.046680 0.0229 Inf 2.041 0.8495
StepF15 - StepF17 0.030887 0.0229 Inf 1.350 0.9972
StepF15 - StepF18 0.064866 0.0229 Inf 2.834 0.2915
StepF16 - StepF17 -0.015792 0.0229 Inf -0.690 1.0000
StepF16 - StepF18 0.018187 0.0229 Inf 0.794 1.0000
StepF17 - StepF18 0.033979 0.0229 Inf 1.484 0.9918
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.08797 0.0229 Inf 3.846 0.0018
StepF3 - StepF2 0.01861 0.0229 Inf 0.813 1.0000
StepF4 - StepF3 -0.10417 0.0229 Inf -4.554 0.0001
StepF5 - StepF4 0.00892 0.0229 Inf 0.390 1.0000
StepF6 - StepF5 0.03360 0.0229 Inf 1.469 1.0000
StepF7 - StepF6 0.02351 0.0229 Inf 1.028 1.0000
StepF8 - StepF7 -0.08297 0.0229 Inf -3.627 0.0040
StepF9 - StepF8 0.02499 0.0229 Inf 1.092 1.0000
StepF10 - StepF9 0.05910 0.0229 Inf 2.584 0.1271
StepF11 - StepF10 -0.00771 0.0229 Inf -0.337 1.0000
StepF12 - StepF11 -0.11752 0.0229 Inf -5.137 <.0001
StepF13 - StepF12 0.01430 0.0229 Inf 0.625 1.0000
StepF14 - StepF13 0.01273 0.0229 Inf 0.557 1.0000
StepF15 - StepF14 0.03703 0.0229 Inf 1.619 1.0000
StepF16 - StepF15 -0.04668 0.0229 Inf -2.041 0.4954
StepF17 - StepF16 0.01579 0.0229 Inf 0.690 1.0000
StepF18 - StepF17 -0.03398 0.0229 Inf -1.484 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.08797 0.0229 Inf 3.846 0.0018
StepF3 - StepF2 0.01861 0.0229 Inf 0.813 1.0000
StepF4 - StepF3 -0.10417 0.0229 Inf -4.554 0.0001
StepF5 - StepF4 0.00892 0.0229 Inf 0.390 1.0000
StepF6 - StepF5 0.03360 0.0229 Inf 1.469 1.0000
StepF7 - StepF6 0.02351 0.0229 Inf 1.028 1.0000
StepF8 - StepF7 -0.08297 0.0229 Inf -3.627 0.0040
StepF9 - StepF8 0.02499 0.0229 Inf 1.092 1.0000
StepF10 - StepF9 0.05910 0.0229 Inf 2.584 0.1271
StepF11 - StepF10 -0.00771 0.0229 Inf -0.337 1.0000
StepF12 - StepF11 -0.11752 0.0229 Inf -5.137 <.0001
StepF13 - StepF12 0.01430 0.0229 Inf 0.625 1.0000
StepF14 - StepF13 0.01273 0.0229 Inf 0.557 1.0000
StepF15 - StepF14 0.03703 0.0229 Inf 1.619 1.0000
StepF16 - StepF15 -0.04668 0.0229 Inf -2.041 0.4954
StepF17 - StepF16 0.01579 0.0229 Inf 0.690 1.0000
StepF18 - StepF17 -0.03398 0.0229 Inf -1.484 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests
==============================
TRAINING | Block 3 (18 steps) | Axis Z
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 153.1381 17 <2e-16 ***
Accuracy 1.0975 1 0.2948
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.22 0.124 Inf 0.972 1.46
2 1.38 0.124 Inf 1.140 1.63
3 1.39 0.124 Inf 1.150 1.64
4 1.25 0.124 Inf 1.007 1.49
5 1.27 0.124 Inf 1.026 1.51
6 1.29 0.124 Inf 1.050 1.54
7 1.30 0.124 Inf 1.059 1.55
8 1.29 0.124 Inf 1.049 1.54
9 1.24 0.124 Inf 1.001 1.49
10 1.29 0.124 Inf 1.045 1.53
11 1.25 0.124 Inf 1.003 1.49
12 1.13 0.124 Inf 0.887 1.37
13 1.19 0.124 Inf 0.950 1.44
14 1.24 0.124 Inf 0.999 1.49
15 1.19 0.124 Inf 0.949 1.44
16 1.13 0.124 Inf 0.888 1.37
17 1.11 0.124 Inf 0.863 1.35
18 1.08 0.124 Inf 0.836 1.32
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.16 0.123 Inf 0.915 1.40
2 1.32 0.123 Inf 1.082 1.57
3 1.33 0.123 Inf 1.092 1.58
4 1.19 0.123 Inf 0.949 1.43
5 1.21 0.123 Inf 0.968 1.45
6 1.23 0.123 Inf 0.992 1.48
7 1.24 0.123 Inf 1.002 1.49
8 1.23 0.123 Inf 0.992 1.48
9 1.18 0.123 Inf 0.943 1.43
10 1.23 0.123 Inf 0.987 1.47
11 1.19 0.123 Inf 0.945 1.43
12 1.07 0.123 Inf 0.829 1.31
13 1.13 0.123 Inf 0.892 1.38
14 1.18 0.123 Inf 0.941 1.43
15 1.13 0.123 Inf 0.891 1.38
16 1.07 0.123 Inf 0.830 1.31
17 1.05 0.123 Inf 0.806 1.29
18 1.02 0.124 Inf 0.778 1.26
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.167063 0.0410 Inf -4.070 0.0060
StepF1 - StepF3 -0.177215 0.0410 Inf -4.318 0.0021
StepF1 - StepF4 -0.034722 0.0410 Inf -0.846 1.0000
StepF1 - StepF5 -0.053457 0.0410 Inf -1.302 0.9982
StepF1 - StepF6 -0.077621 0.0410 Inf -1.891 0.9156
StepF1 - StepF7 -0.086985 0.0410 Inf -2.119 0.8053
StepF1 - StepF8 -0.077022 0.0410 Inf -1.877 0.9207
StepF1 - StepF9 -0.028111 0.0410 Inf -0.685 1.0000
StepF1 - StepF10 -0.072498 0.0410 Inf -1.766 0.9530
StepF1 - StepF11 -0.030733 0.0410 Inf -0.749 1.0000
StepF1 - StepF12 0.085614 0.0410 Inf 2.086 0.8248
StepF1 - StepF13 0.022609 0.0410 Inf 0.551 1.0000
StepF1 - StepF14 -0.026212 0.0410 Inf -0.639 1.0000
StepF1 - StepF15 0.023505 0.0410 Inf 0.573 1.0000
StepF1 - StepF16 0.084456 0.0410 Inf 2.058 0.8404
StepF1 - StepF17 0.109189 0.0410 Inf 2.660 0.4086
StepF1 - StepF18 0.136468 0.0411 Inf 3.323 0.0837
StepF2 - StepF3 -0.010153 0.0410 Inf -0.247 1.0000
StepF2 - StepF4 0.132341 0.0410 Inf 3.224 0.1111
StepF2 - StepF5 0.113606 0.0410 Inf 2.768 0.3335
StepF2 - StepF6 0.089441 0.0410 Inf 2.179 0.7678
StepF2 - StepF7 0.080077 0.0410 Inf 1.951 0.8920
StepF2 - StepF8 0.090041 0.0410 Inf 2.194 0.7581
StepF2 - StepF9 0.138951 0.0410 Inf 3.386 0.0692
StepF2 - StepF10 0.094565 0.0410 Inf 2.304 0.6806
StepF2 - StepF11 0.136330 0.0410 Inf 3.322 0.0839
StepF2 - StepF12 0.252676 0.0410 Inf 6.156 <.0001
StepF2 - StepF13 0.189671 0.0410 Inf 4.621 0.0005
StepF2 - StepF14 0.140850 0.0410 Inf 3.432 0.0599
StepF2 - StepF15 0.190567 0.0410 Inf 4.643 0.0005
StepF2 - StepF16 0.251519 0.0410 Inf 6.128 <.0001
StepF2 - StepF17 0.276251 0.0410 Inf 6.731 <.0001
StepF2 - StepF18 0.303531 0.0411 Inf 7.390 <.0001
StepF3 - StepF4 0.142494 0.0410 Inf 3.472 0.0528
StepF3 - StepF5 0.123758 0.0410 Inf 3.015 0.1925
StepF3 - StepF6 0.099594 0.0410 Inf 2.427 0.5874
StepF3 - StepF7 0.090230 0.0410 Inf 2.198 0.7551
StepF3 - StepF8 0.100194 0.0410 Inf 2.441 0.5760
StepF3 - StepF9 0.149104 0.0410 Inf 3.633 0.0309
StepF3 - StepF10 0.104718 0.0410 Inf 2.551 0.4904
StepF3 - StepF11 0.146482 0.0410 Inf 3.569 0.0384
StepF3 - StepF12 0.262829 0.0410 Inf 6.404 <.0001
StepF3 - StepF13 0.199824 0.0410 Inf 4.869 0.0002
StepF3 - StepF14 0.151003 0.0410 Inf 3.679 0.0263
StepF3 - StepF15 0.200720 0.0410 Inf 4.891 0.0001
StepF3 - StepF16 0.261671 0.0410 Inf 6.376 <.0001
StepF3 - StepF17 0.286404 0.0410 Inf 6.978 <.0001
StepF3 - StepF18 0.313684 0.0411 Inf 7.637 <.0001
StepF4 - StepF5 -0.018735 0.0410 Inf -0.456 1.0000
StepF4 - StepF6 -0.042900 0.0410 Inf -1.045 0.9999
StepF4 - StepF7 -0.052264 0.0410 Inf -1.273 0.9986
StepF4 - StepF8 -0.042300 0.0410 Inf -1.031 0.9999
StepF4 - StepF9 0.006610 0.0410 Inf 0.161 1.0000
StepF4 - StepF10 -0.037776 0.0410 Inf -0.920 1.0000
StepF4 - StepF11 0.003989 0.0410 Inf 0.097 1.0000
StepF4 - StepF12 0.120335 0.0410 Inf 2.932 0.2346
StepF4 - StepF13 0.057330 0.0410 Inf 1.397 0.9959
StepF4 - StepF14 0.008510 0.0410 Inf 0.207 1.0000
StepF4 - StepF15 0.058226 0.0410 Inf 1.419 0.9951
StepF4 - StepF16 0.119178 0.0410 Inf 2.904 0.2502
StepF4 - StepF17 0.143910 0.0410 Inf 3.506 0.0472
StepF4 - StepF18 0.171190 0.0411 Inf 4.168 0.0040
StepF5 - StepF6 -0.024164 0.0410 Inf -0.589 1.0000
StepF5 - StepF7 -0.033528 0.0410 Inf -0.817 1.0000
StepF5 - StepF8 -0.023565 0.0410 Inf -0.574 1.0000
StepF5 - StepF9 0.025345 0.0410 Inf 0.618 1.0000
StepF5 - StepF10 -0.019041 0.0410 Inf -0.464 1.0000
StepF5 - StepF11 0.022724 0.0410 Inf 0.554 1.0000
StepF5 - StepF12 0.139071 0.0410 Inf 3.388 0.0686
StepF5 - StepF13 0.076066 0.0410 Inf 1.853 0.9285
StepF5 - StepF14 0.027245 0.0410 Inf 0.664 1.0000
StepF5 - StepF15 0.076962 0.0410 Inf 1.875 0.9212
StepF5 - StepF16 0.137913 0.0410 Inf 3.360 0.0747
StepF5 - StepF17 0.162646 0.0410 Inf 3.963 0.0092
StepF5 - StepF18 0.189925 0.0411 Inf 4.624 0.0005
StepF6 - StepF7 -0.009364 0.0410 Inf -0.228 1.0000
StepF6 - StepF8 0.000600 0.0410 Inf 0.015 1.0000
StepF6 - StepF9 0.049510 0.0410 Inf 1.206 0.9993
StepF6 - StepF10 0.005124 0.0410 Inf 0.125 1.0000
StepF6 - StepF11 0.046888 0.0410 Inf 1.142 0.9997
StepF6 - StepF12 0.163235 0.0410 Inf 3.977 0.0087
StepF6 - StepF13 0.100230 0.0410 Inf 2.442 0.5753
StepF6 - StepF14 0.051409 0.0410 Inf 1.253 0.9989
StepF6 - StepF15 0.101126 0.0410 Inf 2.464 0.5583
StepF6 - StepF16 0.162077 0.0410 Inf 3.949 0.0097
StepF6 - StepF17 0.186810 0.0410 Inf 4.552 0.0007
StepF6 - StepF18 0.214090 0.0411 Inf 5.212 <.0001
StepF7 - StepF8 0.009964 0.0410 Inf 0.243 1.0000
StepF7 - StepF9 0.058874 0.0410 Inf 1.434 0.9944
StepF7 - StepF10 0.014488 0.0410 Inf 0.353 1.0000
StepF7 - StepF11 0.056252 0.0410 Inf 1.371 0.9967
StepF7 - StepF12 0.172599 0.0410 Inf 4.205 0.0034
StepF7 - StepF13 0.109594 0.0410 Inf 2.670 0.4014
StepF7 - StepF14 0.060773 0.0410 Inf 1.481 0.9920
StepF7 - StepF15 0.110490 0.0410 Inf 2.692 0.3858
StepF7 - StepF16 0.171442 0.0410 Inf 4.177 0.0039
StepF7 - StepF17 0.196174 0.0410 Inf 4.780 0.0003
StepF7 - StepF18 0.223454 0.0411 Inf 5.440 <.0001
StepF8 - StepF9 0.048910 0.0410 Inf 1.192 0.9994
StepF8 - StepF10 0.004524 0.0410 Inf 0.110 1.0000
StepF8 - StepF11 0.046289 0.0410 Inf 1.128 0.9997
StepF8 - StepF12 0.162635 0.0410 Inf 3.963 0.0092
StepF8 - StepF13 0.099630 0.0410 Inf 2.427 0.5867
StepF8 - StepF14 0.050810 0.0410 Inf 1.238 0.9990
StepF8 - StepF15 0.100526 0.0410 Inf 2.449 0.5697
StepF8 - StepF16 0.161478 0.0410 Inf 3.934 0.0103
StepF8 - StepF17 0.186210 0.0410 Inf 4.537 0.0008
StepF8 - StepF18 0.213490 0.0411 Inf 5.198 <.0001
StepF9 - StepF10 -0.044386 0.0410 Inf -1.081 0.9998
StepF9 - StepF11 -0.002622 0.0410 Inf -0.064 1.0000
StepF9 - StepF12 0.113725 0.0410 Inf 2.771 0.3316
StepF9 - StepF13 0.050720 0.0410 Inf 1.236 0.9991
StepF9 - StepF14 0.001899 0.0410 Inf 0.046 1.0000
StepF9 - StepF15 0.051616 0.0410 Inf 1.258 0.9988
StepF9 - StepF16 0.112568 0.0410 Inf 2.743 0.3505
StepF9 - StepF17 0.137300 0.0410 Inf 3.345 0.0782
StepF9 - StepF18 0.164580 0.0411 Inf 4.007 0.0077
StepF10 - StepF11 0.041765 0.0410 Inf 1.018 0.9999
StepF10 - StepF12 0.158111 0.0410 Inf 3.852 0.0140
StepF10 - StepF13 0.095106 0.0410 Inf 2.317 0.6708
StepF10 - StepF14 0.046285 0.0410 Inf 1.128 0.9997
StepF10 - StepF15 0.096002 0.0410 Inf 2.339 0.6544
StepF10 - StepF16 0.156954 0.0410 Inf 3.824 0.0156
StepF10 - StepF17 0.181686 0.0410 Inf 4.427 0.0013
StepF10 - StepF18 0.208966 0.0411 Inf 5.088 0.0001
StepF11 - StepF12 0.116347 0.0410 Inf 2.835 0.2908
StepF11 - StepF13 0.053342 0.0410 Inf 1.300 0.9983
StepF11 - StepF14 0.004521 0.0410 Inf 0.110 1.0000
StepF11 - StepF15 0.054238 0.0410 Inf 1.321 0.9979
StepF11 - StepF16 0.115189 0.0410 Inf 2.807 0.3084
StepF11 - StepF17 0.139922 0.0410 Inf 3.409 0.0643
StepF11 - StepF18 0.167201 0.0411 Inf 4.071 0.0060
StepF12 - StepF13 -0.063005 0.0410 Inf -1.535 0.9882
StepF12 - StepF14 -0.111826 0.0410 Inf -2.725 0.3629
StepF12 - StepF15 -0.062109 0.0410 Inf -1.513 0.9899
StepF12 - StepF16 -0.001157 0.0410 Inf -0.028 1.0000
StepF12 - StepF17 0.023575 0.0410 Inf 0.574 1.0000
StepF12 - StepF18 0.050855 0.0411 Inf 1.238 0.9990
StepF13 - StepF14 -0.048821 0.0410 Inf -1.190 0.9994
StepF13 - StepF15 0.000896 0.0410 Inf 0.022 1.0000
StepF13 - StepF16 0.061847 0.0410 Inf 1.507 0.9903
StepF13 - StepF17 0.086580 0.0410 Inf 2.110 0.8112
StepF13 - StepF18 0.113860 0.0411 Inf 2.772 0.3308
StepF14 - StepF15 0.049717 0.0410 Inf 1.211 0.9993
StepF14 - StepF16 0.110668 0.0410 Inf 2.696 0.3827
StepF14 - StepF17 0.135401 0.0410 Inf 3.299 0.0897
StepF14 - StepF18 0.162680 0.0411 Inf 3.961 0.0093
StepF15 - StepF16 0.060951 0.0410 Inf 1.485 0.9917
StepF15 - StepF17 0.085684 0.0410 Inf 2.088 0.8238
StepF15 - StepF18 0.112963 0.0411 Inf 2.750 0.3454
StepF16 - StepF17 0.024733 0.0410 Inf 0.603 1.0000
StepF16 - StepF18 0.052012 0.0411 Inf 1.266 0.9987
StepF17 - StepF18 0.027279 0.0411 Inf 0.664 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.167063 0.0410 Inf -4.070 0.0060
StepF1 - StepF3 -0.177215 0.0410 Inf -4.318 0.0021
StepF1 - StepF4 -0.034722 0.0410 Inf -0.846 1.0000
StepF1 - StepF5 -0.053457 0.0410 Inf -1.302 0.9982
StepF1 - StepF6 -0.077621 0.0410 Inf -1.891 0.9156
StepF1 - StepF7 -0.086985 0.0410 Inf -2.119 0.8053
StepF1 - StepF8 -0.077022 0.0410 Inf -1.877 0.9207
StepF1 - StepF9 -0.028111 0.0410 Inf -0.685 1.0000
StepF1 - StepF10 -0.072498 0.0410 Inf -1.766 0.9530
StepF1 - StepF11 -0.030733 0.0410 Inf -0.749 1.0000
StepF1 - StepF12 0.085614 0.0410 Inf 2.086 0.8248
StepF1 - StepF13 0.022609 0.0410 Inf 0.551 1.0000
StepF1 - StepF14 -0.026212 0.0410 Inf -0.639 1.0000
StepF1 - StepF15 0.023505 0.0410 Inf 0.573 1.0000
StepF1 - StepF16 0.084456 0.0410 Inf 2.058 0.8404
StepF1 - StepF17 0.109189 0.0410 Inf 2.660 0.4086
StepF1 - StepF18 0.136468 0.0411 Inf 3.323 0.0837
StepF2 - StepF3 -0.010153 0.0410 Inf -0.247 1.0000
StepF2 - StepF4 0.132341 0.0410 Inf 3.224 0.1111
StepF2 - StepF5 0.113606 0.0410 Inf 2.768 0.3335
StepF2 - StepF6 0.089441 0.0410 Inf 2.179 0.7678
StepF2 - StepF7 0.080077 0.0410 Inf 1.951 0.8920
StepF2 - StepF8 0.090041 0.0410 Inf 2.194 0.7581
StepF2 - StepF9 0.138951 0.0410 Inf 3.386 0.0692
StepF2 - StepF10 0.094565 0.0410 Inf 2.304 0.6806
StepF2 - StepF11 0.136330 0.0410 Inf 3.322 0.0839
StepF2 - StepF12 0.252676 0.0410 Inf 6.156 <.0001
StepF2 - StepF13 0.189671 0.0410 Inf 4.621 0.0005
StepF2 - StepF14 0.140850 0.0410 Inf 3.432 0.0599
StepF2 - StepF15 0.190567 0.0410 Inf 4.643 0.0005
StepF2 - StepF16 0.251519 0.0410 Inf 6.128 <.0001
StepF2 - StepF17 0.276251 0.0410 Inf 6.731 <.0001
StepF2 - StepF18 0.303531 0.0411 Inf 7.390 <.0001
StepF3 - StepF4 0.142494 0.0410 Inf 3.472 0.0528
StepF3 - StepF5 0.123758 0.0410 Inf 3.015 0.1925
StepF3 - StepF6 0.099594 0.0410 Inf 2.427 0.5874
StepF3 - StepF7 0.090230 0.0410 Inf 2.198 0.7551
StepF3 - StepF8 0.100194 0.0410 Inf 2.441 0.5760
StepF3 - StepF9 0.149104 0.0410 Inf 3.633 0.0309
StepF3 - StepF10 0.104718 0.0410 Inf 2.551 0.4904
StepF3 - StepF11 0.146482 0.0410 Inf 3.569 0.0384
StepF3 - StepF12 0.262829 0.0410 Inf 6.404 <.0001
StepF3 - StepF13 0.199824 0.0410 Inf 4.869 0.0002
StepF3 - StepF14 0.151003 0.0410 Inf 3.679 0.0263
StepF3 - StepF15 0.200720 0.0410 Inf 4.891 0.0001
StepF3 - StepF16 0.261671 0.0410 Inf 6.376 <.0001
StepF3 - StepF17 0.286404 0.0410 Inf 6.978 <.0001
StepF3 - StepF18 0.313684 0.0411 Inf 7.637 <.0001
StepF4 - StepF5 -0.018735 0.0410 Inf -0.456 1.0000
StepF4 - StepF6 -0.042900 0.0410 Inf -1.045 0.9999
StepF4 - StepF7 -0.052264 0.0410 Inf -1.273 0.9986
StepF4 - StepF8 -0.042300 0.0410 Inf -1.031 0.9999
StepF4 - StepF9 0.006610 0.0410 Inf 0.161 1.0000
StepF4 - StepF10 -0.037776 0.0410 Inf -0.920 1.0000
StepF4 - StepF11 0.003989 0.0410 Inf 0.097 1.0000
StepF4 - StepF12 0.120335 0.0410 Inf 2.932 0.2346
StepF4 - StepF13 0.057330 0.0410 Inf 1.397 0.9959
StepF4 - StepF14 0.008510 0.0410 Inf 0.207 1.0000
StepF4 - StepF15 0.058226 0.0410 Inf 1.419 0.9951
StepF4 - StepF16 0.119178 0.0410 Inf 2.904 0.2502
StepF4 - StepF17 0.143910 0.0410 Inf 3.506 0.0472
StepF4 - StepF18 0.171190 0.0411 Inf 4.168 0.0040
StepF5 - StepF6 -0.024164 0.0410 Inf -0.589 1.0000
StepF5 - StepF7 -0.033528 0.0410 Inf -0.817 1.0000
StepF5 - StepF8 -0.023565 0.0410 Inf -0.574 1.0000
StepF5 - StepF9 0.025345 0.0410 Inf 0.618 1.0000
StepF5 - StepF10 -0.019041 0.0410 Inf -0.464 1.0000
StepF5 - StepF11 0.022724 0.0410 Inf 0.554 1.0000
StepF5 - StepF12 0.139071 0.0410 Inf 3.388 0.0686
StepF5 - StepF13 0.076066 0.0410 Inf 1.853 0.9285
StepF5 - StepF14 0.027245 0.0410 Inf 0.664 1.0000
StepF5 - StepF15 0.076962 0.0410 Inf 1.875 0.9212
StepF5 - StepF16 0.137913 0.0410 Inf 3.360 0.0747
StepF5 - StepF17 0.162646 0.0410 Inf 3.963 0.0092
StepF5 - StepF18 0.189925 0.0411 Inf 4.624 0.0005
StepF6 - StepF7 -0.009364 0.0410 Inf -0.228 1.0000
StepF6 - StepF8 0.000600 0.0410 Inf 0.015 1.0000
StepF6 - StepF9 0.049510 0.0410 Inf 1.206 0.9993
StepF6 - StepF10 0.005124 0.0410 Inf 0.125 1.0000
StepF6 - StepF11 0.046888 0.0410 Inf 1.142 0.9997
StepF6 - StepF12 0.163235 0.0410 Inf 3.977 0.0087
StepF6 - StepF13 0.100230 0.0410 Inf 2.442 0.5753
StepF6 - StepF14 0.051409 0.0410 Inf 1.253 0.9989
StepF6 - StepF15 0.101126 0.0410 Inf 2.464 0.5583
StepF6 - StepF16 0.162077 0.0410 Inf 3.949 0.0097
StepF6 - StepF17 0.186810 0.0410 Inf 4.552 0.0007
StepF6 - StepF18 0.214090 0.0411 Inf 5.212 <.0001
StepF7 - StepF8 0.009964 0.0410 Inf 0.243 1.0000
StepF7 - StepF9 0.058874 0.0410 Inf 1.434 0.9944
StepF7 - StepF10 0.014488 0.0410 Inf 0.353 1.0000
StepF7 - StepF11 0.056252 0.0410 Inf 1.371 0.9967
StepF7 - StepF12 0.172599 0.0410 Inf 4.205 0.0034
StepF7 - StepF13 0.109594 0.0410 Inf 2.670 0.4014
StepF7 - StepF14 0.060773 0.0410 Inf 1.481 0.9920
StepF7 - StepF15 0.110490 0.0410 Inf 2.692 0.3858
StepF7 - StepF16 0.171442 0.0410 Inf 4.177 0.0039
StepF7 - StepF17 0.196174 0.0410 Inf 4.780 0.0003
StepF7 - StepF18 0.223454 0.0411 Inf 5.440 <.0001
StepF8 - StepF9 0.048910 0.0410 Inf 1.192 0.9994
StepF8 - StepF10 0.004524 0.0410 Inf 0.110 1.0000
StepF8 - StepF11 0.046289 0.0410 Inf 1.128 0.9997
StepF8 - StepF12 0.162635 0.0410 Inf 3.963 0.0092
StepF8 - StepF13 0.099630 0.0410 Inf 2.427 0.5867
StepF8 - StepF14 0.050810 0.0410 Inf 1.238 0.9990
StepF8 - StepF15 0.100526 0.0410 Inf 2.449 0.5697
StepF8 - StepF16 0.161478 0.0410 Inf 3.934 0.0103
StepF8 - StepF17 0.186210 0.0410 Inf 4.537 0.0008
StepF8 - StepF18 0.213490 0.0411 Inf 5.198 <.0001
StepF9 - StepF10 -0.044386 0.0410 Inf -1.081 0.9998
StepF9 - StepF11 -0.002622 0.0410 Inf -0.064 1.0000
StepF9 - StepF12 0.113725 0.0410 Inf 2.771 0.3316
StepF9 - StepF13 0.050720 0.0410 Inf 1.236 0.9991
StepF9 - StepF14 0.001899 0.0410 Inf 0.046 1.0000
StepF9 - StepF15 0.051616 0.0410 Inf 1.258 0.9988
StepF9 - StepF16 0.112568 0.0410 Inf 2.743 0.3505
StepF9 - StepF17 0.137300 0.0410 Inf 3.345 0.0782
StepF9 - StepF18 0.164580 0.0411 Inf 4.007 0.0077
StepF10 - StepF11 0.041765 0.0410 Inf 1.018 0.9999
StepF10 - StepF12 0.158111 0.0410 Inf 3.852 0.0140
StepF10 - StepF13 0.095106 0.0410 Inf 2.317 0.6708
StepF10 - StepF14 0.046285 0.0410 Inf 1.128 0.9997
StepF10 - StepF15 0.096002 0.0410 Inf 2.339 0.6544
StepF10 - StepF16 0.156954 0.0410 Inf 3.824 0.0156
StepF10 - StepF17 0.181686 0.0410 Inf 4.427 0.0013
StepF10 - StepF18 0.208966 0.0411 Inf 5.088 0.0001
StepF11 - StepF12 0.116347 0.0410 Inf 2.835 0.2908
StepF11 - StepF13 0.053342 0.0410 Inf 1.300 0.9983
StepF11 - StepF14 0.004521 0.0410 Inf 0.110 1.0000
StepF11 - StepF15 0.054238 0.0410 Inf 1.321 0.9979
StepF11 - StepF16 0.115189 0.0410 Inf 2.807 0.3084
StepF11 - StepF17 0.139922 0.0410 Inf 3.409 0.0643
StepF11 - StepF18 0.167201 0.0411 Inf 4.071 0.0060
StepF12 - StepF13 -0.063005 0.0410 Inf -1.535 0.9882
StepF12 - StepF14 -0.111826 0.0410 Inf -2.725 0.3629
StepF12 - StepF15 -0.062109 0.0410 Inf -1.513 0.9899
StepF12 - StepF16 -0.001157 0.0410 Inf -0.028 1.0000
StepF12 - StepF17 0.023575 0.0410 Inf 0.574 1.0000
StepF12 - StepF18 0.050855 0.0411 Inf 1.238 0.9990
StepF13 - StepF14 -0.048821 0.0410 Inf -1.190 0.9994
StepF13 - StepF15 0.000896 0.0410 Inf 0.022 1.0000
StepF13 - StepF16 0.061847 0.0410 Inf 1.507 0.9903
StepF13 - StepF17 0.086580 0.0410 Inf 2.110 0.8112
StepF13 - StepF18 0.113860 0.0411 Inf 2.772 0.3308
StepF14 - StepF15 0.049717 0.0410 Inf 1.211 0.9993
StepF14 - StepF16 0.110668 0.0410 Inf 2.696 0.3827
StepF14 - StepF17 0.135401 0.0410 Inf 3.299 0.0897
StepF14 - StepF18 0.162680 0.0411 Inf 3.961 0.0093
StepF15 - StepF16 0.060951 0.0410 Inf 1.485 0.9917
StepF15 - StepF17 0.085684 0.0410 Inf 2.088 0.8238
StepF15 - StepF18 0.112963 0.0411 Inf 2.750 0.3454
StepF16 - StepF17 0.024733 0.0410 Inf 0.603 1.0000
StepF16 - StepF18 0.052012 0.0411 Inf 1.266 0.9987
StepF17 - StepF18 0.027279 0.0411 Inf 0.664 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.16706 0.0410 Inf 4.070 0.0008
StepF3 - StepF2 0.01015 0.0410 Inf 0.247 1.0000
StepF4 - StepF3 -0.14249 0.0410 Inf -3.472 0.0083
StepF5 - StepF4 0.01874 0.0410 Inf 0.456 1.0000
StepF6 - StepF5 0.02416 0.0410 Inf 0.589 1.0000
StepF7 - StepF6 0.00936 0.0410 Inf 0.228 1.0000
StepF8 - StepF7 -0.00996 0.0410 Inf -0.243 1.0000
StepF9 - StepF8 -0.04891 0.0410 Inf -1.192 1.0000
StepF10 - StepF9 0.04439 0.0410 Inf 1.081 1.0000
StepF11 - StepF10 -0.04176 0.0410 Inf -1.018 1.0000
StepF12 - StepF11 -0.11635 0.0410 Inf -2.835 0.0688
StepF13 - StepF12 0.06300 0.0410 Inf 1.535 1.0000
StepF14 - StepF13 0.04882 0.0410 Inf 1.190 1.0000
StepF15 - StepF14 -0.04972 0.0410 Inf -1.211 1.0000
StepF16 - StepF15 -0.06095 0.0410 Inf -1.485 1.0000
StepF17 - StepF16 -0.02473 0.0410 Inf -0.603 1.0000
StepF18 - StepF17 -0.02728 0.0411 Inf -0.664 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.16706 0.0410 Inf 4.070 0.0008
StepF3 - StepF2 0.01015 0.0410 Inf 0.247 1.0000
StepF4 - StepF3 -0.14249 0.0410 Inf -3.472 0.0083
StepF5 - StepF4 0.01874 0.0410 Inf 0.456 1.0000
StepF6 - StepF5 0.02416 0.0410 Inf 0.589 1.0000
StepF7 - StepF6 0.00936 0.0410 Inf 0.228 1.0000
StepF8 - StepF7 -0.00996 0.0410 Inf -0.243 1.0000
StepF9 - StepF8 -0.04891 0.0410 Inf -1.192 1.0000
StepF10 - StepF9 0.04439 0.0410 Inf 1.081 1.0000
StepF11 - StepF10 -0.04176 0.0410 Inf -1.018 1.0000
StepF12 - StepF11 -0.11635 0.0410 Inf -2.835 0.0688
StepF13 - StepF12 0.06300 0.0410 Inf 1.535 1.0000
StepF14 - StepF13 0.04882 0.0410 Inf 1.190 1.0000
StepF15 - StepF14 -0.04972 0.0410 Inf -1.211 1.0000
StepF16 - StepF15 -0.06095 0.0410 Inf -1.485 1.0000
StepF17 - StepF16 -0.02473 0.0410 Inf -0.603 1.0000
StepF18 - StepF17 -0.02728 0.0411 Inf -0.664 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests # ==== TEST (Blocks 4–5): stepwise LMM + χ² + EMMs + all-pairs + adjacent (per block × seq length × axis)
suppressPackageStartupMessages({
library(dplyr); library(lme4); library(lmerTest); library(emmeans); library(car)
})
emm_options(lmer.df = "asymptotic")
.report_step_test <- function(df_block, block_label, seq_label) {
for (ax in c("x","y","z")) {
dd <- df_block %>% filter(Axis == ax)
if (nrow(dd) == 0) next
dd <- dd %>%
mutate(
StepF = factor(Step, levels = sort(unique(Step))),
subject = factor(subject),
trial_id = factor(trial_id),
Accuracy = droplevels(Accuracy)
)
cat("\n\n==============================\n",
"TEST | Block ", block_label, " | ", seq_label, " | Axis ", toupper(ax),
"\n==============================\n", sep = "")
m <- suppressWarnings(lmer(RMS ~ StepF + Accuracy + (1|subject) + (1|trial_id),
data = dd, REML = TRUE))
cat("\nType II Wald χ² (StepF & Accuracy):\n")
print(car::Anova(m, type = 2, test.statistic = "Chisq"))
if (nlevels(dd$Accuracy) >= 2) {
em <- emmeans(m, ~ StepF | Accuracy)
cat("\nEMMs per step | Accuracy:\n"); print(summary(em))
cat("\nAll-pairs (Tukey) among steps | Accuracy:\n")
print(pairs(em, adjust = "tukey"))
cat("\nAdjacent steps (consec; Holm) | Accuracy:\n")
print(contrast(em, method = "consec", by = "Accuracy", adjust = "holm"))
} else {
em <- emmeans(m, ~ StepF)
cat("\nEMMs per step:\n"); print(summary(em))
cat("\nAll-pairs (Tukey) among steps:\n")
print(pairs(em, adjust = "tukey"))
cat("\nAdjacent steps (consec; Holm):\n")
print(contrast(em, method = "consec", adjust = "holm"))
}
rm(m, em); invisible(gc())
}
}
# Block 4
.report_step_test(sw_b4_6, "4", "6 steps")
==============================
TEST | Block 4 | 6 steps | Axis X
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 43.1301 5 3.477e-08 ***
Accuracy 0.4237 1 0.5151
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.683 0.0811 Inf 0.523 0.842
2 0.814 0.0811 Inf 0.655 0.974
3 0.807 0.0811 Inf 0.648 0.966
4 0.758 0.0811 Inf 0.599 0.917
5 0.623 0.0811 Inf 0.464 0.782
6 0.649 0.0811 Inf 0.490 0.808
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.650 0.0732 Inf 0.506 0.793
2 0.782 0.0732 Inf 0.638 0.925
3 0.774 0.0732 Inf 0.631 0.917
4 0.725 0.0732 Inf 0.582 0.868
5 0.590 0.0732 Inf 0.447 0.733
6 0.616 0.0732 Inf 0.473 0.760
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.13194 0.0396 Inf -3.335 0.0110
StepF1 - StepF3 -0.12454 0.0396 Inf -3.148 0.0204
StepF1 - StepF4 -0.07542 0.0396 Inf -1.907 0.3980
StepF1 - StepF5 0.05961 0.0396 Inf 1.507 0.6598
StepF1 - StepF6 0.03330 0.0396 Inf 0.842 0.9597
StepF2 - StepF3 0.00741 0.0396 Inf 0.187 1.0000
StepF2 - StepF4 0.05652 0.0396 Inf 1.429 0.7095
StepF2 - StepF5 0.19155 0.0396 Inf 4.842 <.0001
StepF2 - StepF6 0.16524 0.0396 Inf 4.177 0.0004
StepF3 - StepF4 0.04911 0.0396 Inf 1.242 0.8164
StepF3 - StepF5 0.18415 0.0396 Inf 4.655 <.0001
StepF3 - StepF6 0.15783 0.0396 Inf 3.990 0.0009
StepF4 - StepF5 0.13503 0.0396 Inf 3.413 0.0084
StepF4 - StepF6 0.10872 0.0396 Inf 2.748 0.0661
StepF5 - StepF6 -0.02631 0.0396 Inf -0.665 0.9857
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.13194 0.0396 Inf -3.335 0.0110
StepF1 - StepF3 -0.12454 0.0396 Inf -3.148 0.0204
StepF1 - StepF4 -0.07542 0.0396 Inf -1.907 0.3980
StepF1 - StepF5 0.05961 0.0396 Inf 1.507 0.6598
StepF1 - StepF6 0.03330 0.0396 Inf 0.842 0.9597
StepF2 - StepF3 0.00741 0.0396 Inf 0.187 1.0000
StepF2 - StepF4 0.05652 0.0396 Inf 1.429 0.7095
StepF2 - StepF5 0.19155 0.0396 Inf 4.842 <.0001
StepF2 - StepF6 0.16524 0.0396 Inf 4.177 0.0004
StepF3 - StepF4 0.04911 0.0396 Inf 1.242 0.8164
StepF3 - StepF5 0.18415 0.0396 Inf 4.655 <.0001
StepF3 - StepF6 0.15783 0.0396 Inf 3.990 0.0009
StepF4 - StepF5 0.13503 0.0396 Inf 3.413 0.0084
StepF4 - StepF6 0.10872 0.0396 Inf 2.748 0.0661
StepF5 - StepF6 -0.02631 0.0396 Inf -0.665 0.9857
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.13194 0.0396 Inf 3.335 0.0034
StepF3 - StepF2 -0.00741 0.0396 Inf -0.187 1.0000
StepF4 - StepF3 -0.04911 0.0396 Inf -1.242 0.6433
StepF5 - StepF4 -0.13503 0.0396 Inf -3.413 0.0032
StepF6 - StepF5 0.02631 0.0396 Inf 0.665 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.13194 0.0396 Inf 3.335 0.0034
StepF3 - StepF2 -0.00741 0.0396 Inf -0.187 1.0000
StepF4 - StepF3 -0.04911 0.0396 Inf -1.242 0.6433
StepF5 - StepF4 -0.13503 0.0396 Inf -3.413 0.0032
StepF6 - StepF5 0.02631 0.0396 Inf 0.665 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests
==============================
TEST | Block 4 | 6 steps | Axis Y
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 42.2983 5 5.126e-08 ***
Accuracy 0.0035 1 0.9527
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.695 0.0955 Inf 0.508 0.882
2 0.899 0.0955 Inf 0.712 1.086
3 0.876 0.0955 Inf 0.689 1.064
4 0.757 0.0955 Inf 0.570 0.944
5 0.685 0.0955 Inf 0.498 0.872
6 0.749 0.0955 Inf 0.562 0.937
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.692 0.0877 Inf 0.520 0.864
2 0.896 0.0877 Inf 0.724 1.068
3 0.873 0.0877 Inf 0.701 1.045
4 0.754 0.0877 Inf 0.582 0.926
5 0.682 0.0877 Inf 0.510 0.854
6 0.746 0.0877 Inf 0.574 0.918
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.20443 0.0441 Inf -4.633 0.0001
StepF1 - StepF3 -0.18153 0.0441 Inf -4.114 0.0006
StepF1 - StepF4 -0.06227 0.0441 Inf -1.411 0.7203
StepF1 - StepF5 0.00977 0.0441 Inf 0.222 0.9999
StepF1 - StepF6 -0.05459 0.0441 Inf -1.237 0.8186
StepF2 - StepF3 0.02290 0.0441 Inf 0.519 0.9955
StepF2 - StepF4 0.14216 0.0441 Inf 3.222 0.0161
StepF2 - StepF5 0.21421 0.0441 Inf 4.854 <.0001
StepF2 - StepF6 0.14984 0.0441 Inf 3.396 0.0090
StepF3 - StepF4 0.11926 0.0441 Inf 2.703 0.0747
StepF3 - StepF5 0.19130 0.0441 Inf 4.335 0.0002
StepF3 - StepF6 0.12694 0.0441 Inf 2.877 0.0463
StepF4 - StepF5 0.07205 0.0441 Inf 1.633 0.5767
StepF4 - StepF6 0.00768 0.0441 Inf 0.174 1.0000
StepF5 - StepF6 -0.06437 0.0441 Inf -1.459 0.6907
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.20443 0.0441 Inf -4.633 0.0001
StepF1 - StepF3 -0.18153 0.0441 Inf -4.114 0.0006
StepF1 - StepF4 -0.06227 0.0441 Inf -1.411 0.7203
StepF1 - StepF5 0.00977 0.0441 Inf 0.222 0.9999
StepF1 - StepF6 -0.05459 0.0441 Inf -1.237 0.8186
StepF2 - StepF3 0.02290 0.0441 Inf 0.519 0.9955
StepF2 - StepF4 0.14216 0.0441 Inf 3.222 0.0161
StepF2 - StepF5 0.21421 0.0441 Inf 4.854 <.0001
StepF2 - StepF6 0.14984 0.0441 Inf 3.396 0.0090
StepF3 - StepF4 0.11926 0.0441 Inf 2.703 0.0747
StepF3 - StepF5 0.19130 0.0441 Inf 4.335 0.0002
StepF3 - StepF6 0.12694 0.0441 Inf 2.877 0.0463
StepF4 - StepF5 0.07205 0.0441 Inf 1.633 0.5767
StepF4 - StepF6 0.00768 0.0441 Inf 0.174 1.0000
StepF5 - StepF6 -0.06437 0.0441 Inf -1.459 0.6907
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2044 0.0441 Inf 4.633 <.0001
StepF3 - StepF2 -0.0229 0.0441 Inf -0.519 0.6037
StepF4 - StepF3 -0.1193 0.0441 Inf -2.703 0.0275
StepF5 - StepF4 -0.0720 0.0441 Inf -1.633 0.3076
StepF6 - StepF5 0.0644 0.0441 Inf 1.459 0.3076
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2044 0.0441 Inf 4.633 <.0001
StepF3 - StepF2 -0.0229 0.0441 Inf -0.519 0.6037
StepF4 - StepF3 -0.1193 0.0441 Inf -2.703 0.0275
StepF5 - StepF4 -0.0720 0.0441 Inf -1.633 0.3076
StepF6 - StepF5 0.0644 0.0441 Inf 1.459 0.3076
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests
==============================
TEST | Block 4 | 6 steps | Axis Z
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 48.4285 5 2.904e-09 ***
Accuracy 0.1687 1 0.6813
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.39 0.175 Inf 1.047 1.73
2 1.66 0.175 Inf 1.314 2.00
3 1.72 0.175 Inf 1.373 2.06
4 1.57 0.175 Inf 1.228 1.91
5 1.33 0.175 Inf 0.985 1.67
6 1.32 0.175 Inf 0.978 1.66
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.44 0.156 Inf 1.133 1.74
2 1.70 0.156 Inf 1.400 2.01
3 1.76 0.156 Inf 1.459 2.07
4 1.62 0.156 Inf 1.314 1.92
5 1.38 0.156 Inf 1.071 1.68
6 1.37 0.156 Inf 1.063 1.67
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.26663 0.0787 Inf -3.389 0.0092
StepF1 - StepF3 -0.32572 0.0787 Inf -4.141 0.0005
StepF1 - StepF4 -0.18123 0.0787 Inf -2.304 0.1924
StepF1 - StepF5 0.06163 0.0787 Inf 0.783 0.9704
StepF1 - StepF6 0.06944 0.0787 Inf 0.883 0.9507
StepF2 - StepF3 -0.05909 0.0787 Inf -0.751 0.9754
StepF2 - StepF4 0.08540 0.0787 Inf 1.086 0.8873
StepF2 - StepF5 0.32826 0.0787 Inf 4.173 0.0004
StepF2 - StepF6 0.33608 0.0787 Inf 4.272 0.0003
StepF3 - StepF4 0.14449 0.0787 Inf 1.837 0.4419
StepF3 - StepF5 0.38735 0.0787 Inf 4.924 <.0001
StepF3 - StepF6 0.39516 0.0787 Inf 5.023 <.0001
StepF4 - StepF5 0.24286 0.0787 Inf 3.087 0.0247
StepF4 - StepF6 0.25068 0.0787 Inf 3.187 0.0180
StepF5 - StepF6 0.00781 0.0787 Inf 0.099 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.26663 0.0787 Inf -3.389 0.0092
StepF1 - StepF3 -0.32572 0.0787 Inf -4.141 0.0005
StepF1 - StepF4 -0.18123 0.0787 Inf -2.304 0.1924
StepF1 - StepF5 0.06163 0.0787 Inf 0.783 0.9704
StepF1 - StepF6 0.06944 0.0787 Inf 0.883 0.9507
StepF2 - StepF3 -0.05909 0.0787 Inf -0.751 0.9754
StepF2 - StepF4 0.08540 0.0787 Inf 1.086 0.8873
StepF2 - StepF5 0.32826 0.0787 Inf 4.173 0.0004
StepF2 - StepF6 0.33608 0.0787 Inf 4.272 0.0003
StepF3 - StepF4 0.14449 0.0787 Inf 1.837 0.4419
StepF3 - StepF5 0.38735 0.0787 Inf 4.924 <.0001
StepF3 - StepF6 0.39516 0.0787 Inf 5.023 <.0001
StepF4 - StepF5 0.24286 0.0787 Inf 3.087 0.0247
StepF4 - StepF6 0.25068 0.0787 Inf 3.187 0.0180
StepF5 - StepF6 0.00781 0.0787 Inf 0.099 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.26663 0.0787 Inf 3.389 0.0035
StepF3 - StepF2 0.05909 0.0787 Inf 0.751 0.9052
StepF4 - StepF3 -0.14449 0.0787 Inf -1.837 0.1988
StepF5 - StepF4 -0.24286 0.0787 Inf -3.087 0.0081
StepF6 - StepF5 -0.00781 0.0787 Inf -0.099 0.9209
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.26663 0.0787 Inf 3.389 0.0035
StepF3 - StepF2 0.05909 0.0787 Inf 0.751 0.9052
StepF4 - StepF3 -0.14449 0.0787 Inf -1.837 0.1988
StepF5 - StepF4 -0.24286 0.0787 Inf -3.087 0.0081
StepF6 - StepF5 -0.00781 0.0787 Inf -0.099 0.9209
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests .report_step_test(sw_b4_12, "4", "12 steps")
==============================
TEST | Block 4 | 12 steps | Axis X
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 78.2270 11 3.246e-12 ***
Accuracy 0.2016 1 0.6534
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.696 0.0758 Inf 0.547 0.845
2 0.786 0.0758 Inf 0.637 0.934
3 0.783 0.0758 Inf 0.635 0.932
4 0.716 0.0758 Inf 0.568 0.865
5 0.678 0.0758 Inf 0.529 0.826
6 0.763 0.0758 Inf 0.615 0.912
7 0.725 0.0758 Inf 0.576 0.873
8 0.708 0.0758 Inf 0.559 0.856
9 0.678 0.0758 Inf 0.529 0.826
10 0.628 0.0758 Inf 0.479 0.776
11 0.641 0.0758 Inf 0.493 0.790
12 0.538 0.0758 Inf 0.389 0.686
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.681 0.0722 Inf 0.539 0.822
2 0.770 0.0722 Inf 0.629 0.912
3 0.768 0.0722 Inf 0.626 0.909
4 0.701 0.0722 Inf 0.559 0.842
5 0.662 0.0722 Inf 0.521 0.804
6 0.748 0.0722 Inf 0.606 0.889
7 0.709 0.0722 Inf 0.568 0.851
8 0.692 0.0722 Inf 0.551 0.834
9 0.662 0.0722 Inf 0.521 0.804
10 0.612 0.0722 Inf 0.471 0.754
11 0.626 0.0722 Inf 0.484 0.767
12 0.522 0.0722 Inf 0.381 0.664
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.08956 0.0374 Inf -2.392 0.4112
StepF1 - StepF3 -0.08714 0.0374 Inf -2.328 0.4566
StepF1 - StepF4 -0.02027 0.0374 Inf -0.542 1.0000
StepF1 - StepF5 0.01834 0.0374 Inf 0.490 1.0000
StepF1 - StepF6 -0.06717 0.0374 Inf -1.794 0.8216
StepF1 - StepF7 -0.02850 0.0374 Inf -0.761 0.9998
StepF1 - StepF8 -0.01157 0.0374 Inf -0.309 1.0000
StepF1 - StepF9 0.01820 0.0374 Inf 0.486 1.0000
StepF1 - StepF10 0.06840 0.0374 Inf 1.827 0.8033
StepF1 - StepF11 0.05474 0.0374 Inf 1.462 0.9506
StepF1 - StepF12 0.15813 0.0374 Inf 4.224 0.0014
StepF2 - StepF3 0.00242 0.0374 Inf 0.065 1.0000
StepF2 - StepF4 0.06928 0.0374 Inf 1.851 0.7896
StepF2 - StepF5 0.10789 0.0374 Inf 2.882 0.1472
StepF2 - StepF6 0.02239 0.0374 Inf 0.598 1.0000
StepF2 - StepF7 0.06106 0.0374 Inf 1.631 0.8979
StepF2 - StepF8 0.07799 0.0374 Inf 2.083 0.6349
StepF2 - StepF9 0.10775 0.0374 Inf 2.878 0.1485
StepF2 - StepF10 0.15796 0.0374 Inf 4.219 0.0015
StepF2 - StepF11 0.14430 0.0374 Inf 3.855 0.0065
StepF2 - StepF12 0.24769 0.0374 Inf 6.616 <.0001
StepF3 - StepF4 0.06687 0.0374 Inf 1.786 0.8260
StepF3 - StepF5 0.10548 0.0374 Inf 2.818 0.1724
StepF3 - StepF6 0.01997 0.0374 Inf 0.533 1.0000
StepF3 - StepF7 0.05864 0.0374 Inf 1.567 0.9211
StepF3 - StepF8 0.07557 0.0374 Inf 2.019 0.6807
StepF3 - StepF9 0.10534 0.0374 Inf 2.814 0.1739
StepF3 - StepF10 0.15554 0.0374 Inf 4.155 0.0019
StepF3 - StepF11 0.14188 0.0374 Inf 3.790 0.0083
StepF3 - StepF12 0.24527 0.0374 Inf 6.552 <.0001
StepF4 - StepF5 0.03861 0.0374 Inf 1.031 0.9970
StepF4 - StepF6 -0.04689 0.0374 Inf -1.253 0.9846
StepF4 - StepF7 -0.00822 0.0374 Inf -0.220 1.0000
StepF4 - StepF8 0.00871 0.0374 Inf 0.233 1.0000
StepF4 - StepF9 0.03847 0.0374 Inf 1.028 0.9971
StepF4 - StepF10 0.08868 0.0374 Inf 2.369 0.4275
StepF4 - StepF11 0.07502 0.0374 Inf 2.004 0.6909
StepF4 - StepF12 0.17840 0.0374 Inf 4.766 0.0001
StepF5 - StepF6 -0.08551 0.0374 Inf -2.284 0.4880
StepF5 - StepF7 -0.04683 0.0374 Inf -1.251 0.9847
StepF5 - StepF8 -0.02990 0.0374 Inf -0.799 0.9997
StepF5 - StepF9 -0.00014 0.0374 Inf -0.004 1.0000
StepF5 - StepF10 0.05007 0.0374 Inf 1.337 0.9743
StepF5 - StepF11 0.03641 0.0374 Inf 0.973 0.9982
StepF5 - StepF12 0.13979 0.0374 Inf 3.734 0.0102
StepF6 - StepF7 0.03867 0.0374 Inf 1.033 0.9970
StepF6 - StepF8 0.05560 0.0374 Inf 1.485 0.9449
StepF6 - StepF9 0.08537 0.0374 Inf 2.280 0.4907
StepF6 - StepF10 0.13557 0.0374 Inf 3.621 0.0154
StepF6 - StepF11 0.12191 0.0374 Inf 3.257 0.0518
StepF6 - StepF12 0.22530 0.0374 Inf 6.018 <.0001
StepF7 - StepF8 0.01693 0.0374 Inf 0.452 1.0000
StepF7 - StepF9 0.04669 0.0374 Inf 1.247 0.9851
StepF7 - StepF10 0.09690 0.0374 Inf 2.588 0.2861
StepF7 - StepF11 0.08324 0.0374 Inf 2.224 0.5323
StepF7 - StepF12 0.18663 0.0374 Inf 4.985 <.0001
StepF8 - StepF9 0.02976 0.0374 Inf 0.795 0.9997
StepF8 - StepF10 0.07997 0.0374 Inf 2.136 0.5965
StepF8 - StepF11 0.06631 0.0374 Inf 1.771 0.8338
StepF8 - StepF12 0.16970 0.0374 Inf 4.533 0.0004
StepF9 - StepF10 0.05020 0.0374 Inf 1.341 0.9738
StepF9 - StepF11 0.03655 0.0374 Inf 0.976 0.9982
StepF9 - StepF12 0.13993 0.0374 Inf 3.738 0.0101
StepF10 - StepF11 -0.01366 0.0374 Inf -0.365 1.0000
StepF10 - StepF12 0.08973 0.0374 Inf 2.397 0.4080
StepF11 - StepF12 0.10339 0.0374 Inf 2.762 0.1966
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.08956 0.0374 Inf -2.392 0.4112
StepF1 - StepF3 -0.08714 0.0374 Inf -2.328 0.4566
StepF1 - StepF4 -0.02027 0.0374 Inf -0.542 1.0000
StepF1 - StepF5 0.01834 0.0374 Inf 0.490 1.0000
StepF1 - StepF6 -0.06717 0.0374 Inf -1.794 0.8216
StepF1 - StepF7 -0.02850 0.0374 Inf -0.761 0.9998
StepF1 - StepF8 -0.01157 0.0374 Inf -0.309 1.0000
StepF1 - StepF9 0.01820 0.0374 Inf 0.486 1.0000
StepF1 - StepF10 0.06840 0.0374 Inf 1.827 0.8033
StepF1 - StepF11 0.05474 0.0374 Inf 1.462 0.9506
StepF1 - StepF12 0.15813 0.0374 Inf 4.224 0.0014
StepF2 - StepF3 0.00242 0.0374 Inf 0.065 1.0000
StepF2 - StepF4 0.06928 0.0374 Inf 1.851 0.7896
StepF2 - StepF5 0.10789 0.0374 Inf 2.882 0.1472
StepF2 - StepF6 0.02239 0.0374 Inf 0.598 1.0000
StepF2 - StepF7 0.06106 0.0374 Inf 1.631 0.8979
StepF2 - StepF8 0.07799 0.0374 Inf 2.083 0.6349
StepF2 - StepF9 0.10775 0.0374 Inf 2.878 0.1485
StepF2 - StepF10 0.15796 0.0374 Inf 4.219 0.0015
StepF2 - StepF11 0.14430 0.0374 Inf 3.855 0.0065
StepF2 - StepF12 0.24769 0.0374 Inf 6.616 <.0001
StepF3 - StepF4 0.06687 0.0374 Inf 1.786 0.8260
StepF3 - StepF5 0.10548 0.0374 Inf 2.818 0.1724
StepF3 - StepF6 0.01997 0.0374 Inf 0.533 1.0000
StepF3 - StepF7 0.05864 0.0374 Inf 1.567 0.9211
StepF3 - StepF8 0.07557 0.0374 Inf 2.019 0.6807
StepF3 - StepF9 0.10534 0.0374 Inf 2.814 0.1739
StepF3 - StepF10 0.15554 0.0374 Inf 4.155 0.0019
StepF3 - StepF11 0.14188 0.0374 Inf 3.790 0.0083
StepF3 - StepF12 0.24527 0.0374 Inf 6.552 <.0001
StepF4 - StepF5 0.03861 0.0374 Inf 1.031 0.9970
StepF4 - StepF6 -0.04689 0.0374 Inf -1.253 0.9846
StepF4 - StepF7 -0.00822 0.0374 Inf -0.220 1.0000
StepF4 - StepF8 0.00871 0.0374 Inf 0.233 1.0000
StepF4 - StepF9 0.03847 0.0374 Inf 1.028 0.9971
StepF4 - StepF10 0.08868 0.0374 Inf 2.369 0.4275
StepF4 - StepF11 0.07502 0.0374 Inf 2.004 0.6909
StepF4 - StepF12 0.17840 0.0374 Inf 4.766 0.0001
StepF5 - StepF6 -0.08551 0.0374 Inf -2.284 0.4880
StepF5 - StepF7 -0.04683 0.0374 Inf -1.251 0.9847
StepF5 - StepF8 -0.02990 0.0374 Inf -0.799 0.9997
StepF5 - StepF9 -0.00014 0.0374 Inf -0.004 1.0000
StepF5 - StepF10 0.05007 0.0374 Inf 1.337 0.9743
StepF5 - StepF11 0.03641 0.0374 Inf 0.973 0.9982
StepF5 - StepF12 0.13979 0.0374 Inf 3.734 0.0102
StepF6 - StepF7 0.03867 0.0374 Inf 1.033 0.9970
StepF6 - StepF8 0.05560 0.0374 Inf 1.485 0.9449
StepF6 - StepF9 0.08537 0.0374 Inf 2.280 0.4907
StepF6 - StepF10 0.13557 0.0374 Inf 3.621 0.0154
StepF6 - StepF11 0.12191 0.0374 Inf 3.257 0.0518
StepF6 - StepF12 0.22530 0.0374 Inf 6.018 <.0001
StepF7 - StepF8 0.01693 0.0374 Inf 0.452 1.0000
StepF7 - StepF9 0.04669 0.0374 Inf 1.247 0.9851
StepF7 - StepF10 0.09690 0.0374 Inf 2.588 0.2861
StepF7 - StepF11 0.08324 0.0374 Inf 2.224 0.5323
StepF7 - StepF12 0.18663 0.0374 Inf 4.985 <.0001
StepF8 - StepF9 0.02976 0.0374 Inf 0.795 0.9997
StepF8 - StepF10 0.07997 0.0374 Inf 2.136 0.5965
StepF8 - StepF11 0.06631 0.0374 Inf 1.771 0.8338
StepF8 - StepF12 0.16970 0.0374 Inf 4.533 0.0004
StepF9 - StepF10 0.05020 0.0374 Inf 1.341 0.9738
StepF9 - StepF11 0.03655 0.0374 Inf 0.976 0.9982
StepF9 - StepF12 0.13993 0.0374 Inf 3.738 0.0101
StepF10 - StepF11 -0.01366 0.0374 Inf -0.365 1.0000
StepF10 - StepF12 0.08973 0.0374 Inf 2.397 0.4080
StepF11 - StepF12 0.10339 0.0374 Inf 2.762 0.1966
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.08956 0.0374 Inf 2.392 0.1674
StepF3 - StepF2 -0.00242 0.0374 Inf -0.065 1.0000
StepF4 - StepF3 -0.06687 0.0374 Inf -1.786 0.5926
StepF5 - StepF4 -0.03861 0.0374 Inf -1.031 1.0000
StepF6 - StepF5 0.08551 0.0374 Inf 2.284 0.2013
StepF7 - StepF6 -0.03867 0.0374 Inf -1.033 1.0000
StepF8 - StepF7 -0.01693 0.0374 Inf -0.452 1.0000
StepF9 - StepF8 -0.02976 0.0374 Inf -0.795 1.0000
StepF10 - StepF9 -0.05020 0.0374 Inf -1.341 1.0000
StepF11 - StepF10 0.01366 0.0374 Inf 0.365 1.0000
StepF12 - StepF11 -0.10339 0.0374 Inf -2.762 0.0633
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.08956 0.0374 Inf 2.392 0.1674
StepF3 - StepF2 -0.00242 0.0374 Inf -0.065 1.0000
StepF4 - StepF3 -0.06687 0.0374 Inf -1.786 0.5926
StepF5 - StepF4 -0.03861 0.0374 Inf -1.031 1.0000
StepF6 - StepF5 0.08551 0.0374 Inf 2.284 0.2013
StepF7 - StepF6 -0.03867 0.0374 Inf -1.033 1.0000
StepF8 - StepF7 -0.01693 0.0374 Inf -0.452 1.0000
StepF9 - StepF8 -0.02976 0.0374 Inf -0.795 1.0000
StepF10 - StepF9 -0.05020 0.0374 Inf -1.341 1.0000
StepF11 - StepF10 0.01366 0.0374 Inf 0.365 1.0000
StepF12 - StepF11 -0.10339 0.0374 Inf -2.762 0.0633
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests
==============================
TEST | Block 4 | 12 steps | Axis Y
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 111.2583 11 <2e-16 ***
Accuracy 0.3606 1 0.5482
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.726 0.0903 Inf 0.549 0.903
2 0.879 0.0903 Inf 0.702 1.056
3 0.940 0.0903 Inf 0.763 1.117
4 0.780 0.0903 Inf 0.603 0.957
5 0.739 0.0903 Inf 0.562 0.916
6 0.826 0.0903 Inf 0.649 1.003
7 0.802 0.0903 Inf 0.625 0.979
8 0.706 0.0903 Inf 0.529 0.883
9 0.710 0.0903 Inf 0.533 0.887
10 0.729 0.0903 Inf 0.552 0.906
11 0.696 0.0903 Inf 0.519 0.873
12 0.557 0.0903 Inf 0.380 0.734
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.701 0.0859 Inf 0.533 0.869
2 0.854 0.0859 Inf 0.686 1.023
3 0.915 0.0859 Inf 0.746 1.083
4 0.755 0.0859 Inf 0.586 0.923
5 0.714 0.0859 Inf 0.545 0.882
6 0.801 0.0859 Inf 0.632 0.969
7 0.777 0.0859 Inf 0.608 0.945
8 0.681 0.0859 Inf 0.513 0.850
9 0.685 0.0859 Inf 0.517 0.854
10 0.704 0.0859 Inf 0.535 0.872
11 0.671 0.0859 Inf 0.502 0.839
12 0.533 0.0859 Inf 0.364 0.701
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.15337 0.0436 Inf -3.514 0.0225
StepF1 - StepF3 -0.21372 0.0436 Inf -4.897 0.0001
StepF1 - StepF4 -0.05384 0.0436 Inf -1.234 0.9863
StepF1 - StepF5 -0.01276 0.0436 Inf -0.292 1.0000
StepF1 - StepF6 -0.09963 0.0436 Inf -2.283 0.4889
StepF1 - StepF7 -0.07584 0.0436 Inf -1.738 0.8509
StepF1 - StepF8 0.01952 0.0436 Inf 0.447 1.0000
StepF1 - StepF9 0.01551 0.0436 Inf 0.355 1.0000
StepF1 - StepF10 -0.00262 0.0436 Inf -0.060 1.0000
StepF1 - StepF11 0.03026 0.0436 Inf 0.693 0.9999
StepF1 - StepF12 0.16849 0.0436 Inf 3.861 0.0063
StepF2 - StepF3 -0.06035 0.0436 Inf -1.383 0.9670
StepF2 - StepF4 0.09952 0.0436 Inf 2.280 0.4907
StepF2 - StepF5 0.14061 0.0436 Inf 3.222 0.0576
StepF2 - StepF6 0.05373 0.0436 Inf 1.231 0.9866
StepF2 - StepF7 0.07753 0.0436 Inf 1.776 0.8311
StepF2 - StepF8 0.17289 0.0436 Inf 3.961 0.0043
StepF2 - StepF9 0.16887 0.0436 Inf 3.869 0.0061
StepF2 - StepF10 0.15075 0.0436 Inf 3.454 0.0275
StepF2 - StepF11 0.18363 0.0436 Inf 4.207 0.0015
StepF2 - StepF12 0.32186 0.0436 Inf 7.375 <.0001
StepF3 - StepF4 0.15987 0.0436 Inf 3.663 0.0133
StepF3 - StepF5 0.20096 0.0436 Inf 4.604 0.0003
StepF3 - StepF6 0.11408 0.0436 Inf 2.614 0.2716
StepF3 - StepF7 0.13788 0.0436 Inf 3.159 0.0693
StepF3 - StepF8 0.23324 0.0436 Inf 5.344 <.0001
StepF3 - StepF9 0.22922 0.0436 Inf 5.252 <.0001
StepF3 - StepF10 0.21110 0.0436 Inf 4.837 0.0001
StepF3 - StepF11 0.24397 0.0436 Inf 5.590 <.0001
StepF3 - StepF12 0.38221 0.0436 Inf 8.757 <.0001
StepF4 - StepF5 0.04108 0.0436 Inf 0.941 0.9987
StepF4 - StepF6 -0.04579 0.0436 Inf -1.049 0.9965
StepF4 - StepF7 -0.02199 0.0436 Inf -0.504 1.0000
StepF4 - StepF8 0.07336 0.0436 Inf 1.681 0.8773
StepF4 - StepF9 0.06935 0.0436 Inf 1.589 0.9135
StepF4 - StepF10 0.05122 0.0436 Inf 1.174 0.9909
StepF4 - StepF11 0.08410 0.0436 Inf 1.927 0.7424
StepF4 - StepF12 0.22234 0.0436 Inf 5.094 <.0001
StepF5 - StepF6 -0.08687 0.0436 Inf -1.990 0.7002
StepF5 - StepF7 -0.06308 0.0436 Inf -1.445 0.9545
StepF5 - StepF8 0.03228 0.0436 Inf 0.740 0.9999
StepF5 - StepF9 0.02827 0.0436 Inf 0.648 1.0000
StepF5 - StepF10 0.01014 0.0436 Inf 0.232 1.0000
StepF5 - StepF11 0.04302 0.0436 Inf 0.986 0.9980
StepF5 - StepF12 0.18126 0.0436 Inf 4.153 0.0019
StepF6 - StepF7 0.02380 0.0436 Inf 0.545 1.0000
StepF6 - StepF8 0.11915 0.0436 Inf 2.730 0.2113
StepF6 - StepF9 0.11514 0.0436 Inf 2.638 0.2582
StepF6 - StepF10 0.09701 0.0436 Inf 2.223 0.5328
StepF6 - StepF11 0.12989 0.0436 Inf 2.976 0.1155
StepF6 - StepF12 0.26813 0.0436 Inf 6.143 <.0001
StepF7 - StepF8 0.09536 0.0436 Inf 2.185 0.5607
StepF7 - StepF9 0.09134 0.0436 Inf 2.093 0.6279
StepF7 - StepF10 0.07322 0.0436 Inf 1.678 0.8787
StepF7 - StepF11 0.10610 0.0436 Inf 2.431 0.3848
StepF7 - StepF12 0.24433 0.0436 Inf 5.598 <.0001
StepF8 - StepF9 -0.00401 0.0436 Inf -0.092 1.0000
StepF8 - StepF10 -0.02214 0.0436 Inf -0.507 1.0000
StepF8 - StepF11 0.01074 0.0436 Inf 0.246 1.0000
StepF8 - StepF12 0.14897 0.0436 Inf 3.413 0.0315
StepF9 - StepF10 -0.01813 0.0436 Inf -0.415 1.0000
StepF9 - StepF11 0.01475 0.0436 Inf 0.338 1.0000
StepF9 - StepF12 0.15299 0.0436 Inf 3.505 0.0231
StepF10 - StepF11 0.03288 0.0436 Inf 0.753 0.9998
StepF10 - StepF12 0.17112 0.0436 Inf 3.921 0.0050
StepF11 - StepF12 0.13824 0.0436 Inf 3.167 0.0677
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.15337 0.0436 Inf -3.514 0.0225
StepF1 - StepF3 -0.21372 0.0436 Inf -4.897 0.0001
StepF1 - StepF4 -0.05384 0.0436 Inf -1.234 0.9863
StepF1 - StepF5 -0.01276 0.0436 Inf -0.292 1.0000
StepF1 - StepF6 -0.09963 0.0436 Inf -2.283 0.4889
StepF1 - StepF7 -0.07584 0.0436 Inf -1.738 0.8509
StepF1 - StepF8 0.01952 0.0436 Inf 0.447 1.0000
StepF1 - StepF9 0.01551 0.0436 Inf 0.355 1.0000
StepF1 - StepF10 -0.00262 0.0436 Inf -0.060 1.0000
StepF1 - StepF11 0.03026 0.0436 Inf 0.693 0.9999
StepF1 - StepF12 0.16849 0.0436 Inf 3.861 0.0063
StepF2 - StepF3 -0.06035 0.0436 Inf -1.383 0.9670
StepF2 - StepF4 0.09952 0.0436 Inf 2.280 0.4907
StepF2 - StepF5 0.14061 0.0436 Inf 3.222 0.0576
StepF2 - StepF6 0.05373 0.0436 Inf 1.231 0.9866
StepF2 - StepF7 0.07753 0.0436 Inf 1.776 0.8311
StepF2 - StepF8 0.17289 0.0436 Inf 3.961 0.0043
StepF2 - StepF9 0.16887 0.0436 Inf 3.869 0.0061
StepF2 - StepF10 0.15075 0.0436 Inf 3.454 0.0275
StepF2 - StepF11 0.18363 0.0436 Inf 4.207 0.0015
StepF2 - StepF12 0.32186 0.0436 Inf 7.375 <.0001
StepF3 - StepF4 0.15987 0.0436 Inf 3.663 0.0133
StepF3 - StepF5 0.20096 0.0436 Inf 4.604 0.0003
StepF3 - StepF6 0.11408 0.0436 Inf 2.614 0.2716
StepF3 - StepF7 0.13788 0.0436 Inf 3.159 0.0693
StepF3 - StepF8 0.23324 0.0436 Inf 5.344 <.0001
StepF3 - StepF9 0.22922 0.0436 Inf 5.252 <.0001
StepF3 - StepF10 0.21110 0.0436 Inf 4.837 0.0001
StepF3 - StepF11 0.24397 0.0436 Inf 5.590 <.0001
StepF3 - StepF12 0.38221 0.0436 Inf 8.757 <.0001
StepF4 - StepF5 0.04108 0.0436 Inf 0.941 0.9987
StepF4 - StepF6 -0.04579 0.0436 Inf -1.049 0.9965
StepF4 - StepF7 -0.02199 0.0436 Inf -0.504 1.0000
StepF4 - StepF8 0.07336 0.0436 Inf 1.681 0.8773
StepF4 - StepF9 0.06935 0.0436 Inf 1.589 0.9135
StepF4 - StepF10 0.05122 0.0436 Inf 1.174 0.9909
StepF4 - StepF11 0.08410 0.0436 Inf 1.927 0.7424
StepF4 - StepF12 0.22234 0.0436 Inf 5.094 <.0001
StepF5 - StepF6 -0.08687 0.0436 Inf -1.990 0.7002
StepF5 - StepF7 -0.06308 0.0436 Inf -1.445 0.9545
StepF5 - StepF8 0.03228 0.0436 Inf 0.740 0.9999
StepF5 - StepF9 0.02827 0.0436 Inf 0.648 1.0000
StepF5 - StepF10 0.01014 0.0436 Inf 0.232 1.0000
StepF5 - StepF11 0.04302 0.0436 Inf 0.986 0.9980
StepF5 - StepF12 0.18126 0.0436 Inf 4.153 0.0019
StepF6 - StepF7 0.02380 0.0436 Inf 0.545 1.0000
StepF6 - StepF8 0.11915 0.0436 Inf 2.730 0.2113
StepF6 - StepF9 0.11514 0.0436 Inf 2.638 0.2582
StepF6 - StepF10 0.09701 0.0436 Inf 2.223 0.5328
StepF6 - StepF11 0.12989 0.0436 Inf 2.976 0.1155
StepF6 - StepF12 0.26813 0.0436 Inf 6.143 <.0001
StepF7 - StepF8 0.09536 0.0436 Inf 2.185 0.5607
StepF7 - StepF9 0.09134 0.0436 Inf 2.093 0.6279
StepF7 - StepF10 0.07322 0.0436 Inf 1.678 0.8787
StepF7 - StepF11 0.10610 0.0436 Inf 2.431 0.3848
StepF7 - StepF12 0.24433 0.0436 Inf 5.598 <.0001
StepF8 - StepF9 -0.00401 0.0436 Inf -0.092 1.0000
StepF8 - StepF10 -0.02214 0.0436 Inf -0.507 1.0000
StepF8 - StepF11 0.01074 0.0436 Inf 0.246 1.0000
StepF8 - StepF12 0.14897 0.0436 Inf 3.413 0.0315
StepF9 - StepF10 -0.01813 0.0436 Inf -0.415 1.0000
StepF9 - StepF11 0.01475 0.0436 Inf 0.338 1.0000
StepF9 - StepF12 0.15299 0.0436 Inf 3.505 0.0231
StepF10 - StepF11 0.03288 0.0436 Inf 0.753 0.9998
StepF10 - StepF12 0.17112 0.0436 Inf 3.921 0.0050
StepF11 - StepF12 0.13824 0.0436 Inf 3.167 0.0677
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.15337 0.0436 Inf 3.514 0.0044
StepF3 - StepF2 0.06035 0.0436 Inf 1.383 1.0000
StepF4 - StepF3 -0.15987 0.0436 Inf -3.663 0.0027
StepF5 - StepF4 -0.04108 0.0436 Inf -0.941 1.0000
StepF6 - StepF5 0.08687 0.0436 Inf 1.990 0.3258
StepF7 - StepF6 -0.02380 0.0436 Inf -0.545 1.0000
StepF8 - StepF7 -0.09536 0.0436 Inf -2.185 0.2312
StepF9 - StepF8 0.00401 0.0436 Inf 0.092 1.0000
StepF10 - StepF9 0.01813 0.0436 Inf 0.415 1.0000
StepF11 - StepF10 -0.03288 0.0436 Inf -0.753 1.0000
StepF12 - StepF11 -0.13824 0.0436 Inf -3.167 0.0138
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.15337 0.0436 Inf 3.514 0.0044
StepF3 - StepF2 0.06035 0.0436 Inf 1.383 1.0000
StepF4 - StepF3 -0.15987 0.0436 Inf -3.663 0.0027
StepF5 - StepF4 -0.04108 0.0436 Inf -0.941 1.0000
StepF6 - StepF5 0.08687 0.0436 Inf 1.990 0.3258
StepF7 - StepF6 -0.02380 0.0436 Inf -0.545 1.0000
StepF8 - StepF7 -0.09536 0.0436 Inf -2.185 0.2312
StepF9 - StepF8 0.00401 0.0436 Inf 0.092 1.0000
StepF10 - StepF9 0.01813 0.0436 Inf 0.415 1.0000
StepF11 - StepF10 -0.03288 0.0436 Inf -0.753 1.0000
StepF12 - StepF11 -0.13824 0.0436 Inf -3.167 0.0138
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests
==============================
TEST | Block 4 | 12 steps | Axis Z
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 115.2456 11 <2e-16 ***
Accuracy 0.1742 1 0.6764
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.45 0.161 Inf 1.134 1.77
2 1.67 0.161 Inf 1.357 1.99
3 1.70 0.161 Inf 1.384 2.02
4 1.55 0.161 Inf 1.234 1.87
5 1.41 0.161 Inf 1.089 1.72
6 1.46 0.161 Inf 1.140 1.77
7 1.50 0.161 Inf 1.180 1.81
8 1.39 0.161 Inf 1.076 1.71
9 1.28 0.161 Inf 0.959 1.59
10 1.34 0.161 Inf 1.020 1.65
11 1.34 0.161 Inf 1.023 1.66
12 1.10 0.161 Inf 0.787 1.42
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.49 0.152 Inf 1.189 1.78
2 1.71 0.152 Inf 1.411 2.01
3 1.74 0.152 Inf 1.438 2.03
4 1.59 0.152 Inf 1.288 1.88
5 1.44 0.152 Inf 1.143 1.74
6 1.49 0.152 Inf 1.194 1.79
7 1.53 0.152 Inf 1.235 1.83
8 1.43 0.152 Inf 1.130 1.72
9 1.31 0.152 Inf 1.013 1.61
10 1.37 0.152 Inf 1.075 1.67
11 1.37 0.152 Inf 1.077 1.67
12 1.14 0.152 Inf 0.842 1.44
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.22247 0.0724 Inf -3.074 0.0884
StepF1 - StepF3 -0.24944 0.0724 Inf -3.447 0.0282
StepF1 - StepF4 -0.09932 0.0724 Inf -1.373 0.9688
StepF1 - StepF5 0.04563 0.0724 Inf 0.631 1.0000
StepF1 - StepF6 -0.00519 0.0724 Inf -0.072 1.0000
StepF1 - StepF7 -0.04593 0.0724 Inf -0.635 1.0000
StepF1 - StepF8 0.05869 0.0724 Inf 0.811 0.9997
StepF1 - StepF9 0.17577 0.0724 Inf 2.429 0.3862
StepF1 - StepF10 0.11407 0.0724 Inf 1.576 0.9179
StepF1 - StepF11 0.11184 0.0724 Inf 1.545 0.9279
StepF1 - StepF12 0.34719 0.0724 Inf 4.798 0.0001
StepF2 - StepF3 -0.02697 0.0724 Inf -0.373 1.0000
StepF2 - StepF4 0.12315 0.0724 Inf 1.702 0.8679
StepF2 - StepF5 0.26811 0.0724 Inf 3.705 0.0114
StepF2 - StepF6 0.21728 0.0724 Inf 3.003 0.1076
StepF2 - StepF7 0.17655 0.0724 Inf 2.440 0.3790
StepF2 - StepF8 0.28116 0.0724 Inf 3.885 0.0058
StepF2 - StepF9 0.39824 0.0724 Inf 5.503 <.0001
StepF2 - StepF10 0.33654 0.0724 Inf 4.651 0.0002
StepF2 - StepF11 0.33431 0.0724 Inf 4.620 0.0002
StepF2 - StepF12 0.56966 0.0724 Inf 7.872 <.0001
StepF3 - StepF4 0.15012 0.0724 Inf 2.074 0.6413
StepF3 - StepF5 0.29507 0.0724 Inf 4.078 0.0027
StepF3 - StepF6 0.24425 0.0724 Inf 3.375 0.0356
StepF3 - StepF7 0.20351 0.0724 Inf 2.812 0.1746
StepF3 - StepF8 0.30813 0.0724 Inf 4.258 0.0012
StepF3 - StepF9 0.42521 0.0724 Inf 5.876 <.0001
StepF3 - StepF10 0.36351 0.0724 Inf 5.023 <.0001
StepF3 - StepF11 0.36128 0.0724 Inf 4.992 <.0001
StepF3 - StepF12 0.59663 0.0724 Inf 8.245 <.0001
StepF4 - StepF5 0.14495 0.0724 Inf 2.003 0.6915
StepF4 - StepF6 0.09413 0.0724 Inf 1.301 0.9792
StepF4 - StepF7 0.05340 0.0724 Inf 0.738 0.9999
StepF4 - StepF8 0.15801 0.0724 Inf 2.184 0.5617
StepF4 - StepF9 0.27509 0.0724 Inf 3.801 0.0080
StepF4 - StepF10 0.21339 0.0724 Inf 2.949 0.1241
StepF4 - StepF11 0.21116 0.0724 Inf 2.918 0.1344
StepF4 - StepF12 0.44651 0.0724 Inf 6.170 <.0001
StepF5 - StepF6 -0.05082 0.0724 Inf -0.702 0.9999
StepF5 - StepF7 -0.09156 0.0724 Inf -1.265 0.9833
StepF5 - StepF8 0.01306 0.0724 Inf 0.180 1.0000
StepF5 - StepF9 0.13014 0.0724 Inf 1.798 0.8194
StepF5 - StepF10 0.06843 0.0724 Inf 0.946 0.9986
StepF5 - StepF11 0.06621 0.0724 Inf 0.915 0.9990
StepF5 - StepF12 0.30156 0.0724 Inf 4.167 0.0018
StepF6 - StepF7 -0.04073 0.0724 Inf -0.563 1.0000
StepF6 - StepF8 0.06388 0.0724 Inf 0.883 0.9993
StepF6 - StepF9 0.18096 0.0724 Inf 2.501 0.3393
StepF6 - StepF10 0.11926 0.0724 Inf 1.648 0.8912
StepF6 - StepF11 0.11703 0.0724 Inf 1.617 0.9032
StepF6 - StepF12 0.35238 0.0724 Inf 4.869 0.0001
StepF7 - StepF8 0.10462 0.0724 Inf 1.446 0.9544
StepF7 - StepF9 0.22169 0.0724 Inf 3.064 0.0911
StepF7 - StepF10 0.15999 0.0724 Inf 2.211 0.5416
StepF7 - StepF11 0.15776 0.0724 Inf 2.180 0.5642
StepF7 - StepF12 0.39311 0.0724 Inf 5.432 <.0001
StepF8 - StepF9 0.11708 0.0724 Inf 1.618 0.9030
StepF8 - StepF10 0.05538 0.0724 Inf 0.765 0.9998
StepF8 - StepF11 0.05315 0.0724 Inf 0.734 0.9999
StepF8 - StepF12 0.28850 0.0724 Inf 3.987 0.0039
StepF9 - StepF10 -0.06170 0.0724 Inf -0.853 0.9995
StepF9 - StepF11 -0.06393 0.0724 Inf -0.883 0.9993
StepF9 - StepF12 0.17142 0.0724 Inf 2.369 0.4275
StepF10 - StepF11 -0.00223 0.0724 Inf -0.031 1.0000
StepF10 - StepF12 0.23312 0.0724 Inf 3.221 0.0576
StepF11 - StepF12 0.23535 0.0724 Inf 3.252 0.0525
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.22247 0.0724 Inf -3.074 0.0884
StepF1 - StepF3 -0.24944 0.0724 Inf -3.447 0.0282
StepF1 - StepF4 -0.09932 0.0724 Inf -1.373 0.9688
StepF1 - StepF5 0.04563 0.0724 Inf 0.631 1.0000
StepF1 - StepF6 -0.00519 0.0724 Inf -0.072 1.0000
StepF1 - StepF7 -0.04593 0.0724 Inf -0.635 1.0000
StepF1 - StepF8 0.05869 0.0724 Inf 0.811 0.9997
StepF1 - StepF9 0.17577 0.0724 Inf 2.429 0.3862
StepF1 - StepF10 0.11407 0.0724 Inf 1.576 0.9179
StepF1 - StepF11 0.11184 0.0724 Inf 1.545 0.9279
StepF1 - StepF12 0.34719 0.0724 Inf 4.798 0.0001
StepF2 - StepF3 -0.02697 0.0724 Inf -0.373 1.0000
StepF2 - StepF4 0.12315 0.0724 Inf 1.702 0.8679
StepF2 - StepF5 0.26811 0.0724 Inf 3.705 0.0114
StepF2 - StepF6 0.21728 0.0724 Inf 3.003 0.1076
StepF2 - StepF7 0.17655 0.0724 Inf 2.440 0.3790
StepF2 - StepF8 0.28116 0.0724 Inf 3.885 0.0058
StepF2 - StepF9 0.39824 0.0724 Inf 5.503 <.0001
StepF2 - StepF10 0.33654 0.0724 Inf 4.651 0.0002
StepF2 - StepF11 0.33431 0.0724 Inf 4.620 0.0002
StepF2 - StepF12 0.56966 0.0724 Inf 7.872 <.0001
StepF3 - StepF4 0.15012 0.0724 Inf 2.074 0.6413
StepF3 - StepF5 0.29507 0.0724 Inf 4.078 0.0027
StepF3 - StepF6 0.24425 0.0724 Inf 3.375 0.0356
StepF3 - StepF7 0.20351 0.0724 Inf 2.812 0.1746
StepF3 - StepF8 0.30813 0.0724 Inf 4.258 0.0012
StepF3 - StepF9 0.42521 0.0724 Inf 5.876 <.0001
StepF3 - StepF10 0.36351 0.0724 Inf 5.023 <.0001
StepF3 - StepF11 0.36128 0.0724 Inf 4.992 <.0001
StepF3 - StepF12 0.59663 0.0724 Inf 8.245 <.0001
StepF4 - StepF5 0.14495 0.0724 Inf 2.003 0.6915
StepF4 - StepF6 0.09413 0.0724 Inf 1.301 0.9792
StepF4 - StepF7 0.05340 0.0724 Inf 0.738 0.9999
StepF4 - StepF8 0.15801 0.0724 Inf 2.184 0.5617
StepF4 - StepF9 0.27509 0.0724 Inf 3.801 0.0080
StepF4 - StepF10 0.21339 0.0724 Inf 2.949 0.1241
StepF4 - StepF11 0.21116 0.0724 Inf 2.918 0.1344
StepF4 - StepF12 0.44651 0.0724 Inf 6.170 <.0001
StepF5 - StepF6 -0.05082 0.0724 Inf -0.702 0.9999
StepF5 - StepF7 -0.09156 0.0724 Inf -1.265 0.9833
StepF5 - StepF8 0.01306 0.0724 Inf 0.180 1.0000
StepF5 - StepF9 0.13014 0.0724 Inf 1.798 0.8194
StepF5 - StepF10 0.06843 0.0724 Inf 0.946 0.9986
StepF5 - StepF11 0.06621 0.0724 Inf 0.915 0.9990
StepF5 - StepF12 0.30156 0.0724 Inf 4.167 0.0018
StepF6 - StepF7 -0.04073 0.0724 Inf -0.563 1.0000
StepF6 - StepF8 0.06388 0.0724 Inf 0.883 0.9993
StepF6 - StepF9 0.18096 0.0724 Inf 2.501 0.3393
StepF6 - StepF10 0.11926 0.0724 Inf 1.648 0.8912
StepF6 - StepF11 0.11703 0.0724 Inf 1.617 0.9032
StepF6 - StepF12 0.35238 0.0724 Inf 4.869 0.0001
StepF7 - StepF8 0.10462 0.0724 Inf 1.446 0.9544
StepF7 - StepF9 0.22169 0.0724 Inf 3.064 0.0911
StepF7 - StepF10 0.15999 0.0724 Inf 2.211 0.5416
StepF7 - StepF11 0.15776 0.0724 Inf 2.180 0.5642
StepF7 - StepF12 0.39311 0.0724 Inf 5.432 <.0001
StepF8 - StepF9 0.11708 0.0724 Inf 1.618 0.9030
StepF8 - StepF10 0.05538 0.0724 Inf 0.765 0.9998
StepF8 - StepF11 0.05315 0.0724 Inf 0.734 0.9999
StepF8 - StepF12 0.28850 0.0724 Inf 3.987 0.0039
StepF9 - StepF10 -0.06170 0.0724 Inf -0.853 0.9995
StepF9 - StepF11 -0.06393 0.0724 Inf -0.883 0.9993
StepF9 - StepF12 0.17142 0.0724 Inf 2.369 0.4275
StepF10 - StepF11 -0.00223 0.0724 Inf -0.031 1.0000
StepF10 - StepF12 0.23312 0.0724 Inf 3.221 0.0576
StepF11 - StepF12 0.23535 0.0724 Inf 3.252 0.0525
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.22247 0.0724 Inf 3.074 0.0211
StepF3 - StepF2 0.02697 0.0724 Inf 0.373 1.0000
StepF4 - StepF3 -0.15012 0.0724 Inf -2.074 0.3423
StepF5 - StepF4 -0.14495 0.0724 Inf -2.003 0.3613
StepF6 - StepF5 0.05082 0.0724 Inf 0.702 1.0000
StepF7 - StepF6 0.04073 0.0724 Inf 0.563 1.0000
StepF8 - StepF7 -0.10462 0.0724 Inf -1.446 0.8896
StepF9 - StepF8 -0.11708 0.0724 Inf -1.618 0.7399
StepF10 - StepF9 0.06170 0.0724 Inf 0.853 1.0000
StepF11 - StepF10 0.00223 0.0724 Inf 0.031 1.0000
StepF12 - StepF11 -0.23535 0.0724 Inf -3.252 0.0126
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.22247 0.0724 Inf 3.074 0.0211
StepF3 - StepF2 0.02697 0.0724 Inf 0.373 1.0000
StepF4 - StepF3 -0.15012 0.0724 Inf -2.074 0.3423
StepF5 - StepF4 -0.14495 0.0724 Inf -2.003 0.3613
StepF6 - StepF5 0.05082 0.0724 Inf 0.702 1.0000
StepF7 - StepF6 0.04073 0.0724 Inf 0.563 1.0000
StepF8 - StepF7 -0.10462 0.0724 Inf -1.446 0.8896
StepF9 - StepF8 -0.11708 0.0724 Inf -1.618 0.7399
StepF10 - StepF9 0.06170 0.0724 Inf 0.853 1.0000
StepF11 - StepF10 0.00223 0.0724 Inf 0.031 1.0000
StepF12 - StepF11 -0.23535 0.0724 Inf -3.252 0.0126
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests .report_step_test(sw_b4_18, "4", "18 steps")
==============================
TEST | Block 4 | 18 steps | Axis X
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 141.0528 17 <2e-16 ***
Accuracy 0.0788 1 0.7789
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.692 0.0770 Inf 0.541 0.843
2 0.797 0.0770 Inf 0.646 0.948
3 0.768 0.0770 Inf 0.617 0.919
4 0.760 0.0770 Inf 0.609 0.911
5 0.698 0.0770 Inf 0.547 0.849
6 0.813 0.0770 Inf 0.662 0.964
7 0.684 0.0770 Inf 0.533 0.835
8 0.672 0.0770 Inf 0.521 0.823
9 0.740 0.0770 Inf 0.589 0.891
10 0.657 0.0770 Inf 0.506 0.808
11 0.692 0.0770 Inf 0.541 0.843
12 0.597 0.0770 Inf 0.446 0.748
13 0.651 0.0770 Inf 0.500 0.802
14 0.652 0.0770 Inf 0.501 0.803
15 0.689 0.0770 Inf 0.538 0.840
16 0.644 0.0770 Inf 0.493 0.795
17 0.525 0.0770 Inf 0.374 0.676
18 0.528 0.0770 Inf 0.377 0.679
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.702 0.0738 Inf 0.557 0.846
2 0.807 0.0738 Inf 0.663 0.952
3 0.778 0.0738 Inf 0.633 0.923
4 0.770 0.0738 Inf 0.626 0.915
5 0.707 0.0738 Inf 0.563 0.852
6 0.823 0.0738 Inf 0.678 0.968
7 0.693 0.0738 Inf 0.549 0.838
8 0.682 0.0738 Inf 0.537 0.827
9 0.750 0.0738 Inf 0.605 0.894
10 0.667 0.0738 Inf 0.522 0.812
11 0.701 0.0738 Inf 0.557 0.846
12 0.607 0.0738 Inf 0.462 0.752
13 0.661 0.0738 Inf 0.516 0.805
14 0.662 0.0738 Inf 0.518 0.807
15 0.699 0.0738 Inf 0.554 0.844
16 0.654 0.0738 Inf 0.509 0.799
17 0.535 0.0738 Inf 0.390 0.680
18 0.538 0.0738 Inf 0.393 0.682
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.105678 0.039 Inf -2.708 0.3744
StepF1 - StepF3 -0.076465 0.039 Inf -1.960 0.8883
StepF1 - StepF4 -0.068653 0.039 Inf -1.759 0.9546
StepF1 - StepF5 -0.005838 0.039 Inf -0.150 1.0000
StepF1 - StepF6 -0.121476 0.039 Inf -3.113 0.1503
StepF1 - StepF7 0.008102 0.039 Inf 0.208 1.0000
StepF1 - StepF8 0.019427 0.039 Inf 0.498 1.0000
StepF1 - StepF9 -0.047971 0.039 Inf -1.229 0.9991
StepF1 - StepF10 0.034551 0.039 Inf 0.885 1.0000
StepF1 - StepF11 0.000256 0.039 Inf 0.007 1.0000
StepF1 - StepF12 0.094474 0.039 Inf 2.421 0.5916
StepF1 - StepF13 0.040849 0.039 Inf 1.047 0.9999
StepF1 - StepF14 0.039273 0.039 Inf 1.006 0.9999
StepF1 - StepF15 0.002457 0.039 Inf 0.063 1.0000
StepF1 - StepF16 0.047602 0.039 Inf 1.220 0.9992
StepF1 - StepF17 0.166479 0.039 Inf 4.266 0.0027
StepF1 - StepF18 0.163830 0.039 Inf 4.198 0.0035
StepF2 - StepF3 0.029212 0.039 Inf 0.749 1.0000
StepF2 - StepF4 0.037025 0.039 Inf 0.949 1.0000
StepF2 - StepF5 0.099839 0.039 Inf 2.559 0.4849
StepF2 - StepF6 -0.015798 0.039 Inf -0.405 1.0000
StepF2 - StepF7 0.113779 0.039 Inf 2.916 0.2434
StepF2 - StepF8 0.125104 0.039 Inf 3.206 0.1169
StepF2 - StepF9 0.057707 0.039 Inf 1.479 0.9921
StepF2 - StepF10 0.140229 0.039 Inf 3.594 0.0353
StepF2 - StepF11 0.105933 0.039 Inf 2.715 0.3698
StepF2 - StepF12 0.200151 0.039 Inf 5.129 <.0001
StepF2 - StepF13 0.146527 0.039 Inf 3.755 0.0201
StepF2 - StepF14 0.144950 0.039 Inf 3.715 0.0232
StepF2 - StepF15 0.108135 0.039 Inf 2.771 0.3314
StepF2 - StepF16 0.153279 0.039 Inf 3.928 0.0105
StepF2 - StepF17 0.272156 0.039 Inf 6.975 <.0001
StepF2 - StepF18 0.269508 0.039 Inf 6.907 <.0001
StepF3 - StepF4 0.007812 0.039 Inf 0.200 1.0000
StepF3 - StepF5 0.070627 0.039 Inf 1.810 0.9416
StepF3 - StepF6 -0.045011 0.039 Inf -1.153 0.9996
StepF3 - StepF7 0.084567 0.039 Inf 2.167 0.7756
StepF3 - StepF8 0.095892 0.039 Inf 2.457 0.5634
StepF3 - StepF9 0.028495 0.039 Inf 0.730 1.0000
StepF3 - StepF10 0.111017 0.039 Inf 2.845 0.2845
StepF3 - StepF11 0.076721 0.039 Inf 1.966 0.8854
StepF3 - StepF12 0.170939 0.039 Inf 4.381 0.0016
StepF3 - StepF13 0.117315 0.039 Inf 3.006 0.1968
StepF3 - StepF14 0.115738 0.039 Inf 2.966 0.2168
StepF3 - StepF15 0.078923 0.039 Inf 2.023 0.8588
StepF3 - StepF16 0.124067 0.039 Inf 3.179 0.1258
StepF3 - StepF17 0.242944 0.039 Inf 6.226 <.0001
StepF3 - StepF18 0.240295 0.039 Inf 6.158 <.0001
StepF4 - StepF5 0.062815 0.039 Inf 1.610 0.9807
StepF4 - StepF6 -0.052823 0.039 Inf -1.354 0.9971
StepF4 - StepF7 0.076755 0.039 Inf 1.967 0.8851
StepF4 - StepF8 0.088080 0.039 Inf 2.257 0.7145
StepF4 - StepF9 0.020682 0.039 Inf 0.530 1.0000
StepF4 - StepF10 0.103204 0.039 Inf 2.645 0.4200
StepF4 - StepF11 0.068909 0.039 Inf 1.766 0.9531
StepF4 - StepF12 0.163127 0.039 Inf 4.180 0.0038
StepF4 - StepF13 0.109502 0.039 Inf 2.806 0.3087
StepF4 - StepF14 0.107926 0.039 Inf 2.766 0.3350
StepF4 - StepF15 0.071110 0.039 Inf 1.822 0.9381
StepF4 - StepF16 0.116255 0.039 Inf 2.979 0.2101
StepF4 - StepF17 0.235132 0.039 Inf 6.026 <.0001
StepF4 - StepF18 0.232483 0.039 Inf 5.958 <.0001
StepF5 - StepF6 -0.115637 0.039 Inf -2.963 0.2181
StepF5 - StepF7 0.013940 0.039 Inf 0.357 1.0000
StepF5 - StepF8 0.025265 0.039 Inf 0.647 1.0000
StepF5 - StepF9 -0.042132 0.039 Inf -1.080 0.9998
StepF5 - StepF10 0.040390 0.039 Inf 1.035 0.9999
StepF5 - StepF11 0.006094 0.039 Inf 0.156 1.0000
StepF5 - StepF12 0.100312 0.039 Inf 2.571 0.4757
StepF5 - StepF13 0.046688 0.039 Inf 1.196 0.9994
StepF5 - StepF14 0.045111 0.039 Inf 1.156 0.9996
StepF5 - StepF15 0.008296 0.039 Inf 0.213 1.0000
StepF5 - StepF16 0.053440 0.039 Inf 1.370 0.9967
StepF5 - StepF17 0.172317 0.039 Inf 4.416 0.0014
StepF5 - StepF18 0.169669 0.039 Inf 4.348 0.0019
StepF6 - StepF7 0.129577 0.039 Inf 3.321 0.0841
StepF6 - StepF8 0.140903 0.039 Inf 3.611 0.0333
StepF6 - StepF9 0.073505 0.039 Inf 1.884 0.9183
StepF6 - StepF10 0.156027 0.039 Inf 3.998 0.0080
StepF6 - StepF11 0.121732 0.039 Inf 3.120 0.1477
StepF6 - StepF12 0.215950 0.039 Inf 5.534 <.0001
StepF6 - StepF13 0.162325 0.039 Inf 4.160 0.0042
StepF6 - StepF14 0.160749 0.039 Inf 4.119 0.0049
StepF6 - StepF15 0.123933 0.039 Inf 3.176 0.1270
StepF6 - StepF16 0.169078 0.039 Inf 4.333 0.0020
StepF6 - StepF17 0.287954 0.039 Inf 7.379 <.0001
StepF6 - StepF18 0.285306 0.039 Inf 7.312 <.0001
StepF7 - StepF8 0.011325 0.039 Inf 0.290 1.0000
StepF7 - StepF9 -0.056072 0.039 Inf -1.437 0.9943
StepF7 - StepF10 0.026450 0.039 Inf 0.678 1.0000
StepF7 - StepF11 -0.007846 0.039 Inf -0.201 1.0000
StepF7 - StepF12 0.086372 0.039 Inf 2.213 0.7449
StepF7 - StepF13 0.032748 0.039 Inf 0.839 1.0000
StepF7 - StepF14 0.031171 0.039 Inf 0.799 1.0000
StepF7 - StepF15 -0.005644 0.039 Inf -0.145 1.0000
StepF7 - StepF16 0.039500 0.039 Inf 1.012 0.9999
StepF7 - StepF17 0.158377 0.039 Inf 4.059 0.0063
StepF7 - StepF18 0.155729 0.039 Inf 3.991 0.0082
StepF8 - StepF9 -0.067398 0.039 Inf -1.727 0.9617
StepF8 - StepF10 0.015125 0.039 Inf 0.388 1.0000
StepF8 - StepF11 -0.019171 0.039 Inf -0.491 1.0000
StepF8 - StepF12 0.075047 0.039 Inf 1.923 0.9034
StepF8 - StepF13 0.021422 0.039 Inf 0.549 1.0000
StepF8 - StepF14 0.019846 0.039 Inf 0.509 1.0000
StepF8 - StepF15 -0.016969 0.039 Inf -0.435 1.0000
StepF8 - StepF16 0.028175 0.039 Inf 0.722 1.0000
StepF8 - StepF17 0.147052 0.039 Inf 3.768 0.0191
StepF8 - StepF18 0.144403 0.039 Inf 3.701 0.0244
StepF9 - StepF10 0.082522 0.039 Inf 2.115 0.8081
StepF9 - StepF11 0.048227 0.039 Inf 1.236 0.9991
StepF9 - StepF12 0.142444 0.039 Inf 3.650 0.0291
StepF9 - StepF13 0.088820 0.039 Inf 2.276 0.7009
StepF9 - StepF14 0.087243 0.039 Inf 2.236 0.7296
StepF9 - StepF15 0.050428 0.039 Inf 1.292 0.9984
StepF9 - StepF16 0.095573 0.039 Inf 2.449 0.5698
StepF9 - StepF17 0.214449 0.039 Inf 5.496 <.0001
StepF9 - StepF18 0.211801 0.039 Inf 5.428 <.0001
StepF10 - StepF11 -0.034296 0.039 Inf -0.879 1.0000
StepF10 - StepF12 0.059922 0.039 Inf 1.536 0.9881
StepF10 - StepF13 0.006298 0.039 Inf 0.161 1.0000
StepF10 - StepF14 0.004721 0.039 Inf 0.121 1.0000
StepF10 - StepF15 -0.032094 0.039 Inf -0.822 1.0000
StepF10 - StepF16 0.013050 0.039 Inf 0.334 1.0000
StepF10 - StepF17 0.131927 0.039 Inf 3.381 0.0702
StepF10 - StepF18 0.129279 0.039 Inf 3.313 0.0861
StepF11 - StepF12 0.094218 0.039 Inf 2.415 0.5967
StepF11 - StepF13 0.040593 0.039 Inf 1.040 0.9999
StepF11 - StepF14 0.039017 0.039 Inf 1.000 0.9999
StepF11 - StepF15 0.002202 0.039 Inf 0.056 1.0000
StepF11 - StepF16 0.047346 0.039 Inf 1.213 0.9993
StepF11 - StepF17 0.166223 0.039 Inf 4.260 0.0027
StepF11 - StepF18 0.163574 0.039 Inf 4.192 0.0036
StepF12 - StepF13 -0.053624 0.039 Inf -1.374 0.9966
StepF12 - StepF14 -0.055201 0.039 Inf -1.415 0.9952
StepF12 - StepF15 -0.092016 0.039 Inf -2.358 0.6401
StepF12 - StepF16 -0.046872 0.039 Inf -1.201 0.9993
StepF12 - StepF17 0.072005 0.039 Inf 1.845 0.9311
StepF12 - StepF18 0.069356 0.039 Inf 1.777 0.9503
StepF13 - StepF14 -0.001577 0.039 Inf -0.040 1.0000
StepF13 - StepF15 -0.038392 0.039 Inf -0.984 1.0000
StepF13 - StepF16 0.006753 0.039 Inf 0.173 1.0000
StepF13 - StepF17 0.125629 0.039 Inf 3.219 0.1126
StepF13 - StepF18 0.122981 0.039 Inf 3.152 0.1357
StepF14 - StepF15 -0.036815 0.039 Inf -0.943 1.0000
StepF14 - StepF16 0.008329 0.039 Inf 0.213 1.0000
StepF14 - StepF17 0.127206 0.039 Inf 3.260 0.1005
StepF14 - StepF18 0.124557 0.039 Inf 3.192 0.1216
StepF15 - StepF16 0.045144 0.039 Inf 1.157 0.9996
StepF15 - StepF17 0.164021 0.039 Inf 4.203 0.0035
StepF15 - StepF18 0.161373 0.039 Inf 4.135 0.0046
StepF16 - StepF17 0.118877 0.039 Inf 3.046 0.1782
StepF16 - StepF18 0.116228 0.039 Inf 2.979 0.2104
StepF17 - StepF18 -0.002648 0.039 Inf -0.068 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.105678 0.039 Inf -2.708 0.3744
StepF1 - StepF3 -0.076465 0.039 Inf -1.960 0.8883
StepF1 - StepF4 -0.068653 0.039 Inf -1.759 0.9546
StepF1 - StepF5 -0.005838 0.039 Inf -0.150 1.0000
StepF1 - StepF6 -0.121476 0.039 Inf -3.113 0.1503
StepF1 - StepF7 0.008102 0.039 Inf 0.208 1.0000
StepF1 - StepF8 0.019427 0.039 Inf 0.498 1.0000
StepF1 - StepF9 -0.047971 0.039 Inf -1.229 0.9991
StepF1 - StepF10 0.034551 0.039 Inf 0.885 1.0000
StepF1 - StepF11 0.000256 0.039 Inf 0.007 1.0000
StepF1 - StepF12 0.094474 0.039 Inf 2.421 0.5916
StepF1 - StepF13 0.040849 0.039 Inf 1.047 0.9999
StepF1 - StepF14 0.039273 0.039 Inf 1.006 0.9999
StepF1 - StepF15 0.002457 0.039 Inf 0.063 1.0000
StepF1 - StepF16 0.047602 0.039 Inf 1.220 0.9992
StepF1 - StepF17 0.166479 0.039 Inf 4.266 0.0027
StepF1 - StepF18 0.163830 0.039 Inf 4.198 0.0035
StepF2 - StepF3 0.029212 0.039 Inf 0.749 1.0000
StepF2 - StepF4 0.037025 0.039 Inf 0.949 1.0000
StepF2 - StepF5 0.099839 0.039 Inf 2.559 0.4849
StepF2 - StepF6 -0.015798 0.039 Inf -0.405 1.0000
StepF2 - StepF7 0.113779 0.039 Inf 2.916 0.2434
StepF2 - StepF8 0.125104 0.039 Inf 3.206 0.1169
StepF2 - StepF9 0.057707 0.039 Inf 1.479 0.9921
StepF2 - StepF10 0.140229 0.039 Inf 3.594 0.0353
StepF2 - StepF11 0.105933 0.039 Inf 2.715 0.3698
StepF2 - StepF12 0.200151 0.039 Inf 5.129 <.0001
StepF2 - StepF13 0.146527 0.039 Inf 3.755 0.0201
StepF2 - StepF14 0.144950 0.039 Inf 3.715 0.0232
StepF2 - StepF15 0.108135 0.039 Inf 2.771 0.3314
StepF2 - StepF16 0.153279 0.039 Inf 3.928 0.0105
StepF2 - StepF17 0.272156 0.039 Inf 6.975 <.0001
StepF2 - StepF18 0.269508 0.039 Inf 6.907 <.0001
StepF3 - StepF4 0.007812 0.039 Inf 0.200 1.0000
StepF3 - StepF5 0.070627 0.039 Inf 1.810 0.9416
StepF3 - StepF6 -0.045011 0.039 Inf -1.153 0.9996
StepF3 - StepF7 0.084567 0.039 Inf 2.167 0.7756
StepF3 - StepF8 0.095892 0.039 Inf 2.457 0.5634
StepF3 - StepF9 0.028495 0.039 Inf 0.730 1.0000
StepF3 - StepF10 0.111017 0.039 Inf 2.845 0.2845
StepF3 - StepF11 0.076721 0.039 Inf 1.966 0.8854
StepF3 - StepF12 0.170939 0.039 Inf 4.381 0.0016
StepF3 - StepF13 0.117315 0.039 Inf 3.006 0.1968
StepF3 - StepF14 0.115738 0.039 Inf 2.966 0.2168
StepF3 - StepF15 0.078923 0.039 Inf 2.023 0.8588
StepF3 - StepF16 0.124067 0.039 Inf 3.179 0.1258
StepF3 - StepF17 0.242944 0.039 Inf 6.226 <.0001
StepF3 - StepF18 0.240295 0.039 Inf 6.158 <.0001
StepF4 - StepF5 0.062815 0.039 Inf 1.610 0.9807
StepF4 - StepF6 -0.052823 0.039 Inf -1.354 0.9971
StepF4 - StepF7 0.076755 0.039 Inf 1.967 0.8851
StepF4 - StepF8 0.088080 0.039 Inf 2.257 0.7145
StepF4 - StepF9 0.020682 0.039 Inf 0.530 1.0000
StepF4 - StepF10 0.103204 0.039 Inf 2.645 0.4200
StepF4 - StepF11 0.068909 0.039 Inf 1.766 0.9531
StepF4 - StepF12 0.163127 0.039 Inf 4.180 0.0038
StepF4 - StepF13 0.109502 0.039 Inf 2.806 0.3087
StepF4 - StepF14 0.107926 0.039 Inf 2.766 0.3350
StepF4 - StepF15 0.071110 0.039 Inf 1.822 0.9381
StepF4 - StepF16 0.116255 0.039 Inf 2.979 0.2101
StepF4 - StepF17 0.235132 0.039 Inf 6.026 <.0001
StepF4 - StepF18 0.232483 0.039 Inf 5.958 <.0001
StepF5 - StepF6 -0.115637 0.039 Inf -2.963 0.2181
StepF5 - StepF7 0.013940 0.039 Inf 0.357 1.0000
StepF5 - StepF8 0.025265 0.039 Inf 0.647 1.0000
StepF5 - StepF9 -0.042132 0.039 Inf -1.080 0.9998
StepF5 - StepF10 0.040390 0.039 Inf 1.035 0.9999
StepF5 - StepF11 0.006094 0.039 Inf 0.156 1.0000
StepF5 - StepF12 0.100312 0.039 Inf 2.571 0.4757
StepF5 - StepF13 0.046688 0.039 Inf 1.196 0.9994
StepF5 - StepF14 0.045111 0.039 Inf 1.156 0.9996
StepF5 - StepF15 0.008296 0.039 Inf 0.213 1.0000
StepF5 - StepF16 0.053440 0.039 Inf 1.370 0.9967
StepF5 - StepF17 0.172317 0.039 Inf 4.416 0.0014
StepF5 - StepF18 0.169669 0.039 Inf 4.348 0.0019
StepF6 - StepF7 0.129577 0.039 Inf 3.321 0.0841
StepF6 - StepF8 0.140903 0.039 Inf 3.611 0.0333
StepF6 - StepF9 0.073505 0.039 Inf 1.884 0.9183
StepF6 - StepF10 0.156027 0.039 Inf 3.998 0.0080
StepF6 - StepF11 0.121732 0.039 Inf 3.120 0.1477
StepF6 - StepF12 0.215950 0.039 Inf 5.534 <.0001
StepF6 - StepF13 0.162325 0.039 Inf 4.160 0.0042
StepF6 - StepF14 0.160749 0.039 Inf 4.119 0.0049
StepF6 - StepF15 0.123933 0.039 Inf 3.176 0.1270
StepF6 - StepF16 0.169078 0.039 Inf 4.333 0.0020
StepF6 - StepF17 0.287954 0.039 Inf 7.379 <.0001
StepF6 - StepF18 0.285306 0.039 Inf 7.312 <.0001
StepF7 - StepF8 0.011325 0.039 Inf 0.290 1.0000
StepF7 - StepF9 -0.056072 0.039 Inf -1.437 0.9943
StepF7 - StepF10 0.026450 0.039 Inf 0.678 1.0000
StepF7 - StepF11 -0.007846 0.039 Inf -0.201 1.0000
StepF7 - StepF12 0.086372 0.039 Inf 2.213 0.7449
StepF7 - StepF13 0.032748 0.039 Inf 0.839 1.0000
StepF7 - StepF14 0.031171 0.039 Inf 0.799 1.0000
StepF7 - StepF15 -0.005644 0.039 Inf -0.145 1.0000
StepF7 - StepF16 0.039500 0.039 Inf 1.012 0.9999
StepF7 - StepF17 0.158377 0.039 Inf 4.059 0.0063
StepF7 - StepF18 0.155729 0.039 Inf 3.991 0.0082
StepF8 - StepF9 -0.067398 0.039 Inf -1.727 0.9617
StepF8 - StepF10 0.015125 0.039 Inf 0.388 1.0000
StepF8 - StepF11 -0.019171 0.039 Inf -0.491 1.0000
StepF8 - StepF12 0.075047 0.039 Inf 1.923 0.9034
StepF8 - StepF13 0.021422 0.039 Inf 0.549 1.0000
StepF8 - StepF14 0.019846 0.039 Inf 0.509 1.0000
StepF8 - StepF15 -0.016969 0.039 Inf -0.435 1.0000
StepF8 - StepF16 0.028175 0.039 Inf 0.722 1.0000
StepF8 - StepF17 0.147052 0.039 Inf 3.768 0.0191
StepF8 - StepF18 0.144403 0.039 Inf 3.701 0.0244
StepF9 - StepF10 0.082522 0.039 Inf 2.115 0.8081
StepF9 - StepF11 0.048227 0.039 Inf 1.236 0.9991
StepF9 - StepF12 0.142444 0.039 Inf 3.650 0.0291
StepF9 - StepF13 0.088820 0.039 Inf 2.276 0.7009
StepF9 - StepF14 0.087243 0.039 Inf 2.236 0.7296
StepF9 - StepF15 0.050428 0.039 Inf 1.292 0.9984
StepF9 - StepF16 0.095573 0.039 Inf 2.449 0.5698
StepF9 - StepF17 0.214449 0.039 Inf 5.496 <.0001
StepF9 - StepF18 0.211801 0.039 Inf 5.428 <.0001
StepF10 - StepF11 -0.034296 0.039 Inf -0.879 1.0000
StepF10 - StepF12 0.059922 0.039 Inf 1.536 0.9881
StepF10 - StepF13 0.006298 0.039 Inf 0.161 1.0000
StepF10 - StepF14 0.004721 0.039 Inf 0.121 1.0000
StepF10 - StepF15 -0.032094 0.039 Inf -0.822 1.0000
StepF10 - StepF16 0.013050 0.039 Inf 0.334 1.0000
StepF10 - StepF17 0.131927 0.039 Inf 3.381 0.0702
StepF10 - StepF18 0.129279 0.039 Inf 3.313 0.0861
StepF11 - StepF12 0.094218 0.039 Inf 2.415 0.5967
StepF11 - StepF13 0.040593 0.039 Inf 1.040 0.9999
StepF11 - StepF14 0.039017 0.039 Inf 1.000 0.9999
StepF11 - StepF15 0.002202 0.039 Inf 0.056 1.0000
StepF11 - StepF16 0.047346 0.039 Inf 1.213 0.9993
StepF11 - StepF17 0.166223 0.039 Inf 4.260 0.0027
StepF11 - StepF18 0.163574 0.039 Inf 4.192 0.0036
StepF12 - StepF13 -0.053624 0.039 Inf -1.374 0.9966
StepF12 - StepF14 -0.055201 0.039 Inf -1.415 0.9952
StepF12 - StepF15 -0.092016 0.039 Inf -2.358 0.6401
StepF12 - StepF16 -0.046872 0.039 Inf -1.201 0.9993
StepF12 - StepF17 0.072005 0.039 Inf 1.845 0.9311
StepF12 - StepF18 0.069356 0.039 Inf 1.777 0.9503
StepF13 - StepF14 -0.001577 0.039 Inf -0.040 1.0000
StepF13 - StepF15 -0.038392 0.039 Inf -0.984 1.0000
StepF13 - StepF16 0.006753 0.039 Inf 0.173 1.0000
StepF13 - StepF17 0.125629 0.039 Inf 3.219 0.1126
StepF13 - StepF18 0.122981 0.039 Inf 3.152 0.1357
StepF14 - StepF15 -0.036815 0.039 Inf -0.943 1.0000
StepF14 - StepF16 0.008329 0.039 Inf 0.213 1.0000
StepF14 - StepF17 0.127206 0.039 Inf 3.260 0.1005
StepF14 - StepF18 0.124557 0.039 Inf 3.192 0.1216
StepF15 - StepF16 0.045144 0.039 Inf 1.157 0.9996
StepF15 - StepF17 0.164021 0.039 Inf 4.203 0.0035
StepF15 - StepF18 0.161373 0.039 Inf 4.135 0.0046
StepF16 - StepF17 0.118877 0.039 Inf 3.046 0.1782
StepF16 - StepF18 0.116228 0.039 Inf 2.979 0.2104
StepF17 - StepF18 -0.002648 0.039 Inf -0.068 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.10568 0.039 Inf 2.708 0.0947
StepF3 - StepF2 -0.02921 0.039 Inf -0.749 1.0000
StepF4 - StepF3 -0.00781 0.039 Inf -0.200 1.0000
StepF5 - StepF4 -0.06281 0.039 Inf -1.610 1.0000
StepF6 - StepF5 0.11564 0.039 Inf 2.963 0.0456
StepF7 - StepF6 -0.12958 0.039 Inf -3.321 0.0153
StepF8 - StepF7 -0.01133 0.039 Inf -0.290 1.0000
StepF9 - StepF8 0.06740 0.039 Inf 1.727 0.9255
StepF10 - StepF9 -0.08252 0.039 Inf -2.115 0.4134
StepF11 - StepF10 0.03430 0.039 Inf 0.879 1.0000
StepF12 - StepF11 -0.09422 0.039 Inf -2.415 0.2048
StepF13 - StepF12 0.05362 0.039 Inf 1.374 1.0000
StepF14 - StepF13 0.00158 0.039 Inf 0.040 1.0000
StepF15 - StepF14 0.03682 0.039 Inf 0.943 1.0000
StepF16 - StepF15 -0.04514 0.039 Inf -1.157 1.0000
StepF17 - StepF16 -0.11888 0.039 Inf -3.046 0.0371
StepF18 - StepF17 0.00265 0.039 Inf 0.068 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.10568 0.039 Inf 2.708 0.0947
StepF3 - StepF2 -0.02921 0.039 Inf -0.749 1.0000
StepF4 - StepF3 -0.00781 0.039 Inf -0.200 1.0000
StepF5 - StepF4 -0.06281 0.039 Inf -1.610 1.0000
StepF6 - StepF5 0.11564 0.039 Inf 2.963 0.0456
StepF7 - StepF6 -0.12958 0.039 Inf -3.321 0.0153
StepF8 - StepF7 -0.01133 0.039 Inf -0.290 1.0000
StepF9 - StepF8 0.06740 0.039 Inf 1.727 0.9255
StepF10 - StepF9 -0.08252 0.039 Inf -2.115 0.4134
StepF11 - StepF10 0.03430 0.039 Inf 0.879 1.0000
StepF12 - StepF11 -0.09422 0.039 Inf -2.415 0.2048
StepF13 - StepF12 0.05362 0.039 Inf 1.374 1.0000
StepF14 - StepF13 0.00158 0.039 Inf 0.040 1.0000
StepF15 - StepF14 0.03682 0.039 Inf 0.943 1.0000
StepF16 - StepF15 -0.04514 0.039 Inf -1.157 1.0000
StepF17 - StepF16 -0.11888 0.039 Inf -3.046 0.0371
StepF18 - StepF17 0.00265 0.039 Inf 0.068 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests
==============================
TEST | Block 4 | 18 steps | Axis Y
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 179.2988 17 <2e-16 ***
Accuracy 0.4899 1 0.484
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.773 0.0895 Inf 0.597 0.948
2 0.918 0.0895 Inf 0.743 1.094
3 0.886 0.0895 Inf 0.710 1.061
4 0.744 0.0895 Inf 0.569 0.920
5 0.779 0.0895 Inf 0.603 0.954
6 0.864 0.0895 Inf 0.689 1.040
7 0.767 0.0895 Inf 0.592 0.943
8 0.689 0.0895 Inf 0.513 0.864
9 0.738 0.0895 Inf 0.563 0.913
10 0.794 0.0895 Inf 0.618 0.969
11 0.695 0.0895 Inf 0.520 0.871
12 0.622 0.0895 Inf 0.446 0.797
13 0.684 0.0895 Inf 0.509 0.860
14 0.652 0.0895 Inf 0.477 0.828
15 0.700 0.0895 Inf 0.525 0.876
16 0.660 0.0895 Inf 0.485 0.835
17 0.627 0.0895 Inf 0.451 0.802
18 0.562 0.0895 Inf 0.387 0.737
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.800 0.0860 Inf 0.632 0.969
2 0.946 0.0860 Inf 0.777 1.114
3 0.913 0.0860 Inf 0.745 1.082
4 0.772 0.0860 Inf 0.603 0.941
5 0.806 0.0860 Inf 0.637 0.975
6 0.892 0.0860 Inf 0.723 1.060
7 0.795 0.0860 Inf 0.626 0.963
8 0.716 0.0860 Inf 0.548 0.885
9 0.766 0.0860 Inf 0.597 0.934
10 0.821 0.0860 Inf 0.652 0.990
11 0.723 0.0860 Inf 0.554 0.891
12 0.649 0.0860 Inf 0.481 0.818
13 0.712 0.0860 Inf 0.543 0.880
14 0.680 0.0860 Inf 0.511 0.849
15 0.728 0.0860 Inf 0.559 0.896
16 0.687 0.0860 Inf 0.519 0.856
17 0.654 0.0860 Inf 0.486 0.823
18 0.590 0.0860 Inf 0.421 0.758
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.14552 0.0416 Inf -3.500 0.0482
StepF1 - StepF3 -0.11317 0.0416 Inf -2.722 0.3648
StepF1 - StepF4 0.02810 0.0416 Inf 0.676 1.0000
StepF1 - StepF5 -0.00596 0.0416 Inf -0.143 1.0000
StepF1 - StepF6 -0.09175 0.0416 Inf -2.207 0.7494
StepF1 - StepF7 0.00528 0.0416 Inf 0.127 1.0000
StepF1 - StepF8 0.08397 0.0416 Inf 2.020 0.8602
StepF1 - StepF9 0.03451 0.0416 Inf 0.830 1.0000
StepF1 - StepF10 -0.02096 0.0416 Inf -0.504 1.0000
StepF1 - StepF11 0.07722 0.0416 Inf 1.857 0.9273
StepF1 - StepF12 0.15090 0.0416 Inf 3.629 0.0313
StepF1 - StepF13 0.08825 0.0416 Inf 2.123 0.8033
StepF1 - StepF14 0.12014 0.0416 Inf 2.890 0.2581
StepF1 - StepF15 0.07243 0.0416 Inf 1.742 0.9585
StepF1 - StepF16 0.11264 0.0416 Inf 2.709 0.3737
StepF1 - StepF17 0.14587 0.0416 Inf 3.509 0.0469
StepF1 - StepF18 0.21053 0.0416 Inf 5.064 0.0001
StepF2 - StepF3 0.03235 0.0416 Inf 0.778 1.0000
StepF2 - StepF4 0.17362 0.0416 Inf 4.176 0.0039
StepF2 - StepF5 0.13956 0.0416 Inf 3.357 0.0755
StepF2 - StepF6 0.05376 0.0416 Inf 1.293 0.9984
StepF2 - StepF7 0.15079 0.0416 Inf 3.627 0.0316
StepF2 - StepF8 0.22949 0.0416 Inf 5.520 <.0001
StepF2 - StepF9 0.18002 0.0416 Inf 4.330 0.0020
StepF2 - StepF10 0.12456 0.0416 Inf 2.996 0.2019
StepF2 - StepF11 0.22273 0.0416 Inf 5.357 <.0001
StepF2 - StepF12 0.29641 0.0416 Inf 7.129 <.0001
StepF2 - StepF13 0.23377 0.0416 Inf 5.623 <.0001
StepF2 - StepF14 0.26566 0.0416 Inf 6.390 <.0001
StepF2 - StepF15 0.21795 0.0416 Inf 5.242 <.0001
StepF2 - StepF16 0.25816 0.0416 Inf 6.209 <.0001
StepF2 - StepF17 0.29139 0.0416 Inf 7.008 <.0001
StepF2 - StepF18 0.35604 0.0416 Inf 8.564 <.0001
StepF3 - StepF4 0.14127 0.0416 Inf 3.398 0.0666
StepF3 - StepF5 0.10721 0.0416 Inf 2.579 0.4695
StepF3 - StepF6 0.02142 0.0416 Inf 0.515 1.0000
StepF3 - StepF7 0.11845 0.0416 Inf 2.849 0.2821
StepF3 - StepF8 0.19714 0.0416 Inf 4.742 0.0003
StepF3 - StepF9 0.14768 0.0416 Inf 3.552 0.0406
StepF3 - StepF10 0.09221 0.0416 Inf 2.218 0.7419
StepF3 - StepF11 0.19039 0.0416 Inf 4.579 0.0007
StepF3 - StepF12 0.26407 0.0416 Inf 6.351 <.0001
StepF3 - StepF13 0.20142 0.0416 Inf 4.845 0.0002
StepF3 - StepF14 0.23331 0.0416 Inf 5.612 <.0001
StepF3 - StepF15 0.18560 0.0416 Inf 4.464 0.0011
StepF3 - StepF16 0.22581 0.0416 Inf 5.431 <.0001
StepF3 - StepF17 0.25904 0.0416 Inf 6.230 <.0001
StepF3 - StepF18 0.32370 0.0416 Inf 7.786 <.0001
StepF4 - StepF5 -0.03406 0.0416 Inf -0.819 1.0000
StepF4 - StepF6 -0.11985 0.0416 Inf -2.883 0.2622
StepF4 - StepF7 -0.02282 0.0416 Inf -0.549 1.0000
StepF4 - StepF8 0.05587 0.0416 Inf 1.344 0.9974
StepF4 - StepF9 0.00641 0.0416 Inf 0.154 1.0000
StepF4 - StepF10 -0.04906 0.0416 Inf -1.180 0.9995
StepF4 - StepF11 0.04912 0.0416 Inf 1.181 0.9995
StepF4 - StepF12 0.12280 0.0416 Inf 2.954 0.2232
StepF4 - StepF13 0.06015 0.0416 Inf 1.447 0.9938
StepF4 - StepF14 0.09204 0.0416 Inf 2.214 0.7447
StepF4 - StepF15 0.04433 0.0416 Inf 1.066 0.9999
StepF4 - StepF16 0.08454 0.0416 Inf 2.033 0.8533
StepF4 - StepF17 0.11777 0.0416 Inf 2.833 0.2921
StepF4 - StepF18 0.18243 0.0416 Inf 4.388 0.0016
StepF5 - StepF6 -0.08580 0.0416 Inf -2.064 0.8373
StepF5 - StepF7 0.01124 0.0416 Inf 0.270 1.0000
StepF5 - StepF8 0.08993 0.0416 Inf 2.163 0.7783
StepF5 - StepF9 0.04046 0.0416 Inf 0.973 1.0000
StepF5 - StepF10 -0.01500 0.0416 Inf -0.361 1.0000
StepF5 - StepF11 0.08317 0.0416 Inf 2.000 0.8696
StepF5 - StepF12 0.15686 0.0416 Inf 3.773 0.0188
StepF5 - StepF13 0.09421 0.0416 Inf 2.266 0.7083
StepF5 - StepF14 0.12610 0.0416 Inf 3.033 0.1843
StepF5 - StepF15 0.07839 0.0416 Inf 1.885 0.9176
StepF5 - StepF16 0.11860 0.0416 Inf 2.852 0.2800
StepF5 - StepF17 0.15183 0.0416 Inf 3.652 0.0290
StepF5 - StepF18 0.21648 0.0416 Inf 5.207 <.0001
StepF6 - StepF7 0.09703 0.0416 Inf 2.334 0.6584
StepF6 - StepF8 0.17573 0.0416 Inf 4.227 0.0032
StepF6 - StepF9 0.12626 0.0416 Inf 3.037 0.1826
StepF6 - StepF10 0.07079 0.0416 Inf 1.703 0.9665
StepF6 - StepF11 0.16897 0.0416 Inf 4.064 0.0062
StepF6 - StepF12 0.24265 0.0416 Inf 5.836 <.0001
StepF6 - StepF13 0.18001 0.0416 Inf 4.330 0.0020
StepF6 - StepF14 0.21190 0.0416 Inf 5.097 0.0001
StepF6 - StepF15 0.16419 0.0416 Inf 3.949 0.0097
StepF6 - StepF16 0.20439 0.0416 Inf 4.916 0.0001
StepF6 - StepF17 0.23763 0.0416 Inf 5.715 <.0001
StepF6 - StepF18 0.30228 0.0416 Inf 7.270 <.0001
StepF7 - StepF8 0.07869 0.0416 Inf 1.893 0.9150
StepF7 - StepF9 0.02923 0.0416 Inf 0.703 1.0000
StepF7 - StepF10 -0.02624 0.0416 Inf -0.631 1.0000
StepF7 - StepF11 0.07194 0.0416 Inf 1.730 0.9610
StepF7 - StepF12 0.14562 0.0416 Inf 3.502 0.0478
StepF7 - StepF13 0.08298 0.0416 Inf 1.996 0.8719
StepF7 - StepF14 0.11487 0.0416 Inf 2.763 0.3370
StepF7 - StepF15 0.06716 0.0416 Inf 1.615 0.9800
StepF7 - StepF16 0.10736 0.0416 Inf 2.582 0.4668
StepF7 - StepF17 0.14059 0.0416 Inf 3.382 0.0700
StepF7 - StepF18 0.20525 0.0416 Inf 4.937 0.0001
StepF8 - StepF9 -0.04947 0.0416 Inf -1.190 0.9994
StepF8 - StepF10 -0.10493 0.0416 Inf -2.524 0.5117
StepF8 - StepF11 -0.00676 0.0416 Inf -0.162 1.0000
StepF8 - StepF12 0.06693 0.0416 Inf 1.610 0.9807
StepF8 - StepF13 0.00428 0.0416 Inf 0.103 1.0000
StepF8 - StepF14 0.03617 0.0416 Inf 0.870 1.0000
StepF8 - StepF15 -0.01154 0.0416 Inf -0.278 1.0000
StepF8 - StepF16 0.02867 0.0416 Inf 0.690 1.0000
StepF8 - StepF17 0.06190 0.0416 Inf 1.489 0.9915
StepF8 - StepF18 0.12656 0.0416 Inf 3.044 0.1794
StepF9 - StepF10 -0.05547 0.0416 Inf -1.334 0.9976
StepF9 - StepF11 0.04271 0.0416 Inf 1.027 0.9999
StepF9 - StepF12 0.11639 0.0416 Inf 2.799 0.3130
StepF9 - StepF13 0.05375 0.0416 Inf 1.293 0.9984
StepF9 - StepF14 0.08564 0.0416 Inf 2.060 0.8393
StepF9 - StepF15 0.03793 0.0416 Inf 0.912 1.0000
StepF9 - StepF16 0.07813 0.0416 Inf 1.879 0.9198
StepF9 - StepF17 0.11137 0.0416 Inf 2.679 0.3954
StepF9 - StepF18 0.17602 0.0416 Inf 4.234 0.0031
StepF10 - StepF11 0.09818 0.0416 Inf 2.361 0.6376
StepF10 - StepF12 0.17186 0.0416 Inf 4.134 0.0046
StepF10 - StepF13 0.10921 0.0416 Inf 2.627 0.4333
StepF10 - StepF14 0.14110 0.0416 Inf 3.394 0.0674
StepF10 - StepF15 0.09339 0.0416 Inf 2.246 0.7222
StepF10 - StepF16 0.13360 0.0416 Inf 3.213 0.1146
StepF10 - StepF17 0.16683 0.0416 Inf 4.013 0.0076
StepF10 - StepF18 0.23149 0.0416 Inf 5.568 <.0001
StepF11 - StepF12 0.07368 0.0416 Inf 1.772 0.9515
StepF11 - StepF13 0.01104 0.0416 Inf 0.265 1.0000
StepF11 - StepF14 0.04293 0.0416 Inf 1.032 0.9999
StepF11 - StepF15 -0.00478 0.0416 Inf -0.115 1.0000
StepF11 - StepF16 0.03542 0.0416 Inf 0.852 1.0000
StepF11 - StepF17 0.06865 0.0416 Inf 1.651 0.9751
StepF11 - StepF18 0.13331 0.0416 Inf 3.206 0.1168
StepF12 - StepF13 -0.06265 0.0416 Inf -1.507 0.9903
StepF12 - StepF14 -0.03076 0.0416 Inf -0.740 1.0000
StepF12 - StepF15 -0.07846 0.0416 Inf -1.887 0.9170
StepF12 - StepF16 -0.03826 0.0416 Inf -0.920 1.0000
StepF12 - StepF17 -0.00503 0.0416 Inf -0.121 1.0000
StepF12 - StepF18 0.05963 0.0416 Inf 1.434 0.9944
StepF13 - StepF14 0.03189 0.0416 Inf 0.767 1.0000
StepF13 - StepF15 -0.01582 0.0416 Inf -0.380 1.0000
StepF13 - StepF16 0.02439 0.0416 Inf 0.587 1.0000
StepF13 - StepF17 0.05762 0.0416 Inf 1.386 0.9962
StepF13 - StepF18 0.12227 0.0416 Inf 2.941 0.2298
StepF14 - StepF15 -0.04771 0.0416 Inf -1.148 0.9996
StepF14 - StepF16 -0.00750 0.0416 Inf -0.180 1.0000
StepF14 - StepF17 0.02573 0.0416 Inf 0.619 1.0000
StepF14 - StepF18 0.09038 0.0416 Inf 2.174 0.7712
StepF15 - StepF16 0.04021 0.0416 Inf 0.967 1.0000
StepF15 - StepF17 0.07344 0.0416 Inf 1.766 0.9530
StepF15 - StepF18 0.13809 0.0416 Inf 3.321 0.0839
StepF16 - StepF17 0.03323 0.0416 Inf 0.799 1.0000
StepF16 - StepF18 0.09789 0.0416 Inf 2.354 0.6429
StepF17 - StepF18 0.06466 0.0416 Inf 1.555 0.9865
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.14552 0.0416 Inf -3.500 0.0482
StepF1 - StepF3 -0.11317 0.0416 Inf -2.722 0.3648
StepF1 - StepF4 0.02810 0.0416 Inf 0.676 1.0000
StepF1 - StepF5 -0.00596 0.0416 Inf -0.143 1.0000
StepF1 - StepF6 -0.09175 0.0416 Inf -2.207 0.7494
StepF1 - StepF7 0.00528 0.0416 Inf 0.127 1.0000
StepF1 - StepF8 0.08397 0.0416 Inf 2.020 0.8602
StepF1 - StepF9 0.03451 0.0416 Inf 0.830 1.0000
StepF1 - StepF10 -0.02096 0.0416 Inf -0.504 1.0000
StepF1 - StepF11 0.07722 0.0416 Inf 1.857 0.9273
StepF1 - StepF12 0.15090 0.0416 Inf 3.629 0.0313
StepF1 - StepF13 0.08825 0.0416 Inf 2.123 0.8033
StepF1 - StepF14 0.12014 0.0416 Inf 2.890 0.2581
StepF1 - StepF15 0.07243 0.0416 Inf 1.742 0.9585
StepF1 - StepF16 0.11264 0.0416 Inf 2.709 0.3737
StepF1 - StepF17 0.14587 0.0416 Inf 3.509 0.0469
StepF1 - StepF18 0.21053 0.0416 Inf 5.064 0.0001
StepF2 - StepF3 0.03235 0.0416 Inf 0.778 1.0000
StepF2 - StepF4 0.17362 0.0416 Inf 4.176 0.0039
StepF2 - StepF5 0.13956 0.0416 Inf 3.357 0.0755
StepF2 - StepF6 0.05376 0.0416 Inf 1.293 0.9984
StepF2 - StepF7 0.15079 0.0416 Inf 3.627 0.0316
StepF2 - StepF8 0.22949 0.0416 Inf 5.520 <.0001
StepF2 - StepF9 0.18002 0.0416 Inf 4.330 0.0020
StepF2 - StepF10 0.12456 0.0416 Inf 2.996 0.2019
StepF2 - StepF11 0.22273 0.0416 Inf 5.357 <.0001
StepF2 - StepF12 0.29641 0.0416 Inf 7.129 <.0001
StepF2 - StepF13 0.23377 0.0416 Inf 5.623 <.0001
StepF2 - StepF14 0.26566 0.0416 Inf 6.390 <.0001
StepF2 - StepF15 0.21795 0.0416 Inf 5.242 <.0001
StepF2 - StepF16 0.25816 0.0416 Inf 6.209 <.0001
StepF2 - StepF17 0.29139 0.0416 Inf 7.008 <.0001
StepF2 - StepF18 0.35604 0.0416 Inf 8.564 <.0001
StepF3 - StepF4 0.14127 0.0416 Inf 3.398 0.0666
StepF3 - StepF5 0.10721 0.0416 Inf 2.579 0.4695
StepF3 - StepF6 0.02142 0.0416 Inf 0.515 1.0000
StepF3 - StepF7 0.11845 0.0416 Inf 2.849 0.2821
StepF3 - StepF8 0.19714 0.0416 Inf 4.742 0.0003
StepF3 - StepF9 0.14768 0.0416 Inf 3.552 0.0406
StepF3 - StepF10 0.09221 0.0416 Inf 2.218 0.7419
StepF3 - StepF11 0.19039 0.0416 Inf 4.579 0.0007
StepF3 - StepF12 0.26407 0.0416 Inf 6.351 <.0001
StepF3 - StepF13 0.20142 0.0416 Inf 4.845 0.0002
StepF3 - StepF14 0.23331 0.0416 Inf 5.612 <.0001
StepF3 - StepF15 0.18560 0.0416 Inf 4.464 0.0011
StepF3 - StepF16 0.22581 0.0416 Inf 5.431 <.0001
StepF3 - StepF17 0.25904 0.0416 Inf 6.230 <.0001
StepF3 - StepF18 0.32370 0.0416 Inf 7.786 <.0001
StepF4 - StepF5 -0.03406 0.0416 Inf -0.819 1.0000
StepF4 - StepF6 -0.11985 0.0416 Inf -2.883 0.2622
StepF4 - StepF7 -0.02282 0.0416 Inf -0.549 1.0000
StepF4 - StepF8 0.05587 0.0416 Inf 1.344 0.9974
StepF4 - StepF9 0.00641 0.0416 Inf 0.154 1.0000
StepF4 - StepF10 -0.04906 0.0416 Inf -1.180 0.9995
StepF4 - StepF11 0.04912 0.0416 Inf 1.181 0.9995
StepF4 - StepF12 0.12280 0.0416 Inf 2.954 0.2232
StepF4 - StepF13 0.06015 0.0416 Inf 1.447 0.9938
StepF4 - StepF14 0.09204 0.0416 Inf 2.214 0.7447
StepF4 - StepF15 0.04433 0.0416 Inf 1.066 0.9999
StepF4 - StepF16 0.08454 0.0416 Inf 2.033 0.8533
StepF4 - StepF17 0.11777 0.0416 Inf 2.833 0.2921
StepF4 - StepF18 0.18243 0.0416 Inf 4.388 0.0016
StepF5 - StepF6 -0.08580 0.0416 Inf -2.064 0.8373
StepF5 - StepF7 0.01124 0.0416 Inf 0.270 1.0000
StepF5 - StepF8 0.08993 0.0416 Inf 2.163 0.7783
StepF5 - StepF9 0.04046 0.0416 Inf 0.973 1.0000
StepF5 - StepF10 -0.01500 0.0416 Inf -0.361 1.0000
StepF5 - StepF11 0.08317 0.0416 Inf 2.000 0.8696
StepF5 - StepF12 0.15686 0.0416 Inf 3.773 0.0188
StepF5 - StepF13 0.09421 0.0416 Inf 2.266 0.7083
StepF5 - StepF14 0.12610 0.0416 Inf 3.033 0.1843
StepF5 - StepF15 0.07839 0.0416 Inf 1.885 0.9176
StepF5 - StepF16 0.11860 0.0416 Inf 2.852 0.2800
StepF5 - StepF17 0.15183 0.0416 Inf 3.652 0.0290
StepF5 - StepF18 0.21648 0.0416 Inf 5.207 <.0001
StepF6 - StepF7 0.09703 0.0416 Inf 2.334 0.6584
StepF6 - StepF8 0.17573 0.0416 Inf 4.227 0.0032
StepF6 - StepF9 0.12626 0.0416 Inf 3.037 0.1826
StepF6 - StepF10 0.07079 0.0416 Inf 1.703 0.9665
StepF6 - StepF11 0.16897 0.0416 Inf 4.064 0.0062
StepF6 - StepF12 0.24265 0.0416 Inf 5.836 <.0001
StepF6 - StepF13 0.18001 0.0416 Inf 4.330 0.0020
StepF6 - StepF14 0.21190 0.0416 Inf 5.097 0.0001
StepF6 - StepF15 0.16419 0.0416 Inf 3.949 0.0097
StepF6 - StepF16 0.20439 0.0416 Inf 4.916 0.0001
StepF6 - StepF17 0.23763 0.0416 Inf 5.715 <.0001
StepF6 - StepF18 0.30228 0.0416 Inf 7.270 <.0001
StepF7 - StepF8 0.07869 0.0416 Inf 1.893 0.9150
StepF7 - StepF9 0.02923 0.0416 Inf 0.703 1.0000
StepF7 - StepF10 -0.02624 0.0416 Inf -0.631 1.0000
StepF7 - StepF11 0.07194 0.0416 Inf 1.730 0.9610
StepF7 - StepF12 0.14562 0.0416 Inf 3.502 0.0478
StepF7 - StepF13 0.08298 0.0416 Inf 1.996 0.8719
StepF7 - StepF14 0.11487 0.0416 Inf 2.763 0.3370
StepF7 - StepF15 0.06716 0.0416 Inf 1.615 0.9800
StepF7 - StepF16 0.10736 0.0416 Inf 2.582 0.4668
StepF7 - StepF17 0.14059 0.0416 Inf 3.382 0.0700
StepF7 - StepF18 0.20525 0.0416 Inf 4.937 0.0001
StepF8 - StepF9 -0.04947 0.0416 Inf -1.190 0.9994
StepF8 - StepF10 -0.10493 0.0416 Inf -2.524 0.5117
StepF8 - StepF11 -0.00676 0.0416 Inf -0.162 1.0000
StepF8 - StepF12 0.06693 0.0416 Inf 1.610 0.9807
StepF8 - StepF13 0.00428 0.0416 Inf 0.103 1.0000
StepF8 - StepF14 0.03617 0.0416 Inf 0.870 1.0000
StepF8 - StepF15 -0.01154 0.0416 Inf -0.278 1.0000
StepF8 - StepF16 0.02867 0.0416 Inf 0.690 1.0000
StepF8 - StepF17 0.06190 0.0416 Inf 1.489 0.9915
StepF8 - StepF18 0.12656 0.0416 Inf 3.044 0.1794
StepF9 - StepF10 -0.05547 0.0416 Inf -1.334 0.9976
StepF9 - StepF11 0.04271 0.0416 Inf 1.027 0.9999
StepF9 - StepF12 0.11639 0.0416 Inf 2.799 0.3130
StepF9 - StepF13 0.05375 0.0416 Inf 1.293 0.9984
StepF9 - StepF14 0.08564 0.0416 Inf 2.060 0.8393
StepF9 - StepF15 0.03793 0.0416 Inf 0.912 1.0000
StepF9 - StepF16 0.07813 0.0416 Inf 1.879 0.9198
StepF9 - StepF17 0.11137 0.0416 Inf 2.679 0.3954
StepF9 - StepF18 0.17602 0.0416 Inf 4.234 0.0031
StepF10 - StepF11 0.09818 0.0416 Inf 2.361 0.6376
StepF10 - StepF12 0.17186 0.0416 Inf 4.134 0.0046
StepF10 - StepF13 0.10921 0.0416 Inf 2.627 0.4333
StepF10 - StepF14 0.14110 0.0416 Inf 3.394 0.0674
StepF10 - StepF15 0.09339 0.0416 Inf 2.246 0.7222
StepF10 - StepF16 0.13360 0.0416 Inf 3.213 0.1146
StepF10 - StepF17 0.16683 0.0416 Inf 4.013 0.0076
StepF10 - StepF18 0.23149 0.0416 Inf 5.568 <.0001
StepF11 - StepF12 0.07368 0.0416 Inf 1.772 0.9515
StepF11 - StepF13 0.01104 0.0416 Inf 0.265 1.0000
StepF11 - StepF14 0.04293 0.0416 Inf 1.032 0.9999
StepF11 - StepF15 -0.00478 0.0416 Inf -0.115 1.0000
StepF11 - StepF16 0.03542 0.0416 Inf 0.852 1.0000
StepF11 - StepF17 0.06865 0.0416 Inf 1.651 0.9751
StepF11 - StepF18 0.13331 0.0416 Inf 3.206 0.1168
StepF12 - StepF13 -0.06265 0.0416 Inf -1.507 0.9903
StepF12 - StepF14 -0.03076 0.0416 Inf -0.740 1.0000
StepF12 - StepF15 -0.07846 0.0416 Inf -1.887 0.9170
StepF12 - StepF16 -0.03826 0.0416 Inf -0.920 1.0000
StepF12 - StepF17 -0.00503 0.0416 Inf -0.121 1.0000
StepF12 - StepF18 0.05963 0.0416 Inf 1.434 0.9944
StepF13 - StepF14 0.03189 0.0416 Inf 0.767 1.0000
StepF13 - StepF15 -0.01582 0.0416 Inf -0.380 1.0000
StepF13 - StepF16 0.02439 0.0416 Inf 0.587 1.0000
StepF13 - StepF17 0.05762 0.0416 Inf 1.386 0.9962
StepF13 - StepF18 0.12227 0.0416 Inf 2.941 0.2298
StepF14 - StepF15 -0.04771 0.0416 Inf -1.148 0.9996
StepF14 - StepF16 -0.00750 0.0416 Inf -0.180 1.0000
StepF14 - StepF17 0.02573 0.0416 Inf 0.619 1.0000
StepF14 - StepF18 0.09038 0.0416 Inf 2.174 0.7712
StepF15 - StepF16 0.04021 0.0416 Inf 0.967 1.0000
StepF15 - StepF17 0.07344 0.0416 Inf 1.766 0.9530
StepF15 - StepF18 0.13809 0.0416 Inf 3.321 0.0839
StepF16 - StepF17 0.03323 0.0416 Inf 0.799 1.0000
StepF16 - StepF18 0.09789 0.0416 Inf 2.354 0.6429
StepF17 - StepF18 0.06466 0.0416 Inf 1.555 0.9865
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1455 0.0416 Inf 3.500 0.0079
StepF3 - StepF2 -0.0323 0.0416 Inf -0.778 1.0000
StepF4 - StepF3 -0.1413 0.0416 Inf -3.398 0.0109
StepF5 - StepF4 0.0341 0.0416 Inf 0.819 1.0000
StepF6 - StepF5 0.0858 0.0416 Inf 2.064 0.5077
StepF7 - StepF6 -0.0970 0.0416 Inf -2.334 0.2745
StepF8 - StepF7 -0.0787 0.0416 Inf -1.893 0.7007
StepF9 - StepF8 0.0495 0.0416 Inf 1.190 1.0000
StepF10 - StepF9 0.0555 0.0416 Inf 1.334 1.0000
StepF11 - StepF10 -0.0982 0.0416 Inf -2.361 0.2731
StepF12 - StepF11 -0.0737 0.0416 Inf -1.772 0.8400
StepF13 - StepF12 0.0626 0.0416 Inf 1.507 1.0000
StepF14 - StepF13 -0.0319 0.0416 Inf -0.767 1.0000
StepF15 - StepF14 0.0477 0.0416 Inf 1.148 1.0000
StepF16 - StepF15 -0.0402 0.0416 Inf -0.967 1.0000
StepF17 - StepF16 -0.0332 0.0416 Inf -0.799 1.0000
StepF18 - StepF17 -0.0647 0.0416 Inf -1.555 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1455 0.0416 Inf 3.500 0.0079
StepF3 - StepF2 -0.0323 0.0416 Inf -0.778 1.0000
StepF4 - StepF3 -0.1413 0.0416 Inf -3.398 0.0109
StepF5 - StepF4 0.0341 0.0416 Inf 0.819 1.0000
StepF6 - StepF5 0.0858 0.0416 Inf 2.064 0.5077
StepF7 - StepF6 -0.0970 0.0416 Inf -2.334 0.2745
StepF8 - StepF7 -0.0787 0.0416 Inf -1.893 0.7007
StepF9 - StepF8 0.0495 0.0416 Inf 1.190 1.0000
StepF10 - StepF9 0.0555 0.0416 Inf 1.334 1.0000
StepF11 - StepF10 -0.0982 0.0416 Inf -2.361 0.2731
StepF12 - StepF11 -0.0737 0.0416 Inf -1.772 0.8400
StepF13 - StepF12 0.0626 0.0416 Inf 1.507 1.0000
StepF14 - StepF13 -0.0319 0.0416 Inf -0.767 1.0000
StepF15 - StepF14 0.0477 0.0416 Inf 1.148 1.0000
StepF16 - StepF15 -0.0402 0.0416 Inf -0.967 1.0000
StepF17 - StepF16 -0.0332 0.0416 Inf -0.799 1.0000
StepF18 - StepF17 -0.0647 0.0416 Inf -1.555 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests
==============================
TEST | Block 4 | 18 steps | Axis Z
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 199.940 17 <2e-16 ***
Accuracy 0.659 1 0.4169
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.573 0.164 Inf 1.252 1.89
2 1.743 0.164 Inf 1.423 2.06
3 1.674 0.164 Inf 1.353 1.99
4 1.559 0.164 Inf 1.239 1.88
5 1.455 0.164 Inf 1.134 1.78
6 1.535 0.164 Inf 1.214 1.86
7 1.458 0.164 Inf 1.138 1.78
8 1.486 0.164 Inf 1.166 1.81
9 1.435 0.164 Inf 1.115 1.76
10 1.482 0.164 Inf 1.161 1.80
11 1.495 0.164 Inf 1.174 1.82
12 1.293 0.164 Inf 0.972 1.61
13 1.341 0.164 Inf 1.020 1.66
14 1.434 0.164 Inf 1.113 1.75
15 1.430 0.164 Inf 1.110 1.75
16 1.204 0.164 Inf 0.884 1.52
17 1.195 0.164 Inf 0.875 1.52
18 0.973 0.164 Inf 0.652 1.29
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.635 0.156 Inf 1.328 1.94
2 1.805 0.156 Inf 1.499 2.11
3 1.736 0.156 Inf 1.429 2.04
4 1.621 0.156 Inf 1.315 1.93
5 1.517 0.156 Inf 1.210 1.82
6 1.597 0.156 Inf 1.290 1.90
7 1.520 0.156 Inf 1.214 1.83
8 1.548 0.156 Inf 1.242 1.85
9 1.497 0.156 Inf 1.191 1.80
10 1.544 0.156 Inf 1.237 1.85
11 1.557 0.156 Inf 1.251 1.86
12 1.355 0.156 Inf 1.048 1.66
13 1.403 0.156 Inf 1.096 1.71
14 1.496 0.156 Inf 1.189 1.80
15 1.492 0.156 Inf 1.186 1.80
16 1.266 0.156 Inf 0.960 1.57
17 1.257 0.156 Inf 0.951 1.56
18 1.035 0.156 Inf 0.728 1.34
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.17053 0.0748 Inf -2.281 0.6977
StepF1 - StepF3 -0.10093 0.0748 Inf -1.350 0.9972
StepF1 - StepF4 0.01334 0.0748 Inf 0.178 1.0000
StepF1 - StepF5 0.11815 0.0748 Inf 1.580 0.9840
StepF1 - StepF6 0.03815 0.0748 Inf 0.510 1.0000
StepF1 - StepF7 0.11434 0.0748 Inf 1.529 0.9887
StepF1 - StepF8 0.08646 0.0748 Inf 1.156 0.9996
StepF1 - StepF9 0.13733 0.0748 Inf 1.837 0.9338
StepF1 - StepF10 0.09098 0.0748 Inf 1.217 0.9992
StepF1 - StepF11 0.07779 0.0748 Inf 1.040 0.9999
StepF1 - StepF12 0.28019 0.0748 Inf 3.747 0.0207
StepF1 - StepF13 0.23191 0.0748 Inf 3.101 0.1549
StepF1 - StepF14 0.13888 0.0748 Inf 1.857 0.9272
StepF1 - StepF15 0.14275 0.0748 Inf 1.909 0.9090
StepF1 - StepF16 0.36866 0.0748 Inf 4.930 0.0001
StepF1 - StepF17 0.37735 0.0748 Inf 5.046 0.0001
StepF1 - StepF18 0.59990 0.0748 Inf 8.023 <.0001
StepF2 - StepF3 0.06961 0.0748 Inf 0.931 1.0000
StepF2 - StepF4 0.18387 0.0748 Inf 2.459 0.5622
StepF2 - StepF5 0.28868 0.0748 Inf 3.861 0.0136
StepF2 - StepF6 0.20868 0.0748 Inf 2.791 0.3186
StepF2 - StepF7 0.28487 0.0748 Inf 3.810 0.0165
StepF2 - StepF8 0.25700 0.0748 Inf 3.437 0.0590
StepF2 - StepF9 0.30786 0.0748 Inf 4.117 0.0050
StepF2 - StepF10 0.26151 0.0748 Inf 3.497 0.0486
StepF2 - StepF11 0.24832 0.0748 Inf 3.321 0.0841
StepF2 - StepF12 0.45072 0.0748 Inf 6.028 <.0001
StepF2 - StepF13 0.40244 0.0748 Inf 5.382 <.0001
StepF2 - StepF14 0.30942 0.0748 Inf 4.138 0.0046
StepF2 - StepF15 0.31328 0.0748 Inf 4.190 0.0037
StepF2 - StepF16 0.53919 0.0748 Inf 7.211 <.0001
StepF2 - StepF17 0.54788 0.0748 Inf 7.327 <.0001
StepF2 - StepF18 0.77043 0.0748 Inf 10.303 <.0001
StepF3 - StepF4 0.11427 0.0748 Inf 1.528 0.9887
StepF3 - StepF5 0.21907 0.0748 Inf 2.930 0.2358
StepF3 - StepF6 0.13908 0.0748 Inf 1.860 0.9264
StepF3 - StepF7 0.21527 0.0748 Inf 2.879 0.2644
StepF3 - StepF8 0.18739 0.0748 Inf 2.506 0.5255
StepF3 - StepF9 0.23826 0.0748 Inf 3.186 0.1235
StepF3 - StepF10 0.19190 0.0748 Inf 2.566 0.4790
StepF3 - StepF11 0.17872 0.0748 Inf 2.390 0.6156
StepF3 - StepF12 0.38111 0.0748 Inf 5.097 0.0001
StepF3 - StepF13 0.33284 0.0748 Inf 4.451 0.0012
StepF3 - StepF14 0.23981 0.0748 Inf 3.207 0.1166
StepF3 - StepF15 0.24367 0.0748 Inf 3.259 0.1008
StepF3 - StepF16 0.46959 0.0748 Inf 6.280 <.0001
StepF3 - StepF17 0.47827 0.0748 Inf 6.396 <.0001
StepF3 - StepF18 0.70083 0.0748 Inf 9.372 <.0001
StepF4 - StepF5 0.10481 0.0748 Inf 1.402 0.9957
StepF4 - StepF6 0.02481 0.0748 Inf 0.332 1.0000
StepF4 - StepF7 0.10100 0.0748 Inf 1.351 0.9972
StepF4 - StepF8 0.07312 0.0748 Inf 0.978 1.0000
StepF4 - StepF9 0.12399 0.0748 Inf 1.658 0.9740
StepF4 - StepF10 0.07764 0.0748 Inf 1.038 0.9999
StepF4 - StepF11 0.06445 0.0748 Inf 0.862 1.0000
StepF4 - StepF12 0.26684 0.0748 Inf 3.569 0.0384
StepF4 - StepF13 0.21857 0.0748 Inf 2.923 0.2395
StepF4 - StepF14 0.12554 0.0748 Inf 1.679 0.9707
StepF4 - StepF15 0.12940 0.0748 Inf 1.731 0.9610
StepF4 - StepF16 0.35532 0.0748 Inf 4.752 0.0003
StepF4 - StepF17 0.36401 0.0748 Inf 4.868 0.0002
StepF4 - StepF18 0.58656 0.0748 Inf 7.844 <.0001
StepF5 - StepF6 -0.08000 0.0748 Inf -1.070 0.9999
StepF5 - StepF7 -0.00381 0.0748 Inf -0.051 1.0000
StepF5 - StepF8 -0.03168 0.0748 Inf -0.424 1.0000
StepF5 - StepF9 0.01918 0.0748 Inf 0.257 1.0000
StepF5 - StepF10 -0.02717 0.0748 Inf -0.363 1.0000
StepF5 - StepF11 -0.04036 0.0748 Inf -0.540 1.0000
StepF5 - StepF12 0.16204 0.0748 Inf 2.167 0.7757
StepF5 - StepF13 0.11376 0.0748 Inf 1.521 0.9893
StepF5 - StepF14 0.02074 0.0748 Inf 0.277 1.0000
StepF5 - StepF15 0.02460 0.0748 Inf 0.329 1.0000
StepF5 - StepF16 0.25051 0.0748 Inf 3.350 0.0770
StepF5 - StepF17 0.25920 0.0748 Inf 3.466 0.0537
StepF5 - StepF18 0.48176 0.0748 Inf 6.443 <.0001
StepF6 - StepF7 0.07619 0.0748 Inf 1.019 0.9999
StepF6 - StepF8 0.04831 0.0748 Inf 0.646 1.0000
StepF6 - StepF9 0.09918 0.0748 Inf 1.326 0.9978
StepF6 - StepF10 0.05283 0.0748 Inf 0.706 1.0000
StepF6 - StepF11 0.03964 0.0748 Inf 0.530 1.0000
StepF6 - StepF12 0.24204 0.0748 Inf 3.237 0.1073
StepF6 - StepF13 0.19376 0.0748 Inf 2.591 0.4600
StepF6 - StepF14 0.10073 0.0748 Inf 1.347 0.9973
StepF6 - StepF15 0.10460 0.0748 Inf 1.399 0.9958
StepF6 - StepF16 0.33051 0.0748 Inf 4.420 0.0014
StepF6 - StepF17 0.33920 0.0748 Inf 4.536 0.0008
StepF6 - StepF18 0.56175 0.0748 Inf 7.512 <.0001
StepF7 - StepF8 -0.02788 0.0748 Inf -0.373 1.0000
StepF7 - StepF9 0.02299 0.0748 Inf 0.307 1.0000
StepF7 - StepF10 -0.02336 0.0748 Inf -0.312 1.0000
StepF7 - StepF11 -0.03655 0.0748 Inf -0.489 1.0000
StepF7 - StepF12 0.16585 0.0748 Inf 2.218 0.7419
StepF7 - StepF13 0.11757 0.0748 Inf 1.572 0.9848
StepF7 - StepF14 0.02454 0.0748 Inf 0.328 1.0000
StepF7 - StepF15 0.02841 0.0748 Inf 0.380 1.0000
StepF7 - StepF16 0.25432 0.0748 Inf 3.401 0.0659
StepF7 - StepF17 0.26301 0.0748 Inf 3.517 0.0456
StepF7 - StepF18 0.48556 0.0748 Inf 6.494 <.0001
StepF8 - StepF9 0.05087 0.0748 Inf 0.680 1.0000
StepF8 - StepF10 0.00451 0.0748 Inf 0.060 1.0000
StepF8 - StepF11 -0.00867 0.0748 Inf -0.116 1.0000
StepF8 - StepF12 0.19372 0.0748 Inf 2.591 0.4604
StepF8 - StepF13 0.14545 0.0748 Inf 1.945 0.8945
StepF8 - StepF14 0.05242 0.0748 Inf 0.701 1.0000
StepF8 - StepF15 0.05628 0.0748 Inf 0.753 1.0000
StepF8 - StepF16 0.28220 0.0748 Inf 3.774 0.0188
StepF8 - StepF17 0.29088 0.0748 Inf 3.890 0.0122
StepF8 - StepF18 0.51344 0.0748 Inf 6.866 <.0001
StepF9 - StepF10 -0.04635 0.0748 Inf -0.620 1.0000
StepF9 - StepF11 -0.05954 0.0748 Inf -0.796 1.0000
StepF9 - StepF12 0.14286 0.0748 Inf 1.910 0.9084
StepF9 - StepF13 0.09458 0.0748 Inf 1.265 0.9987
StepF9 - StepF14 0.00155 0.0748 Inf 0.021 1.0000
StepF9 - StepF15 0.00542 0.0748 Inf 0.072 1.0000
StepF9 - StepF16 0.23133 0.0748 Inf 3.094 0.1581
StepF9 - StepF17 0.24002 0.0748 Inf 3.210 0.1157
StepF9 - StepF18 0.46257 0.0748 Inf 6.186 <.0001
StepF10 - StepF11 -0.01319 0.0748 Inf -0.176 1.0000
StepF10 - StepF12 0.18921 0.0748 Inf 2.530 0.5067
StepF10 - StepF13 0.14094 0.0748 Inf 1.885 0.9179
StepF10 - StepF14 0.04791 0.0748 Inf 0.641 1.0000
StepF10 - StepF15 0.05177 0.0748 Inf 0.692 1.0000
StepF10 - StepF16 0.27768 0.0748 Inf 3.714 0.0233
StepF10 - StepF17 0.28637 0.0748 Inf 3.830 0.0153
StepF10 - StepF18 0.50893 0.0748 Inf 6.806 <.0001
StepF11 - StepF12 0.20239 0.0748 Inf 2.707 0.3755
StepF11 - StepF13 0.15412 0.0748 Inf 2.061 0.8386
StepF11 - StepF14 0.06109 0.0748 Inf 0.817 1.0000
StepF11 - StepF15 0.06495 0.0748 Inf 0.869 1.0000
StepF11 - StepF16 0.29087 0.0748 Inf 3.890 0.0122
StepF11 - StepF17 0.29956 0.0748 Inf 4.006 0.0078
StepF11 - StepF18 0.52211 0.0748 Inf 6.982 <.0001
StepF12 - StepF13 -0.04827 0.0748 Inf -0.646 1.0000
StepF12 - StepF14 -0.14130 0.0748 Inf -1.890 0.9161
StepF12 - StepF15 -0.13744 0.0748 Inf -1.838 0.9334
StepF12 - StepF16 0.08847 0.0748 Inf 1.183 0.9995
StepF12 - StepF17 0.09716 0.0748 Inf 1.299 0.9983
StepF12 - StepF18 0.31972 0.0748 Inf 4.276 0.0026
StepF13 - StepF14 -0.09303 0.0748 Inf -1.244 0.9990
StepF13 - StepF15 -0.08917 0.0748 Inf -1.192 0.9994
StepF13 - StepF16 0.13675 0.0748 Inf 1.829 0.9362
StepF13 - StepF17 0.14543 0.0748 Inf 1.945 0.8946
StepF13 - StepF18 0.36799 0.0748 Inf 4.921 0.0001
StepF14 - StepF15 0.00386 0.0748 Inf 0.052 1.0000
StepF14 - StepF16 0.22978 0.0748 Inf 3.073 0.1667
StepF14 - StepF17 0.23846 0.0748 Inf 3.189 0.1226
StepF14 - StepF18 0.46102 0.0748 Inf 6.165 <.0001
StepF15 - StepF16 0.22591 0.0748 Inf 3.021 0.1898
StepF15 - StepF17 0.23460 0.0748 Inf 3.137 0.1409
StepF15 - StepF18 0.45716 0.0748 Inf 6.114 <.0001
StepF16 - StepF17 0.00869 0.0748 Inf 0.116 1.0000
StepF16 - StepF18 0.23124 0.0748 Inf 3.092 0.1586
StepF17 - StepF18 0.22256 0.0748 Inf 2.976 0.2115
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.17053 0.0748 Inf -2.281 0.6977
StepF1 - StepF3 -0.10093 0.0748 Inf -1.350 0.9972
StepF1 - StepF4 0.01334 0.0748 Inf 0.178 1.0000
StepF1 - StepF5 0.11815 0.0748 Inf 1.580 0.9840
StepF1 - StepF6 0.03815 0.0748 Inf 0.510 1.0000
StepF1 - StepF7 0.11434 0.0748 Inf 1.529 0.9887
StepF1 - StepF8 0.08646 0.0748 Inf 1.156 0.9996
StepF1 - StepF9 0.13733 0.0748 Inf 1.837 0.9338
StepF1 - StepF10 0.09098 0.0748 Inf 1.217 0.9992
StepF1 - StepF11 0.07779 0.0748 Inf 1.040 0.9999
StepF1 - StepF12 0.28019 0.0748 Inf 3.747 0.0207
StepF1 - StepF13 0.23191 0.0748 Inf 3.101 0.1549
StepF1 - StepF14 0.13888 0.0748 Inf 1.857 0.9272
StepF1 - StepF15 0.14275 0.0748 Inf 1.909 0.9090
StepF1 - StepF16 0.36866 0.0748 Inf 4.930 0.0001
StepF1 - StepF17 0.37735 0.0748 Inf 5.046 0.0001
StepF1 - StepF18 0.59990 0.0748 Inf 8.023 <.0001
StepF2 - StepF3 0.06961 0.0748 Inf 0.931 1.0000
StepF2 - StepF4 0.18387 0.0748 Inf 2.459 0.5622
StepF2 - StepF5 0.28868 0.0748 Inf 3.861 0.0136
StepF2 - StepF6 0.20868 0.0748 Inf 2.791 0.3186
StepF2 - StepF7 0.28487 0.0748 Inf 3.810 0.0165
StepF2 - StepF8 0.25700 0.0748 Inf 3.437 0.0590
StepF2 - StepF9 0.30786 0.0748 Inf 4.117 0.0050
StepF2 - StepF10 0.26151 0.0748 Inf 3.497 0.0486
StepF2 - StepF11 0.24832 0.0748 Inf 3.321 0.0841
StepF2 - StepF12 0.45072 0.0748 Inf 6.028 <.0001
StepF2 - StepF13 0.40244 0.0748 Inf 5.382 <.0001
StepF2 - StepF14 0.30942 0.0748 Inf 4.138 0.0046
StepF2 - StepF15 0.31328 0.0748 Inf 4.190 0.0037
StepF2 - StepF16 0.53919 0.0748 Inf 7.211 <.0001
StepF2 - StepF17 0.54788 0.0748 Inf 7.327 <.0001
StepF2 - StepF18 0.77043 0.0748 Inf 10.303 <.0001
StepF3 - StepF4 0.11427 0.0748 Inf 1.528 0.9887
StepF3 - StepF5 0.21907 0.0748 Inf 2.930 0.2358
StepF3 - StepF6 0.13908 0.0748 Inf 1.860 0.9264
StepF3 - StepF7 0.21527 0.0748 Inf 2.879 0.2644
StepF3 - StepF8 0.18739 0.0748 Inf 2.506 0.5255
StepF3 - StepF9 0.23826 0.0748 Inf 3.186 0.1235
StepF3 - StepF10 0.19190 0.0748 Inf 2.566 0.4790
StepF3 - StepF11 0.17872 0.0748 Inf 2.390 0.6156
StepF3 - StepF12 0.38111 0.0748 Inf 5.097 0.0001
StepF3 - StepF13 0.33284 0.0748 Inf 4.451 0.0012
StepF3 - StepF14 0.23981 0.0748 Inf 3.207 0.1166
StepF3 - StepF15 0.24367 0.0748 Inf 3.259 0.1008
StepF3 - StepF16 0.46959 0.0748 Inf 6.280 <.0001
StepF3 - StepF17 0.47827 0.0748 Inf 6.396 <.0001
StepF3 - StepF18 0.70083 0.0748 Inf 9.372 <.0001
StepF4 - StepF5 0.10481 0.0748 Inf 1.402 0.9957
StepF4 - StepF6 0.02481 0.0748 Inf 0.332 1.0000
StepF4 - StepF7 0.10100 0.0748 Inf 1.351 0.9972
StepF4 - StepF8 0.07312 0.0748 Inf 0.978 1.0000
StepF4 - StepF9 0.12399 0.0748 Inf 1.658 0.9740
StepF4 - StepF10 0.07764 0.0748 Inf 1.038 0.9999
StepF4 - StepF11 0.06445 0.0748 Inf 0.862 1.0000
StepF4 - StepF12 0.26684 0.0748 Inf 3.569 0.0384
StepF4 - StepF13 0.21857 0.0748 Inf 2.923 0.2395
StepF4 - StepF14 0.12554 0.0748 Inf 1.679 0.9707
StepF4 - StepF15 0.12940 0.0748 Inf 1.731 0.9610
StepF4 - StepF16 0.35532 0.0748 Inf 4.752 0.0003
StepF4 - StepF17 0.36401 0.0748 Inf 4.868 0.0002
StepF4 - StepF18 0.58656 0.0748 Inf 7.844 <.0001
StepF5 - StepF6 -0.08000 0.0748 Inf -1.070 0.9999
StepF5 - StepF7 -0.00381 0.0748 Inf -0.051 1.0000
StepF5 - StepF8 -0.03168 0.0748 Inf -0.424 1.0000
StepF5 - StepF9 0.01918 0.0748 Inf 0.257 1.0000
StepF5 - StepF10 -0.02717 0.0748 Inf -0.363 1.0000
StepF5 - StepF11 -0.04036 0.0748 Inf -0.540 1.0000
StepF5 - StepF12 0.16204 0.0748 Inf 2.167 0.7757
StepF5 - StepF13 0.11376 0.0748 Inf 1.521 0.9893
StepF5 - StepF14 0.02074 0.0748 Inf 0.277 1.0000
StepF5 - StepF15 0.02460 0.0748 Inf 0.329 1.0000
StepF5 - StepF16 0.25051 0.0748 Inf 3.350 0.0770
StepF5 - StepF17 0.25920 0.0748 Inf 3.466 0.0537
StepF5 - StepF18 0.48176 0.0748 Inf 6.443 <.0001
StepF6 - StepF7 0.07619 0.0748 Inf 1.019 0.9999
StepF6 - StepF8 0.04831 0.0748 Inf 0.646 1.0000
StepF6 - StepF9 0.09918 0.0748 Inf 1.326 0.9978
StepF6 - StepF10 0.05283 0.0748 Inf 0.706 1.0000
StepF6 - StepF11 0.03964 0.0748 Inf 0.530 1.0000
StepF6 - StepF12 0.24204 0.0748 Inf 3.237 0.1073
StepF6 - StepF13 0.19376 0.0748 Inf 2.591 0.4600
StepF6 - StepF14 0.10073 0.0748 Inf 1.347 0.9973
StepF6 - StepF15 0.10460 0.0748 Inf 1.399 0.9958
StepF6 - StepF16 0.33051 0.0748 Inf 4.420 0.0014
StepF6 - StepF17 0.33920 0.0748 Inf 4.536 0.0008
StepF6 - StepF18 0.56175 0.0748 Inf 7.512 <.0001
StepF7 - StepF8 -0.02788 0.0748 Inf -0.373 1.0000
StepF7 - StepF9 0.02299 0.0748 Inf 0.307 1.0000
StepF7 - StepF10 -0.02336 0.0748 Inf -0.312 1.0000
StepF7 - StepF11 -0.03655 0.0748 Inf -0.489 1.0000
StepF7 - StepF12 0.16585 0.0748 Inf 2.218 0.7419
StepF7 - StepF13 0.11757 0.0748 Inf 1.572 0.9848
StepF7 - StepF14 0.02454 0.0748 Inf 0.328 1.0000
StepF7 - StepF15 0.02841 0.0748 Inf 0.380 1.0000
StepF7 - StepF16 0.25432 0.0748 Inf 3.401 0.0659
StepF7 - StepF17 0.26301 0.0748 Inf 3.517 0.0456
StepF7 - StepF18 0.48556 0.0748 Inf 6.494 <.0001
StepF8 - StepF9 0.05087 0.0748 Inf 0.680 1.0000
StepF8 - StepF10 0.00451 0.0748 Inf 0.060 1.0000
StepF8 - StepF11 -0.00867 0.0748 Inf -0.116 1.0000
StepF8 - StepF12 0.19372 0.0748 Inf 2.591 0.4604
StepF8 - StepF13 0.14545 0.0748 Inf 1.945 0.8945
StepF8 - StepF14 0.05242 0.0748 Inf 0.701 1.0000
StepF8 - StepF15 0.05628 0.0748 Inf 0.753 1.0000
StepF8 - StepF16 0.28220 0.0748 Inf 3.774 0.0188
StepF8 - StepF17 0.29088 0.0748 Inf 3.890 0.0122
StepF8 - StepF18 0.51344 0.0748 Inf 6.866 <.0001
StepF9 - StepF10 -0.04635 0.0748 Inf -0.620 1.0000
StepF9 - StepF11 -0.05954 0.0748 Inf -0.796 1.0000
StepF9 - StepF12 0.14286 0.0748 Inf 1.910 0.9084
StepF9 - StepF13 0.09458 0.0748 Inf 1.265 0.9987
StepF9 - StepF14 0.00155 0.0748 Inf 0.021 1.0000
StepF9 - StepF15 0.00542 0.0748 Inf 0.072 1.0000
StepF9 - StepF16 0.23133 0.0748 Inf 3.094 0.1581
StepF9 - StepF17 0.24002 0.0748 Inf 3.210 0.1157
StepF9 - StepF18 0.46257 0.0748 Inf 6.186 <.0001
StepF10 - StepF11 -0.01319 0.0748 Inf -0.176 1.0000
StepF10 - StepF12 0.18921 0.0748 Inf 2.530 0.5067
StepF10 - StepF13 0.14094 0.0748 Inf 1.885 0.9179
StepF10 - StepF14 0.04791 0.0748 Inf 0.641 1.0000
StepF10 - StepF15 0.05177 0.0748 Inf 0.692 1.0000
StepF10 - StepF16 0.27768 0.0748 Inf 3.714 0.0233
StepF10 - StepF17 0.28637 0.0748 Inf 3.830 0.0153
StepF10 - StepF18 0.50893 0.0748 Inf 6.806 <.0001
StepF11 - StepF12 0.20239 0.0748 Inf 2.707 0.3755
StepF11 - StepF13 0.15412 0.0748 Inf 2.061 0.8386
StepF11 - StepF14 0.06109 0.0748 Inf 0.817 1.0000
StepF11 - StepF15 0.06495 0.0748 Inf 0.869 1.0000
StepF11 - StepF16 0.29087 0.0748 Inf 3.890 0.0122
StepF11 - StepF17 0.29956 0.0748 Inf 4.006 0.0078
StepF11 - StepF18 0.52211 0.0748 Inf 6.982 <.0001
StepF12 - StepF13 -0.04827 0.0748 Inf -0.646 1.0000
StepF12 - StepF14 -0.14130 0.0748 Inf -1.890 0.9161
StepF12 - StepF15 -0.13744 0.0748 Inf -1.838 0.9334
StepF12 - StepF16 0.08847 0.0748 Inf 1.183 0.9995
StepF12 - StepF17 0.09716 0.0748 Inf 1.299 0.9983
StepF12 - StepF18 0.31972 0.0748 Inf 4.276 0.0026
StepF13 - StepF14 -0.09303 0.0748 Inf -1.244 0.9990
StepF13 - StepF15 -0.08917 0.0748 Inf -1.192 0.9994
StepF13 - StepF16 0.13675 0.0748 Inf 1.829 0.9362
StepF13 - StepF17 0.14543 0.0748 Inf 1.945 0.8946
StepF13 - StepF18 0.36799 0.0748 Inf 4.921 0.0001
StepF14 - StepF15 0.00386 0.0748 Inf 0.052 1.0000
StepF14 - StepF16 0.22978 0.0748 Inf 3.073 0.1667
StepF14 - StepF17 0.23846 0.0748 Inf 3.189 0.1226
StepF14 - StepF18 0.46102 0.0748 Inf 6.165 <.0001
StepF15 - StepF16 0.22591 0.0748 Inf 3.021 0.1898
StepF15 - StepF17 0.23460 0.0748 Inf 3.137 0.1409
StepF15 - StepF18 0.45716 0.0748 Inf 6.114 <.0001
StepF16 - StepF17 0.00869 0.0748 Inf 0.116 1.0000
StepF16 - StepF18 0.23124 0.0748 Inf 3.092 0.1586
StepF17 - StepF18 0.22256 0.0748 Inf 2.976 0.2115
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.17053 0.0748 Inf 2.281 0.3160
StepF3 - StepF2 -0.06961 0.0748 Inf -0.931 1.0000
StepF4 - StepF3 -0.11427 0.0748 Inf -1.528 1.0000
StepF5 - StepF4 -0.10481 0.0748 Inf -1.402 1.0000
StepF6 - StepF5 0.08000 0.0748 Inf 1.070 1.0000
StepF7 - StepF6 -0.07619 0.0748 Inf -1.019 1.0000
StepF8 - StepF7 0.02788 0.0748 Inf 0.373 1.0000
StepF9 - StepF8 -0.05087 0.0748 Inf -0.680 1.0000
StepF10 - StepF9 0.04635 0.0748 Inf 0.620 1.0000
StepF11 - StepF10 0.01319 0.0748 Inf 0.176 1.0000
StepF12 - StepF11 -0.20239 0.0748 Inf -2.707 0.1019
StepF13 - StepF12 0.04827 0.0748 Inf 0.646 1.0000
StepF14 - StepF13 0.09303 0.0748 Inf 1.244 1.0000
StepF15 - StepF14 -0.00386 0.0748 Inf -0.052 1.0000
StepF16 - StepF15 -0.22591 0.0748 Inf -3.021 0.0428
StepF17 - StepF16 -0.00869 0.0748 Inf -0.116 1.0000
StepF18 - StepF17 -0.22256 0.0748 Inf -2.976 0.0467
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.17053 0.0748 Inf 2.281 0.3160
StepF3 - StepF2 -0.06961 0.0748 Inf -0.931 1.0000
StepF4 - StepF3 -0.11427 0.0748 Inf -1.528 1.0000
StepF5 - StepF4 -0.10481 0.0748 Inf -1.402 1.0000
StepF6 - StepF5 0.08000 0.0748 Inf 1.070 1.0000
StepF7 - StepF6 -0.07619 0.0748 Inf -1.019 1.0000
StepF8 - StepF7 0.02788 0.0748 Inf 0.373 1.0000
StepF9 - StepF8 -0.05087 0.0748 Inf -0.680 1.0000
StepF10 - StepF9 0.04635 0.0748 Inf 0.620 1.0000
StepF11 - StepF10 0.01319 0.0748 Inf 0.176 1.0000
StepF12 - StepF11 -0.20239 0.0748 Inf -2.707 0.1019
StepF13 - StepF12 0.04827 0.0748 Inf 0.646 1.0000
StepF14 - StepF13 0.09303 0.0748 Inf 1.244 1.0000
StepF15 - StepF14 -0.00386 0.0748 Inf -0.052 1.0000
StepF16 - StepF15 -0.22591 0.0748 Inf -3.021 0.0428
StepF17 - StepF16 -0.00869 0.0748 Inf -0.116 1.0000
StepF18 - StepF17 -0.22256 0.0748 Inf -2.976 0.0467
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests # Block 5
.report_step_test(sw_b5_6, "5", "6 steps")
==============================
TEST | Block 5 | 6 steps | Axis X
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 11.8973 5 0.03622 *
Accuracy 1.2959 1 0.25497
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.645 0.0490 Inf 0.549 0.741
2 0.605 0.0490 Inf 0.509 0.701
3 0.638 0.0490 Inf 0.542 0.734
4 0.617 0.0490 Inf 0.521 0.713
5 0.573 0.0490 Inf 0.477 0.669
6 0.562 0.0490 Inf 0.466 0.658
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.610 0.0488 Inf 0.514 0.706
2 0.571 0.0488 Inf 0.475 0.667
3 0.603 0.0488 Inf 0.507 0.699
4 0.583 0.0488 Inf 0.487 0.678
5 0.538 0.0488 Inf 0.442 0.634
6 0.527 0.0488 Inf 0.432 0.623
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.03933 0.0309 Inf 1.273 0.8001
StepF1 - StepF3 0.00698 0.0309 Inf 0.226 0.9999
StepF1 - StepF4 0.02762 0.0309 Inf 0.894 0.9482
StepF1 - StepF5 0.07217 0.0309 Inf 2.335 0.1799
StepF1 - StepF6 0.08270 0.0309 Inf 2.676 0.0800
StepF2 - StepF3 -0.03235 0.0309 Inf -1.047 0.9021
StepF2 - StepF4 -0.01171 0.0309 Inf -0.379 0.9990
StepF2 - StepF5 0.03284 0.0309 Inf 1.063 0.8961
StepF2 - StepF6 0.04337 0.0309 Inf 1.403 0.7250
StepF3 - StepF4 0.02063 0.0309 Inf 0.668 0.9854
StepF3 - StepF5 0.06519 0.0309 Inf 2.109 0.2823
StepF3 - StepF6 0.07572 0.0309 Inf 2.450 0.1393
StepF4 - StepF5 0.04456 0.0309 Inf 1.442 0.7014
StepF4 - StepF6 0.05509 0.0309 Inf 1.782 0.4772
StepF5 - StepF6 0.01053 0.0309 Inf 0.341 0.9994
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.03933 0.0309 Inf 1.273 0.8001
StepF1 - StepF3 0.00698 0.0309 Inf 0.226 0.9999
StepF1 - StepF4 0.02762 0.0309 Inf 0.894 0.9482
StepF1 - StepF5 0.07217 0.0309 Inf 2.335 0.1799
StepF1 - StepF6 0.08270 0.0309 Inf 2.676 0.0800
StepF2 - StepF3 -0.03235 0.0309 Inf -1.047 0.9021
StepF2 - StepF4 -0.01171 0.0309 Inf -0.379 0.9990
StepF2 - StepF5 0.03284 0.0309 Inf 1.063 0.8961
StepF2 - StepF6 0.04337 0.0309 Inf 1.403 0.7250
StepF3 - StepF4 0.02063 0.0309 Inf 0.668 0.9854
StepF3 - StepF5 0.06519 0.0309 Inf 2.109 0.2823
StepF3 - StepF6 0.07572 0.0309 Inf 2.450 0.1393
StepF4 - StepF5 0.04456 0.0309 Inf 1.442 0.7014
StepF4 - StepF6 0.05509 0.0309 Inf 1.782 0.4772
StepF5 - StepF6 0.01053 0.0309 Inf 0.341 0.9994
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.0393 0.0309 Inf -1.273 0.8126
StepF3 - StepF2 0.0323 0.0309 Inf 1.047 0.8858
StepF4 - StepF3 -0.0206 0.0309 Inf -0.668 1.0000
StepF5 - StepF4 -0.0446 0.0309 Inf -1.442 0.7469
StepF6 - StepF5 -0.0105 0.0309 Inf -0.341 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.0393 0.0309 Inf -1.273 0.8126
StepF3 - StepF2 0.0323 0.0309 Inf 1.047 0.8858
StepF4 - StepF3 -0.0206 0.0309 Inf -0.668 1.0000
StepF5 - StepF4 -0.0446 0.0309 Inf -1.442 0.7469
StepF6 - StepF5 -0.0105 0.0309 Inf -0.341 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests
==============================
TEST | Block 5 | 6 steps | Axis Y
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 32.3897 5 4.974e-06 ***
Accuracy 2.7081 1 0.09984 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.641 0.0591 Inf 0.525 0.757
2 0.758 0.0591 Inf 0.642 0.874
3 0.672 0.0591 Inf 0.556 0.788
4 0.687 0.0591 Inf 0.572 0.803
5 0.611 0.0591 Inf 0.495 0.726
6 0.591 0.0591 Inf 0.476 0.707
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.589 0.0589 Inf 0.474 0.705
2 0.706 0.0589 Inf 0.591 0.822
3 0.621 0.0589 Inf 0.505 0.736
4 0.636 0.0589 Inf 0.520 0.751
5 0.559 0.0589 Inf 0.444 0.675
6 0.540 0.0589 Inf 0.424 0.655
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.1168 0.0333 Inf -3.507 0.0060
StepF1 - StepF3 -0.0312 0.0333 Inf -0.936 0.9371
StepF1 - StepF4 -0.0463 0.0333 Inf -1.391 0.7323
StepF1 - StepF5 0.0303 0.0333 Inf 0.910 0.9442
StepF1 - StepF6 0.0496 0.0333 Inf 1.490 0.6706
StepF2 - StepF3 0.0856 0.0333 Inf 2.571 0.1045
StepF2 - StepF4 0.0705 0.0333 Inf 2.116 0.2790
StepF2 - StepF5 0.1471 0.0333 Inf 4.417 0.0001
StepF2 - StepF6 0.1664 0.0333 Inf 4.997 <.0001
StepF3 - StepF4 -0.0152 0.0333 Inf -0.455 0.9976
StepF3 - StepF5 0.0615 0.0333 Inf 1.846 0.4359
StepF3 - StepF6 0.0808 0.0333 Inf 2.427 0.1470
StepF4 - StepF5 0.0766 0.0333 Inf 2.301 0.1934
StepF4 - StepF6 0.0960 0.0333 Inf 2.882 0.0457
StepF5 - StepF6 0.0193 0.0333 Inf 0.580 0.9923
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.1168 0.0333 Inf -3.507 0.0060
StepF1 - StepF3 -0.0312 0.0333 Inf -0.936 0.9371
StepF1 - StepF4 -0.0463 0.0333 Inf -1.391 0.7323
StepF1 - StepF5 0.0303 0.0333 Inf 0.910 0.9442
StepF1 - StepF6 0.0496 0.0333 Inf 1.490 0.6706
StepF2 - StepF3 0.0856 0.0333 Inf 2.571 0.1045
StepF2 - StepF4 0.0705 0.0333 Inf 2.116 0.2790
StepF2 - StepF5 0.1471 0.0333 Inf 4.417 0.0001
StepF2 - StepF6 0.1664 0.0333 Inf 4.997 <.0001
StepF3 - StepF4 -0.0152 0.0333 Inf -0.455 0.9976
StepF3 - StepF5 0.0615 0.0333 Inf 1.846 0.4359
StepF3 - StepF6 0.0808 0.0333 Inf 2.427 0.1470
StepF4 - StepF5 0.0766 0.0333 Inf 2.301 0.1934
StepF4 - StepF6 0.0960 0.0333 Inf 2.882 0.0457
StepF5 - StepF6 0.0193 0.0333 Inf 0.580 0.9923
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1168 0.0333 Inf 3.507 0.0023
StepF3 - StepF2 -0.0856 0.0333 Inf -2.571 0.0406
StepF4 - StepF3 0.0152 0.0333 Inf 0.455 1.0000
StepF5 - StepF4 -0.0766 0.0333 Inf -2.301 0.0641
StepF6 - StepF5 -0.0193 0.0333 Inf -0.580 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1168 0.0333 Inf 3.507 0.0023
StepF3 - StepF2 -0.0856 0.0333 Inf -2.571 0.0406
StepF4 - StepF3 0.0152 0.0333 Inf 0.455 1.0000
StepF5 - StepF4 -0.0766 0.0333 Inf -2.301 0.0641
StepF6 - StepF5 -0.0193 0.0333 Inf -0.580 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests
==============================
TEST | Block 5 | 6 steps | Axis Z
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 24.4407 5 0.0001786 ***
Accuracy 3.0721 1 0.0796481 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.33 0.129 Inf 1.075 1.58
2 1.51 0.129 Inf 1.258 1.76
3 1.45 0.129 Inf 1.196 1.70
4 1.38 0.129 Inf 1.130 1.63
5 1.29 0.129 Inf 1.034 1.54
6 1.23 0.129 Inf 0.979 1.48
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.19 0.128 Inf 0.941 1.44
2 1.37 0.128 Inf 1.123 1.63
3 1.31 0.128 Inf 1.061 1.56
4 1.25 0.128 Inf 0.996 1.50
5 1.15 0.128 Inf 0.900 1.40
6 1.10 0.128 Inf 0.845 1.35
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.1823 0.0662 Inf -2.752 0.0655
StepF1 - StepF3 -0.1202 0.0662 Inf -1.815 0.4557
StepF1 - StepF4 -0.0546 0.0662 Inf -0.825 0.9630
StepF1 - StepF5 0.0413 0.0662 Inf 0.624 0.9893
StepF1 - StepF6 0.0963 0.0662 Inf 1.454 0.6938
StepF2 - StepF3 0.0620 0.0662 Inf 0.936 0.9372
StepF2 - StepF4 0.1276 0.0662 Inf 1.927 0.3855
StepF2 - StepF5 0.2236 0.0662 Inf 3.376 0.0096
StepF2 - StepF6 0.2786 0.0662 Inf 4.205 0.0004
StepF3 - StepF4 0.0656 0.0662 Inf 0.990 0.9211
StepF3 - StepF5 0.1616 0.0662 Inf 2.440 0.1427
StepF3 - StepF6 0.2165 0.0662 Inf 3.269 0.0138
StepF4 - StepF5 0.0960 0.0662 Inf 1.449 0.6968
StepF4 - StepF6 0.1509 0.0662 Inf 2.279 0.2027
StepF5 - StepF6 0.0549 0.0662 Inf 0.830 0.9621
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.1823 0.0662 Inf -2.752 0.0655
StepF1 - StepF3 -0.1202 0.0662 Inf -1.815 0.4557
StepF1 - StepF4 -0.0546 0.0662 Inf -0.825 0.9630
StepF1 - StepF5 0.0413 0.0662 Inf 0.624 0.9893
StepF1 - StepF6 0.0963 0.0662 Inf 1.454 0.6938
StepF2 - StepF3 0.0620 0.0662 Inf 0.936 0.9372
StepF2 - StepF4 0.1276 0.0662 Inf 1.927 0.3855
StepF2 - StepF5 0.2236 0.0662 Inf 3.376 0.0096
StepF2 - StepF6 0.2786 0.0662 Inf 4.205 0.0004
StepF3 - StepF4 0.0656 0.0662 Inf 0.990 0.9211
StepF3 - StepF5 0.1616 0.0662 Inf 2.440 0.1427
StepF3 - StepF6 0.2165 0.0662 Inf 3.269 0.0138
StepF4 - StepF5 0.0960 0.0662 Inf 1.449 0.6968
StepF4 - StepF6 0.1509 0.0662 Inf 2.279 0.2027
StepF5 - StepF6 0.0549 0.0662 Inf 0.830 0.9621
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1823 0.0662 Inf 2.752 0.0296
StepF3 - StepF2 -0.0620 0.0662 Inf -0.936 0.9658
StepF4 - StepF3 -0.0656 0.0662 Inf -0.990 0.9658
StepF5 - StepF4 -0.0960 0.0662 Inf -1.449 0.5893
StepF6 - StepF5 -0.0549 0.0662 Inf -0.830 0.9658
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1823 0.0662 Inf 2.752 0.0296
StepF3 - StepF2 -0.0620 0.0662 Inf -0.936 0.9658
StepF4 - StepF3 -0.0656 0.0662 Inf -0.990 0.9658
StepF5 - StepF4 -0.0960 0.0662 Inf -1.449 0.5893
StepF6 - StepF5 -0.0549 0.0662 Inf -0.830 0.9658
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests .report_step_test(sw_b5_12, "5", "12 steps")
==============================
TEST | Block 5 | 12 steps | Axis X
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 53.5986 11 1.397e-07 ***
Accuracy 1.1562 1 0.2822
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.655 0.0535 Inf 0.550 0.760
2 0.642 0.0535 Inf 0.537 0.747
3 0.694 0.0535 Inf 0.589 0.799
4 0.601 0.0535 Inf 0.496 0.706
5 0.582 0.0535 Inf 0.477 0.687
6 0.612 0.0535 Inf 0.507 0.717
7 0.601 0.0535 Inf 0.496 0.706
8 0.586 0.0535 Inf 0.481 0.691
9 0.518 0.0535 Inf 0.413 0.623
10 0.576 0.0535 Inf 0.471 0.681
11 0.620 0.0535 Inf 0.515 0.725
12 0.536 0.0535 Inf 0.431 0.641
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.628 0.0539 Inf 0.522 0.734
2 0.615 0.0539 Inf 0.510 0.721
3 0.667 0.0539 Inf 0.561 0.773
4 0.574 0.0539 Inf 0.469 0.680
5 0.556 0.0539 Inf 0.450 0.661
6 0.585 0.0539 Inf 0.479 0.691
7 0.575 0.0539 Inf 0.469 0.680
8 0.559 0.0539 Inf 0.454 0.665
9 0.491 0.0539 Inf 0.385 0.597
10 0.550 0.0539 Inf 0.444 0.655
11 0.593 0.0539 Inf 0.488 0.699
12 0.509 0.0539 Inf 0.403 0.615
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.012531 0.0312 Inf 0.402 1.0000
StepF1 - StepF3 -0.039262 0.0312 Inf -1.258 0.9840
StepF1 - StepF4 0.053540 0.0312 Inf 1.716 0.8613
StepF1 - StepF5 0.072397 0.0312 Inf 2.320 0.4617
StepF1 - StepF6 0.042730 0.0312 Inf 1.370 0.9693
StepF1 - StepF7 0.053229 0.0312 Inf 1.706 0.8659
StepF1 - StepF8 0.068538 0.0312 Inf 2.197 0.5519
StepF1 - StepF9 0.137112 0.0312 Inf 4.395 0.0007
StepF1 - StepF10 0.078255 0.0312 Inf 2.508 0.3345
StepF1 - StepF11 0.034455 0.0312 Inf 1.104 0.9946
StepF1 - StepF12 0.118943 0.0312 Inf 3.812 0.0076
StepF2 - StepF3 -0.051793 0.0312 Inf -1.660 0.8862
StepF2 - StepF4 0.041008 0.0312 Inf 1.314 0.9775
StepF2 - StepF5 0.059866 0.0312 Inf 1.919 0.7477
StepF2 - StepF6 0.030199 0.0312 Inf 0.968 0.9983
StepF2 - StepF7 0.040698 0.0312 Inf 1.304 0.9788
StepF2 - StepF8 0.056007 0.0312 Inf 1.795 0.8211
StepF2 - StepF9 0.124581 0.0312 Inf 3.993 0.0038
StepF2 - StepF10 0.065724 0.0312 Inf 2.107 0.6181
StepF2 - StepF11 0.021923 0.0312 Inf 0.703 0.9999
StepF2 - StepF12 0.106411 0.0312 Inf 3.411 0.0318
StepF3 - StepF4 0.092801 0.0312 Inf 2.974 0.1160
StepF3 - StepF5 0.111659 0.0312 Inf 3.579 0.0179
StepF3 - StepF6 0.081992 0.0312 Inf 2.628 0.2638
StepF3 - StepF7 0.092491 0.0312 Inf 2.965 0.1191
StepF3 - StepF8 0.107800 0.0312 Inf 3.455 0.0274
StepF3 - StepF9 0.176374 0.0312 Inf 5.653 <.0001
StepF3 - StepF10 0.117517 0.0312 Inf 3.767 0.0091
StepF3 - StepF11 0.073716 0.0312 Inf 2.363 0.4317
StepF3 - StepF12 0.158204 0.0312 Inf 5.071 <.0001
StepF4 - StepF5 0.018857 0.0312 Inf 0.604 1.0000
StepF4 - StepF6 -0.010810 0.0312 Inf -0.346 1.0000
StepF4 - StepF7 -0.000311 0.0312 Inf -0.010 1.0000
StepF4 - StepF8 0.014999 0.0312 Inf 0.481 1.0000
StepF4 - StepF9 0.083573 0.0312 Inf 2.679 0.2368
StepF4 - StepF10 0.024715 0.0312 Inf 0.792 0.9997
StepF4 - StepF11 -0.019085 0.0312 Inf -0.612 1.0000
StepF4 - StepF12 0.065403 0.0312 Inf 2.096 0.6255
StepF5 - StepF6 -0.029667 0.0312 Inf -0.951 0.9986
StepF5 - StepF7 -0.019168 0.0312 Inf -0.614 1.0000
StepF5 - StepF8 -0.003859 0.0312 Inf -0.124 1.0000
StepF5 - StepF9 0.064715 0.0312 Inf 2.074 0.6414
StepF5 - StepF10 0.005858 0.0312 Inf 0.188 1.0000
StepF5 - StepF11 -0.037942 0.0312 Inf -1.216 0.9878
StepF5 - StepF12 0.046546 0.0312 Inf 1.492 0.9432
StepF6 - StepF7 0.010499 0.0312 Inf 0.337 1.0000
StepF6 - StepF8 0.025808 0.0312 Inf 0.827 0.9996
StepF6 - StepF9 0.094382 0.0312 Inf 3.025 0.1013
StepF6 - StepF10 0.035525 0.0312 Inf 1.139 0.9930
StepF6 - StepF11 -0.008275 0.0312 Inf -0.265 1.0000
StepF6 - StepF12 0.076213 0.0312 Inf 2.443 0.3769
StepF7 - StepF8 0.015309 0.0312 Inf 0.491 1.0000
StepF7 - StepF9 0.083883 0.0312 Inf 2.689 0.2317
StepF7 - StepF10 0.025026 0.0312 Inf 0.802 0.9997
StepF7 - StepF11 -0.018774 0.0312 Inf -0.602 1.0000
StepF7 - StepF12 0.065714 0.0312 Inf 2.106 0.6183
StepF8 - StepF9 0.068574 0.0312 Inf 2.198 0.5511
StepF8 - StepF10 0.009717 0.0312 Inf 0.311 1.0000
StepF8 - StepF11 -0.034084 0.0312 Inf -1.092 0.9951
StepF8 - StepF12 0.050405 0.0312 Inf 1.616 0.9038
StepF9 - StepF10 -0.058858 0.0312 Inf -1.886 0.7680
StepF9 - StepF11 -0.102658 0.0312 Inf -3.290 0.0467
StepF9 - StepF12 -0.018170 0.0312 Inf -0.582 1.0000
StepF10 - StepF11 -0.043800 0.0312 Inf -1.404 0.9631
StepF10 - StepF12 0.040688 0.0312 Inf 1.304 0.9788
StepF11 - StepF12 0.084488 0.0312 Inf 2.708 0.2220
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.012531 0.0312 Inf 0.402 1.0000
StepF1 - StepF3 -0.039262 0.0312 Inf -1.258 0.9840
StepF1 - StepF4 0.053540 0.0312 Inf 1.716 0.8613
StepF1 - StepF5 0.072397 0.0312 Inf 2.320 0.4617
StepF1 - StepF6 0.042730 0.0312 Inf 1.370 0.9693
StepF1 - StepF7 0.053229 0.0312 Inf 1.706 0.8659
StepF1 - StepF8 0.068538 0.0312 Inf 2.197 0.5519
StepF1 - StepF9 0.137112 0.0312 Inf 4.395 0.0007
StepF1 - StepF10 0.078255 0.0312 Inf 2.508 0.3345
StepF1 - StepF11 0.034455 0.0312 Inf 1.104 0.9946
StepF1 - StepF12 0.118943 0.0312 Inf 3.812 0.0076
StepF2 - StepF3 -0.051793 0.0312 Inf -1.660 0.8862
StepF2 - StepF4 0.041008 0.0312 Inf 1.314 0.9775
StepF2 - StepF5 0.059866 0.0312 Inf 1.919 0.7477
StepF2 - StepF6 0.030199 0.0312 Inf 0.968 0.9983
StepF2 - StepF7 0.040698 0.0312 Inf 1.304 0.9788
StepF2 - StepF8 0.056007 0.0312 Inf 1.795 0.8211
StepF2 - StepF9 0.124581 0.0312 Inf 3.993 0.0038
StepF2 - StepF10 0.065724 0.0312 Inf 2.107 0.6181
StepF2 - StepF11 0.021923 0.0312 Inf 0.703 0.9999
StepF2 - StepF12 0.106411 0.0312 Inf 3.411 0.0318
StepF3 - StepF4 0.092801 0.0312 Inf 2.974 0.1160
StepF3 - StepF5 0.111659 0.0312 Inf 3.579 0.0179
StepF3 - StepF6 0.081992 0.0312 Inf 2.628 0.2638
StepF3 - StepF7 0.092491 0.0312 Inf 2.965 0.1191
StepF3 - StepF8 0.107800 0.0312 Inf 3.455 0.0274
StepF3 - StepF9 0.176374 0.0312 Inf 5.653 <.0001
StepF3 - StepF10 0.117517 0.0312 Inf 3.767 0.0091
StepF3 - StepF11 0.073716 0.0312 Inf 2.363 0.4317
StepF3 - StepF12 0.158204 0.0312 Inf 5.071 <.0001
StepF4 - StepF5 0.018857 0.0312 Inf 0.604 1.0000
StepF4 - StepF6 -0.010810 0.0312 Inf -0.346 1.0000
StepF4 - StepF7 -0.000311 0.0312 Inf -0.010 1.0000
StepF4 - StepF8 0.014999 0.0312 Inf 0.481 1.0000
StepF4 - StepF9 0.083573 0.0312 Inf 2.679 0.2368
StepF4 - StepF10 0.024715 0.0312 Inf 0.792 0.9997
StepF4 - StepF11 -0.019085 0.0312 Inf -0.612 1.0000
StepF4 - StepF12 0.065403 0.0312 Inf 2.096 0.6255
StepF5 - StepF6 -0.029667 0.0312 Inf -0.951 0.9986
StepF5 - StepF7 -0.019168 0.0312 Inf -0.614 1.0000
StepF5 - StepF8 -0.003859 0.0312 Inf -0.124 1.0000
StepF5 - StepF9 0.064715 0.0312 Inf 2.074 0.6414
StepF5 - StepF10 0.005858 0.0312 Inf 0.188 1.0000
StepF5 - StepF11 -0.037942 0.0312 Inf -1.216 0.9878
StepF5 - StepF12 0.046546 0.0312 Inf 1.492 0.9432
StepF6 - StepF7 0.010499 0.0312 Inf 0.337 1.0000
StepF6 - StepF8 0.025808 0.0312 Inf 0.827 0.9996
StepF6 - StepF9 0.094382 0.0312 Inf 3.025 0.1013
StepF6 - StepF10 0.035525 0.0312 Inf 1.139 0.9930
StepF6 - StepF11 -0.008275 0.0312 Inf -0.265 1.0000
StepF6 - StepF12 0.076213 0.0312 Inf 2.443 0.3769
StepF7 - StepF8 0.015309 0.0312 Inf 0.491 1.0000
StepF7 - StepF9 0.083883 0.0312 Inf 2.689 0.2317
StepF7 - StepF10 0.025026 0.0312 Inf 0.802 0.9997
StepF7 - StepF11 -0.018774 0.0312 Inf -0.602 1.0000
StepF7 - StepF12 0.065714 0.0312 Inf 2.106 0.6183
StepF8 - StepF9 0.068574 0.0312 Inf 2.198 0.5511
StepF8 - StepF10 0.009717 0.0312 Inf 0.311 1.0000
StepF8 - StepF11 -0.034084 0.0312 Inf -1.092 0.9951
StepF8 - StepF12 0.050405 0.0312 Inf 1.616 0.9038
StepF9 - StepF10 -0.058858 0.0312 Inf -1.886 0.7680
StepF9 - StepF11 -0.102658 0.0312 Inf -3.290 0.0467
StepF9 - StepF12 -0.018170 0.0312 Inf -0.582 1.0000
StepF10 - StepF11 -0.043800 0.0312 Inf -1.404 0.9631
StepF10 - StepF12 0.040688 0.0312 Inf 1.304 0.9788
StepF11 - StepF12 0.084488 0.0312 Inf 2.708 0.2220
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.0125 0.0312 Inf -0.402 1.0000
StepF3 - StepF2 0.0518 0.0312 Inf 1.660 0.6783
StepF4 - StepF3 -0.0928 0.0312 Inf -2.974 0.0323
StepF5 - StepF4 -0.0189 0.0312 Inf -0.604 1.0000
StepF6 - StepF5 0.0297 0.0312 Inf 0.951 1.0000
StepF7 - StepF6 -0.0105 0.0312 Inf -0.337 1.0000
StepF8 - StepF7 -0.0153 0.0312 Inf -0.491 1.0000
StepF9 - StepF8 -0.0686 0.0312 Inf -2.198 0.2516
StepF10 - StepF9 0.0589 0.0312 Inf 1.886 0.4738
StepF11 - StepF10 0.0438 0.0312 Inf 1.404 0.9621
StepF12 - StepF11 -0.0845 0.0312 Inf -2.708 0.0677
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.0125 0.0312 Inf -0.402 1.0000
StepF3 - StepF2 0.0518 0.0312 Inf 1.660 0.6783
StepF4 - StepF3 -0.0928 0.0312 Inf -2.974 0.0323
StepF5 - StepF4 -0.0189 0.0312 Inf -0.604 1.0000
StepF6 - StepF5 0.0297 0.0312 Inf 0.951 1.0000
StepF7 - StepF6 -0.0105 0.0312 Inf -0.337 1.0000
StepF8 - StepF7 -0.0153 0.0312 Inf -0.491 1.0000
StepF9 - StepF8 -0.0686 0.0312 Inf -2.198 0.2516
StepF10 - StepF9 0.0589 0.0312 Inf 1.886 0.4738
StepF11 - StepF10 0.0438 0.0312 Inf 1.404 0.9621
StepF12 - StepF11 -0.0845 0.0312 Inf -2.708 0.0677
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests
==============================
TEST | Block 5 | 12 steps | Axis Y
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 117.2792 11 < 2e-16 ***
Accuracy 3.6975 1 0.05449 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.624 0.0614 Inf 0.504 0.745
2 0.813 0.0614 Inf 0.692 0.933
3 0.692 0.0614 Inf 0.572 0.812
4 0.692 0.0614 Inf 0.571 0.812
5 0.624 0.0614 Inf 0.504 0.744
6 0.542 0.0614 Inf 0.422 0.662
7 0.605 0.0614 Inf 0.484 0.725
8 0.635 0.0614 Inf 0.514 0.755
9 0.554 0.0614 Inf 0.434 0.674
10 0.660 0.0614 Inf 0.539 0.780
11 0.616 0.0614 Inf 0.496 0.736
12 0.549 0.0614 Inf 0.429 0.669
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.579 0.0617 Inf 0.458 0.700
2 0.767 0.0617 Inf 0.646 0.888
3 0.647 0.0617 Inf 0.526 0.768
4 0.646 0.0617 Inf 0.525 0.767
5 0.579 0.0617 Inf 0.458 0.700
6 0.497 0.0617 Inf 0.376 0.618
7 0.559 0.0617 Inf 0.438 0.680
8 0.589 0.0617 Inf 0.468 0.710
9 0.509 0.0617 Inf 0.388 0.630
10 0.614 0.0617 Inf 0.493 0.735
11 0.570 0.0617 Inf 0.450 0.691
12 0.504 0.0617 Inf 0.383 0.624
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.188225 0.0327 Inf -5.756 <.0001
StepF1 - StepF3 -0.067807 0.0327 Inf -2.074 0.6419
StepF1 - StepF4 -0.067441 0.0327 Inf -2.062 0.6499
StepF1 - StepF5 0.000246 0.0327 Inf 0.008 1.0000
StepF1 - StepF6 0.082295 0.0327 Inf 2.517 0.3292
StepF1 - StepF7 0.019506 0.0327 Inf 0.597 1.0000
StepF1 - StepF8 -0.010447 0.0327 Inf -0.319 1.0000
StepF1 - StepF9 0.070247 0.0327 Inf 2.148 0.5876
StepF1 - StepF10 -0.035477 0.0327 Inf -1.085 0.9953
StepF1 - StepF11 0.008374 0.0327 Inf 0.256 1.0000
StepF1 - StepF12 0.075329 0.0327 Inf 2.304 0.4739
StepF2 - StepF3 0.120418 0.0327 Inf 3.682 0.0124
StepF2 - StepF4 0.120783 0.0327 Inf 3.694 0.0119
StepF2 - StepF5 0.188471 0.0327 Inf 5.764 <.0001
StepF2 - StepF6 0.270520 0.0327 Inf 8.273 <.0001
StepF2 - StepF7 0.207731 0.0327 Inf 6.353 <.0001
StepF2 - StepF8 0.177778 0.0327 Inf 5.437 <.0001
StepF2 - StepF9 0.258472 0.0327 Inf 7.904 <.0001
StepF2 - StepF10 0.152748 0.0327 Inf 4.671 0.0002
StepF2 - StepF11 0.196598 0.0327 Inf 6.012 <.0001
StepF2 - StepF12 0.263554 0.0327 Inf 8.060 <.0001
StepF3 - StepF4 0.000366 0.0327 Inf 0.011 1.0000
StepF3 - StepF5 0.068053 0.0327 Inf 2.081 0.6365
StepF3 - StepF6 0.150102 0.0327 Inf 4.590 0.0003
StepF3 - StepF7 0.087313 0.0327 Inf 2.670 0.2412
StepF3 - StepF8 0.057361 0.0327 Inf 1.754 0.8427
StepF3 - StepF9 0.138055 0.0327 Inf 4.222 0.0015
StepF3 - StepF10 0.032330 0.0327 Inf 0.989 0.9980
StepF3 - StepF11 0.076181 0.0327 Inf 2.330 0.4552
StepF3 - StepF12 0.143136 0.0327 Inf 4.377 0.0007
StepF4 - StepF5 0.067687 0.0327 Inf 2.070 0.6445
StepF4 - StepF6 0.149737 0.0327 Inf 4.579 0.0003
StepF4 - StepF7 0.086948 0.0327 Inf 2.659 0.2471
StepF4 - StepF8 0.056995 0.0327 Inf 1.743 0.8483
StepF4 - StepF9 0.137689 0.0327 Inf 4.211 0.0015
StepF4 - StepF10 0.031964 0.0327 Inf 0.977 0.9982
StepF4 - StepF11 0.075815 0.0327 Inf 2.318 0.4632
StepF4 - StepF12 0.142770 0.0327 Inf 4.366 0.0008
StepF5 - StepF6 0.082050 0.0327 Inf 2.509 0.3339
StepF5 - StepF7 0.019261 0.0327 Inf 0.589 1.0000
StepF5 - StepF8 -0.010692 0.0327 Inf -0.327 1.0000
StepF5 - StepF9 0.070002 0.0327 Inf 2.141 0.5931
StepF5 - StepF10 -0.035723 0.0327 Inf -1.092 0.9951
StepF5 - StepF11 0.008128 0.0327 Inf 0.249 1.0000
StepF5 - StepF12 0.075083 0.0327 Inf 2.296 0.4793
StepF6 - StepF7 -0.062789 0.0327 Inf -1.920 0.7468
StepF6 - StepF8 -0.092742 0.0327 Inf -2.836 0.1648
StepF6 - StepF9 -0.012048 0.0327 Inf -0.368 1.0000
StepF6 - StepF10 -0.117772 0.0327 Inf -3.602 0.0166
StepF6 - StepF11 -0.073922 0.0327 Inf -2.261 0.5051
StepF6 - StepF12 -0.006966 0.0327 Inf -0.213 1.0000
StepF7 - StepF8 -0.029953 0.0327 Inf -0.916 0.9990
StepF7 - StepF9 0.050741 0.0327 Inf 1.552 0.9259
StepF7 - StepF10 -0.054983 0.0327 Inf -1.681 0.8770
StepF7 - StepF11 -0.011133 0.0327 Inf -0.340 1.0000
StepF7 - StepF12 0.055823 0.0327 Inf 1.707 0.8655
StepF8 - StepF9 0.080694 0.0327 Inf 2.468 0.3605
StepF8 - StepF10 -0.025030 0.0327 Inf -0.765 0.9998
StepF8 - StepF11 0.018820 0.0327 Inf 0.576 1.0000
StepF8 - StepF12 0.085775 0.0327 Inf 2.623 0.2665
StepF9 - StepF10 -0.105724 0.0327 Inf -3.233 0.0556
StepF9 - StepF11 -0.061874 0.0327 Inf -1.892 0.7645
StepF9 - StepF12 0.005081 0.0327 Inf 0.155 1.0000
StepF10 - StepF11 0.043851 0.0327 Inf 1.341 0.9738
StepF10 - StepF12 0.110806 0.0327 Inf 3.389 0.0341
StepF11 - StepF12 0.066955 0.0327 Inf 2.048 0.6604
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.188225 0.0327 Inf -5.756 <.0001
StepF1 - StepF3 -0.067807 0.0327 Inf -2.074 0.6419
StepF1 - StepF4 -0.067441 0.0327 Inf -2.062 0.6499
StepF1 - StepF5 0.000246 0.0327 Inf 0.008 1.0000
StepF1 - StepF6 0.082295 0.0327 Inf 2.517 0.3292
StepF1 - StepF7 0.019506 0.0327 Inf 0.597 1.0000
StepF1 - StepF8 -0.010447 0.0327 Inf -0.319 1.0000
StepF1 - StepF9 0.070247 0.0327 Inf 2.148 0.5876
StepF1 - StepF10 -0.035477 0.0327 Inf -1.085 0.9953
StepF1 - StepF11 0.008374 0.0327 Inf 0.256 1.0000
StepF1 - StepF12 0.075329 0.0327 Inf 2.304 0.4739
StepF2 - StepF3 0.120418 0.0327 Inf 3.682 0.0124
StepF2 - StepF4 0.120783 0.0327 Inf 3.694 0.0119
StepF2 - StepF5 0.188471 0.0327 Inf 5.764 <.0001
StepF2 - StepF6 0.270520 0.0327 Inf 8.273 <.0001
StepF2 - StepF7 0.207731 0.0327 Inf 6.353 <.0001
StepF2 - StepF8 0.177778 0.0327 Inf 5.437 <.0001
StepF2 - StepF9 0.258472 0.0327 Inf 7.904 <.0001
StepF2 - StepF10 0.152748 0.0327 Inf 4.671 0.0002
StepF2 - StepF11 0.196598 0.0327 Inf 6.012 <.0001
StepF2 - StepF12 0.263554 0.0327 Inf 8.060 <.0001
StepF3 - StepF4 0.000366 0.0327 Inf 0.011 1.0000
StepF3 - StepF5 0.068053 0.0327 Inf 2.081 0.6365
StepF3 - StepF6 0.150102 0.0327 Inf 4.590 0.0003
StepF3 - StepF7 0.087313 0.0327 Inf 2.670 0.2412
StepF3 - StepF8 0.057361 0.0327 Inf 1.754 0.8427
StepF3 - StepF9 0.138055 0.0327 Inf 4.222 0.0015
StepF3 - StepF10 0.032330 0.0327 Inf 0.989 0.9980
StepF3 - StepF11 0.076181 0.0327 Inf 2.330 0.4552
StepF3 - StepF12 0.143136 0.0327 Inf 4.377 0.0007
StepF4 - StepF5 0.067687 0.0327 Inf 2.070 0.6445
StepF4 - StepF6 0.149737 0.0327 Inf 4.579 0.0003
StepF4 - StepF7 0.086948 0.0327 Inf 2.659 0.2471
StepF4 - StepF8 0.056995 0.0327 Inf 1.743 0.8483
StepF4 - StepF9 0.137689 0.0327 Inf 4.211 0.0015
StepF4 - StepF10 0.031964 0.0327 Inf 0.977 0.9982
StepF4 - StepF11 0.075815 0.0327 Inf 2.318 0.4632
StepF4 - StepF12 0.142770 0.0327 Inf 4.366 0.0008
StepF5 - StepF6 0.082050 0.0327 Inf 2.509 0.3339
StepF5 - StepF7 0.019261 0.0327 Inf 0.589 1.0000
StepF5 - StepF8 -0.010692 0.0327 Inf -0.327 1.0000
StepF5 - StepF9 0.070002 0.0327 Inf 2.141 0.5931
StepF5 - StepF10 -0.035723 0.0327 Inf -1.092 0.9951
StepF5 - StepF11 0.008128 0.0327 Inf 0.249 1.0000
StepF5 - StepF12 0.075083 0.0327 Inf 2.296 0.4793
StepF6 - StepF7 -0.062789 0.0327 Inf -1.920 0.7468
StepF6 - StepF8 -0.092742 0.0327 Inf -2.836 0.1648
StepF6 - StepF9 -0.012048 0.0327 Inf -0.368 1.0000
StepF6 - StepF10 -0.117772 0.0327 Inf -3.602 0.0166
StepF6 - StepF11 -0.073922 0.0327 Inf -2.261 0.5051
StepF6 - StepF12 -0.006966 0.0327 Inf -0.213 1.0000
StepF7 - StepF8 -0.029953 0.0327 Inf -0.916 0.9990
StepF7 - StepF9 0.050741 0.0327 Inf 1.552 0.9259
StepF7 - StepF10 -0.054983 0.0327 Inf -1.681 0.8770
StepF7 - StepF11 -0.011133 0.0327 Inf -0.340 1.0000
StepF7 - StepF12 0.055823 0.0327 Inf 1.707 0.8655
StepF8 - StepF9 0.080694 0.0327 Inf 2.468 0.3605
StepF8 - StepF10 -0.025030 0.0327 Inf -0.765 0.9998
StepF8 - StepF11 0.018820 0.0327 Inf 0.576 1.0000
StepF8 - StepF12 0.085775 0.0327 Inf 2.623 0.2665
StepF9 - StepF10 -0.105724 0.0327 Inf -3.233 0.0556
StepF9 - StepF11 -0.061874 0.0327 Inf -1.892 0.7645
StepF9 - StepF12 0.005081 0.0327 Inf 0.155 1.0000
StepF10 - StepF11 0.043851 0.0327 Inf 1.341 0.9738
StepF10 - StepF12 0.110806 0.0327 Inf 3.389 0.0341
StepF11 - StepF12 0.066955 0.0327 Inf 2.048 0.6604
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.188225 0.0327 Inf 5.756 <.0001
StepF3 - StepF2 -0.120418 0.0327 Inf -3.682 0.0023
StepF4 - StepF3 -0.000366 0.0327 Inf -0.011 0.9911
StepF5 - StepF4 -0.067687 0.0327 Inf -2.070 0.2308
StepF6 - StepF5 -0.082050 0.0327 Inf -2.509 0.0968
StepF7 - StepF6 0.062789 0.0327 Inf 1.920 0.2308
StepF8 - StepF7 0.029953 0.0327 Inf 0.916 0.7193
StepF9 - StepF8 -0.080694 0.0327 Inf -2.468 0.0968
StepF10 - StepF9 0.105724 0.0327 Inf 3.233 0.0110
StepF11 - StepF10 -0.043851 0.0327 Inf -1.341 0.5398
StepF12 - StepF11 -0.066955 0.0327 Inf -2.048 0.2308
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.188225 0.0327 Inf 5.756 <.0001
StepF3 - StepF2 -0.120418 0.0327 Inf -3.682 0.0023
StepF4 - StepF3 -0.000366 0.0327 Inf -0.011 0.9911
StepF5 - StepF4 -0.067687 0.0327 Inf -2.070 0.2308
StepF6 - StepF5 -0.082050 0.0327 Inf -2.509 0.0968
StepF7 - StepF6 0.062789 0.0327 Inf 1.920 0.2308
StepF8 - StepF7 0.029953 0.0327 Inf 0.916 0.7193
StepF9 - StepF8 -0.080694 0.0327 Inf -2.468 0.0968
StepF10 - StepF9 0.105724 0.0327 Inf 3.233 0.0110
StepF11 - StepF10 -0.043851 0.0327 Inf -1.341 0.5398
StepF12 - StepF11 -0.066955 0.0327 Inf -2.048 0.2308
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests
==============================
TEST | Block 5 | 12 steps | Axis Z
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 90.8425 11 1.14e-14 ***
Accuracy 3.0281 1 0.08183 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.36 0.141 Inf 1.081 1.63
2 1.60 0.141 Inf 1.322 1.87
3 1.44 0.141 Inf 1.169 1.72
4 1.31 0.141 Inf 1.032 1.58
5 1.31 0.141 Inf 1.039 1.59
6 1.30 0.141 Inf 1.024 1.58
7 1.31 0.141 Inf 1.038 1.59
8 1.25 0.141 Inf 0.971 1.52
9 1.21 0.141 Inf 0.932 1.48
10 1.28 0.141 Inf 1.002 1.55
11 1.26 0.141 Inf 0.981 1.53
12 1.15 0.141 Inf 0.871 1.42
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.24 0.142 Inf 0.964 1.52
2 1.48 0.142 Inf 1.205 1.76
3 1.33 0.142 Inf 1.052 1.61
4 1.19 0.142 Inf 0.915 1.47
5 1.20 0.142 Inf 0.922 1.48
6 1.19 0.142 Inf 0.908 1.46
7 1.20 0.142 Inf 0.921 1.48
8 1.13 0.142 Inf 0.854 1.41
9 1.09 0.142 Inf 0.815 1.37
10 1.16 0.142 Inf 0.886 1.44
11 1.14 0.142 Inf 0.864 1.42
12 1.03 0.142 Inf 0.754 1.31
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.24108 0.0571 Inf -4.225 0.0014
StepF1 - StepF3 -0.08839 0.0571 Inf -1.549 0.9267
StepF1 - StepF4 0.04883 0.0571 Inf 0.856 0.9995
StepF1 - StepF5 0.04145 0.0571 Inf 0.726 0.9999
StepF1 - StepF6 0.05614 0.0571 Inf 0.984 0.9980
StepF1 - StepF7 0.04264 0.0571 Inf 0.747 0.9999
StepF1 - StepF8 0.10992 0.0571 Inf 1.926 0.7428
StepF1 - StepF9 0.14863 0.0571 Inf 2.605 0.2766
StepF1 - StepF10 0.07829 0.0571 Inf 1.372 0.9688
StepF1 - StepF11 0.09958 0.0571 Inf 1.745 0.8472
StepF1 - StepF12 0.20968 0.0571 Inf 3.675 0.0127
StepF2 - StepF3 0.15269 0.0571 Inf 2.676 0.2382
StepF2 - StepF4 0.28991 0.0571 Inf 5.081 <.0001
StepF2 - StepF5 0.28253 0.0571 Inf 4.951 <.0001
StepF2 - StepF6 0.29722 0.0571 Inf 5.209 <.0001
StepF2 - StepF7 0.28372 0.0571 Inf 4.972 <.0001
StepF2 - StepF8 0.35100 0.0571 Inf 6.152 <.0001
StepF2 - StepF9 0.38971 0.0571 Inf 6.830 <.0001
StepF2 - StepF10 0.31937 0.0571 Inf 5.597 <.0001
StepF2 - StepF11 0.34066 0.0571 Inf 5.970 <.0001
StepF2 - StepF12 0.45076 0.0571 Inf 7.900 <.0001
StepF3 - StepF4 0.13722 0.0571 Inf 2.405 0.4025
StepF3 - StepF5 0.12984 0.0571 Inf 2.276 0.4942
StepF3 - StepF6 0.14454 0.0571 Inf 2.533 0.3190
StepF3 - StepF7 0.13103 0.0571 Inf 2.296 0.4791
StepF3 - StepF8 0.19832 0.0571 Inf 3.476 0.0256
StepF3 - StepF9 0.23703 0.0571 Inf 4.154 0.0019
StepF3 - StepF10 0.16668 0.0571 Inf 2.921 0.1333
StepF3 - StepF11 0.18797 0.0571 Inf 3.294 0.0461
StepF3 - StepF12 0.29808 0.0571 Inf 5.224 <.0001
StepF4 - StepF5 -0.00738 0.0571 Inf -0.129 1.0000
StepF4 - StepF6 0.00731 0.0571 Inf 0.128 1.0000
StepF4 - StepF7 -0.00619 0.0571 Inf -0.109 1.0000
StepF4 - StepF8 0.06109 0.0571 Inf 1.071 0.9958
StepF4 - StepF9 0.09980 0.0571 Inf 1.749 0.8452
StepF4 - StepF10 0.02946 0.0571 Inf 0.516 1.0000
StepF4 - StepF11 0.05075 0.0571 Inf 0.889 0.9992
StepF4 - StepF12 0.16085 0.0571 Inf 2.819 0.1718
StepF5 - StepF6 0.01470 0.0571 Inf 0.258 1.0000
StepF5 - StepF7 0.00119 0.0571 Inf 0.021 1.0000
StepF5 - StepF8 0.06848 0.0571 Inf 1.200 0.9891
StepF5 - StepF9 0.10719 0.0571 Inf 1.879 0.7729
StepF5 - StepF10 0.03684 0.0571 Inf 0.646 1.0000
StepF5 - StepF11 0.05813 0.0571 Inf 1.019 0.9973
StepF5 - StepF12 0.16824 0.0571 Inf 2.948 0.1242
StepF6 - StepF7 -0.01350 0.0571 Inf -0.237 1.0000
StepF6 - StepF8 0.05378 0.0571 Inf 0.943 0.9987
StepF6 - StepF9 0.09249 0.0571 Inf 1.621 0.9018
StepF6 - StepF10 0.02215 0.0571 Inf 0.388 1.0000
StepF6 - StepF11 0.04344 0.0571 Inf 0.761 0.9998
StepF6 - StepF12 0.15354 0.0571 Inf 2.691 0.2306
StepF7 - StepF8 0.06728 0.0571 Inf 1.179 0.9906
StepF7 - StepF9 0.10600 0.0571 Inf 1.858 0.7855
StepF7 - StepF10 0.03565 0.0571 Inf 0.625 1.0000
StepF7 - StepF11 0.05694 0.0571 Inf 0.998 0.9978
StepF7 - StepF12 0.16704 0.0571 Inf 2.928 0.1311
StepF8 - StepF9 0.03871 0.0571 Inf 0.678 0.9999
StepF8 - StepF10 -0.03163 0.0571 Inf -0.554 1.0000
StepF8 - StepF11 -0.01034 0.0571 Inf -0.181 1.0000
StepF8 - StepF12 0.09976 0.0571 Inf 1.748 0.8456
StepF9 - StepF10 -0.07035 0.0571 Inf -1.233 0.9864
StepF9 - StepF11 -0.04906 0.0571 Inf -0.860 0.9994
StepF9 - StepF12 0.06105 0.0571 Inf 1.070 0.9959
StepF10 - StepF11 0.02129 0.0571 Inf 0.373 1.0000
StepF10 - StepF12 0.13139 0.0571 Inf 2.303 0.4745
StepF11 - StepF12 0.11010 0.0571 Inf 1.930 0.7407
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.24108 0.0571 Inf -4.225 0.0014
StepF1 - StepF3 -0.08839 0.0571 Inf -1.549 0.9267
StepF1 - StepF4 0.04883 0.0571 Inf 0.856 0.9995
StepF1 - StepF5 0.04145 0.0571 Inf 0.726 0.9999
StepF1 - StepF6 0.05614 0.0571 Inf 0.984 0.9980
StepF1 - StepF7 0.04264 0.0571 Inf 0.747 0.9999
StepF1 - StepF8 0.10992 0.0571 Inf 1.926 0.7428
StepF1 - StepF9 0.14863 0.0571 Inf 2.605 0.2766
StepF1 - StepF10 0.07829 0.0571 Inf 1.372 0.9688
StepF1 - StepF11 0.09958 0.0571 Inf 1.745 0.8472
StepF1 - StepF12 0.20968 0.0571 Inf 3.675 0.0127
StepF2 - StepF3 0.15269 0.0571 Inf 2.676 0.2382
StepF2 - StepF4 0.28991 0.0571 Inf 5.081 <.0001
StepF2 - StepF5 0.28253 0.0571 Inf 4.951 <.0001
StepF2 - StepF6 0.29722 0.0571 Inf 5.209 <.0001
StepF2 - StepF7 0.28372 0.0571 Inf 4.972 <.0001
StepF2 - StepF8 0.35100 0.0571 Inf 6.152 <.0001
StepF2 - StepF9 0.38971 0.0571 Inf 6.830 <.0001
StepF2 - StepF10 0.31937 0.0571 Inf 5.597 <.0001
StepF2 - StepF11 0.34066 0.0571 Inf 5.970 <.0001
StepF2 - StepF12 0.45076 0.0571 Inf 7.900 <.0001
StepF3 - StepF4 0.13722 0.0571 Inf 2.405 0.4025
StepF3 - StepF5 0.12984 0.0571 Inf 2.276 0.4942
StepF3 - StepF6 0.14454 0.0571 Inf 2.533 0.3190
StepF3 - StepF7 0.13103 0.0571 Inf 2.296 0.4791
StepF3 - StepF8 0.19832 0.0571 Inf 3.476 0.0256
StepF3 - StepF9 0.23703 0.0571 Inf 4.154 0.0019
StepF3 - StepF10 0.16668 0.0571 Inf 2.921 0.1333
StepF3 - StepF11 0.18797 0.0571 Inf 3.294 0.0461
StepF3 - StepF12 0.29808 0.0571 Inf 5.224 <.0001
StepF4 - StepF5 -0.00738 0.0571 Inf -0.129 1.0000
StepF4 - StepF6 0.00731 0.0571 Inf 0.128 1.0000
StepF4 - StepF7 -0.00619 0.0571 Inf -0.109 1.0000
StepF4 - StepF8 0.06109 0.0571 Inf 1.071 0.9958
StepF4 - StepF9 0.09980 0.0571 Inf 1.749 0.8452
StepF4 - StepF10 0.02946 0.0571 Inf 0.516 1.0000
StepF4 - StepF11 0.05075 0.0571 Inf 0.889 0.9992
StepF4 - StepF12 0.16085 0.0571 Inf 2.819 0.1718
StepF5 - StepF6 0.01470 0.0571 Inf 0.258 1.0000
StepF5 - StepF7 0.00119 0.0571 Inf 0.021 1.0000
StepF5 - StepF8 0.06848 0.0571 Inf 1.200 0.9891
StepF5 - StepF9 0.10719 0.0571 Inf 1.879 0.7729
StepF5 - StepF10 0.03684 0.0571 Inf 0.646 1.0000
StepF5 - StepF11 0.05813 0.0571 Inf 1.019 0.9973
StepF5 - StepF12 0.16824 0.0571 Inf 2.948 0.1242
StepF6 - StepF7 -0.01350 0.0571 Inf -0.237 1.0000
StepF6 - StepF8 0.05378 0.0571 Inf 0.943 0.9987
StepF6 - StepF9 0.09249 0.0571 Inf 1.621 0.9018
StepF6 - StepF10 0.02215 0.0571 Inf 0.388 1.0000
StepF6 - StepF11 0.04344 0.0571 Inf 0.761 0.9998
StepF6 - StepF12 0.15354 0.0571 Inf 2.691 0.2306
StepF7 - StepF8 0.06728 0.0571 Inf 1.179 0.9906
StepF7 - StepF9 0.10600 0.0571 Inf 1.858 0.7855
StepF7 - StepF10 0.03565 0.0571 Inf 0.625 1.0000
StepF7 - StepF11 0.05694 0.0571 Inf 0.998 0.9978
StepF7 - StepF12 0.16704 0.0571 Inf 2.928 0.1311
StepF8 - StepF9 0.03871 0.0571 Inf 0.678 0.9999
StepF8 - StepF10 -0.03163 0.0571 Inf -0.554 1.0000
StepF8 - StepF11 -0.01034 0.0571 Inf -0.181 1.0000
StepF8 - StepF12 0.09976 0.0571 Inf 1.748 0.8456
StepF9 - StepF10 -0.07035 0.0571 Inf -1.233 0.9864
StepF9 - StepF11 -0.04906 0.0571 Inf -0.860 0.9994
StepF9 - StepF12 0.06105 0.0571 Inf 1.070 0.9959
StepF10 - StepF11 0.02129 0.0571 Inf 0.373 1.0000
StepF10 - StepF12 0.13139 0.0571 Inf 2.303 0.4745
StepF11 - StepF12 0.11010 0.0571 Inf 1.930 0.7407
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.24108 0.0571 Inf 4.225 0.0003
StepF3 - StepF2 -0.15269 0.0571 Inf -2.676 0.0745
StepF4 - StepF3 -0.13722 0.0571 Inf -2.405 0.1456
StepF5 - StepF4 0.00738 0.0571 Inf 0.129 1.0000
StepF6 - StepF5 -0.01470 0.0571 Inf -0.258 1.0000
StepF7 - StepF6 0.01350 0.0571 Inf 0.237 1.0000
StepF8 - StepF7 -0.06728 0.0571 Inf -1.179 1.0000
StepF9 - StepF8 -0.03871 0.0571 Inf -0.678 1.0000
StepF10 - StepF9 0.07035 0.0571 Inf 1.233 1.0000
StepF11 - StepF10 -0.02129 0.0571 Inf -0.373 1.0000
StepF12 - StepF11 -0.11010 0.0571 Inf -1.930 0.4292
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.24108 0.0571 Inf 4.225 0.0003
StepF3 - StepF2 -0.15269 0.0571 Inf -2.676 0.0745
StepF4 - StepF3 -0.13722 0.0571 Inf -2.405 0.1456
StepF5 - StepF4 0.00738 0.0571 Inf 0.129 1.0000
StepF6 - StepF5 -0.01470 0.0571 Inf -0.258 1.0000
StepF7 - StepF6 0.01350 0.0571 Inf 0.237 1.0000
StepF8 - StepF7 -0.06728 0.0571 Inf -1.179 1.0000
StepF9 - StepF8 -0.03871 0.0571 Inf -0.678 1.0000
StepF10 - StepF9 0.07035 0.0571 Inf 1.233 1.0000
StepF11 - StepF10 -0.02129 0.0571 Inf -0.373 1.0000
StepF12 - StepF11 -0.11010 0.0571 Inf -1.930 0.4292
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests .report_step_test(sw_b5_18, "5", "18 steps")
==============================
TEST | Block 5 | 18 steps | Axis X
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 71.8346 17 1.04e-08 ***
Accuracy 0.3972 1 0.5285
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.671 0.0555 Inf 0.562 0.780
2 0.625 0.0555 Inf 0.516 0.734
3 0.691 0.0555 Inf 0.582 0.799
4 0.613 0.0555 Inf 0.505 0.722
5 0.572 0.0555 Inf 0.464 0.681
6 0.608 0.0555 Inf 0.500 0.717
7 0.635 0.0555 Inf 0.526 0.743
8 0.571 0.0555 Inf 0.463 0.680
9 0.574 0.0555 Inf 0.465 0.683
10 0.617 0.0555 Inf 0.508 0.725
11 0.649 0.0555 Inf 0.540 0.757
12 0.532 0.0555 Inf 0.423 0.641
13 0.572 0.0555 Inf 0.463 0.681
14 0.609 0.0555 Inf 0.500 0.717
15 0.598 0.0555 Inf 0.489 0.707
16 0.544 0.0555 Inf 0.436 0.653
17 0.529 0.0555 Inf 0.420 0.638
18 0.523 0.0555 Inf 0.415 0.632
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.650 0.0563 Inf 0.539 0.760
2 0.604 0.0563 Inf 0.494 0.714
3 0.669 0.0563 Inf 0.559 0.780
4 0.592 0.0563 Inf 0.482 0.703
5 0.551 0.0563 Inf 0.441 0.661
6 0.587 0.0563 Inf 0.477 0.698
7 0.613 0.0563 Inf 0.503 0.724
8 0.550 0.0563 Inf 0.440 0.660
9 0.553 0.0563 Inf 0.442 0.663
10 0.595 0.0563 Inf 0.485 0.706
11 0.627 0.0563 Inf 0.517 0.738
12 0.511 0.0563 Inf 0.400 0.621
13 0.551 0.0563 Inf 0.440 0.661
14 0.587 0.0563 Inf 0.477 0.698
15 0.577 0.0563 Inf 0.466 0.687
16 0.523 0.0563 Inf 0.413 0.633
17 0.508 0.0563 Inf 0.397 0.618
18 0.502 0.0563 Inf 0.392 0.612
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.045863 0.0332 Inf 1.381 0.9964
StepF1 - StepF3 -0.019678 0.0332 Inf -0.593 1.0000
StepF1 - StepF4 0.057557 0.0332 Inf 1.733 0.9604
StepF1 - StepF5 0.098689 0.0332 Inf 2.972 0.2138
StepF1 - StepF6 0.062569 0.0332 Inf 1.884 0.9181
StepF1 - StepF7 0.036409 0.0332 Inf 1.096 0.9998
StepF1 - StepF8 0.099823 0.0332 Inf 3.006 0.1970
StepF1 - StepF9 0.097142 0.0332 Inf 2.925 0.2383
StepF1 - StepF10 0.054389 0.0332 Inf 1.638 0.9770
StepF1 - StepF11 0.022364 0.0332 Inf 0.673 1.0000
StepF1 - StepF12 0.139236 0.0332 Inf 4.193 0.0036
StepF1 - StepF13 0.099210 0.0332 Inf 2.987 0.2060
StepF1 - StepF14 0.062368 0.0332 Inf 1.878 0.9202
StepF1 - StepF15 0.073131 0.0332 Inf 2.202 0.7526
StepF1 - StepF16 0.126787 0.0332 Inf 3.818 0.0160
StepF1 - StepF17 0.142068 0.0332 Inf 4.278 0.0025
StepF1 - StepF18 0.147845 0.0332 Inf 4.452 0.0012
StepF2 - StepF3 -0.065541 0.0332 Inf -1.974 0.8821
StepF2 - StepF4 0.011694 0.0332 Inf 0.352 1.0000
StepF2 - StepF5 0.052826 0.0332 Inf 1.591 0.9829
StepF2 - StepF6 0.016706 0.0332 Inf 0.503 1.0000
StepF2 - StepF7 -0.009454 0.0332 Inf -0.285 1.0000
StepF2 - StepF8 0.053961 0.0332 Inf 1.625 0.9787
StepF2 - StepF9 0.051279 0.0332 Inf 1.544 0.9874
StepF2 - StepF10 0.008526 0.0332 Inf 0.257 1.0000
StepF2 - StepF11 -0.023498 0.0332 Inf -0.708 1.0000
StepF2 - StepF12 0.093373 0.0332 Inf 2.812 0.3052
StepF2 - StepF13 0.053348 0.0332 Inf 1.606 0.9811
StepF2 - StepF14 0.016506 0.0332 Inf 0.497 1.0000
StepF2 - StepF15 0.027268 0.0332 Inf 0.821 1.0000
StepF2 - StepF16 0.080924 0.0332 Inf 2.437 0.5794
StepF2 - StepF17 0.096205 0.0332 Inf 2.897 0.2540
StepF2 - StepF18 0.101982 0.0332 Inf 3.071 0.1676
StepF3 - StepF4 0.077235 0.0332 Inf 2.326 0.6644
StepF3 - StepF5 0.118368 0.0332 Inf 3.564 0.0390
StepF3 - StepF6 0.082247 0.0332 Inf 2.477 0.5484
StepF3 - StepF7 0.056087 0.0332 Inf 1.689 0.9690
StepF3 - StepF8 0.119502 0.0332 Inf 3.599 0.0348
StepF3 - StepF9 0.116820 0.0332 Inf 3.518 0.0455
StepF3 - StepF10 0.074067 0.0332 Inf 2.230 0.7333
StepF3 - StepF11 0.042043 0.0332 Inf 1.266 0.9987
StepF3 - StepF12 0.158914 0.0332 Inf 4.785 0.0002
StepF3 - StepF13 0.118889 0.0332 Inf 3.580 0.0370
StepF3 - StepF14 0.082047 0.0332 Inf 2.471 0.5531
StepF3 - StepF15 0.092809 0.0332 Inf 2.795 0.3160
StepF3 - StepF16 0.146466 0.0332 Inf 4.410 0.0014
StepF3 - StepF17 0.161746 0.0332 Inf 4.871 0.0002
StepF3 - StepF18 0.167523 0.0332 Inf 5.045 0.0001
StepF4 - StepF5 0.041132 0.0332 Inf 1.239 0.9990
StepF4 - StepF6 0.005012 0.0332 Inf 0.151 1.0000
StepF4 - StepF7 -0.021148 0.0332 Inf -0.637 1.0000
StepF4 - StepF8 0.042266 0.0332 Inf 1.273 0.9986
StepF4 - StepF9 0.039585 0.0332 Inf 1.192 0.9994
StepF4 - StepF10 -0.003168 0.0332 Inf -0.095 1.0000
StepF4 - StepF11 -0.035192 0.0332 Inf -1.060 0.9999
StepF4 - StepF12 0.081679 0.0332 Inf 2.460 0.5617
StepF4 - StepF13 0.041653 0.0332 Inf 1.254 0.9989
StepF4 - StepF14 0.004812 0.0332 Inf 0.145 1.0000
StepF4 - StepF15 0.015574 0.0332 Inf 0.469 1.0000
StepF4 - StepF16 0.069230 0.0332 Inf 2.085 0.8255
StepF4 - StepF17 0.084511 0.0332 Inf 2.545 0.4955
StepF4 - StepF18 0.090288 0.0332 Inf 2.719 0.3670
StepF5 - StepF6 -0.036121 0.0332 Inf -1.088 0.9998
StepF5 - StepF7 -0.062280 0.0332 Inf -1.875 0.9212
StepF5 - StepF8 0.001134 0.0332 Inf 0.034 1.0000
StepF5 - StepF9 -0.001547 0.0332 Inf -0.047 1.0000
StepF5 - StepF10 -0.044300 0.0332 Inf -1.334 0.9976
StepF5 - StepF11 -0.076325 0.0332 Inf -2.298 0.6848
StepF5 - StepF12 0.040547 0.0332 Inf 1.221 0.9992
StepF5 - StepF13 0.000521 0.0332 Inf 0.016 1.0000
StepF5 - StepF14 -0.036321 0.0332 Inf -1.094 0.9998
StepF5 - StepF15 -0.025558 0.0332 Inf -0.770 1.0000
StepF5 - StepF16 0.028098 0.0332 Inf 0.846 1.0000
StepF5 - StepF17 0.043379 0.0332 Inf 1.306 0.9981
StepF5 - StepF18 0.049155 0.0332 Inf 1.480 0.9920
StepF6 - StepF7 -0.026160 0.0332 Inf -0.788 1.0000
StepF6 - StepF8 0.037255 0.0332 Inf 1.122 0.9997
StepF6 - StepF9 0.034573 0.0332 Inf 1.041 0.9999
StepF6 - StepF10 -0.008180 0.0332 Inf -0.246 1.0000
StepF6 - StepF11 -0.040204 0.0332 Inf -1.211 0.9993
StepF6 - StepF12 0.076667 0.0332 Inf 2.309 0.6772
StepF6 - StepF13 0.036642 0.0332 Inf 1.103 0.9998
StepF6 - StepF14 -0.000200 0.0332 Inf -0.006 1.0000
StepF6 - StepF15 0.010562 0.0332 Inf 0.318 1.0000
StepF6 - StepF16 0.064219 0.0332 Inf 1.934 0.8992
StepF6 - StepF17 0.079499 0.0332 Inf 2.394 0.6126
StepF6 - StepF18 0.085276 0.0332 Inf 2.568 0.4778
StepF7 - StepF8 0.063414 0.0332 Inf 1.910 0.9087
StepF7 - StepF9 0.060733 0.0332 Inf 1.829 0.9362
StepF7 - StepF10 0.017980 0.0332 Inf 0.541 1.0000
StepF7 - StepF11 -0.014044 0.0332 Inf -0.423 1.0000
StepF7 - StepF12 0.102827 0.0332 Inf 3.096 0.1570
StepF7 - StepF13 0.062801 0.0332 Inf 1.891 0.9156
StepF7 - StepF14 0.025960 0.0332 Inf 0.782 1.0000
StepF7 - StepF15 0.036722 0.0332 Inf 1.106 0.9998
StepF7 - StepF16 0.090378 0.0332 Inf 2.722 0.3651
StepF7 - StepF17 0.105659 0.0332 Inf 3.182 0.1251
StepF7 - StepF18 0.111436 0.0332 Inf 3.356 0.0758
StepF8 - StepF9 -0.002681 0.0332 Inf -0.081 1.0000
StepF8 - StepF10 -0.045434 0.0332 Inf -1.368 0.9968
StepF8 - StepF11 -0.077459 0.0332 Inf -2.332 0.6594
StepF8 - StepF12 0.039412 0.0332 Inf 1.187 0.9994
StepF8 - StepF13 -0.000613 0.0332 Inf -0.018 1.0000
StepF8 - StepF14 -0.037455 0.0332 Inf -1.128 0.9997
StepF8 - StepF15 -0.026693 0.0332 Inf -0.804 1.0000
StepF8 - StepF16 0.026964 0.0332 Inf 0.812 1.0000
StepF8 - StepF17 0.042245 0.0332 Inf 1.272 0.9987
StepF8 - StepF18 0.048021 0.0332 Inf 1.446 0.9939
StepF9 - StepF10 -0.042753 0.0332 Inf -1.287 0.9984
StepF9 - StepF11 -0.074778 0.0332 Inf -2.252 0.7184
StepF9 - StepF12 0.042094 0.0332 Inf 1.268 0.9987
StepF9 - StepF13 0.002068 0.0332 Inf 0.062 1.0000
StepF9 - StepF14 -0.034774 0.0332 Inf -1.047 0.9999
StepF9 - StepF15 -0.024011 0.0332 Inf -0.723 1.0000
StepF9 - StepF16 0.029645 0.0332 Inf 0.893 1.0000
StepF9 - StepF17 0.044926 0.0332 Inf 1.353 0.9972
StepF9 - StepF18 0.050703 0.0332 Inf 1.527 0.9889
StepF10 - StepF11 -0.032024 0.0332 Inf -0.964 1.0000
StepF10 - StepF12 0.084847 0.0332 Inf 2.555 0.4877
StepF10 - StepF13 0.044821 0.0332 Inf 1.350 0.9972
StepF10 - StepF14 0.007980 0.0332 Inf 0.240 1.0000
StepF10 - StepF15 0.018742 0.0332 Inf 0.564 1.0000
StepF10 - StepF16 0.072398 0.0332 Inf 2.180 0.7672
StepF10 - StepF17 0.087679 0.0332 Inf 2.640 0.4234
StepF10 - StepF18 0.093456 0.0332 Inf 2.814 0.3036
StepF11 - StepF12 0.116871 0.0332 Inf 3.519 0.0453
StepF11 - StepF13 0.076846 0.0332 Inf 2.314 0.6732
StepF11 - StepF14 0.040004 0.0332 Inf 1.205 0.9993
StepF11 - StepF15 0.050766 0.0332 Inf 1.529 0.9887
StepF11 - StepF16 0.104423 0.0332 Inf 3.144 0.1383
StepF11 - StepF17 0.119703 0.0332 Inf 3.605 0.0341
StepF11 - StepF18 0.125480 0.0332 Inf 3.779 0.0184
StepF12 - StepF13 -0.040025 0.0332 Inf -1.205 0.9993
StepF12 - StepF14 -0.076867 0.0332 Inf -2.315 0.6727
StepF12 - StepF15 -0.066105 0.0332 Inf -1.991 0.8743
StepF12 - StepF16 -0.012449 0.0332 Inf -0.375 1.0000
StepF12 - StepF17 0.002832 0.0332 Inf 0.085 1.0000
StepF12 - StepF18 0.008609 0.0332 Inf 0.259 1.0000
StepF13 - StepF14 -0.036842 0.0332 Inf -1.109 0.9998
StepF13 - StepF15 -0.026079 0.0332 Inf -0.785 1.0000
StepF13 - StepF16 0.027577 0.0332 Inf 0.830 1.0000
StepF13 - StepF17 0.042858 0.0332 Inf 1.291 0.9984
StepF13 - StepF18 0.048634 0.0332 Inf 1.465 0.9929
StepF14 - StepF15 0.010762 0.0332 Inf 0.324 1.0000
StepF14 - StepF16 0.064419 0.0332 Inf 1.940 0.8967
StepF14 - StepF17 0.079699 0.0332 Inf 2.400 0.6080
StepF14 - StepF18 0.085476 0.0332 Inf 2.574 0.4732
StepF15 - StepF16 0.053656 0.0332 Inf 1.616 0.9799
StepF15 - StepF17 0.068937 0.0332 Inf 2.076 0.8305
StepF15 - StepF18 0.074714 0.0332 Inf 2.250 0.7197
StepF16 - StepF17 0.015281 0.0332 Inf 0.460 1.0000
StepF16 - StepF18 0.021057 0.0332 Inf 0.634 1.0000
StepF17 - StepF18 0.005777 0.0332 Inf 0.174 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.045863 0.0332 Inf 1.381 0.9964
StepF1 - StepF3 -0.019678 0.0332 Inf -0.593 1.0000
StepF1 - StepF4 0.057557 0.0332 Inf 1.733 0.9604
StepF1 - StepF5 0.098689 0.0332 Inf 2.972 0.2138
StepF1 - StepF6 0.062569 0.0332 Inf 1.884 0.9181
StepF1 - StepF7 0.036409 0.0332 Inf 1.096 0.9998
StepF1 - StepF8 0.099823 0.0332 Inf 3.006 0.1970
StepF1 - StepF9 0.097142 0.0332 Inf 2.925 0.2383
StepF1 - StepF10 0.054389 0.0332 Inf 1.638 0.9770
StepF1 - StepF11 0.022364 0.0332 Inf 0.673 1.0000
StepF1 - StepF12 0.139236 0.0332 Inf 4.193 0.0036
StepF1 - StepF13 0.099210 0.0332 Inf 2.987 0.2060
StepF1 - StepF14 0.062368 0.0332 Inf 1.878 0.9202
StepF1 - StepF15 0.073131 0.0332 Inf 2.202 0.7526
StepF1 - StepF16 0.126787 0.0332 Inf 3.818 0.0160
StepF1 - StepF17 0.142068 0.0332 Inf 4.278 0.0025
StepF1 - StepF18 0.147845 0.0332 Inf 4.452 0.0012
StepF2 - StepF3 -0.065541 0.0332 Inf -1.974 0.8821
StepF2 - StepF4 0.011694 0.0332 Inf 0.352 1.0000
StepF2 - StepF5 0.052826 0.0332 Inf 1.591 0.9829
StepF2 - StepF6 0.016706 0.0332 Inf 0.503 1.0000
StepF2 - StepF7 -0.009454 0.0332 Inf -0.285 1.0000
StepF2 - StepF8 0.053961 0.0332 Inf 1.625 0.9787
StepF2 - StepF9 0.051279 0.0332 Inf 1.544 0.9874
StepF2 - StepF10 0.008526 0.0332 Inf 0.257 1.0000
StepF2 - StepF11 -0.023498 0.0332 Inf -0.708 1.0000
StepF2 - StepF12 0.093373 0.0332 Inf 2.812 0.3052
StepF2 - StepF13 0.053348 0.0332 Inf 1.606 0.9811
StepF2 - StepF14 0.016506 0.0332 Inf 0.497 1.0000
StepF2 - StepF15 0.027268 0.0332 Inf 0.821 1.0000
StepF2 - StepF16 0.080924 0.0332 Inf 2.437 0.5794
StepF2 - StepF17 0.096205 0.0332 Inf 2.897 0.2540
StepF2 - StepF18 0.101982 0.0332 Inf 3.071 0.1676
StepF3 - StepF4 0.077235 0.0332 Inf 2.326 0.6644
StepF3 - StepF5 0.118368 0.0332 Inf 3.564 0.0390
StepF3 - StepF6 0.082247 0.0332 Inf 2.477 0.5484
StepF3 - StepF7 0.056087 0.0332 Inf 1.689 0.9690
StepF3 - StepF8 0.119502 0.0332 Inf 3.599 0.0348
StepF3 - StepF9 0.116820 0.0332 Inf 3.518 0.0455
StepF3 - StepF10 0.074067 0.0332 Inf 2.230 0.7333
StepF3 - StepF11 0.042043 0.0332 Inf 1.266 0.9987
StepF3 - StepF12 0.158914 0.0332 Inf 4.785 0.0002
StepF3 - StepF13 0.118889 0.0332 Inf 3.580 0.0370
StepF3 - StepF14 0.082047 0.0332 Inf 2.471 0.5531
StepF3 - StepF15 0.092809 0.0332 Inf 2.795 0.3160
StepF3 - StepF16 0.146466 0.0332 Inf 4.410 0.0014
StepF3 - StepF17 0.161746 0.0332 Inf 4.871 0.0002
StepF3 - StepF18 0.167523 0.0332 Inf 5.045 0.0001
StepF4 - StepF5 0.041132 0.0332 Inf 1.239 0.9990
StepF4 - StepF6 0.005012 0.0332 Inf 0.151 1.0000
StepF4 - StepF7 -0.021148 0.0332 Inf -0.637 1.0000
StepF4 - StepF8 0.042266 0.0332 Inf 1.273 0.9986
StepF4 - StepF9 0.039585 0.0332 Inf 1.192 0.9994
StepF4 - StepF10 -0.003168 0.0332 Inf -0.095 1.0000
StepF4 - StepF11 -0.035192 0.0332 Inf -1.060 0.9999
StepF4 - StepF12 0.081679 0.0332 Inf 2.460 0.5617
StepF4 - StepF13 0.041653 0.0332 Inf 1.254 0.9989
StepF4 - StepF14 0.004812 0.0332 Inf 0.145 1.0000
StepF4 - StepF15 0.015574 0.0332 Inf 0.469 1.0000
StepF4 - StepF16 0.069230 0.0332 Inf 2.085 0.8255
StepF4 - StepF17 0.084511 0.0332 Inf 2.545 0.4955
StepF4 - StepF18 0.090288 0.0332 Inf 2.719 0.3670
StepF5 - StepF6 -0.036121 0.0332 Inf -1.088 0.9998
StepF5 - StepF7 -0.062280 0.0332 Inf -1.875 0.9212
StepF5 - StepF8 0.001134 0.0332 Inf 0.034 1.0000
StepF5 - StepF9 -0.001547 0.0332 Inf -0.047 1.0000
StepF5 - StepF10 -0.044300 0.0332 Inf -1.334 0.9976
StepF5 - StepF11 -0.076325 0.0332 Inf -2.298 0.6848
StepF5 - StepF12 0.040547 0.0332 Inf 1.221 0.9992
StepF5 - StepF13 0.000521 0.0332 Inf 0.016 1.0000
StepF5 - StepF14 -0.036321 0.0332 Inf -1.094 0.9998
StepF5 - StepF15 -0.025558 0.0332 Inf -0.770 1.0000
StepF5 - StepF16 0.028098 0.0332 Inf 0.846 1.0000
StepF5 - StepF17 0.043379 0.0332 Inf 1.306 0.9981
StepF5 - StepF18 0.049155 0.0332 Inf 1.480 0.9920
StepF6 - StepF7 -0.026160 0.0332 Inf -0.788 1.0000
StepF6 - StepF8 0.037255 0.0332 Inf 1.122 0.9997
StepF6 - StepF9 0.034573 0.0332 Inf 1.041 0.9999
StepF6 - StepF10 -0.008180 0.0332 Inf -0.246 1.0000
StepF6 - StepF11 -0.040204 0.0332 Inf -1.211 0.9993
StepF6 - StepF12 0.076667 0.0332 Inf 2.309 0.6772
StepF6 - StepF13 0.036642 0.0332 Inf 1.103 0.9998
StepF6 - StepF14 -0.000200 0.0332 Inf -0.006 1.0000
StepF6 - StepF15 0.010562 0.0332 Inf 0.318 1.0000
StepF6 - StepF16 0.064219 0.0332 Inf 1.934 0.8992
StepF6 - StepF17 0.079499 0.0332 Inf 2.394 0.6126
StepF6 - StepF18 0.085276 0.0332 Inf 2.568 0.4778
StepF7 - StepF8 0.063414 0.0332 Inf 1.910 0.9087
StepF7 - StepF9 0.060733 0.0332 Inf 1.829 0.9362
StepF7 - StepF10 0.017980 0.0332 Inf 0.541 1.0000
StepF7 - StepF11 -0.014044 0.0332 Inf -0.423 1.0000
StepF7 - StepF12 0.102827 0.0332 Inf 3.096 0.1570
StepF7 - StepF13 0.062801 0.0332 Inf 1.891 0.9156
StepF7 - StepF14 0.025960 0.0332 Inf 0.782 1.0000
StepF7 - StepF15 0.036722 0.0332 Inf 1.106 0.9998
StepF7 - StepF16 0.090378 0.0332 Inf 2.722 0.3651
StepF7 - StepF17 0.105659 0.0332 Inf 3.182 0.1251
StepF7 - StepF18 0.111436 0.0332 Inf 3.356 0.0758
StepF8 - StepF9 -0.002681 0.0332 Inf -0.081 1.0000
StepF8 - StepF10 -0.045434 0.0332 Inf -1.368 0.9968
StepF8 - StepF11 -0.077459 0.0332 Inf -2.332 0.6594
StepF8 - StepF12 0.039412 0.0332 Inf 1.187 0.9994
StepF8 - StepF13 -0.000613 0.0332 Inf -0.018 1.0000
StepF8 - StepF14 -0.037455 0.0332 Inf -1.128 0.9997
StepF8 - StepF15 -0.026693 0.0332 Inf -0.804 1.0000
StepF8 - StepF16 0.026964 0.0332 Inf 0.812 1.0000
StepF8 - StepF17 0.042245 0.0332 Inf 1.272 0.9987
StepF8 - StepF18 0.048021 0.0332 Inf 1.446 0.9939
StepF9 - StepF10 -0.042753 0.0332 Inf -1.287 0.9984
StepF9 - StepF11 -0.074778 0.0332 Inf -2.252 0.7184
StepF9 - StepF12 0.042094 0.0332 Inf 1.268 0.9987
StepF9 - StepF13 0.002068 0.0332 Inf 0.062 1.0000
StepF9 - StepF14 -0.034774 0.0332 Inf -1.047 0.9999
StepF9 - StepF15 -0.024011 0.0332 Inf -0.723 1.0000
StepF9 - StepF16 0.029645 0.0332 Inf 0.893 1.0000
StepF9 - StepF17 0.044926 0.0332 Inf 1.353 0.9972
StepF9 - StepF18 0.050703 0.0332 Inf 1.527 0.9889
StepF10 - StepF11 -0.032024 0.0332 Inf -0.964 1.0000
StepF10 - StepF12 0.084847 0.0332 Inf 2.555 0.4877
StepF10 - StepF13 0.044821 0.0332 Inf 1.350 0.9972
StepF10 - StepF14 0.007980 0.0332 Inf 0.240 1.0000
StepF10 - StepF15 0.018742 0.0332 Inf 0.564 1.0000
StepF10 - StepF16 0.072398 0.0332 Inf 2.180 0.7672
StepF10 - StepF17 0.087679 0.0332 Inf 2.640 0.4234
StepF10 - StepF18 0.093456 0.0332 Inf 2.814 0.3036
StepF11 - StepF12 0.116871 0.0332 Inf 3.519 0.0453
StepF11 - StepF13 0.076846 0.0332 Inf 2.314 0.6732
StepF11 - StepF14 0.040004 0.0332 Inf 1.205 0.9993
StepF11 - StepF15 0.050766 0.0332 Inf 1.529 0.9887
StepF11 - StepF16 0.104423 0.0332 Inf 3.144 0.1383
StepF11 - StepF17 0.119703 0.0332 Inf 3.605 0.0341
StepF11 - StepF18 0.125480 0.0332 Inf 3.779 0.0184
StepF12 - StepF13 -0.040025 0.0332 Inf -1.205 0.9993
StepF12 - StepF14 -0.076867 0.0332 Inf -2.315 0.6727
StepF12 - StepF15 -0.066105 0.0332 Inf -1.991 0.8743
StepF12 - StepF16 -0.012449 0.0332 Inf -0.375 1.0000
StepF12 - StepF17 0.002832 0.0332 Inf 0.085 1.0000
StepF12 - StepF18 0.008609 0.0332 Inf 0.259 1.0000
StepF13 - StepF14 -0.036842 0.0332 Inf -1.109 0.9998
StepF13 - StepF15 -0.026079 0.0332 Inf -0.785 1.0000
StepF13 - StepF16 0.027577 0.0332 Inf 0.830 1.0000
StepF13 - StepF17 0.042858 0.0332 Inf 1.291 0.9984
StepF13 - StepF18 0.048634 0.0332 Inf 1.465 0.9929
StepF14 - StepF15 0.010762 0.0332 Inf 0.324 1.0000
StepF14 - StepF16 0.064419 0.0332 Inf 1.940 0.8967
StepF14 - StepF17 0.079699 0.0332 Inf 2.400 0.6080
StepF14 - StepF18 0.085476 0.0332 Inf 2.574 0.4732
StepF15 - StepF16 0.053656 0.0332 Inf 1.616 0.9799
StepF15 - StepF17 0.068937 0.0332 Inf 2.076 0.8305
StepF15 - StepF18 0.074714 0.0332 Inf 2.250 0.7197
StepF16 - StepF17 0.015281 0.0332 Inf 0.460 1.0000
StepF16 - StepF18 0.021057 0.0332 Inf 0.634 1.0000
StepF17 - StepF18 0.005777 0.0332 Inf 0.174 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.04586 0.0332 Inf -1.381 1.0000
StepF3 - StepF2 0.06554 0.0332 Inf 1.974 0.7264
StepF4 - StepF3 -0.07724 0.0332 Inf -2.326 0.3205
StepF5 - StepF4 -0.04113 0.0332 Inf -1.239 1.0000
StepF6 - StepF5 0.03612 0.0332 Inf 1.088 1.0000
StepF7 - StepF6 0.02616 0.0332 Inf 0.788 1.0000
StepF8 - StepF7 -0.06341 0.0332 Inf -1.910 0.7866
StepF9 - StepF8 0.00268 0.0332 Inf 0.081 1.0000
StepF10 - StepF9 0.04275 0.0332 Inf 1.287 1.0000
StepF11 - StepF10 0.03202 0.0332 Inf 0.964 1.0000
StepF12 - StepF11 -0.11687 0.0332 Inf -3.519 0.0074
StepF13 - StepF12 0.04003 0.0332 Inf 1.205 1.0000
StepF14 - StepF13 0.03684 0.0332 Inf 1.109 1.0000
StepF15 - StepF14 -0.01076 0.0332 Inf -0.324 1.0000
StepF16 - StepF15 -0.05366 0.0332 Inf -1.616 1.0000
StepF17 - StepF16 -0.01528 0.0332 Inf -0.460 1.0000
StepF18 - StepF17 -0.00578 0.0332 Inf -0.174 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.04586 0.0332 Inf -1.381 1.0000
StepF3 - StepF2 0.06554 0.0332 Inf 1.974 0.7264
StepF4 - StepF3 -0.07724 0.0332 Inf -2.326 0.3205
StepF5 - StepF4 -0.04113 0.0332 Inf -1.239 1.0000
StepF6 - StepF5 0.03612 0.0332 Inf 1.088 1.0000
StepF7 - StepF6 0.02616 0.0332 Inf 0.788 1.0000
StepF8 - StepF7 -0.06341 0.0332 Inf -1.910 0.7866
StepF9 - StepF8 0.00268 0.0332 Inf 0.081 1.0000
StepF10 - StepF9 0.04275 0.0332 Inf 1.287 1.0000
StepF11 - StepF10 0.03202 0.0332 Inf 0.964 1.0000
StepF12 - StepF11 -0.11687 0.0332 Inf -3.519 0.0074
StepF13 - StepF12 0.04003 0.0332 Inf 1.205 1.0000
StepF14 - StepF13 0.03684 0.0332 Inf 1.109 1.0000
StepF15 - StepF14 -0.01076 0.0332 Inf -0.324 1.0000
StepF16 - StepF15 -0.05366 0.0332 Inf -1.616 1.0000
StepF17 - StepF16 -0.01528 0.0332 Inf -0.460 1.0000
StepF18 - StepF17 -0.00578 0.0332 Inf -0.174 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests
==============================
TEST | Block 5 | 18 steps | Axis Y
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 57.4981 17 2.703e-06 ***
Accuracy 0.6287 1 0.4278
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.656 0.0827 Inf 0.494 0.818
2 0.836 0.0827 Inf 0.674 0.998
3 0.780 0.0827 Inf 0.618 0.942
4 0.669 0.0827 Inf 0.507 0.831
5 0.579 0.0827 Inf 0.417 0.741
6 0.650 0.0827 Inf 0.488 0.812
7 0.646 0.0827 Inf 0.484 0.808
8 0.686 0.0827 Inf 0.524 0.849
9 0.744 0.0827 Inf 0.582 0.906
10 0.762 0.0827 Inf 0.600 0.924
11 0.712 0.0827 Inf 0.550 0.874
12 0.602 0.0827 Inf 0.439 0.764
13 0.563 0.0827 Inf 0.401 0.725
14 0.652 0.0827 Inf 0.489 0.814
15 0.656 0.0827 Inf 0.494 0.818
16 0.602 0.0827 Inf 0.439 0.764
17 0.551 0.0827 Inf 0.389 0.713
18 0.544 0.0827 Inf 0.382 0.706
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.597 0.0855 Inf 0.430 0.765
2 0.777 0.0855 Inf 0.609 0.945
3 0.721 0.0855 Inf 0.553 0.888
4 0.610 0.0855 Inf 0.443 0.778
5 0.520 0.0855 Inf 0.353 0.688
6 0.591 0.0855 Inf 0.424 0.759
7 0.587 0.0855 Inf 0.420 0.755
8 0.628 0.0855 Inf 0.460 0.795
9 0.685 0.0855 Inf 0.518 0.853
10 0.703 0.0855 Inf 0.536 0.871
11 0.653 0.0855 Inf 0.485 0.820
12 0.543 0.0855 Inf 0.375 0.710
13 0.504 0.0855 Inf 0.336 0.671
14 0.593 0.0855 Inf 0.425 0.760
15 0.597 0.0855 Inf 0.429 0.765
16 0.543 0.0855 Inf 0.375 0.710
17 0.492 0.0855 Inf 0.325 0.660
18 0.485 0.0855 Inf 0.317 0.652
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -1.80e-01 0.0632 Inf -2.841 0.2871
StepF1 - StepF3 -1.23e-01 0.0632 Inf -1.951 0.8919
StepF1 - StepF4 -1.28e-02 0.0632 Inf -0.203 1.0000
StepF1 - StepF5 7.72e-02 0.0632 Inf 1.221 0.9992
StepF1 - StepF6 6.26e-03 0.0632 Inf 0.099 1.0000
StepF1 - StepF7 1.01e-02 0.0632 Inf 0.159 1.0000
StepF1 - StepF8 -3.01e-02 0.0632 Inf -0.476 1.0000
StepF1 - StepF9 -8.78e-02 0.0632 Inf -1.389 0.9961
StepF1 - StepF10 -1.06e-01 0.0632 Inf -1.674 0.9715
StepF1 - StepF11 -5.54e-02 0.0632 Inf -0.876 1.0000
StepF1 - StepF12 5.49e-02 0.0632 Inf 0.868 1.0000
StepF1 - StepF13 9.37e-02 0.0632 Inf 1.483 0.9919
StepF1 - StepF14 4.86e-03 0.0632 Inf 0.077 1.0000
StepF1 - StepF15 4.70e-04 0.0632 Inf 0.007 1.0000
StepF1 - StepF16 5.49e-02 0.0632 Inf 0.868 1.0000
StepF1 - StepF17 1.05e-01 0.0632 Inf 1.664 0.9730
StepF1 - StepF18 1.13e-01 0.0632 Inf 1.782 0.9491
StepF2 - StepF3 5.62e-02 0.0632 Inf 0.890 1.0000
StepF2 - StepF4 1.67e-01 0.0632 Inf 2.638 0.4253
StepF2 - StepF5 2.57e-01 0.0632 Inf 4.062 0.0062
StepF2 - StepF6 1.86e-01 0.0632 Inf 2.940 0.2304
StepF2 - StepF7 1.90e-01 0.0632 Inf 3.000 0.1999
StepF2 - StepF8 1.50e-01 0.0632 Inf 2.365 0.6349
StepF2 - StepF9 9.18e-02 0.0632 Inf 1.452 0.9936
StepF2 - StepF10 7.38e-02 0.0632 Inf 1.167 0.9996
StepF2 - StepF11 1.24e-01 0.0632 Inf 1.965 0.8859
StepF2 - StepF12 2.34e-01 0.0632 Inf 3.709 0.0237
StepF2 - StepF13 2.73e-01 0.0632 Inf 4.323 0.0021
StepF2 - StepF14 1.84e-01 0.0632 Inf 2.918 0.2424
StepF2 - StepF15 1.80e-01 0.0632 Inf 2.848 0.2825
StepF2 - StepF16 2.34e-01 0.0632 Inf 3.709 0.0237
StepF2 - StepF17 2.85e-01 0.0632 Inf 4.505 0.0009
StepF2 - StepF18 2.92e-01 0.0632 Inf 4.623 0.0005
StepF3 - StepF4 1.11e-01 0.0632 Inf 1.748 0.9572
StepF3 - StepF5 2.01e-01 0.0632 Inf 3.172 0.1283
StepF3 - StepF6 1.30e-01 0.0632 Inf 2.050 0.8444
StepF3 - StepF7 1.33e-01 0.0632 Inf 2.110 0.8106
StepF3 - StepF8 9.33e-02 0.0632 Inf 1.475 0.9923
StepF3 - StepF9 3.56e-02 0.0632 Inf 0.562 1.0000
StepF3 - StepF10 1.75e-02 0.0632 Inf 0.277 1.0000
StepF3 - StepF11 6.80e-02 0.0632 Inf 1.076 0.9998
StepF3 - StepF12 1.78e-01 0.0632 Inf 2.819 0.3005
StepF3 - StepF13 2.17e-01 0.0632 Inf 3.434 0.0595
StepF3 - StepF14 1.28e-01 0.0632 Inf 2.028 0.8559
StepF3 - StepF15 1.24e-01 0.0632 Inf 1.959 0.8887
StepF3 - StepF16 1.78e-01 0.0632 Inf 2.819 0.3004
StepF3 - StepF17 2.29e-01 0.0632 Inf 3.616 0.0328
StepF3 - StepF18 2.36e-01 0.0632 Inf 3.733 0.0217
StepF4 - StepF5 9.01e-02 0.0632 Inf 1.424 0.9948
StepF4 - StepF6 1.91e-02 0.0632 Inf 0.302 1.0000
StepF4 - StepF7 2.29e-02 0.0632 Inf 0.362 1.0000
StepF4 - StepF8 -1.72e-02 0.0632 Inf -0.273 1.0000
StepF4 - StepF9 -7.50e-02 0.0632 Inf -1.186 0.9994
StepF4 - StepF10 -9.30e-02 0.0632 Inf -1.471 0.9926
StepF4 - StepF11 -4.25e-02 0.0632 Inf -0.672 1.0000
StepF4 - StepF12 6.77e-02 0.0632 Inf 1.071 0.9999
StepF4 - StepF13 1.07e-01 0.0632 Inf 1.686 0.9695
StepF4 - StepF14 1.77e-02 0.0632 Inf 0.280 1.0000
StepF4 - StepF15 1.33e-02 0.0632 Inf 0.211 1.0000
StepF4 - StepF16 6.77e-02 0.0632 Inf 1.071 0.9999
StepF4 - StepF17 1.18e-01 0.0632 Inf 1.868 0.9238
StepF4 - StepF18 1.26e-01 0.0632 Inf 1.985 0.8769
StepF5 - StepF6 -7.09e-02 0.0632 Inf -1.122 0.9997
StepF5 - StepF7 -6.71e-02 0.0632 Inf -1.062 0.9999
StepF5 - StepF8 -1.07e-01 0.0632 Inf -1.697 0.9675
StepF5 - StepF9 -1.65e-01 0.0632 Inf -2.610 0.4459
StepF5 - StepF10 -1.83e-01 0.0632 Inf -2.895 0.2550
StepF5 - StepF11 -1.33e-01 0.0632 Inf -2.097 0.8186
StepF5 - StepF12 -2.23e-02 0.0632 Inf -0.353 1.0000
StepF5 - StepF13 1.65e-02 0.0632 Inf 0.261 1.0000
StepF5 - StepF14 -7.23e-02 0.0632 Inf -1.144 0.9997
StepF5 - StepF15 -7.67e-02 0.0632 Inf -1.214 0.9993
StepF5 - StepF16 -2.23e-02 0.0632 Inf -0.353 1.0000
StepF5 - StepF17 2.80e-02 0.0632 Inf 0.443 1.0000
StepF5 - StepF18 3.55e-02 0.0632 Inf 0.561 1.0000
StepF6 - StepF7 3.80e-03 0.0632 Inf 0.060 1.0000
StepF6 - StepF8 -3.64e-02 0.0632 Inf -0.575 1.0000
StepF6 - StepF9 -9.41e-02 0.0632 Inf -1.488 0.9916
StepF6 - StepF10 -1.12e-01 0.0632 Inf -1.773 0.9513
StepF6 - StepF11 -6.16e-02 0.0632 Inf -0.975 1.0000
StepF6 - StepF12 4.86e-02 0.0632 Inf 0.769 1.0000
StepF6 - StepF13 8.75e-02 0.0632 Inf 1.384 0.9963
StepF6 - StepF14 -1.40e-03 0.0632 Inf -0.022 1.0000
StepF6 - StepF15 -5.79e-03 0.0632 Inf -0.092 1.0000
StepF6 - StepF16 4.86e-02 0.0632 Inf 0.769 1.0000
StepF6 - StepF17 9.90e-02 0.0632 Inf 1.565 0.9855
StepF6 - StepF18 1.06e-01 0.0632 Inf 1.683 0.9700
StepF7 - StepF8 -4.02e-02 0.0632 Inf -0.635 1.0000
StepF7 - StepF9 -9.79e-02 0.0632 Inf -1.548 0.9871
StepF7 - StepF10 -1.16e-01 0.0632 Inf -1.833 0.9348
StepF7 - StepF11 -6.54e-02 0.0632 Inf -1.035 0.9999
StepF7 - StepF12 4.48e-02 0.0632 Inf 0.709 1.0000
StepF7 - StepF13 8.37e-02 0.0632 Inf 1.323 0.9978
StepF7 - StepF14 -5.20e-03 0.0632 Inf -0.082 1.0000
StepF7 - StepF15 -9.59e-03 0.0632 Inf -0.152 1.0000
StepF7 - StepF16 4.48e-02 0.0632 Inf 0.709 1.0000
StepF7 - StepF17 9.52e-02 0.0632 Inf 1.505 0.9904
StepF7 - StepF18 1.03e-01 0.0632 Inf 1.623 0.9790
StepF8 - StepF9 -5.77e-02 0.0632 Inf -0.913 1.0000
StepF8 - StepF10 -7.58e-02 0.0632 Inf -1.198 0.9994
StepF8 - StepF11 -2.53e-02 0.0632 Inf -0.400 1.0000
StepF8 - StepF12 8.50e-02 0.0632 Inf 1.344 0.9974
StepF8 - StepF13 1.24e-01 0.0632 Inf 1.959 0.8888
StepF8 - StepF14 3.50e-02 0.0632 Inf 0.553 1.0000
StepF8 - StepF15 3.06e-02 0.0632 Inf 0.483 1.0000
StepF8 - StepF16 8.50e-02 0.0632 Inf 1.344 0.9974
StepF8 - StepF17 1.35e-01 0.0632 Inf 2.140 0.7924
StepF8 - StepF18 1.43e-01 0.0632 Inf 2.258 0.7141
StepF9 - StepF10 -1.80e-02 0.0632 Inf -0.285 1.0000
StepF9 - StepF11 3.25e-02 0.0632 Inf 0.513 1.0000
StepF9 - StepF12 1.43e-01 0.0632 Inf 2.257 0.7148
StepF9 - StepF13 1.82e-01 0.0632 Inf 2.871 0.2687
StepF9 - StepF14 9.27e-02 0.0632 Inf 1.466 0.9929
StepF9 - StepF15 8.83e-02 0.0632 Inf 1.396 0.9959
StepF9 - StepF16 1.43e-01 0.0632 Inf 2.257 0.7147
StepF9 - StepF17 1.93e-01 0.0632 Inf 3.053 0.1752
StepF9 - StepF18 2.00e-01 0.0632 Inf 3.171 0.1288
StepF10 - StepF11 5.05e-02 0.0632 Inf 0.798 1.0000
StepF10 - StepF12 1.61e-01 0.0632 Inf 2.542 0.4977
StepF10 - StepF13 2.00e-01 0.0632 Inf 3.157 0.1338
StepF10 - StepF14 1.11e-01 0.0632 Inf 1.751 0.9565
StepF10 - StepF15 1.06e-01 0.0632 Inf 1.682 0.9703
StepF10 - StepF16 1.61e-01 0.0632 Inf 2.542 0.4976
StepF10 - StepF17 2.11e-01 0.0632 Inf 3.339 0.0798
StepF10 - StepF18 2.19e-01 0.0632 Inf 3.456 0.0555
StepF11 - StepF12 1.10e-01 0.0632 Inf 1.744 0.9582
StepF11 - StepF13 1.49e-01 0.0632 Inf 2.358 0.6399
StepF11 - StepF14 6.02e-02 0.0632 Inf 0.953 1.0000
StepF11 - StepF15 5.58e-02 0.0632 Inf 0.883 1.0000
StepF11 - StepF16 1.10e-01 0.0632 Inf 1.744 0.9582
StepF11 - StepF17 1.61e-01 0.0632 Inf 2.540 0.4991
StepF11 - StepF18 1.68e-01 0.0632 Inf 2.658 0.4107
StepF12 - StepF13 3.89e-02 0.0632 Inf 0.615 1.0000
StepF12 - StepF14 -5.00e-02 0.0632 Inf -0.791 1.0000
StepF12 - StepF15 -5.44e-02 0.0632 Inf -0.860 1.0000
StepF12 - StepF16 4.13e-06 0.0632 Inf 0.000 1.0000
StepF12 - StepF17 5.04e-02 0.0632 Inf 0.797 1.0000
StepF12 - StepF18 5.78e-02 0.0632 Inf 0.914 1.0000
StepF13 - StepF14 -8.89e-02 0.0632 Inf -1.406 0.9956
StepF13 - StepF15 -9.33e-02 0.0632 Inf -1.475 0.9923
StepF13 - StepF16 -3.89e-02 0.0632 Inf -0.615 1.0000
StepF13 - StepF17 1.15e-02 0.0632 Inf 0.182 1.0000
StepF13 - StepF18 1.89e-02 0.0632 Inf 0.299 1.0000
StepF14 - StepF15 -4.39e-03 0.0632 Inf -0.069 1.0000
StepF14 - StepF16 5.00e-02 0.0632 Inf 0.791 1.0000
StepF14 - StepF17 1.00e-01 0.0632 Inf 1.588 0.9832
StepF14 - StepF18 1.08e-01 0.0632 Inf 1.705 0.9661
StepF15 - StepF16 5.44e-02 0.0632 Inf 0.861 1.0000
StepF15 - StepF17 1.05e-01 0.0632 Inf 1.657 0.9742
StepF15 - StepF18 1.12e-01 0.0632 Inf 1.774 0.9510
StepF16 - StepF17 5.04e-02 0.0632 Inf 0.797 1.0000
StepF16 - StepF18 5.78e-02 0.0632 Inf 0.914 1.0000
StepF17 - StepF18 7.42e-03 0.0632 Inf 0.117 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -1.80e-01 0.0632 Inf -2.841 0.2871
StepF1 - StepF3 -1.23e-01 0.0632 Inf -1.951 0.8919
StepF1 - StepF4 -1.28e-02 0.0632 Inf -0.203 1.0000
StepF1 - StepF5 7.72e-02 0.0632 Inf 1.221 0.9992
StepF1 - StepF6 6.26e-03 0.0632 Inf 0.099 1.0000
StepF1 - StepF7 1.01e-02 0.0632 Inf 0.159 1.0000
StepF1 - StepF8 -3.01e-02 0.0632 Inf -0.476 1.0000
StepF1 - StepF9 -8.78e-02 0.0632 Inf -1.389 0.9961
StepF1 - StepF10 -1.06e-01 0.0632 Inf -1.674 0.9715
StepF1 - StepF11 -5.54e-02 0.0632 Inf -0.876 1.0000
StepF1 - StepF12 5.49e-02 0.0632 Inf 0.868 1.0000
StepF1 - StepF13 9.37e-02 0.0632 Inf 1.483 0.9919
StepF1 - StepF14 4.86e-03 0.0632 Inf 0.077 1.0000
StepF1 - StepF15 4.70e-04 0.0632 Inf 0.007 1.0000
StepF1 - StepF16 5.49e-02 0.0632 Inf 0.868 1.0000
StepF1 - StepF17 1.05e-01 0.0632 Inf 1.664 0.9730
StepF1 - StepF18 1.13e-01 0.0632 Inf 1.782 0.9491
StepF2 - StepF3 5.62e-02 0.0632 Inf 0.890 1.0000
StepF2 - StepF4 1.67e-01 0.0632 Inf 2.638 0.4253
StepF2 - StepF5 2.57e-01 0.0632 Inf 4.062 0.0062
StepF2 - StepF6 1.86e-01 0.0632 Inf 2.940 0.2304
StepF2 - StepF7 1.90e-01 0.0632 Inf 3.000 0.1999
StepF2 - StepF8 1.50e-01 0.0632 Inf 2.365 0.6349
StepF2 - StepF9 9.18e-02 0.0632 Inf 1.452 0.9936
StepF2 - StepF10 7.38e-02 0.0632 Inf 1.167 0.9996
StepF2 - StepF11 1.24e-01 0.0632 Inf 1.965 0.8859
StepF2 - StepF12 2.34e-01 0.0632 Inf 3.709 0.0237
StepF2 - StepF13 2.73e-01 0.0632 Inf 4.323 0.0021
StepF2 - StepF14 1.84e-01 0.0632 Inf 2.918 0.2424
StepF2 - StepF15 1.80e-01 0.0632 Inf 2.848 0.2825
StepF2 - StepF16 2.34e-01 0.0632 Inf 3.709 0.0237
StepF2 - StepF17 2.85e-01 0.0632 Inf 4.505 0.0009
StepF2 - StepF18 2.92e-01 0.0632 Inf 4.623 0.0005
StepF3 - StepF4 1.11e-01 0.0632 Inf 1.748 0.9572
StepF3 - StepF5 2.01e-01 0.0632 Inf 3.172 0.1283
StepF3 - StepF6 1.30e-01 0.0632 Inf 2.050 0.8444
StepF3 - StepF7 1.33e-01 0.0632 Inf 2.110 0.8106
StepF3 - StepF8 9.33e-02 0.0632 Inf 1.475 0.9923
StepF3 - StepF9 3.56e-02 0.0632 Inf 0.562 1.0000
StepF3 - StepF10 1.75e-02 0.0632 Inf 0.277 1.0000
StepF3 - StepF11 6.80e-02 0.0632 Inf 1.076 0.9998
StepF3 - StepF12 1.78e-01 0.0632 Inf 2.819 0.3005
StepF3 - StepF13 2.17e-01 0.0632 Inf 3.434 0.0595
StepF3 - StepF14 1.28e-01 0.0632 Inf 2.028 0.8559
StepF3 - StepF15 1.24e-01 0.0632 Inf 1.959 0.8887
StepF3 - StepF16 1.78e-01 0.0632 Inf 2.819 0.3004
StepF3 - StepF17 2.29e-01 0.0632 Inf 3.616 0.0328
StepF3 - StepF18 2.36e-01 0.0632 Inf 3.733 0.0217
StepF4 - StepF5 9.01e-02 0.0632 Inf 1.424 0.9948
StepF4 - StepF6 1.91e-02 0.0632 Inf 0.302 1.0000
StepF4 - StepF7 2.29e-02 0.0632 Inf 0.362 1.0000
StepF4 - StepF8 -1.72e-02 0.0632 Inf -0.273 1.0000
StepF4 - StepF9 -7.50e-02 0.0632 Inf -1.186 0.9994
StepF4 - StepF10 -9.30e-02 0.0632 Inf -1.471 0.9926
StepF4 - StepF11 -4.25e-02 0.0632 Inf -0.672 1.0000
StepF4 - StepF12 6.77e-02 0.0632 Inf 1.071 0.9999
StepF4 - StepF13 1.07e-01 0.0632 Inf 1.686 0.9695
StepF4 - StepF14 1.77e-02 0.0632 Inf 0.280 1.0000
StepF4 - StepF15 1.33e-02 0.0632 Inf 0.211 1.0000
StepF4 - StepF16 6.77e-02 0.0632 Inf 1.071 0.9999
StepF4 - StepF17 1.18e-01 0.0632 Inf 1.868 0.9238
StepF4 - StepF18 1.26e-01 0.0632 Inf 1.985 0.8769
StepF5 - StepF6 -7.09e-02 0.0632 Inf -1.122 0.9997
StepF5 - StepF7 -6.71e-02 0.0632 Inf -1.062 0.9999
StepF5 - StepF8 -1.07e-01 0.0632 Inf -1.697 0.9675
StepF5 - StepF9 -1.65e-01 0.0632 Inf -2.610 0.4459
StepF5 - StepF10 -1.83e-01 0.0632 Inf -2.895 0.2550
StepF5 - StepF11 -1.33e-01 0.0632 Inf -2.097 0.8186
StepF5 - StepF12 -2.23e-02 0.0632 Inf -0.353 1.0000
StepF5 - StepF13 1.65e-02 0.0632 Inf 0.261 1.0000
StepF5 - StepF14 -7.23e-02 0.0632 Inf -1.144 0.9997
StepF5 - StepF15 -7.67e-02 0.0632 Inf -1.214 0.9993
StepF5 - StepF16 -2.23e-02 0.0632 Inf -0.353 1.0000
StepF5 - StepF17 2.80e-02 0.0632 Inf 0.443 1.0000
StepF5 - StepF18 3.55e-02 0.0632 Inf 0.561 1.0000
StepF6 - StepF7 3.80e-03 0.0632 Inf 0.060 1.0000
StepF6 - StepF8 -3.64e-02 0.0632 Inf -0.575 1.0000
StepF6 - StepF9 -9.41e-02 0.0632 Inf -1.488 0.9916
StepF6 - StepF10 -1.12e-01 0.0632 Inf -1.773 0.9513
StepF6 - StepF11 -6.16e-02 0.0632 Inf -0.975 1.0000
StepF6 - StepF12 4.86e-02 0.0632 Inf 0.769 1.0000
StepF6 - StepF13 8.75e-02 0.0632 Inf 1.384 0.9963
StepF6 - StepF14 -1.40e-03 0.0632 Inf -0.022 1.0000
StepF6 - StepF15 -5.79e-03 0.0632 Inf -0.092 1.0000
StepF6 - StepF16 4.86e-02 0.0632 Inf 0.769 1.0000
StepF6 - StepF17 9.90e-02 0.0632 Inf 1.565 0.9855
StepF6 - StepF18 1.06e-01 0.0632 Inf 1.683 0.9700
StepF7 - StepF8 -4.02e-02 0.0632 Inf -0.635 1.0000
StepF7 - StepF9 -9.79e-02 0.0632 Inf -1.548 0.9871
StepF7 - StepF10 -1.16e-01 0.0632 Inf -1.833 0.9348
StepF7 - StepF11 -6.54e-02 0.0632 Inf -1.035 0.9999
StepF7 - StepF12 4.48e-02 0.0632 Inf 0.709 1.0000
StepF7 - StepF13 8.37e-02 0.0632 Inf 1.323 0.9978
StepF7 - StepF14 -5.20e-03 0.0632 Inf -0.082 1.0000
StepF7 - StepF15 -9.59e-03 0.0632 Inf -0.152 1.0000
StepF7 - StepF16 4.48e-02 0.0632 Inf 0.709 1.0000
StepF7 - StepF17 9.52e-02 0.0632 Inf 1.505 0.9904
StepF7 - StepF18 1.03e-01 0.0632 Inf 1.623 0.9790
StepF8 - StepF9 -5.77e-02 0.0632 Inf -0.913 1.0000
StepF8 - StepF10 -7.58e-02 0.0632 Inf -1.198 0.9994
StepF8 - StepF11 -2.53e-02 0.0632 Inf -0.400 1.0000
StepF8 - StepF12 8.50e-02 0.0632 Inf 1.344 0.9974
StepF8 - StepF13 1.24e-01 0.0632 Inf 1.959 0.8888
StepF8 - StepF14 3.50e-02 0.0632 Inf 0.553 1.0000
StepF8 - StepF15 3.06e-02 0.0632 Inf 0.483 1.0000
StepF8 - StepF16 8.50e-02 0.0632 Inf 1.344 0.9974
StepF8 - StepF17 1.35e-01 0.0632 Inf 2.140 0.7924
StepF8 - StepF18 1.43e-01 0.0632 Inf 2.258 0.7141
StepF9 - StepF10 -1.80e-02 0.0632 Inf -0.285 1.0000
StepF9 - StepF11 3.25e-02 0.0632 Inf 0.513 1.0000
StepF9 - StepF12 1.43e-01 0.0632 Inf 2.257 0.7148
StepF9 - StepF13 1.82e-01 0.0632 Inf 2.871 0.2687
StepF9 - StepF14 9.27e-02 0.0632 Inf 1.466 0.9929
StepF9 - StepF15 8.83e-02 0.0632 Inf 1.396 0.9959
StepF9 - StepF16 1.43e-01 0.0632 Inf 2.257 0.7147
StepF9 - StepF17 1.93e-01 0.0632 Inf 3.053 0.1752
StepF9 - StepF18 2.00e-01 0.0632 Inf 3.171 0.1288
StepF10 - StepF11 5.05e-02 0.0632 Inf 0.798 1.0000
StepF10 - StepF12 1.61e-01 0.0632 Inf 2.542 0.4977
StepF10 - StepF13 2.00e-01 0.0632 Inf 3.157 0.1338
StepF10 - StepF14 1.11e-01 0.0632 Inf 1.751 0.9565
StepF10 - StepF15 1.06e-01 0.0632 Inf 1.682 0.9703
StepF10 - StepF16 1.61e-01 0.0632 Inf 2.542 0.4976
StepF10 - StepF17 2.11e-01 0.0632 Inf 3.339 0.0798
StepF10 - StepF18 2.19e-01 0.0632 Inf 3.456 0.0555
StepF11 - StepF12 1.10e-01 0.0632 Inf 1.744 0.9582
StepF11 - StepF13 1.49e-01 0.0632 Inf 2.358 0.6399
StepF11 - StepF14 6.02e-02 0.0632 Inf 0.953 1.0000
StepF11 - StepF15 5.58e-02 0.0632 Inf 0.883 1.0000
StepF11 - StepF16 1.10e-01 0.0632 Inf 1.744 0.9582
StepF11 - StepF17 1.61e-01 0.0632 Inf 2.540 0.4991
StepF11 - StepF18 1.68e-01 0.0632 Inf 2.658 0.4107
StepF12 - StepF13 3.89e-02 0.0632 Inf 0.615 1.0000
StepF12 - StepF14 -5.00e-02 0.0632 Inf -0.791 1.0000
StepF12 - StepF15 -5.44e-02 0.0632 Inf -0.860 1.0000
StepF12 - StepF16 4.13e-06 0.0632 Inf 0.000 1.0000
StepF12 - StepF17 5.04e-02 0.0632 Inf 0.797 1.0000
StepF12 - StepF18 5.78e-02 0.0632 Inf 0.914 1.0000
StepF13 - StepF14 -8.89e-02 0.0632 Inf -1.406 0.9956
StepF13 - StepF15 -9.33e-02 0.0632 Inf -1.475 0.9923
StepF13 - StepF16 -3.89e-02 0.0632 Inf -0.615 1.0000
StepF13 - StepF17 1.15e-02 0.0632 Inf 0.182 1.0000
StepF13 - StepF18 1.89e-02 0.0632 Inf 0.299 1.0000
StepF14 - StepF15 -4.39e-03 0.0632 Inf -0.069 1.0000
StepF14 - StepF16 5.00e-02 0.0632 Inf 0.791 1.0000
StepF14 - StepF17 1.00e-01 0.0632 Inf 1.588 0.9832
StepF14 - StepF18 1.08e-01 0.0632 Inf 1.705 0.9661
StepF15 - StepF16 5.44e-02 0.0632 Inf 0.861 1.0000
StepF15 - StepF17 1.05e-01 0.0632 Inf 1.657 0.9742
StepF15 - StepF18 1.12e-01 0.0632 Inf 1.774 0.9510
StepF16 - StepF17 5.04e-02 0.0632 Inf 0.797 1.0000
StepF16 - StepF18 5.78e-02 0.0632 Inf 0.914 1.0000
StepF17 - StepF18 7.42e-03 0.0632 Inf 0.117 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.17962 0.0632 Inf 2.841 0.0765
StepF3 - StepF2 -0.05624 0.0632 Inf -0.890 1.0000
StepF4 - StepF3 -0.11053 0.0632 Inf -1.748 1.0000
StepF5 - StepF4 -0.09006 0.0632 Inf -1.424 1.0000
StepF6 - StepF5 0.07095 0.0632 Inf 1.122 1.0000
StepF7 - StepF6 -0.00380 0.0632 Inf -0.060 1.0000
StepF8 - StepF7 0.04016 0.0632 Inf 0.635 1.0000
StepF9 - StepF8 0.05772 0.0632 Inf 0.913 1.0000
StepF10 - StepF9 0.01803 0.0632 Inf 0.285 1.0000
StepF11 - StepF10 -0.05048 0.0632 Inf -0.798 1.0000
StepF12 - StepF11 -0.11024 0.0632 Inf -1.744 1.0000
StepF13 - StepF12 -0.03886 0.0632 Inf -0.615 1.0000
StepF14 - StepF13 0.08888 0.0632 Inf 1.406 1.0000
StepF15 - StepF14 0.00439 0.0632 Inf 0.069 1.0000
StepF16 - StepF15 -0.05441 0.0632 Inf -0.861 1.0000
StepF17 - StepF16 -0.05036 0.0632 Inf -0.797 1.0000
StepF18 - StepF17 -0.00742 0.0632 Inf -0.117 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.17962 0.0632 Inf 2.841 0.0765
StepF3 - StepF2 -0.05624 0.0632 Inf -0.890 1.0000
StepF4 - StepF3 -0.11053 0.0632 Inf -1.748 1.0000
StepF5 - StepF4 -0.09006 0.0632 Inf -1.424 1.0000
StepF6 - StepF5 0.07095 0.0632 Inf 1.122 1.0000
StepF7 - StepF6 -0.00380 0.0632 Inf -0.060 1.0000
StepF8 - StepF7 0.04016 0.0632 Inf 0.635 1.0000
StepF9 - StepF8 0.05772 0.0632 Inf 0.913 1.0000
StepF10 - StepF9 0.01803 0.0632 Inf 0.285 1.0000
StepF11 - StepF10 -0.05048 0.0632 Inf -0.798 1.0000
StepF12 - StepF11 -0.11024 0.0632 Inf -1.744 1.0000
StepF13 - StepF12 -0.03886 0.0632 Inf -0.615 1.0000
StepF14 - StepF13 0.08888 0.0632 Inf 1.406 1.0000
StepF15 - StepF14 0.00439 0.0632 Inf 0.069 1.0000
StepF16 - StepF15 -0.05441 0.0632 Inf -0.861 1.0000
StepF17 - StepF16 -0.05036 0.0632 Inf -0.797 1.0000
StepF18 - StepF17 -0.00742 0.0632 Inf -0.117 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests
==============================
TEST | Block 5 | 18 steps | Axis Z
==============================
Type II Wald χ² (StepF & Accuracy):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 89.8575 17 6.48e-12 ***
Accuracy 0.7698 1 0.3803
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy:
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.46 0.177 Inf 1.111 1.80
2 1.67 0.177 Inf 1.322 2.02
3 1.53 0.177 Inf 1.180 1.87
4 1.44 0.177 Inf 1.089 1.78
5 1.38 0.177 Inf 1.037 1.73
6 1.37 0.177 Inf 1.027 1.72
7 1.48 0.177 Inf 1.136 1.83
8 1.44 0.177 Inf 1.089 1.78
9 1.40 0.177 Inf 1.054 1.75
10 1.46 0.177 Inf 1.109 1.80
11 1.33 0.177 Inf 0.987 1.68
12 1.23 0.177 Inf 0.886 1.58
13 1.27 0.177 Inf 0.920 1.61
14 1.40 0.177 Inf 1.050 1.74
15 1.32 0.177 Inf 0.972 1.67
16 1.22 0.177 Inf 0.877 1.57
17 1.17 0.177 Inf 0.821 1.51
18 1.18 0.177 Inf 0.831 1.52
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.37 0.179 Inf 1.023 1.73
2 1.59 0.179 Inf 1.234 1.94
3 1.44 0.179 Inf 1.092 1.79
4 1.35 0.179 Inf 1.001 1.70
5 1.30 0.179 Inf 0.949 1.65
6 1.29 0.179 Inf 0.939 1.64
7 1.40 0.179 Inf 1.048 1.75
8 1.35 0.179 Inf 1.001 1.70
9 1.32 0.179 Inf 0.966 1.67
10 1.37 0.179 Inf 1.021 1.72
11 1.25 0.179 Inf 0.899 1.60
12 1.15 0.179 Inf 0.798 1.50
13 1.18 0.179 Inf 0.832 1.53
14 1.31 0.179 Inf 0.962 1.66
15 1.24 0.179 Inf 0.884 1.59
16 1.14 0.179 Inf 0.789 1.49
17 1.08 0.179 Inf 0.733 1.43
18 1.09 0.179 Inf 0.742 1.44
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -2.11e-01 0.0797 Inf -2.653 0.4138
StepF1 - StepF3 -6.87e-02 0.0797 Inf -0.862 1.0000
StepF1 - StepF4 2.19e-02 0.0797 Inf 0.276 1.0000
StepF1 - StepF5 7.43e-02 0.0797 Inf 0.933 1.0000
StepF1 - StepF6 8.44e-02 0.0797 Inf 1.060 0.9999
StepF1 - StepF7 -2.51e-02 0.0797 Inf -0.315 1.0000
StepF1 - StepF8 2.19e-02 0.0797 Inf 0.275 1.0000
StepF1 - StepF9 5.74e-02 0.0797 Inf 0.721 1.0000
StepF1 - StepF10 2.01e-03 0.0797 Inf 0.025 1.0000
StepF1 - StepF11 1.24e-01 0.0797 Inf 1.553 0.9866
StepF1 - StepF12 2.25e-01 0.0797 Inf 2.829 0.2942
StepF1 - StepF13 1.91e-01 0.0797 Inf 2.398 0.6093
StepF1 - StepF14 6.10e-02 0.0797 Inf 0.766 1.0000
StepF1 - StepF15 1.39e-01 0.0797 Inf 1.742 0.9586
StepF1 - StepF16 2.34e-01 0.0797 Inf 2.939 0.2311
StepF1 - StepF17 2.91e-01 0.0797 Inf 3.647 0.0294
StepF1 - StepF18 2.81e-01 0.0797 Inf 3.522 0.0449
StepF2 - StepF3 1.43e-01 0.0797 Inf 1.791 0.9468
StepF2 - StepF4 2.33e-01 0.0797 Inf 2.929 0.2364
StepF2 - StepF5 2.86e-01 0.0797 Inf 3.586 0.0363
StepF2 - StepF6 2.96e-01 0.0797 Inf 3.713 0.0234
StepF2 - StepF7 1.86e-01 0.0797 Inf 2.338 0.6553
StepF2 - StepF8 2.33e-01 0.0797 Inf 2.928 0.2367
StepF2 - StepF9 2.69e-01 0.0797 Inf 3.374 0.0717
StepF2 - StepF10 2.13e-01 0.0797 Inf 2.678 0.3955
StepF2 - StepF11 3.35e-01 0.0797 Inf 4.207 0.0034
StepF2 - StepF12 4.37e-01 0.0797 Inf 5.482 <.0001
StepF2 - StepF13 4.02e-01 0.0797 Inf 5.051 0.0001
StepF2 - StepF14 2.72e-01 0.0797 Inf 3.419 0.0623
StepF2 - StepF15 3.50e-01 0.0797 Inf 4.395 0.0015
StepF2 - StepF16 4.45e-01 0.0797 Inf 5.592 <.0001
StepF2 - StepF17 5.02e-01 0.0797 Inf 6.301 <.0001
StepF2 - StepF18 4.92e-01 0.0797 Inf 6.175 <.0001
StepF3 - StepF4 9.06e-02 0.0797 Inf 1.138 0.9997
StepF3 - StepF5 1.43e-01 0.0797 Inf 1.795 0.9457
StepF3 - StepF6 1.53e-01 0.0797 Inf 1.922 0.9038
StepF3 - StepF7 4.36e-02 0.0797 Inf 0.547 1.0000
StepF3 - StepF8 9.06e-02 0.0797 Inf 1.137 0.9997
StepF3 - StepF9 1.26e-01 0.0797 Inf 1.583 0.9837
StepF3 - StepF10 7.07e-02 0.0797 Inf 0.888 1.0000
StepF3 - StepF11 1.92e-01 0.0797 Inf 2.416 0.5957
StepF3 - StepF12 2.94e-01 0.0797 Inf 3.692 0.0252
StepF3 - StepF13 2.60e-01 0.0797 Inf 3.261 0.1002
StepF3 - StepF14 1.30e-01 0.0797 Inf 1.629 0.9783
StepF3 - StepF15 2.07e-01 0.0797 Inf 2.604 0.4504
StepF3 - StepF16 3.03e-01 0.0797 Inf 3.801 0.0170
StepF3 - StepF17 3.59e-01 0.0797 Inf 4.510 0.0009
StepF3 - StepF18 3.49e-01 0.0797 Inf 4.384 0.0016
StepF4 - StepF5 5.24e-02 0.0797 Inf 0.657 1.0000
StepF4 - StepF6 6.25e-02 0.0797 Inf 0.784 1.0000
StepF4 - StepF7 -4.71e-02 0.0797 Inf -0.591 1.0000
StepF4 - StepF8 -3.94e-05 0.0797 Inf 0.000 1.0000
StepF4 - StepF9 3.55e-02 0.0797 Inf 0.445 1.0000
StepF4 - StepF10 -1.99e-02 0.0797 Inf -0.250 1.0000
StepF4 - StepF11 1.02e-01 0.0797 Inf 1.278 0.9986
StepF4 - StepF12 2.03e-01 0.0797 Inf 2.554 0.4887
StepF4 - StepF13 1.69e-01 0.0797 Inf 2.123 0.8033
StepF4 - StepF14 3.91e-02 0.0797 Inf 0.491 1.0000
StepF4 - StepF15 1.17e-01 0.0797 Inf 1.466 0.9928
StepF4 - StepF16 2.12e-01 0.0797 Inf 2.663 0.4066
StepF4 - StepF17 2.69e-01 0.0797 Inf 3.372 0.0722
StepF4 - StepF18 2.59e-01 0.0797 Inf 3.246 0.1044
StepF5 - StepF6 1.01e-02 0.0797 Inf 0.127 1.0000
StepF5 - StepF7 -9.94e-02 0.0797 Inf -1.248 0.9989
StepF5 - StepF8 -5.24e-02 0.0797 Inf -0.658 1.0000
StepF5 - StepF9 -1.69e-02 0.0797 Inf -0.212 1.0000
StepF5 - StepF10 -7.23e-02 0.0797 Inf -0.908 1.0000
StepF5 - StepF11 4.94e-02 0.0797 Inf 0.621 1.0000
StepF5 - StepF12 1.51e-01 0.0797 Inf 1.896 0.9137
StepF5 - StepF13 1.17e-01 0.0797 Inf 1.465 0.9929
StepF5 - StepF14 -1.33e-02 0.0797 Inf -0.167 1.0000
StepF5 - StepF15 6.44e-02 0.0797 Inf 0.809 1.0000
StepF5 - StepF16 1.60e-01 0.0797 Inf 2.006 0.8671
StepF5 - StepF17 2.16e-01 0.0797 Inf 2.714 0.3700
StepF5 - StepF18 2.06e-01 0.0797 Inf 2.589 0.4617
StepF6 - StepF7 -1.10e-01 0.0797 Inf -1.375 0.9966
StepF6 - StepF8 -6.25e-02 0.0797 Inf -0.785 1.0000
StepF6 - StepF9 -2.70e-02 0.0797 Inf -0.339 1.0000
StepF6 - StepF10 -8.24e-02 0.0797 Inf -1.035 0.9999
StepF6 - StepF11 3.93e-02 0.0797 Inf 0.494 1.0000
StepF6 - StepF12 1.41e-01 0.0797 Inf 1.769 0.9522
StepF6 - StepF13 1.07e-01 0.0797 Inf 1.338 0.9975
StepF6 - StepF14 -2.34e-02 0.0797 Inf -0.294 1.0000
StepF6 - StepF15 5.43e-02 0.0797 Inf 0.682 1.0000
StepF6 - StepF16 1.50e-01 0.0797 Inf 1.879 0.9200
StepF6 - StepF17 2.06e-01 0.0797 Inf 2.587 0.4629
StepF6 - StepF18 1.96e-01 0.0797 Inf 2.462 0.5598
StepF7 - StepF8 4.70e-02 0.0797 Inf 0.590 1.0000
StepF7 - StepF9 8.25e-02 0.0797 Inf 1.036 0.9999
StepF7 - StepF10 2.71e-02 0.0797 Inf 0.341 1.0000
StepF7 - StepF11 1.49e-01 0.0797 Inf 1.869 0.9234
StepF7 - StepF12 2.50e-01 0.0797 Inf 3.145 0.1383
StepF7 - StepF13 2.16e-01 0.0797 Inf 2.714 0.3707
StepF7 - StepF14 8.61e-02 0.0797 Inf 1.081 0.9998
StepF7 - StepF15 1.64e-01 0.0797 Inf 2.057 0.8409
StepF7 - StepF16 2.59e-01 0.0797 Inf 3.254 0.1022
StepF7 - StepF17 3.16e-01 0.0797 Inf 3.963 0.0092
StepF7 - StepF18 3.06e-01 0.0797 Inf 3.837 0.0149
StepF8 - StepF9 3.55e-02 0.0797 Inf 0.446 1.0000
StepF8 - StepF10 -1.99e-02 0.0797 Inf -0.250 1.0000
StepF8 - StepF11 1.02e-01 0.0797 Inf 1.278 0.9986
StepF8 - StepF12 2.03e-01 0.0797 Inf 2.554 0.4883
StepF8 - StepF13 1.69e-01 0.0797 Inf 2.123 0.8030
StepF8 - StepF14 3.91e-02 0.0797 Inf 0.491 1.0000
StepF8 - StepF15 1.17e-01 0.0797 Inf 1.467 0.9928
StepF8 - StepF16 2.12e-01 0.0797 Inf 2.664 0.4063
StepF8 - StepF17 2.69e-01 0.0797 Inf 3.372 0.0720
StepF8 - StepF18 2.59e-01 0.0797 Inf 3.247 0.1043
StepF9 - StepF10 -5.54e-02 0.0797 Inf -0.696 1.0000
StepF9 - StepF11 6.63e-02 0.0797 Inf 0.833 1.0000
StepF9 - StepF12 1.68e-01 0.0797 Inf 2.108 0.8118
StepF9 - StepF13 1.34e-01 0.0797 Inf 1.677 0.9709
StepF9 - StepF14 3.61e-03 0.0797 Inf 0.045 1.0000
StepF9 - StepF15 8.13e-02 0.0797 Inf 1.021 0.9999
StepF9 - StepF16 1.77e-01 0.0797 Inf 2.218 0.7420
StepF9 - StepF17 2.33e-01 0.0797 Inf 2.926 0.2376
StepF9 - StepF18 2.23e-01 0.0797 Inf 2.801 0.3120
StepF10 - StepF11 1.22e-01 0.0797 Inf 1.528 0.9887
StepF10 - StepF12 2.23e-01 0.0797 Inf 2.804 0.3101
StepF10 - StepF13 1.89e-01 0.0797 Inf 2.373 0.6287
StepF10 - StepF14 5.90e-02 0.0797 Inf 0.741 1.0000
StepF10 - StepF15 1.37e-01 0.0797 Inf 1.716 0.9639
StepF10 - StepF16 2.32e-01 0.0797 Inf 2.913 0.2448
StepF10 - StepF17 2.89e-01 0.0797 Inf 3.622 0.0321
StepF10 - StepF18 2.79e-01 0.0797 Inf 3.497 0.0487
StepF11 - StepF12 1.02e-01 0.0797 Inf 1.276 0.9986
StepF11 - StepF13 6.73e-02 0.0797 Inf 0.845 1.0000
StepF11 - StepF14 -6.27e-02 0.0797 Inf -0.787 1.0000
StepF11 - StepF15 1.50e-02 0.0797 Inf 0.188 1.0000
StepF11 - StepF16 1.10e-01 0.0797 Inf 1.385 0.9963
StepF11 - StepF17 1.67e-01 0.0797 Inf 2.094 0.8203
StepF11 - StepF18 1.57e-01 0.0797 Inf 1.968 0.8844
StepF12 - StepF13 -3.43e-02 0.0797 Inf -0.431 1.0000
StepF12 - StepF14 -1.64e-01 0.0797 Inf -2.063 0.8375
StepF12 - StepF15 -8.66e-02 0.0797 Inf -1.088 0.9998
StepF12 - StepF16 8.71e-03 0.0797 Inf 0.109 1.0000
StepF12 - StepF17 6.52e-02 0.0797 Inf 0.818 1.0000
StepF12 - StepF18 5.52e-02 0.0797 Inf 0.693 1.0000
StepF13 - StepF14 -1.30e-01 0.0797 Inf -1.632 0.9778
StepF13 - StepF15 -5.23e-02 0.0797 Inf -0.657 1.0000
StepF13 - StepF16 4.30e-02 0.0797 Inf 0.540 1.0000
StepF13 - StepF17 9.95e-02 0.0797 Inf 1.249 0.9989
StepF13 - StepF18 8.95e-02 0.0797 Inf 1.124 0.9997
StepF14 - StepF15 7.77e-02 0.0797 Inf 0.975 1.0000
StepF14 - StepF16 1.73e-01 0.0797 Inf 2.172 0.7722
StepF14 - StepF17 2.30e-01 0.0797 Inf 2.881 0.2630
StepF14 - StepF18 2.20e-01 0.0797 Inf 2.756 0.3417
StepF15 - StepF16 9.54e-02 0.0797 Inf 1.197 0.9994
StepF15 - StepF17 1.52e-01 0.0797 Inf 1.906 0.9102
StepF15 - StepF18 1.42e-01 0.0797 Inf 1.780 0.9495
StepF16 - StepF17 5.65e-02 0.0797 Inf 0.709 1.0000
StepF16 - StepF18 4.65e-02 0.0797 Inf 0.583 1.0000
StepF17 - StepF18 -1.00e-02 0.0797 Inf -0.125 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -2.11e-01 0.0797 Inf -2.653 0.4138
StepF1 - StepF3 -6.87e-02 0.0797 Inf -0.862 1.0000
StepF1 - StepF4 2.19e-02 0.0797 Inf 0.276 1.0000
StepF1 - StepF5 7.43e-02 0.0797 Inf 0.933 1.0000
StepF1 - StepF6 8.44e-02 0.0797 Inf 1.060 0.9999
StepF1 - StepF7 -2.51e-02 0.0797 Inf -0.315 1.0000
StepF1 - StepF8 2.19e-02 0.0797 Inf 0.275 1.0000
StepF1 - StepF9 5.74e-02 0.0797 Inf 0.721 1.0000
StepF1 - StepF10 2.01e-03 0.0797 Inf 0.025 1.0000
StepF1 - StepF11 1.24e-01 0.0797 Inf 1.553 0.9866
StepF1 - StepF12 2.25e-01 0.0797 Inf 2.829 0.2942
StepF1 - StepF13 1.91e-01 0.0797 Inf 2.398 0.6093
StepF1 - StepF14 6.10e-02 0.0797 Inf 0.766 1.0000
StepF1 - StepF15 1.39e-01 0.0797 Inf 1.742 0.9586
StepF1 - StepF16 2.34e-01 0.0797 Inf 2.939 0.2311
StepF1 - StepF17 2.91e-01 0.0797 Inf 3.647 0.0294
StepF1 - StepF18 2.81e-01 0.0797 Inf 3.522 0.0449
StepF2 - StepF3 1.43e-01 0.0797 Inf 1.791 0.9468
StepF2 - StepF4 2.33e-01 0.0797 Inf 2.929 0.2364
StepF2 - StepF5 2.86e-01 0.0797 Inf 3.586 0.0363
StepF2 - StepF6 2.96e-01 0.0797 Inf 3.713 0.0234
StepF2 - StepF7 1.86e-01 0.0797 Inf 2.338 0.6553
StepF2 - StepF8 2.33e-01 0.0797 Inf 2.928 0.2367
StepF2 - StepF9 2.69e-01 0.0797 Inf 3.374 0.0717
StepF2 - StepF10 2.13e-01 0.0797 Inf 2.678 0.3955
StepF2 - StepF11 3.35e-01 0.0797 Inf 4.207 0.0034
StepF2 - StepF12 4.37e-01 0.0797 Inf 5.482 <.0001
StepF2 - StepF13 4.02e-01 0.0797 Inf 5.051 0.0001
StepF2 - StepF14 2.72e-01 0.0797 Inf 3.419 0.0623
StepF2 - StepF15 3.50e-01 0.0797 Inf 4.395 0.0015
StepF2 - StepF16 4.45e-01 0.0797 Inf 5.592 <.0001
StepF2 - StepF17 5.02e-01 0.0797 Inf 6.301 <.0001
StepF2 - StepF18 4.92e-01 0.0797 Inf 6.175 <.0001
StepF3 - StepF4 9.06e-02 0.0797 Inf 1.138 0.9997
StepF3 - StepF5 1.43e-01 0.0797 Inf 1.795 0.9457
StepF3 - StepF6 1.53e-01 0.0797 Inf 1.922 0.9038
StepF3 - StepF7 4.36e-02 0.0797 Inf 0.547 1.0000
StepF3 - StepF8 9.06e-02 0.0797 Inf 1.137 0.9997
StepF3 - StepF9 1.26e-01 0.0797 Inf 1.583 0.9837
StepF3 - StepF10 7.07e-02 0.0797 Inf 0.888 1.0000
StepF3 - StepF11 1.92e-01 0.0797 Inf 2.416 0.5957
StepF3 - StepF12 2.94e-01 0.0797 Inf 3.692 0.0252
StepF3 - StepF13 2.60e-01 0.0797 Inf 3.261 0.1002
StepF3 - StepF14 1.30e-01 0.0797 Inf 1.629 0.9783
StepF3 - StepF15 2.07e-01 0.0797 Inf 2.604 0.4504
StepF3 - StepF16 3.03e-01 0.0797 Inf 3.801 0.0170
StepF3 - StepF17 3.59e-01 0.0797 Inf 4.510 0.0009
StepF3 - StepF18 3.49e-01 0.0797 Inf 4.384 0.0016
StepF4 - StepF5 5.24e-02 0.0797 Inf 0.657 1.0000
StepF4 - StepF6 6.25e-02 0.0797 Inf 0.784 1.0000
StepF4 - StepF7 -4.71e-02 0.0797 Inf -0.591 1.0000
StepF4 - StepF8 -3.94e-05 0.0797 Inf 0.000 1.0000
StepF4 - StepF9 3.55e-02 0.0797 Inf 0.445 1.0000
StepF4 - StepF10 -1.99e-02 0.0797 Inf -0.250 1.0000
StepF4 - StepF11 1.02e-01 0.0797 Inf 1.278 0.9986
StepF4 - StepF12 2.03e-01 0.0797 Inf 2.554 0.4887
StepF4 - StepF13 1.69e-01 0.0797 Inf 2.123 0.8033
StepF4 - StepF14 3.91e-02 0.0797 Inf 0.491 1.0000
StepF4 - StepF15 1.17e-01 0.0797 Inf 1.466 0.9928
StepF4 - StepF16 2.12e-01 0.0797 Inf 2.663 0.4066
StepF4 - StepF17 2.69e-01 0.0797 Inf 3.372 0.0722
StepF4 - StepF18 2.59e-01 0.0797 Inf 3.246 0.1044
StepF5 - StepF6 1.01e-02 0.0797 Inf 0.127 1.0000
StepF5 - StepF7 -9.94e-02 0.0797 Inf -1.248 0.9989
StepF5 - StepF8 -5.24e-02 0.0797 Inf -0.658 1.0000
StepF5 - StepF9 -1.69e-02 0.0797 Inf -0.212 1.0000
StepF5 - StepF10 -7.23e-02 0.0797 Inf -0.908 1.0000
StepF5 - StepF11 4.94e-02 0.0797 Inf 0.621 1.0000
StepF5 - StepF12 1.51e-01 0.0797 Inf 1.896 0.9137
StepF5 - StepF13 1.17e-01 0.0797 Inf 1.465 0.9929
StepF5 - StepF14 -1.33e-02 0.0797 Inf -0.167 1.0000
StepF5 - StepF15 6.44e-02 0.0797 Inf 0.809 1.0000
StepF5 - StepF16 1.60e-01 0.0797 Inf 2.006 0.8671
StepF5 - StepF17 2.16e-01 0.0797 Inf 2.714 0.3700
StepF5 - StepF18 2.06e-01 0.0797 Inf 2.589 0.4617
StepF6 - StepF7 -1.10e-01 0.0797 Inf -1.375 0.9966
StepF6 - StepF8 -6.25e-02 0.0797 Inf -0.785 1.0000
StepF6 - StepF9 -2.70e-02 0.0797 Inf -0.339 1.0000
StepF6 - StepF10 -8.24e-02 0.0797 Inf -1.035 0.9999
StepF6 - StepF11 3.93e-02 0.0797 Inf 0.494 1.0000
StepF6 - StepF12 1.41e-01 0.0797 Inf 1.769 0.9522
StepF6 - StepF13 1.07e-01 0.0797 Inf 1.338 0.9975
StepF6 - StepF14 -2.34e-02 0.0797 Inf -0.294 1.0000
StepF6 - StepF15 5.43e-02 0.0797 Inf 0.682 1.0000
StepF6 - StepF16 1.50e-01 0.0797 Inf 1.879 0.9200
StepF6 - StepF17 2.06e-01 0.0797 Inf 2.587 0.4629
StepF6 - StepF18 1.96e-01 0.0797 Inf 2.462 0.5598
StepF7 - StepF8 4.70e-02 0.0797 Inf 0.590 1.0000
StepF7 - StepF9 8.25e-02 0.0797 Inf 1.036 0.9999
StepF7 - StepF10 2.71e-02 0.0797 Inf 0.341 1.0000
StepF7 - StepF11 1.49e-01 0.0797 Inf 1.869 0.9234
StepF7 - StepF12 2.50e-01 0.0797 Inf 3.145 0.1383
StepF7 - StepF13 2.16e-01 0.0797 Inf 2.714 0.3707
StepF7 - StepF14 8.61e-02 0.0797 Inf 1.081 0.9998
StepF7 - StepF15 1.64e-01 0.0797 Inf 2.057 0.8409
StepF7 - StepF16 2.59e-01 0.0797 Inf 3.254 0.1022
StepF7 - StepF17 3.16e-01 0.0797 Inf 3.963 0.0092
StepF7 - StepF18 3.06e-01 0.0797 Inf 3.837 0.0149
StepF8 - StepF9 3.55e-02 0.0797 Inf 0.446 1.0000
StepF8 - StepF10 -1.99e-02 0.0797 Inf -0.250 1.0000
StepF8 - StepF11 1.02e-01 0.0797 Inf 1.278 0.9986
StepF8 - StepF12 2.03e-01 0.0797 Inf 2.554 0.4883
StepF8 - StepF13 1.69e-01 0.0797 Inf 2.123 0.8030
StepF8 - StepF14 3.91e-02 0.0797 Inf 0.491 1.0000
StepF8 - StepF15 1.17e-01 0.0797 Inf 1.467 0.9928
StepF8 - StepF16 2.12e-01 0.0797 Inf 2.664 0.4063
StepF8 - StepF17 2.69e-01 0.0797 Inf 3.372 0.0720
StepF8 - StepF18 2.59e-01 0.0797 Inf 3.247 0.1043
StepF9 - StepF10 -5.54e-02 0.0797 Inf -0.696 1.0000
StepF9 - StepF11 6.63e-02 0.0797 Inf 0.833 1.0000
StepF9 - StepF12 1.68e-01 0.0797 Inf 2.108 0.8118
StepF9 - StepF13 1.34e-01 0.0797 Inf 1.677 0.9709
StepF9 - StepF14 3.61e-03 0.0797 Inf 0.045 1.0000
StepF9 - StepF15 8.13e-02 0.0797 Inf 1.021 0.9999
StepF9 - StepF16 1.77e-01 0.0797 Inf 2.218 0.7420
StepF9 - StepF17 2.33e-01 0.0797 Inf 2.926 0.2376
StepF9 - StepF18 2.23e-01 0.0797 Inf 2.801 0.3120
StepF10 - StepF11 1.22e-01 0.0797 Inf 1.528 0.9887
StepF10 - StepF12 2.23e-01 0.0797 Inf 2.804 0.3101
StepF10 - StepF13 1.89e-01 0.0797 Inf 2.373 0.6287
StepF10 - StepF14 5.90e-02 0.0797 Inf 0.741 1.0000
StepF10 - StepF15 1.37e-01 0.0797 Inf 1.716 0.9639
StepF10 - StepF16 2.32e-01 0.0797 Inf 2.913 0.2448
StepF10 - StepF17 2.89e-01 0.0797 Inf 3.622 0.0321
StepF10 - StepF18 2.79e-01 0.0797 Inf 3.497 0.0487
StepF11 - StepF12 1.02e-01 0.0797 Inf 1.276 0.9986
StepF11 - StepF13 6.73e-02 0.0797 Inf 0.845 1.0000
StepF11 - StepF14 -6.27e-02 0.0797 Inf -0.787 1.0000
StepF11 - StepF15 1.50e-02 0.0797 Inf 0.188 1.0000
StepF11 - StepF16 1.10e-01 0.0797 Inf 1.385 0.9963
StepF11 - StepF17 1.67e-01 0.0797 Inf 2.094 0.8203
StepF11 - StepF18 1.57e-01 0.0797 Inf 1.968 0.8844
StepF12 - StepF13 -3.43e-02 0.0797 Inf -0.431 1.0000
StepF12 - StepF14 -1.64e-01 0.0797 Inf -2.063 0.8375
StepF12 - StepF15 -8.66e-02 0.0797 Inf -1.088 0.9998
StepF12 - StepF16 8.71e-03 0.0797 Inf 0.109 1.0000
StepF12 - StepF17 6.52e-02 0.0797 Inf 0.818 1.0000
StepF12 - StepF18 5.52e-02 0.0797 Inf 0.693 1.0000
StepF13 - StepF14 -1.30e-01 0.0797 Inf -1.632 0.9778
StepF13 - StepF15 -5.23e-02 0.0797 Inf -0.657 1.0000
StepF13 - StepF16 4.30e-02 0.0797 Inf 0.540 1.0000
StepF13 - StepF17 9.95e-02 0.0797 Inf 1.249 0.9989
StepF13 - StepF18 8.95e-02 0.0797 Inf 1.124 0.9997
StepF14 - StepF15 7.77e-02 0.0797 Inf 0.975 1.0000
StepF14 - StepF16 1.73e-01 0.0797 Inf 2.172 0.7722
StepF14 - StepF17 2.30e-01 0.0797 Inf 2.881 0.2630
StepF14 - StepF18 2.20e-01 0.0797 Inf 2.756 0.3417
StepF15 - StepF16 9.54e-02 0.0797 Inf 1.197 0.9994
StepF15 - StepF17 1.52e-01 0.0797 Inf 1.906 0.9102
StepF15 - StepF18 1.42e-01 0.0797 Inf 1.780 0.9495
StepF16 - StepF17 5.65e-02 0.0797 Inf 0.709 1.0000
StepF16 - StepF18 4.65e-02 0.0797 Inf 0.583 1.0000
StepF17 - StepF18 -1.00e-02 0.0797 Inf -0.125 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2114 0.0797 Inf 2.653 0.1355
StepF3 - StepF2 -0.1427 0.0797 Inf -1.791 1.0000
StepF4 - StepF3 -0.0906 0.0797 Inf -1.138 1.0000
StepF5 - StepF4 -0.0524 0.0797 Inf -0.657 1.0000
StepF6 - StepF5 -0.0101 0.0797 Inf -0.127 1.0000
StepF7 - StepF6 0.1095 0.0797 Inf 1.375 1.0000
StepF8 - StepF7 -0.0470 0.0797 Inf -0.590 1.0000
StepF9 - StepF8 -0.0355 0.0797 Inf -0.446 1.0000
StepF10 - StepF9 0.0554 0.0797 Inf 0.696 1.0000
StepF11 - StepF10 -0.1217 0.0797 Inf -1.528 1.0000
StepF12 - StepF11 -0.1016 0.0797 Inf -1.276 1.0000
StepF13 - StepF12 0.0343 0.0797 Inf 0.431 1.0000
StepF14 - StepF13 0.1300 0.0797 Inf 1.632 1.0000
StepF15 - StepF14 -0.0777 0.0797 Inf -0.975 1.0000
StepF16 - StepF15 -0.0954 0.0797 Inf -1.197 1.0000
StepF17 - StepF16 -0.0565 0.0797 Inf -0.709 1.0000
StepF18 - StepF17 0.0100 0.0797 Inf 0.125 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2114 0.0797 Inf 2.653 0.1355
StepF3 - StepF2 -0.1427 0.0797 Inf -1.791 1.0000
StepF4 - StepF3 -0.0906 0.0797 Inf -1.138 1.0000
StepF5 - StepF4 -0.0524 0.0797 Inf -0.657 1.0000
StepF6 - StepF5 -0.0101 0.0797 Inf -0.127 1.0000
StepF7 - StepF6 0.1095 0.0797 Inf 1.375 1.0000
StepF8 - StepF7 -0.0470 0.0797 Inf -0.590 1.0000
StepF9 - StepF8 -0.0355 0.0797 Inf -0.446 1.0000
StepF10 - StepF9 0.0554 0.0797 Inf 0.696 1.0000
StepF11 - StepF10 -0.1217 0.0797 Inf -1.528 1.0000
StepF12 - StepF11 -0.1016 0.0797 Inf -1.276 1.0000
StepF13 - StepF12 0.0343 0.0797 Inf 0.431 1.0000
StepF14 - StepF13 0.1300 0.0797 Inf 1.632 1.0000
StepF15 - StepF14 -0.0777 0.0797 Inf -0.975 1.0000
StepF16 - StepF15 -0.0954 0.0797 Inf -1.197 1.0000
StepF17 - StepF16 -0.0565 0.0797 Inf -0.709 1.0000
StepF18 - StepF17 0.0100 0.0797 Inf 0.125 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests #B3.2 Concatenation plots
# ==== TRAINING (Blocks 1–3): Step-wise EMM ± SD (Correct-only) + All-axes overlay ====
suppressPackageStartupMessages({
library(dplyr); library(ggplot2); library(lme4); library(emmeans); library(patchwork)
})
emm_options(lmer.df = "asymptotic")
.get_emm_sd_correct <- function(df_axis) {
if (nrow(df_axis) == 0) return(NULL)
dd <- df_axis %>%
filter(as.character(Accuracy) == "1") %>%
mutate(
StepF = factor(Step, levels = sort(unique(Step))),
subject = factor(subject),
trial_id = factor(trial_id)
)
if (nrow(dd) == 0) return(NULL)
m <- suppressWarnings(lmer(RMS ~ StepF + (1|subject) + (1|trial_id), data = dd, REML = TRUE))
em_df <- as.data.frame(emmeans(m, ~ StepF)) %>%
transmute(Step = as.numeric(as.character(StepF)),
emmean = emmean)
sd_df <- dd %>%
group_by(StepF) %>%
summarise(sd = sd(RMS, na.rm = TRUE), .groups = "drop") %>%
transmute(Step = as.numeric(as.character(StepF)), sd = sd)
em_df %>%
left_join(sd_df, by = "Step") %>%
mutate(ymin = pmax(0, emmean - sd),
ymax = emmean + sd)
}
.plot_block_stepwise_sd_correct <- function(df_block, block_title) {
axes_map <- c("x"="X","y"="Y","z"="Z")
out <- lapply(names(axes_map), function(ax) {
tbl <- .get_emm_sd_correct(df_block %>% filter(Axis == ax))
if (is.null(tbl)) return(NULL)
tbl %>% mutate(Axis = axes_map[[ax]])
})
emms_tbl <- bind_rows(out)
if (nrow(emms_tbl) == 0) return(invisible(NULL))
# (1) Faceted by Axis (as before)
p_facets <- ggplot(emms_tbl, aes(x = Step, y = emmean)) +
geom_ribbon(aes(ymin = ymin, ymax = ymax), alpha = 0.18) +
geom_line(size = 0.9) +
geom_point(size = 1.2) +
facet_wrap(~ Axis, nrow = 1, scales = "free_y") +
labs(title = paste0(block_title, " — Step-wise EMMs (±SD) — Correct trials"),
x = "Step", y = "EMM RMS") +
theme_classic() +
theme(legend.position = "none")
# (2) All axes in one panel
p_overlay <- ggplot(emms_tbl, aes(x = Step, y = emmean, color = Axis, fill = Axis, group = Axis)) +
geom_ribbon(aes(ymin = ymin, ymax = ymax), alpha = 0.15, color = NA) +
geom_line(size = 0.9) +
geom_point(size = 1.2) +
labs(title = paste0(block_title, " — Step-wise EMMs (±SD) — Correct trials (X/Y/Z overlaid)"),
x = "Step", y = "EMM RMS") +
theme_classic() +
theme(legend.position = "bottom")
list(facets = p_facets, overlay = p_overlay)
}
# Render for each training block
res_b1 <- .plot_block_stepwise_sd_correct(stepwise_6, "Block 1 (6 steps)")Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.res_b2 <- .plot_block_stepwise_sd_correct(stepwise_12, "Block 2 (12 steps)")
res_b3 <- .plot_block_stepwise_sd_correct(stepwise_18, "Block 3 (18 steps)")
if (!is.null(res_b1)) { print(res_b1$facets); print(res_b1$overlay) }
if (!is.null(res_b2)) { print(res_b2$facets); print(res_b2$overlay) }
if (!is.null(res_b3)) { print(res_b3$facets); print(res_b3$overlay) }
# ==== TEST (Blocks 4–5): Step-wise EMM ± SD (Correct-only) + All-axes overlay per seq length ====
suppressPackageStartupMessages({
library(dplyr); library(ggplot2); library(lme4); library(emmeans); library(patchwork)
})
emm_options(lmer.df = "asymptotic")
.get_emm_sd_correct <- function(df_axis) {
if (nrow(df_axis) == 0) return(NULL)
dd <- df_axis %>%
filter(as.character(Accuracy) == "1") %>%
mutate(
StepF = factor(Step, levels = sort(unique(Step))),
subject = factor(subject),
trial_id = factor(trial_id)
)
if (nrow(dd) == 0) return(NULL)
m <- suppressWarnings(lmer(RMS ~ StepF + (1|subject) + (1|trial_id), data = dd, REML = TRUE))
em_df <- as.data.frame(emmeans(m, ~ StepF)) %>%
transmute(Step = as.numeric(as.character(StepF)),
emmean = emmean)
sd_df <- dd %>%
group_by(StepF) %>%
summarise(sd = sd(RMS, na.rm = TRUE), .groups = "drop") %>%
transmute(Step = as.numeric(as.character(StepF)), sd = sd)
em_df %>%
left_join(sd_df, by = "Step") %>%
mutate(ymin = pmax(0, emmean - sd),
ymax = emmean + sd)
}
.plot_block_len_sd_correct <- function(df_block, title_prefix) {
axes_map <- c("x"="X","y"="Y","z"="Z")
out <- lapply(names(axes_map), function(ax) {
tbl <- .get_emm_sd_correct(df_block %>% filter(Axis == ax))
if (is.null(tbl)) return(NULL)
tbl %>% mutate(Axis = axes_map[[ax]])
})
emms_tbl <- bind_rows(out)
if (nrow(emms_tbl) == 0) return(invisible(NULL))
# (1) Faceted by Axis (as before)
p_facets <- ggplot(emms_tbl, aes(x = Step, y = emmean)) +
geom_ribbon(aes(ymin = ymin, ymax = ymax), alpha = 0.18) +
geom_line(size = 0.9) +
geom_point(size = 1.2) +
facet_wrap(~ Axis, nrow = 1, scales = "free_y") +
labs(title = paste0(title_prefix, " — Step-wise EMMs (±SD) — Correct trials"),
x = "Step", y = "EMM RMS") +
theme_classic() +
theme(legend.position = "none")
# (2) All axes in one panel
p_overlay <- ggplot(emms_tbl, aes(x = Step, y = emmean, color = Axis, fill = Axis, group = Axis)) +
geom_ribbon(aes(ymin = ymin, ymax = ymax), alpha = 0.15, color = NA) +
geom_line(size = 0.9) +
geom_point(size = 1.2) +
labs(title = paste0(title_prefix, " — Step-wise EMMs (±SD) — Correct trials (X/Y/Z overlaid)"),
x = "Step", y = "EMM RMS") +
theme_classic() +
theme(legend.position = "bottom")
list(facets = p_facets, overlay = p_overlay)
}
# Block 4
res_b4_6 <- .plot_block_len_sd_correct(sw_b4_6, "Block 4 — 6 steps")
res_b4_12 <- .plot_block_len_sd_correct(sw_b4_12, "Block 4 — 12 steps")
res_b4_18 <- .plot_block_len_sd_correct(sw_b4_18, "Block 4 — 18 steps")
if (!is.null(res_b4_6)) { print(res_b4_6$facets); print(res_b4_6$overlay) }
if (!is.null(res_b4_12)) { print(res_b4_12$facets); print(res_b4_12$overlay) }
if (!is.null(res_b4_18)) { print(res_b4_18$facets); print(res_b4_18$overlay) }
# Block 5
res_b5_6 <- .plot_block_len_sd_correct(sw_b5_6, "Block 5 — 6 steps")
res_b5_12 <- .plot_block_len_sd_correct(sw_b5_12, "Block 5 — 12 steps")
res_b5_18 <- .plot_block_len_sd_correct(sw_b5_18, "Block 5 — 18 steps")
if (!is.null(res_b5_6)) { print(res_b5_6$facets); print(res_b5_6$overlay) }
if (!is.null(res_b5_12)) { print(res_b5_12$facets); print(res_b5_12$overlay) }
if (!is.null(res_b5_18)) { print(res_b5_18$facets); print(res_b5_18$overlay) }
#additional analysis comparing which axis shows the highest variability
# ==== #1.3bis: RMS differences among axes (X/Y/Z) across blocks ====
# RMS ~ Axis * Block + Accuracy + (1 | subject) + (1 | Trial)
# and EMMs + Tukey pairwise comparisons for Axis (overall and per block).
suppressPackageStartupMessages({
library(dplyr); library(tidyr)
library(lme4); library(lmerTest)
library(emmeans); library(car)
})
emm_options(lmer.df = "asymptotic")
# Prepare long format once
axes_long <- rms_combined %>%
dplyr::select(subject, Trial, Block, phase, Accuracy, rms_x, rms_y, rms_z) %>%
tidyr::pivot_longer(
cols = c(rms_x, rms_y, rms_z),
names_to = "Axis",
values_to = "RMS"
) %>%
mutate(
Axis = dplyr::recode(Axis, rms_x = "X", rms_y = "Y", rms_z = "Z"),
Axis = factor(Axis, levels = c("X","Y","Z")),
Block = factor(Block), # already factor upstream
phase = factor(phase, levels = c("Preparation","Execution"))
) %>%
tidyr::drop_na(RMS) %>%
droplevels()
analyze_axes_vs_blocks <- function(df, label = "OVERALL (collapsed across phase)") {
cat("\n\n==============================\n",
"AXES × BLOCKS — ", label,
"\n==============================\n", sep = "")
m <- lmer(RMS ~ Axis * Block + Accuracy + (1 | subject) + (1 | Trial), data = df, REML = TRUE)
cat("\n--- Model Summary (lmerTest; Satterthwaite t-tests) ---\n")
print(summary(m))
cat("\n--- lmerTest ANOVA (F-tests; Satterthwaite) ---\n")
print(anova(m))
cat("\n--- Type II Wald χ² (car::Anova) ---\n")
print(car::Anova(m, type = 2, test.statistic = "Chisq"))
cat("\n--- Type III Wald χ² (car::Anova; sum contrasts) ---\n")
print(car::Anova(m, type = 3, test.statistic = "Chisq"))
# EMMs for Axis (averaged over Blocks & Accuracy levels present)
em_axis <- emmeans(m, ~ Axis)
cat("\n--- EMMs by Axis (averaged over Blocks) ---\n")
print(summary(em_axis))
cat("\n--- Pairwise (Tukey) Axis comparisons (overall) ---\n")
print(pairs(em_axis, adjust = "tukey"))
# Simple effects: Axis differences within each Block
em_axis_by_block <- emmeans(m, ~ Axis | Block)
cat("\n--- EMMs by Axis within each Block ---\n")
print(summary(em_axis_by_block))
cat("\n--- Pairwise (Tukey) Axis comparisons within each Block ---\n")
print(pairs(em_axis_by_block, adjust = "tukey"))
invisible(TRUE)
}
# 1) Collapsed across phase (overall)
analyze_axes_vs_blocks(axes_long, label = "OVERALL")
==============================
AXES × BLOCKS — OVERALL
==============================
--- Model Summary (lmerTest; Satterthwaite t-tests) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ Axis * Block + Accuracy + (1 | subject) + (1 | Trial)
Data: df
REML criterion at convergence: 54387.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.2450 -0.7332 -0.0719 0.5559 13.7440
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.001358 0.03684
subject (Intercept) 0.029239 0.17099
Residual 0.278967 0.52817
Number of obs: 34656, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 4.891e-01 4.076e-02 1.761e+01 12.000 6.59e-10 ***
Axis1 -9.296e-02 4.029e-03 3.457e+04 -23.073 < 2e-16 ***
Axis2 -7.535e-02 4.029e-03 3.457e+04 -18.701 < 2e-16 ***
Block1 1.759e-02 6.125e-03 3.462e+04 2.871 0.00409 **
Block2 2.802e-02 5.699e-03 3.462e+04 4.916 8.88e-07 ***
Block3 -1.481e-02 5.738e-03 3.442e+04 -2.580 0.00987 **
Block4 2.674e-02 5.740e-03 3.461e+04 4.659 3.19e-06 ***
Accuracy1 -2.407e-02 3.040e-03 3.329e+04 -7.915 2.54e-15 ***
Axis1:Block1 1.025e-03 8.502e-03 3.457e+04 0.121 0.90405
Axis2:Block1 6.267e-03 8.502e-03 3.457e+04 0.737 0.46102
Axis1:Block2 -1.060e-02 8.044e-03 3.457e+04 -1.317 0.18772
Axis2:Block2 -2.157e-03 8.044e-03 3.457e+04 -0.268 0.78857
Axis1:Block3 -2.407e-04 8.013e-03 3.457e+04 -0.030 0.97604
Axis2:Block3 5.460e-04 8.013e-03 3.457e+04 0.068 0.94568
Axis1:Block4 -4.620e-03 8.072e-03 3.457e+04 -0.572 0.56712
Axis2:Block4 -1.201e-02 8.072e-03 3.457e+04 -1.487 0.13692
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 16 > 12.
Use print(summary(m), correlation=TRUE) or
vcov(summary(m)) if you need it
--- lmerTest ANOVA (F-tests; Satterthwaite) ---
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Axis 488.58 244.289 2 34569 875.6926 < 2.2e-16 ***
Block 38.11 9.527 4 34619 34.1505 < 2.2e-16 ***
Accuracy 17.48 17.479 1 33291 62.6548 2.539e-15 ***
Axis:Block 3.79 0.473 8 34569 1.6972 0.09349 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type II Wald χ² (car::Anova) ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Axis 1755.332 2 < 2.2e-16 ***
Block 136.602 4 < 2.2e-16 ***
Accuracy 62.655 1 2.463e-15 ***
Axis:Block 13.578 8 0.09345 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (car::Anova; sum contrasts) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 144.003 1 < 2.2e-16 ***
Axis 1751.385 2 < 2.2e-16 ***
Block 136.602 4 < 2.2e-16 ***
Accuracy 62.655 1 2.463e-15 ***
Axis:Block 13.578 8 0.09345 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NOTE: Results may be misleading due to involvement in interactions
--- EMMs by Axis (averaged over Blocks) ---
Axis emmean SE df asymp.LCL asymp.UCL
X 0.396 0.041 Inf 0.316 0.476
Y 0.414 0.041 Inf 0.334 0.494
Z 0.657 0.041 Inf 0.577 0.738
Results are averaged over the levels of: Block, Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) Axis comparisons (overall) ---
contrast estimate SE df z.ratio p.value
X - Y -0.0176 0.00698 Inf -2.524 0.0312
X - Z -0.2613 0.00698 Inf -37.439 <.0001
Y - Z -0.2436 0.00698 Inf -34.915 <.0001
Results are averaged over the levels of: Block, Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
--- EMMs by Axis within each Block ---
Block = 1:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.415 0.0424 Inf 0.332 0.498
Y 0.438 0.0424 Inf 0.355 0.521
Z 0.668 0.0424 Inf 0.585 0.751
Block = 2:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.414 0.0421 Inf 0.331 0.496
Y 0.440 0.0421 Inf 0.357 0.522
Z 0.698 0.0421 Inf 0.616 0.781
Block = 3:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.381 0.0421 Inf 0.299 0.464
Y 0.400 0.0421 Inf 0.317 0.482
Z 0.642 0.0421 Inf 0.560 0.725
Block = 4:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.418 0.0421 Inf 0.336 0.501
Y 0.429 0.0421 Inf 0.346 0.511
Z 0.701 0.0421 Inf 0.618 0.783
Block = 5:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.353 0.0419 Inf 0.271 0.435
Y 0.364 0.0419 Inf 0.281 0.446
Z 0.578 0.0419 Inf 0.496 0.660
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) Axis comparisons within each Block ---
Block = 1:
contrast estimate SE df z.ratio p.value
X - Y -0.0229 0.0167 Inf -1.365 0.3592
X - Z -0.2529 0.0167 Inf -15.110 <.0001
Y - Z -0.2301 0.0167 Inf -13.745 <.0001
Block = 2:
contrast estimate SE df z.ratio p.value
X - Y -0.0261 0.0156 Inf -1.673 0.2154
X - Z -0.2846 0.0156 Inf -18.282 <.0001
Y - Z -0.2586 0.0156 Inf -16.608 <.0001
Block = 3:
contrast estimate SE df z.ratio p.value
X - Y -0.0184 0.0155 Inf -1.188 0.4603
X - Z -0.2612 0.0155 Inf -16.865 <.0001
Y - Z -0.2428 0.0155 Inf -15.677 <.0001
Block = 4:
contrast estimate SE df z.ratio p.value
X - Y -0.0102 0.0156 Inf -0.654 0.7901
X - Z -0.2825 0.0156 Inf -18.063 <.0001
Y - Z -0.2723 0.0156 Inf -17.410 <.0001
Block = 5:
contrast estimate SE df z.ratio p.value
X - Y -0.0105 0.0145 Inf -0.726 0.7480
X - Z -0.2250 0.0145 Inf -15.516 <.0001
Y - Z -0.2145 0.0145 Inf -14.790 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates # 2) Within each phase (to match your within-phase reporting elsewhere)
for (ph in c("Preparation","Execution")) {
df_ph <- axes_long %>% filter(phase == ph) %>% droplevels()
if (nrow(df_ph) > 0) analyze_axes_vs_blocks(df_ph, label = paste("PHASE:", ph))
}
==============================
AXES × BLOCKS — PHASE: Preparation
==============================
--- Model Summary (lmerTest; Satterthwaite t-tests) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ Axis * Block + Accuracy + (1 | subject) + (1 | Trial)
Data: df
REML criterion at convergence: 2758.8
Scaled residuals:
Min 1Q Median 3Q Max
-2.0492 -0.3806 -0.1162 0.1486 21.5763
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.011441 0.10696
subject (Intercept) 0.001264 0.03556
Residual 0.067024 0.25889
Number of obs: 17613, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.372e-01 1.769e-02 6.214e+01 7.755 1.04e-10 ***
Axis1 -1.253e-02 2.768e-03 1.753e+04 -4.526 6.05e-06 ***
Axis2 -1.094e-02 2.768e-03 1.753e+04 -3.952 7.78e-05 ***
Block1 -7.772e-02 4.180e-03 1.754e+04 -18.595 < 2e-16 ***
Block2 4.705e-03 3.926e-03 1.755e+04 1.198 0.230754
Block3 7.096e-02 3.956e-03 1.755e+04 17.934 < 2e-16 ***
Block4 2.978e-03 3.957e-03 1.754e+04 0.753 0.451728
Accuracy1 -7.036e-03 2.096e-03 1.751e+04 -3.356 0.000792 ***
Axis1:Block1 1.349e-02 5.798e-03 1.753e+04 2.326 0.020021 *
Axis2:Block1 1.144e-02 5.798e-03 1.753e+04 1.973 0.048547 *
Axis1:Block2 -6.818e-03 5.535e-03 1.753e+04 -1.232 0.218004
Axis2:Block2 -5.683e-04 5.535e-03 1.753e+04 -0.103 0.918224
Axis1:Block3 -1.523e-02 5.516e-03 1.753e+04 -2.762 0.005755 **
Axis2:Block3 -4.979e-03 5.516e-03 1.753e+04 -0.903 0.366649
Axis1:Block4 2.938e-03 5.560e-03 1.753e+04 0.528 0.597286
Axis2:Block4 -3.952e-03 5.560e-03 1.753e+04 -0.711 0.477288
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 16 > 12.
Use print(summary(m), correlation=TRUE) or
vcov(summary(m)) if you need it
--- lmerTest ANOVA (F-tests; Satterthwaite) ---
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Axis 4.825 2.4123 2 17533 35.9918 2.517e-16 ***
Block 34.989 8.7473 4 17545 130.5101 < 2.2e-16 ***
Accuracy 0.755 0.7550 1 17506 11.2649 0.0007915 ***
Axis:Block 1.959 0.2449 8 17533 3.6544 0.0002906 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type II Wald χ² (car::Anova) ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Axis 75.547 2 < 2.2e-16 ***
Block 522.040 4 < 2.2e-16 ***
Accuracy 11.265 1 0.0007899 ***
Axis:Block 29.235 8 0.0002883 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (car::Anova; sum contrasts) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 60.136 1 8.853e-15 ***
Axis 71.984 2 2.339e-16 ***
Block 522.040 4 < 2.2e-16 ***
Accuracy 11.265 1 0.0007899 ***
Axis:Block 29.235 8 0.0002883 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NOTE: Results may be misleading due to involvement in interactions
--- EMMs by Axis (averaged over Blocks) ---
Axis emmean SE df asymp.LCL asymp.UCL
X 0.125 0.0179 Inf 0.0896 0.160
Y 0.126 0.0179 Inf 0.0912 0.161
Z 0.161 0.0179 Inf 0.1256 0.196
Results are averaged over the levels of: Block, Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) Axis comparisons (overall) ---
contrast estimate SE df z.ratio p.value
X - Y -0.00159 0.00479 Inf -0.331 0.9413
X - Z -0.03600 0.00479 Inf -7.508 <.0001
Y - Z -0.03441 0.00479 Inf -7.176 <.0001
Results are averaged over the levels of: Block, Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
--- EMMs by Axis within each Block ---
Block = 1:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.0604 0.0194 Inf 0.0225 0.0984
Y 0.0600 0.0194 Inf 0.0220 0.0979
Z 0.0580 0.0194 Inf 0.0201 0.0960
Block = 2:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.1226 0.0191 Inf 0.0850 0.1601
Y 0.1304 0.0191 Inf 0.0929 0.1679
Z 0.1728 0.0191 Inf 0.1352 0.2103
Block = 3:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.1804 0.0191 Inf 0.1429 0.2179
Y 0.1922 0.0191 Inf 0.1547 0.2298
Z 0.2518 0.0191 Inf 0.2143 0.2894
Block = 4:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.1306 0.0192 Inf 0.0930 0.1682
Y 0.1253 0.0192 Inf 0.0877 0.1629
Z 0.1647 0.0192 Inf 0.1271 0.2022
Block = 5:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.1294 0.0190 Inf 0.0922 0.1665
Y 0.1234 0.0190 Inf 0.0862 0.1606
Z 0.1561 0.0190 Inf 0.1189 0.1932
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) Axis comparisons within each Block ---
Block = 1:
contrast estimate SE df z.ratio p.value
X - Y 0.000461 0.0114 Inf 0.040 0.9991
X - Z 0.002415 0.0114 Inf 0.212 0.9755
Y - Z 0.001954 0.0114 Inf 0.172 0.9839
Block = 2:
contrast estimate SE df z.ratio p.value
X - Y -0.007839 0.0107 Inf -0.731 0.7448
X - Z -0.050202 0.0107 Inf -4.684 <.0001
Y - Z -0.042363 0.0107 Inf -3.953 0.0002
Block = 3:
contrast estimate SE df z.ratio p.value
X - Y -0.011842 0.0107 Inf -1.110 0.5078
X - Z -0.071442 0.0107 Inf -6.697 <.0001
Y - Z -0.059600 0.0107 Inf -5.587 <.0001
Block = 4:
contrast estimate SE df z.ratio p.value
X - Y 0.005300 0.0108 Inf 0.492 0.8753
X - Z -0.034073 0.0108 Inf -3.160 0.0045
Y - Z -0.039374 0.0108 Inf -3.652 0.0008
Block = 5:
contrast estimate SE df z.ratio p.value
X - Y 0.005976 0.0100 Inf 0.598 0.8214
X - Z -0.026682 0.0100 Inf -2.668 0.0209
Y - Z -0.032658 0.0100 Inf -3.265 0.0031
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
==============================
AXES × BLOCKS — PHASE: Execution
==============================
--- Model Summary (lmerTest; Satterthwaite t-tests) ---
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ Axis * Block + Accuracy + (1 | subject) + (1 | Trial)
Data: df
REML criterion at convergence: 14946.6
Scaled residuals:
Min 1Q Median 3Q Max
-5.3930 -0.4971 -0.0242 0.4173 18.2710
Random effects:
Groups Name Variance Std.Dev.
Trial (Intercept) 0.008388 0.09159
subject (Intercept) 0.104887 0.32386
Residual 0.137640 0.37100
Number of obs: 17043, groups: Trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 8.538e-01 7.753e-02 1.803e+01 11.012 1.94e-09 ***
Axis1 -1.764e-01 4.039e-03 1.695e+04 -43.663 < 2e-16 ***
Axis2 -1.421e-01 4.039e-03 1.695e+04 -35.176 < 2e-16 ***
Block1 1.373e-01 6.195e-03 1.697e+04 22.171 < 2e-16 ***
Block2 4.779e-02 5.708e-03 1.697e+04 8.372 < 2e-16 ***
Block3 -1.081e-01 5.755e-03 1.699e+04 -18.783 < 2e-16 ***
Block4 4.479e-02 5.739e-03 1.697e+04 7.803 6.38e-15 ***
Accuracy1 -3.904e-02 3.055e-03 1.701e+04 -12.778 < 2e-16 ***
Axis1:Block1 -1.574e-02 8.590e-03 1.695e+04 -1.833 0.0669 .
Axis2:Block1 -2.024e-03 8.590e-03 1.695e+04 -0.236 0.8137
Axis1:Block2 -1.378e-02 8.051e-03 1.695e+04 -1.712 0.0869 .
Axis2:Block2 -3.281e-03 8.051e-03 1.695e+04 -0.408 0.6836
Axis1:Block3 1.601e-02 8.017e-03 1.695e+04 1.997 0.0459 *
Axis2:Block3 6.858e-03 8.017e-03 1.695e+04 0.855 0.3923
Axis1:Block4 -1.116e-02 8.070e-03 1.695e+04 -1.383 0.1667
Axis2:Block4 -1.934e-02 8.070e-03 1.695e+04 -2.397 0.0166 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 16 > 12.
Use print(summary(m), correlation=TRUE) or
vcov(summary(m)) if you need it
--- lmerTest ANOVA (F-tests; Satterthwaite) ---
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Axis 858.81 429.40 2 16955 3119.7640 < 2.2e-16 ***
Block 155.50 38.87 4 16972 282.4317 < 2.2e-16 ***
Accuracy 22.47 22.47 1 17005 163.2750 < 2.2e-16 ***
Axis:Block 7.21 0.90 8 16955 6.5458 1.476e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type II Wald χ² (car::Anova) ---
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
Axis 6223.795 2 < 2.2e-16 ***
Block 1129.727 4 < 2.2e-16 ***
Accuracy 163.275 1 < 2.2e-16 ***
Axis:Block 52.367 8 1.43e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
--- Type III Wald χ² (car::Anova; sum contrasts) ---
Analysis of Deviance Table (Type III Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
(Intercept) 121.272 1 < 2.2e-16 ***
Axis 6239.528 2 < 2.2e-16 ***
Block 1129.727 4 < 2.2e-16 ***
Accuracy 163.275 1 < 2.2e-16 ***
Axis:Block 52.367 8 1.43e-08 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1NOTE: Results may be misleading due to involvement in interactions
--- EMMs by Axis (averaged over Blocks) ---
Axis emmean SE df asymp.LCL asymp.UCL
X 0.677 0.0776 Inf 0.525 0.830
Y 0.712 0.0776 Inf 0.560 0.864
Z 1.172 0.0776 Inf 1.020 1.324
Results are averaged over the levels of: Block, Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) Axis comparisons (overall) ---
contrast estimate SE df z.ratio p.value
X - Y -0.0343 0.007 Inf -4.900 <.0001
X - Z -0.4948 0.007 Inf -70.726 <.0001
Y - Z -0.4605 0.007 Inf -65.826 <.0001
Results are averaged over the levels of: Block, Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates
--- EMMs by Axis within each Block ---
Block = 1:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.799 0.0784 Inf 0.645 0.953
Y 0.847 0.0784 Inf 0.693 1.001
Z 1.327 0.0784 Inf 1.174 1.481
Block = 2:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.711 0.0783 Inf 0.558 0.865
Y 0.756 0.0783 Inf 0.603 0.910
Z 1.237 0.0783 Inf 1.084 1.390
Block = 3:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.585 0.0783 Inf 0.432 0.739
Y 0.610 0.0783 Inf 0.457 0.764
Z 1.041 0.0783 Inf 0.888 1.195
Block = 4:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.711 0.0783 Inf 0.558 0.864
Y 0.737 0.0783 Inf 0.584 0.891
Z 1.248 0.0783 Inf 1.094 1.401
Block = 5:
Axis emmean SE df asymp.LCL asymp.UCL
X 0.580 0.0782 Inf 0.427 0.733
Y 0.608 0.0782 Inf 0.455 0.761
Z 1.008 0.0782 Inf 0.855 1.161
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
--- Pairwise (Tukey) Axis comparisons within each Block ---
Block = 1:
contrast estimate SE df z.ratio p.value
X - Y -0.0480 0.0170 Inf -2.832 0.0129
X - Z -0.5283 0.0170 Inf -31.165 <.0001
Y - Z -0.4803 0.0170 Inf -28.334 <.0001
Block = 2:
contrast estimate SE df z.ratio p.value
X - Y -0.0448 0.0156 Inf -2.875 0.0113
X - Z -0.5256 0.0156 Inf -33.752 <.0001
Y - Z -0.4809 0.0156 Inf -30.876 <.0001
Block = 3:
contrast estimate SE df z.ratio p.value
X - Y -0.0251 0.0155 Inf -1.623 0.2360
X - Z -0.4559 0.0155 Inf -29.442 <.0001
Y - Z -0.4308 0.0155 Inf -27.819 <.0001
Block = 4:
contrast estimate SE df z.ratio p.value
X - Y -0.0261 0.0156 Inf -1.671 0.2165
X - Z -0.5364 0.0156 Inf -34.339 <.0001
Y - Z -0.5103 0.0156 Inf -32.669 <.0001
Block = 5:
contrast estimate SE df z.ratio p.value
X - Y -0.0274 0.0145 Inf -1.891 0.1413
X - Z -0.4276 0.0145 Inf -29.523 <.0001
Y - Z -0.4003 0.0145 Inf -27.632 <.0001
Results are averaged over the levels of: Accuracy
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 3 estimates #B3.2.2 concatenation analysis with RT as fixed effect
# ---- 3.1b SETUP (robust, with key-type harmonization): ensure stepwise_all exists, import step-wise RT, join, and prepare subsets ----
suppressPackageStartupMessages({
library(dplyr); library(tidyr); library(lme4); library(lmerTest)
library(emmeans); library(car); library(rlang); library(ggplot2); library(patchwork)
})
emm_options(lmer.df = "asymptotic")
# Helper: pick first existing object name
.pick_first_existing <- function(cands) {
for (nm in cands) if (exists(nm, inherits = TRUE)) return(get(nm, inherits = TRUE))
NULL
}
# ---- A) Ensure stepwise_all is available (build if missing) ----
.ensure_stepwise_all <- function() {
if (exists("stepwise_all", inherits = TRUE)) {
return(get("stepwise_all", inherits = TRUE))
}
comp_fun <- if (exists("compute_stepwise_rms", inherits = TRUE)) get("compute_stepwise_rms", inherits = TRUE) else NULL
if (is.null(comp_fun)) stop("compute_stepwise_rms() is not available. Please run the earlier parts of the script.")
tagged_src <- .pick_first_existing(c("tagged_data","tagged_exec_df","tagged_exec","tagged_df"))
if (is.null(tagged_src)) stop("No tagged step data found (e.g., 'tagged_data'). Please run the earlier tagging steps.")
swa <- comp_fun(tagged_src, max_steps_keep = 18)
# Add Accuracy via your helper if present; else compute from all_data_mixed
if (exists("add_accuracy_to", inherits = TRUE)) {
add_acc <- get("add_accuracy_to", inherits = TRUE)
swa <- add_acc(swa, .pick_first_existing(c("all_data_mixed","all_data","adm")))
} else {
adm <- .pick_first_existing(c("all_data_mixed","all_data","adm"))
if (!is.null(adm) && all(c("subject","Block","trial") %in% names(adm)) && "feedback.ACC" %in% names(adm)) {
acc_df <- adm %>%
group_by(subject, Block, trial) %>%
summarise(Accuracy = as.factor(as.integer(all(feedback.ACC == 1, na.rm = TRUE))), .groups = "drop")
swa <- swa %>%
left_join(acc_df, by = c("subject","Block","trial"))
} else {
warning("Could not find add_accuracy_to() or a suitable all_data_mixed table; proceeding without Accuracy.")
if (!("Accuracy" %in% names(swa))) swa$Accuracy <- factor(NA)
}
}
# Standardise columns
swa <- swa %>%
mutate(
Block = if ("Block" %in% names(.)) as.integer(Block) else as.integer(session),
Step = as.integer(Step),
Axis = factor(as.character(Axis), levels = c("x","y","z")),
# ensure trial exists if possible
trial = if ("trial" %in% names(.)) trial else NA_integer_,
trial_id = if ("trial_id" %in% names(.)) trial_id else interaction(subject, Block, trial, drop = TRUE)
)
# Ensure step_count exists (actual length per trial)
if (!("step_count" %in% names(swa))) {
swa <- swa %>%
group_by(subject, Block, trial) %>%
mutate(step_count = suppressWarnings(max(Step, na.rm = TRUE))) %>%
ungroup()
}
assign("stepwise_all", swa, envir = .GlobalEnv)
swa
}
stepwise_all_local <- .ensure_stepwise_all()
# ---- B) Import step-wise RTs from Behaviour env (robust) ----
.standardize_rt_table <- function(tbl) {
nm <- names(tbl)
# subject
if ("Subject" %in% nm && !("subject" %in% nm)) tbl <- tbl %>% rename(subject = Subject)
stopifnot("subject" %in% names(tbl))
# Block / session
if (!("Block" %in% names(tbl))) {
if ("session" %in% names(tbl)) tbl <- tbl %>% mutate(Block = as.integer(session))
else if ("Session" %in% names(tbl)) tbl <- tbl %>% mutate(Block = as.integer(Session))
else stop("RT table lacks Block/session info.")
} else {
tbl <- tbl %>% mutate(Block = as.integer(Block))
}
# Step
if (!("Step" %in% names(tbl))) {
if ("step" %in% names(tbl)) tbl <- tbl %>% mutate(Step = as.integer(step))
else if ("sub.trial.number" %in% names(tbl)) tbl <- tbl %>% mutate(Step = as.integer(as.character(sub.trial.number)))
else stop("RT table lacks Step info (Step/step/sub.trial.number).")
} else {
tbl <- tbl %>% mutate(Step = as.integer(Step))
}
# Trial & trial_id
if (!("trial" %in% names(tbl))) tbl <- tbl %>% mutate(trial = NA_integer_)
if (!("trial_id" %in% names(tbl))) {
tbl <- tbl %>% mutate(trial_id = interaction(subject, Block, trial, drop = TRUE))
}
# RT column
if ("rt_ms" %in% names(tbl)) {
# ok
} else if ("rt" %in% names(tbl)) {
tbl <- tbl %>% rename(rt_ms = rt)
} else if ("RT" %in% names(tbl)) {
tbl <- tbl %>% rename(rt_ms = RT)
} else if ("feedback.RT" %in% names(tbl)) {
tbl <- tbl %>% rename(rt_ms = feedback.RT)
} else stop("RT column not found (rt_ms/rt/RT/feedback.RT).")
tbl %>% select(subject, Block, trial, trial_id, Step, rt_ms)
}
# Try ready-made step-wise RT object first
rt_ready <- .pick_first_existing(c(
"rt_step_all","rt_stepwise","stepwise_rt","rt_by_step","beh_rt_step",
"rt_steps","step_rt","RT_step"
))
if (!is.null(rt_ready)) {
rt_step_all <- .standardize_rt_table(rt_ready)
} else {
# Build from Behaviour base (df_acc_base / RTR common)
beh_src <- .pick_first_existing(c("df_acc_base","RTR","behaviour_df","beh_df","behaviour_base","behaviour_all"))
if (is.null(beh_src)) {
# Fall back to all_data_mixed if available
adm <- .pick_first_existing(c("all_data_mixed","all_data","adm"))
if (is.null(adm) || !all(c("subject","Block","trial","sub.trial.number","feedback.RT") %in% names(adm))) {
stop("Could not locate step-wise RTs. Please knit/run the Behaviour script first.")
}
rt_step_all <- adm %>%
group_by(subject, Block, trial, Step = as.integer(as.character(sub.trial.number))) %>%
summarise(rt_ms = mean(feedback.RT, na.rm = TRUE), .groups = "drop") %>%
mutate(trial_id = interaction(subject, Block, trial, drop = TRUE)) %>%
select(subject, Block, trial, trial_id, Step, rt_ms)
} else {
nm <- names(beh_src)
sess_col <- if ("session" %in% nm) "session" else if ("Session" %in% nm) "Session" else if ("Block" %in% nm) "Block" else NULL
if (is.null(sess_col) || !("sub.trial.number" %in% nm) || !("feedback.RT" %in% nm)) {
stop("Behaviour base is missing session/sub.trial.number/feedback.RT columns.")
}
if (!("trial" %in% nm)) {
beh_src <- beh_src %>%
group_by(.data[["subject"]], .data[[sess_col]]) %>%
mutate(trial = cumsum(as.integer(as.character(sub.trial.number)) == 1L)) %>%
ungroup()
}
rt_step_all <- beh_src %>%
group_by(
subject,
session = .data[[sess_col]],
trial,
Step = as.integer(as.character(sub.trial.number))
) %>%
summarise(rt_ms = mean(feedback.RT, na.rm = TRUE), .groups = "drop") %>%
mutate(
Block = as.integer(session),
trial_id = interaction(subject, Block, trial, drop = TRUE)
) %>%
select(subject, Block, trial, trial_id, Step, rt_ms)
}
}
# ---- C) Harmonize join key types on BOTH tables, then join ----
# Cast keys to common types: subject/ trial_id as character; Block/Step as integer
stepwise_all_local <- stepwise_all_local %>%
mutate(
subject = as.character(subject),
Block = as.integer(Block),
Step = as.integer(Step),
# rebuild a consistent string trial_id if trial exists
trial = if ("trial" %in% names(.)) as.integer(trial) else NA_integer_,
trial_id = if ("trial" %in% names(.)) paste(subject, Block, trial, sep = ".") else as.character(trial_id)
)
rt_step_all <- rt_step_all %>%
mutate(
subject = as.character(subject),
Block = as.integer(Block),
Step = as.integer(Step),
trial = if ("trial" %in% names(.)) as.integer(trial) else NA_integer_,
trial_id = if ("trial" %in% names(.)) paste(subject, Block, trial, sep = ".") else as.character(trial_id)
)
# Now the types align; perform the join
stepwise_all_rt <- stepwise_all_local %>%
inner_join(rt_step_all, by = c("subject","Block","trial_id","Step"))
message(sprintf("Matched %d/%d RMS step rows with RT.", nrow(stepwise_all_rt), nrow(stepwise_all_local)))Matched 136944/136944 RMS step rows with RT.# ---- D) Center RT within trial and split subsets ----
stepwise_all_rt <- stepwise_all_rt %>%
group_by(subject, Block, trial_id) %>%
mutate(
rt_bt = mean(rt_ms, na.rm = TRUE),
rt_wi = rt_ms - rt_bt,
rt_wi_sc = as.numeric(scale(rt_wi)),
rt_bt_sc = as.numeric(scale(rt_bt))
) %>%
ungroup()
# TRAINING subsets
stepwise_6_rt <- stepwise_all_rt %>% filter(Block == 1)
stepwise_12_rt <- stepwise_all_rt %>% filter(Block == 2)
stepwise_18_rt <- stepwise_all_rt %>% filter(Block == 3)
# TEST subsets by actual step_count
sw_b4_6_rt <- stepwise_all_rt %>% filter(Block == 4, step_count == 6)
sw_b4_12_rt <- stepwise_all_rt %>% filter(Block == 4, step_count == 12)
sw_b4_18_rt <- stepwise_all_rt %>% filter(Block == 4, step_count == 18)
sw_b5_6_rt <- stepwise_all_rt %>% filter(Block == 5, step_count == 6)
sw_b5_12_rt <- stepwise_all_rt %>% filter(Block == 5, step_count == 12)
sw_b5_18_rt <- stepwise_all_rt %>% filter(Block == 5, step_count == 18)# ---- 3.1b ANALYSIS (rt_ms): Training & Test LMMs using actual per-step RT ----
suppressPackageStartupMessages({
library(dplyr); library(lme4); library(lmerTest); library(emmeans); library(car)
})
emm_options(lmer.df = "asymptotic")
# TRAINING (Blocks 1–3): per-axis models
.report_step_block_rtms <- function(df_block, block_label) {
for (ax in c("x","y","z")) {
dd <- df_block %>% filter(Axis == ax)
if (nrow(dd) == 0) next
dd <- dd %>%
mutate(
StepF = factor(Step, levels = sort(unique(Step))),
subject = factor(subject),
trial_id = factor(trial_id),
Accuracy = droplevels(Accuracy)
)
cat("\n\n==============================\n",
"TRAINING (rt_ms) | Block ", block_label, " | Axis ", toupper(ax),
"\n==============================\n", sep = "")
# Primary model: actual per-step RT as covariate
m <- suppressWarnings(lmer(
RMS ~ StepF + Accuracy + rt_ms + (1|subject) + (1|trial_id),
data = dd, REML = TRUE
))
cat("\nType II Wald χ² (StepF, Accuracy, rt_ms):\n")
print(car::Anova(m, type = 2, test.statistic = "Chisq"))
if (nlevels(dd$Accuracy) >= 2) {
em <- emmeans(m, ~ StepF | Accuracy) # EMMs at mean(rt_ms)
cat("\nEMMs per step | Accuracy (adjusted at mean rt_ms):\n"); print(summary(em))
cat("\nAll-pairs (Tukey) among steps | Accuracy:\n")
print(pairs(em, adjust = "tukey"))
cat("\nAdjacent steps (consec; Holm) | Accuracy:\n")
print(contrast(em, method = "consec", by = "Accuracy", adjust = "holm"))
} else {
em <- emmeans(m, ~ StepF) # EMMs at mean(rt_ms)
cat("\nEMMs per step (adjusted at mean rt_ms):\n"); print(summary(em))
cat("\nAll-pairs (Tukey) among steps:\n")
print(pairs(em, adjust = "tukey"))
cat("\nAdjacent steps (consec; Holm):\n")
print(contrast(em, method = "consec", adjust = "holm"))
}
# Optional: allow subject-specific sensitivity to rt_ms (best-effort)
try({
m_rs <- lmer(
RMS ~ StepF + Accuracy + rt_ms + (1 + rt_ms | subject) + (1|trial_id),
data = dd, REML = TRUE
)
cat("\n[Optional] Random-slope model for rt_ms | subject (summary):\n")
print(summary(m_rs))
rm(m_rs)
}, silent = TRUE)
rm(m, em); invisible(gc())
}
}
# TEST (Blocks 4–5): per-length × axis
.report_step_test_rtms <- function(df_block, block_label, seq_label) {
for (ax in c("x","y","z")) {
dd <- df_block %>% filter(Axis == ax)
if (nrow(dd) == 0) next
dd <- dd %>%
mutate(
StepF = factor(Step, levels = sort(unique(Step))),
subject = factor(subject),
trial_id = factor(trial_id),
Accuracy = droplevels(Accuracy)
)
cat("\n\n==============================\n",
"TEST (rt_ms) | Block ", block_label, " | ", seq_label, " | Axis ", toupper(ax),
"\n==============================\n", sep = "")
m <- suppressWarnings(lmer(
RMS ~ StepF + Accuracy + rt_ms + (1|subject) + (1|trial_id),
data = dd, REML = TRUE
))
cat("\nType II Wald χ² (StepF, Accuracy, rt_ms):\n")
print(car::Anova(m, type = 2, test.statistic = "Chisq"))
if (nlevels(dd$Accuracy) >= 2) {
em <- emmeans(m, ~ StepF | Accuracy) # EMMs at mean(rt_ms)
cat("\nEMMs per step | Accuracy (adjusted at mean rt_ms):\n"); print(summary(em))
cat("\nAll-pairs (Tukey) among steps | Accuracy:\n")
print(pairs(em, adjust = "tukey"))
cat("\nAdjacent steps (consec; Holm) | Accuracy:\n")
print(contrast(em, method = "consec", by = "Accuracy", adjust = "holm"))
} else {
em <- emmeans(m, ~ StepF)
cat("\nEMMs per step (adjusted at mean rt_ms):\n"); print(summary(em))
cat("\nAll-pairs (Tukey) among steps:\n")
print(pairs(em, adjust = "tukey"))
cat("\nAdjacent steps (consec; Holm):\n")
print(contrast(em, method = "consec", adjust = "holm"))
}
rm(m, em); invisible(gc())
}
}
# ---- RUN: TRAINING (Blocks 1–3) ----
.report_step_block_rtms(stepwise_6_rt, "1 (6 steps)")
==============================
TRAINING (rt_ms) | Block 1 (6 steps) | Axis X
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 155.8383 5 < 2.2e-16 ***
Accuracy 6.0775 1 0.01369 *
rt_ms 21.7397 1 3.123e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.682 0.114 Inf 0.459 0.905
2 0.853 0.114 Inf 0.631 1.076
3 0.965 0.114 Inf 0.742 1.188
4 0.868 0.114 Inf 0.645 1.090
5 0.870 0.114 Inf 0.647 1.092
6 0.731 0.114 Inf 0.508 0.953
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.737 0.112 Inf 0.517 0.957
2 0.908 0.112 Inf 0.688 1.128
3 1.020 0.112 Inf 0.800 1.240
4 0.922 0.112 Inf 0.702 1.142
5 0.924 0.112 Inf 0.704 1.144
6 0.785 0.112 Inf 0.565 1.005
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.17130 0.0260 Inf -6.586 <.0001
StepF1 - StepF3 -0.28311 0.0260 Inf -10.874 <.0001
StepF1 - StepF4 -0.18577 0.0260 Inf -7.140 <.0001
StepF1 - StepF5 -0.18770 0.0260 Inf -7.213 <.0001
StepF1 - StepF6 -0.04865 0.0260 Inf -1.874 0.4186
StepF2 - StepF3 -0.11181 0.0258 Inf -4.329 0.0002
StepF2 - StepF4 -0.01447 0.0258 Inf -0.560 0.9935
StepF2 - StepF5 -0.01640 0.0259 Inf -0.634 0.9885
StepF2 - StepF6 0.12264 0.0261 Inf 4.708 <.0001
StepF3 - StepF4 0.09734 0.0258 Inf 3.769 0.0023
StepF3 - StepF5 0.09541 0.0259 Inf 3.689 0.0031
StepF3 - StepF6 0.23446 0.0261 Inf 8.993 <.0001
StepF4 - StepF5 -0.00193 0.0259 Inf -0.075 1.0000
StepF4 - StepF6 0.13711 0.0261 Inf 5.262 <.0001
StepF5 - StepF6 0.13905 0.0261 Inf 5.334 <.0001
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.17130 0.0260 Inf -6.586 <.0001
StepF1 - StepF3 -0.28311 0.0260 Inf -10.874 <.0001
StepF1 - StepF4 -0.18577 0.0260 Inf -7.140 <.0001
StepF1 - StepF5 -0.18770 0.0260 Inf -7.213 <.0001
StepF1 - StepF6 -0.04865 0.0260 Inf -1.874 0.4186
StepF2 - StepF3 -0.11181 0.0258 Inf -4.329 0.0002
StepF2 - StepF4 -0.01447 0.0258 Inf -0.560 0.9935
StepF2 - StepF5 -0.01640 0.0259 Inf -0.634 0.9885
StepF2 - StepF6 0.12264 0.0261 Inf 4.708 <.0001
StepF3 - StepF4 0.09734 0.0258 Inf 3.769 0.0023
StepF3 - StepF5 0.09541 0.0259 Inf 3.689 0.0031
StepF3 - StepF6 0.23446 0.0261 Inf 8.993 <.0001
StepF4 - StepF5 -0.00193 0.0259 Inf -0.075 1.0000
StepF4 - StepF6 0.13711 0.0261 Inf 5.262 <.0001
StepF5 - StepF6 0.13905 0.0261 Inf 5.334 <.0001
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.17130 0.0260 Inf 6.586 <.0001
StepF3 - StepF2 0.11181 0.0258 Inf 4.329 <.0001
StepF4 - StepF3 -0.09734 0.0258 Inf -3.769 0.0003
StepF5 - StepF4 0.00193 0.0259 Inf 0.075 0.9404
StepF6 - StepF5 -0.13905 0.0261 Inf -5.334 <.0001
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.17130 0.0260 Inf 6.586 <.0001
StepF3 - StepF2 0.11181 0.0258 Inf 4.329 <.0001
StepF4 - StepF3 -0.09734 0.0258 Inf -3.769 0.0003
StepF5 - StepF4 0.00193 0.0259 Inf 0.075 0.9404
StepF6 - StepF5 -0.13905 0.0261 Inf -5.334 <.0001
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests Warning: Some predictor variables are on very different scales: consider
rescalingWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
unable to evaluate scaled gradientWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge: degenerate Hessian with 1 negative eigenvaluesWarning: Some predictor variables are on very different scales: consider
rescalingWarning: Model failed to converge with 1 negative eigenvalue: -1.4e+03
[Optional] Random-slope model for rt_ms | subject (summary):
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ StepF + Accuracy + rt_ms + (1 + rt_ms | subject) + (1 |
trial_id)
Data: dd
REML criterion at convergence: 6633.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.0900 -0.5072 -0.1016 0.3598 7.8198
Random effects:
Groups Name Variance Std.Dev. Corr
trial_id (Intercept) 2.293e-05 0.0047886
subject (Intercept) 1.886e-01 0.4342422
rt_ms 1.176e-07 0.0003429 -0.50
Residual 2.483e-01 0.4982509
Number of obs: 4452, groups: trial_id, 745; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 8.772e-01 1.035e-01 3.029e+01 8.478 1.71e-09 ***
StepF1 -1.338e-01 1.733e-02 2.592e+03 -7.721 1.64e-14 ***
StepF2 2.062e-02 1.677e-02 4.411e+03 1.229 0.21901
StepF3 1.318e-01 1.680e-02 4.399e+03 7.845 5.40e-15 ***
StepF4 3.370e-02 1.685e-02 4.306e+03 2.000 0.04552 *
StepF5 4.500e-02 1.680e-02 4.414e+03 2.678 0.00743 **
Accuracy1 -2.706e-02 1.017e-02 4.383e+03 -2.660 0.00785 **
rt_ms -7.196e-05 8.414e-05 4.710e-01 -0.855 0.64749
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) StepF1 StepF2 StepF3 StepF4 StepF5 Accrc1
StepF1 0.026
StepF2 -0.009 -0.211
StepF3 -0.010 -0.214 -0.193
StepF4 -0.010 -0.214 -0.189 -0.183
StepF5 -0.006 -0.207 -0.191 -0.193 -0.198
Accuracy1 0.080 0.040 -0.018 -0.022 -0.023 -0.011
rt_ms -0.501 -0.068 0.024 0.017 0.021 0.018 -0.043
fit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
unable to evaluate scaled gradient
Model failed to converge: degenerate Hessian with 1 negative eigenvalues
==============================
TRAINING (rt_ms) | Block 1 (6 steps) | Axis Y
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 64.6238 5 1.341e-12 ***
Accuracy 4.9036 1 0.0268 *
rt_ms 21.7801 1 3.057e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.797 0.137 Inf 0.530 1.06
2 0.862 0.136 Inf 0.595 1.13
3 0.996 0.136 Inf 0.728 1.26
4 0.893 0.136 Inf 0.626 1.16
5 0.814 0.136 Inf 0.547 1.08
6 0.861 0.137 Inf 0.593 1.13
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.854 0.135 Inf 0.589 1.12
2 0.918 0.135 Inf 0.654 1.18
3 1.052 0.135 Inf 0.788 1.32
4 0.950 0.135 Inf 0.685 1.21
5 0.871 0.135 Inf 0.606 1.14
6 0.917 0.135 Inf 0.653 1.18
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.06480 0.0278 Inf -2.329 0.1823
StepF1 - StepF3 -0.19854 0.0278 Inf -7.130 <.0001
StepF1 - StepF4 -0.09612 0.0278 Inf -3.454 0.0073
StepF1 - StepF5 -0.01702 0.0278 Inf -0.612 0.9902
StepF1 - StepF6 -0.06356 0.0278 Inf -2.289 0.1986
StepF2 - StepF3 -0.13374 0.0276 Inf -4.842 <.0001
StepF2 - StepF4 -0.03132 0.0276 Inf -1.134 0.8673
StepF2 - StepF5 0.04778 0.0277 Inf 1.727 0.5135
StepF2 - StepF6 0.00124 0.0279 Inf 0.044 1.0000
StepF3 - StepF4 0.10242 0.0276 Inf 3.708 0.0029
StepF3 - StepF5 0.18152 0.0277 Inf 6.562 <.0001
StepF3 - StepF6 0.13498 0.0279 Inf 4.840 <.0001
StepF4 - StepF5 0.07910 0.0277 Inf 2.860 0.0486
StepF4 - StepF6 0.03256 0.0279 Inf 1.168 0.8521
StepF5 - StepF6 -0.04654 0.0279 Inf -1.669 0.5523
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.06480 0.0278 Inf -2.329 0.1823
StepF1 - StepF3 -0.19854 0.0278 Inf -7.130 <.0001
StepF1 - StepF4 -0.09612 0.0278 Inf -3.454 0.0073
StepF1 - StepF5 -0.01702 0.0278 Inf -0.612 0.9902
StepF1 - StepF6 -0.06356 0.0278 Inf -2.289 0.1986
StepF2 - StepF3 -0.13374 0.0276 Inf -4.842 <.0001
StepF2 - StepF4 -0.03132 0.0276 Inf -1.134 0.8673
StepF2 - StepF5 0.04778 0.0277 Inf 1.727 0.5135
StepF2 - StepF6 0.00124 0.0279 Inf 0.044 1.0000
StepF3 - StepF4 0.10242 0.0276 Inf 3.708 0.0029
StepF3 - StepF5 0.18152 0.0277 Inf 6.562 <.0001
StepF3 - StepF6 0.13498 0.0279 Inf 4.840 <.0001
StepF4 - StepF5 0.07910 0.0277 Inf 2.860 0.0486
StepF4 - StepF6 0.03256 0.0279 Inf 1.168 0.8521
StepF5 - StepF6 -0.04654 0.0279 Inf -1.669 0.5523
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.0648 0.0278 Inf 2.329 0.0397
StepF3 - StepF2 0.1337 0.0276 Inf 4.842 <.0001
StepF4 - StepF3 -0.1024 0.0276 Inf -3.708 0.0008
StepF5 - StepF4 -0.0791 0.0277 Inf -2.860 0.0127
StepF6 - StepF5 0.0465 0.0279 Inf 1.669 0.0951
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.0648 0.0278 Inf 2.329 0.0397
StepF3 - StepF2 0.1337 0.0276 Inf 4.842 <.0001
StepF4 - StepF3 -0.1024 0.0276 Inf -3.708 0.0008
StepF5 - StepF4 -0.0791 0.0277 Inf -2.860 0.0127
StepF6 - StepF5 0.0465 0.0279 Inf 1.669 0.0951
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests Warning: Some predictor variables are on very different scales: consider
rescalingWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 4.90647 (tol = 0.002, component 1)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?Warning: Some predictor variables are on very different scales: consider
rescaling
[Optional] Random-slope model for rt_ms | subject (summary):
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ StepF + Accuracy + rt_ms + (1 + rt_ms | subject) + (1 |
trial_id)
Data: dd
REML criterion at convergence: 7388.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.3021 -0.4451 -0.1015 0.3093 7.6119
Random effects:
Groups Name Variance Std.Dev. Corr
trial_id (Intercept) 9.901e-03 0.0995014
subject (Intercept) 3.467e-01 0.5888375
rt_ms 7.588e-08 0.0002755 -0.57
Residual 2.855e-01 0.5343589
Number of obs: 4452, groups: trial_id, 745; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 9.292e-01 1.398e-01 1.977e+01 6.646 1.91e-06 ***
StepF1 -6.583e-02 1.855e-02 3.327e+03 -3.549 0.000391 ***
StepF2 -1.212e-02 1.798e-02 3.331e+03 -0.674 0.500314
StepF3 1.260e-01 1.802e-02 3.339e+03 6.994 3.21e-12 ***
StepF4 2.139e-02 1.805e-02 3.353e+03 1.185 0.236089
StepF5 -5.468e-02 1.801e-02 3.338e+03 -3.036 0.002417 **
Accuracy1 -3.114e-02 1.193e-02 6.583e+02 -2.609 0.009276 **
rt_ms -9.905e-05 6.925e-05 7.440e+00 -1.430 0.193231
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) StepF1 StepF2 StepF3 StepF4 StepF5 Accrc1
StepF1 0.020
StepF2 -0.007 -0.210
StepF3 -0.008 -0.214 -0.192
StepF4 -0.008 -0.214 -0.189 -0.184
StepF5 -0.005 -0.207 -0.192 -0.193 -0.197
Accuracy1 0.067 0.039 -0.017 -0.021 -0.022 -0.010
rt_ms -0.554 -0.083 0.029 0.023 0.028 0.021 -0.053
fit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 4.90647 (tol = 0.002, component 1)
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?
Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
==============================
TRAINING (rt_ms) | Block 1 (6 steps) | Axis Z
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 95.1847 5 < 2.2e-16 ***
Accuracy 4.5466 1 0.03298 *
rt_ms 19.1941 1 1.181e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.47 0.212 Inf 1.06 1.89
2 1.72 0.212 Inf 1.31 2.14
3 1.85 0.212 Inf 1.43 2.26
4 1.73 0.212 Inf 1.32 2.15
5 1.80 0.212 Inf 1.39 2.22
6 1.53 0.212 Inf 1.12 1.95
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.58 0.208 Inf 1.17 1.99
2 1.83 0.208 Inf 1.42 2.23
3 1.95 0.208 Inf 1.55 2.36
4 1.84 0.208 Inf 1.43 2.25
5 1.91 0.208 Inf 1.50 2.32
6 1.64 0.208 Inf 1.23 2.04
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.2484 0.0483 Inf -5.144 <.0001
StepF1 - StepF3 -0.3755 0.0483 Inf -7.767 <.0001
StepF1 - StepF4 -0.2602 0.0483 Inf -5.387 <.0001
StepF1 - StepF5 -0.3299 0.0483 Inf -6.828 <.0001
StepF1 - StepF6 -0.0578 0.0482 Inf -1.198 0.8380
StepF2 - StepF3 -0.1271 0.0479 Inf -2.651 0.0854
StepF2 - StepF4 -0.0118 0.0479 Inf -0.247 0.9999
StepF2 - StepF5 -0.0815 0.0480 Inf -1.698 0.5330
StepF2 - StepF6 0.1906 0.0484 Inf 3.941 0.0011
StepF3 - StepF4 0.1152 0.0479 Inf 2.404 0.1546
StepF3 - StepF5 0.0456 0.0480 Inf 0.949 0.9336
StepF3 - StepF6 0.3177 0.0484 Inf 6.563 <.0001
StepF4 - StepF5 -0.0697 0.0480 Inf -1.452 0.6952
StepF4 - StepF6 0.2025 0.0484 Inf 4.185 0.0004
StepF5 - StepF6 0.2722 0.0484 Inf 5.623 <.0001
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.2484 0.0483 Inf -5.144 <.0001
StepF1 - StepF3 -0.3755 0.0483 Inf -7.767 <.0001
StepF1 - StepF4 -0.2602 0.0483 Inf -5.387 <.0001
StepF1 - StepF5 -0.3299 0.0483 Inf -6.828 <.0001
StepF1 - StepF6 -0.0578 0.0482 Inf -1.198 0.8380
StepF2 - StepF3 -0.1271 0.0479 Inf -2.651 0.0854
StepF2 - StepF4 -0.0118 0.0479 Inf -0.247 0.9999
StepF2 - StepF5 -0.0815 0.0480 Inf -1.698 0.5330
StepF2 - StepF6 0.1906 0.0484 Inf 3.941 0.0011
StepF3 - StepF4 0.1152 0.0479 Inf 2.404 0.1546
StepF3 - StepF5 0.0456 0.0480 Inf 0.949 0.9336
StepF3 - StepF6 0.3177 0.0484 Inf 6.563 <.0001
StepF4 - StepF5 -0.0697 0.0480 Inf -1.452 0.6952
StepF4 - StepF6 0.2025 0.0484 Inf 4.185 0.0004
StepF5 - StepF6 0.2722 0.0484 Inf 5.623 <.0001
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2484 0.0483 Inf 5.144 <.0001
StepF3 - StepF2 0.1271 0.0479 Inf 2.651 0.0241
StepF4 - StepF3 -0.1152 0.0479 Inf -2.404 0.0324
StepF5 - StepF4 0.0697 0.0480 Inf 1.452 0.1466
StepF6 - StepF5 -0.2722 0.0484 Inf -5.623 <.0001
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2484 0.0483 Inf 5.144 <.0001
StepF3 - StepF2 0.1271 0.0479 Inf 2.651 0.0241
StepF4 - StepF3 -0.1152 0.0479 Inf -2.404 0.0324
StepF5 - StepF4 0.0697 0.0480 Inf 1.452 0.1466
StepF6 - StepF5 -0.2722 0.0484 Inf -5.623 <.0001
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests Warning: Some predictor variables are on very different scales: consider
rescalingWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 7.92992 (tol = 0.002, component 1)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?Warning: Some predictor variables are on very different scales: consider
rescaling
[Optional] Random-slope model for rt_ms | subject (summary):
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ StepF + Accuracy + rt_ms + (1 + rt_ms | subject) + (1 |
trial_id)
Data: dd
REML criterion at convergence: 12430
Scaled residuals:
Min 1Q Median 3Q Max
-3.6749 -0.4953 -0.1005 0.3735 10.3115
Random effects:
Groups Name Variance Std.Dev. Corr
trial_id (Intercept) 1.331e-01 0.3647932
subject (Intercept) 1.404e+00 1.1849316
rt_ms 1.297e-07 0.0003601 -0.79
Residual 8.217e-01 0.9064855
Number of obs: 4452, groups: trial_id, 745; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.774e+00 2.812e-01 7.912e+00 6.311 0.000241 ***
StepF1 -2.137e-01 3.133e-02 3.698e+03 -6.820 1.06e-11 ***
StepF2 3.163e-02 3.049e-02 3.769e+03 1.037 0.299589
StepF3 1.659e-01 3.056e-02 3.774e+03 5.428 6.07e-08 ***
StepF4 4.766e-02 3.057e-02 3.773e+03 1.559 0.119047
StepF5 1.223e-01 3.052e-02 3.769e+03 4.007 6.26e-05 ***
Accuracy1 -5.398e-02 2.561e-02 6.980e+02 -2.108 0.035395 *
rt_ms -1.012e-04 9.272e-05 8.863e+00 -1.091 0.304004
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) StepF1 StepF2 StepF3 StepF4 StepF5 Accrc1
StepF1 0.016
StepF2 -0.006 -0.209
StepF3 -0.007 -0.215 -0.191
StepF4 -0.006 -0.213 -0.189 -0.185
StepF5 -0.004 -0.208 -0.193 -0.192 -0.195
Accuracy1 0.067 0.033 -0.014 -0.018 -0.018 -0.009
rt_ms -0.746 -0.091 0.031 0.032 0.034 0.023 -0.056
fit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 7.92992 (tol = 0.002, component 1)
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?
Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?.report_step_block_rtms(stepwise_12_rt, "2 (12 steps)")
==============================
TRAINING (rt_ms) | Block 2 (12 steps) | Axis X
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 202.4649 11 < 2.2e-16 ***
Accuracy 1.3551 1 0.2444
rt_ms 23.7765 1 1.082e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.641 0.0717 Inf 0.501 0.782
2 0.820 0.0715 Inf 0.679 0.960
3 0.797 0.0715 Inf 0.656 0.937
4 0.686 0.0715 Inf 0.545 0.826
5 0.697 0.0715 Inf 0.557 0.837
6 0.700 0.0715 Inf 0.560 0.841
7 0.637 0.0715 Inf 0.497 0.777
8 0.633 0.0715 Inf 0.493 0.773
9 0.651 0.0715 Inf 0.511 0.792
10 0.661 0.0715 Inf 0.521 0.801
11 0.613 0.0715 Inf 0.473 0.753
12 0.549 0.0716 Inf 0.409 0.690
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.668 0.0706 Inf 0.530 0.806
2 0.846 0.0706 Inf 0.708 0.985
3 0.823 0.0706 Inf 0.685 0.962
4 0.712 0.0706 Inf 0.574 0.851
5 0.724 0.0705 Inf 0.586 0.862
6 0.727 0.0706 Inf 0.589 0.865
7 0.664 0.0705 Inf 0.525 0.802
8 0.660 0.0705 Inf 0.522 0.798
9 0.678 0.0705 Inf 0.540 0.816
10 0.687 0.0706 Inf 0.549 0.826
11 0.640 0.0706 Inf 0.501 0.778
12 0.576 0.0706 Inf 0.438 0.714
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.17832 0.0249 Inf -7.153 <.0001
StepF1 - StepF3 -0.15532 0.0250 Inf -6.211 <.0001
StepF1 - StepF4 -0.04444 0.0250 Inf -1.774 0.8322
StepF1 - StepF5 -0.05605 0.0248 Inf -2.263 0.5031
StepF1 - StepF6 -0.05913 0.0249 Inf -2.375 0.4230
StepF1 - StepF7 0.00421 0.0248 Inf 0.170 1.0000
StepF1 - StepF8 0.00792 0.0248 Inf 0.320 1.0000
StepF1 - StepF9 -0.01019 0.0248 Inf -0.411 1.0000
StepF1 - StepF10 -0.01949 0.0248 Inf -0.784 0.9998
StepF1 - StepF11 0.02823 0.0249 Inf 1.134 0.9932
StepF1 - StepF12 0.09193 0.0248 Inf 3.712 0.0111
StepF2 - StepF3 0.02300 0.0245 Inf 0.937 0.9987
StepF2 - StepF4 0.13388 0.0245 Inf 5.455 <.0001
StepF2 - StepF5 0.12227 0.0246 Inf 4.979 <.0001
StepF2 - StepF6 0.11920 0.0245 Inf 4.858 0.0001
StepF2 - StepF7 0.18254 0.0246 Inf 7.432 <.0001
StepF2 - StepF8 0.18625 0.0246 Inf 7.586 <.0001
StepF2 - StepF9 0.16814 0.0246 Inf 6.848 <.0001
StepF2 - StepF10 0.15884 0.0245 Inf 6.472 <.0001
StepF2 - StepF11 0.20655 0.0245 Inf 8.418 <.0001
StepF2 - StepF12 0.27025 0.0246 Inf 10.988 <.0001
StepF3 - StepF4 0.11088 0.0245 Inf 4.519 0.0004
StepF3 - StepF5 0.09927 0.0246 Inf 4.038 0.0031
StepF3 - StepF6 0.09620 0.0245 Inf 3.919 0.0051
StepF3 - StepF7 0.15954 0.0246 Inf 6.489 <.0001
StepF3 - StepF8 0.16325 0.0246 Inf 6.644 <.0001
StepF3 - StepF9 0.14514 0.0246 Inf 5.906 <.0001
StepF3 - StepF10 0.13584 0.0246 Inf 5.532 <.0001
StepF3 - StepF11 0.18355 0.0245 Inf 7.478 <.0001
StepF3 - StepF12 0.24725 0.0246 Inf 10.042 <.0001
StepF4 - StepF5 -0.01161 0.0246 Inf -0.472 1.0000
StepF4 - StepF6 -0.01469 0.0246 Inf -0.598 1.0000
StepF4 - StepF7 0.04865 0.0246 Inf 1.978 0.7087
StepF4 - StepF8 0.05236 0.0246 Inf 2.130 0.6007
StepF4 - StepF9 0.03425 0.0246 Inf 1.393 0.9651
StepF4 - StepF10 0.02495 0.0246 Inf 1.016 0.9974
StepF4 - StepF11 0.07267 0.0246 Inf 2.960 0.1205
StepF4 - StepF12 0.13637 0.0246 Inf 5.536 <.0001
StepF5 - StepF6 -0.00308 0.0246 Inf -0.125 1.0000
StepF5 - StepF7 0.06026 0.0245 Inf 2.456 0.3681
StepF5 - StepF8 0.06398 0.0245 Inf 2.607 0.2753
StepF5 - StepF9 0.04587 0.0245 Inf 1.869 0.7786
StepF5 - StepF10 0.03657 0.0245 Inf 1.490 0.9437
StepF5 - StepF11 0.08428 0.0246 Inf 3.433 0.0295
StepF5 - StepF12 0.14798 0.0246 Inf 6.024 <.0001
StepF6 - StepF7 0.06334 0.0246 Inf 2.580 0.2911
StepF6 - StepF8 0.06705 0.0245 Inf 2.732 0.2105
StepF6 - StepF9 0.04894 0.0245 Inf 1.994 0.6979
StepF6 - StepF10 0.03964 0.0245 Inf 1.615 0.9039
StepF6 - StepF11 0.08736 0.0245 Inf 3.560 0.0191
StepF6 - StepF12 0.15106 0.0246 Inf 6.144 <.0001
StepF7 - StepF8 0.00371 0.0245 Inf 0.151 1.0000
StepF7 - StepF9 -0.01440 0.0245 Inf -0.587 1.0000
StepF7 - StepF10 -0.02370 0.0245 Inf -0.966 0.9984
StepF7 - StepF11 0.02402 0.0246 Inf 0.978 0.9981
StepF7 - StepF12 0.08772 0.0246 Inf 3.571 0.0184
StepF8 - StepF9 -0.01811 0.0245 Inf -0.738 0.9999
StepF8 - StepF10 -0.02741 0.0245 Inf -1.117 0.9940
StepF8 - StepF11 0.02031 0.0245 Inf 0.827 0.9996
StepF8 - StepF12 0.08401 0.0246 Inf 3.420 0.0308
StepF9 - StepF10 -0.00930 0.0245 Inf -0.379 1.0000
StepF9 - StepF11 0.03842 0.0245 Inf 1.565 0.9216
StepF9 - StepF12 0.10212 0.0246 Inf 4.157 0.0019
StepF10 - StepF11 0.04772 0.0245 Inf 1.945 0.7310
StepF10 - StepF12 0.11142 0.0246 Inf 4.534 0.0004
StepF11 - StepF12 0.06370 0.0246 Inf 2.591 0.2846
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.17832 0.0249 Inf -7.153 <.0001
StepF1 - StepF3 -0.15532 0.0250 Inf -6.211 <.0001
StepF1 - StepF4 -0.04444 0.0250 Inf -1.774 0.8322
StepF1 - StepF5 -0.05605 0.0248 Inf -2.263 0.5031
StepF1 - StepF6 -0.05913 0.0249 Inf -2.375 0.4230
StepF1 - StepF7 0.00421 0.0248 Inf 0.170 1.0000
StepF1 - StepF8 0.00792 0.0248 Inf 0.320 1.0000
StepF1 - StepF9 -0.01019 0.0248 Inf -0.411 1.0000
StepF1 - StepF10 -0.01949 0.0248 Inf -0.784 0.9998
StepF1 - StepF11 0.02823 0.0249 Inf 1.134 0.9932
StepF1 - StepF12 0.09193 0.0248 Inf 3.712 0.0111
StepF2 - StepF3 0.02300 0.0245 Inf 0.937 0.9987
StepF2 - StepF4 0.13388 0.0245 Inf 5.455 <.0001
StepF2 - StepF5 0.12227 0.0246 Inf 4.979 <.0001
StepF2 - StepF6 0.11920 0.0245 Inf 4.858 0.0001
StepF2 - StepF7 0.18254 0.0246 Inf 7.432 <.0001
StepF2 - StepF8 0.18625 0.0246 Inf 7.586 <.0001
StepF2 - StepF9 0.16814 0.0246 Inf 6.848 <.0001
StepF2 - StepF10 0.15884 0.0245 Inf 6.472 <.0001
StepF2 - StepF11 0.20655 0.0245 Inf 8.418 <.0001
StepF2 - StepF12 0.27025 0.0246 Inf 10.988 <.0001
StepF3 - StepF4 0.11088 0.0245 Inf 4.519 0.0004
StepF3 - StepF5 0.09927 0.0246 Inf 4.038 0.0031
StepF3 - StepF6 0.09620 0.0245 Inf 3.919 0.0051
StepF3 - StepF7 0.15954 0.0246 Inf 6.489 <.0001
StepF3 - StepF8 0.16325 0.0246 Inf 6.644 <.0001
StepF3 - StepF9 0.14514 0.0246 Inf 5.906 <.0001
StepF3 - StepF10 0.13584 0.0246 Inf 5.532 <.0001
StepF3 - StepF11 0.18355 0.0245 Inf 7.478 <.0001
StepF3 - StepF12 0.24725 0.0246 Inf 10.042 <.0001
StepF4 - StepF5 -0.01161 0.0246 Inf -0.472 1.0000
StepF4 - StepF6 -0.01469 0.0246 Inf -0.598 1.0000
StepF4 - StepF7 0.04865 0.0246 Inf 1.978 0.7087
StepF4 - StepF8 0.05236 0.0246 Inf 2.130 0.6007
StepF4 - StepF9 0.03425 0.0246 Inf 1.393 0.9651
StepF4 - StepF10 0.02495 0.0246 Inf 1.016 0.9974
StepF4 - StepF11 0.07267 0.0246 Inf 2.960 0.1205
StepF4 - StepF12 0.13637 0.0246 Inf 5.536 <.0001
StepF5 - StepF6 -0.00308 0.0246 Inf -0.125 1.0000
StepF5 - StepF7 0.06026 0.0245 Inf 2.456 0.3681
StepF5 - StepF8 0.06398 0.0245 Inf 2.607 0.2753
StepF5 - StepF9 0.04587 0.0245 Inf 1.869 0.7786
StepF5 - StepF10 0.03657 0.0245 Inf 1.490 0.9437
StepF5 - StepF11 0.08428 0.0246 Inf 3.433 0.0295
StepF5 - StepF12 0.14798 0.0246 Inf 6.024 <.0001
StepF6 - StepF7 0.06334 0.0246 Inf 2.580 0.2911
StepF6 - StepF8 0.06705 0.0245 Inf 2.732 0.2105
StepF6 - StepF9 0.04894 0.0245 Inf 1.994 0.6979
StepF6 - StepF10 0.03964 0.0245 Inf 1.615 0.9039
StepF6 - StepF11 0.08736 0.0245 Inf 3.560 0.0191
StepF6 - StepF12 0.15106 0.0246 Inf 6.144 <.0001
StepF7 - StepF8 0.00371 0.0245 Inf 0.151 1.0000
StepF7 - StepF9 -0.01440 0.0245 Inf -0.587 1.0000
StepF7 - StepF10 -0.02370 0.0245 Inf -0.966 0.9984
StepF7 - StepF11 0.02402 0.0246 Inf 0.978 0.9981
StepF7 - StepF12 0.08772 0.0246 Inf 3.571 0.0184
StepF8 - StepF9 -0.01811 0.0245 Inf -0.738 0.9999
StepF8 - StepF10 -0.02741 0.0245 Inf -1.117 0.9940
StepF8 - StepF11 0.02031 0.0245 Inf 0.827 0.9996
StepF8 - StepF12 0.08401 0.0246 Inf 3.420 0.0308
StepF9 - StepF10 -0.00930 0.0245 Inf -0.379 1.0000
StepF9 - StepF11 0.03842 0.0245 Inf 1.565 0.9216
StepF9 - StepF12 0.10212 0.0246 Inf 4.157 0.0019
StepF10 - StepF11 0.04772 0.0245 Inf 1.945 0.7310
StepF10 - StepF12 0.11142 0.0246 Inf 4.534 0.0004
StepF11 - StepF12 0.06370 0.0246 Inf 2.591 0.2846
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.17832 0.0249 Inf 7.153 <.0001
StepF3 - StepF2 -0.02300 0.0245 Inf -0.937 1.0000
StepF4 - StepF3 -0.11088 0.0245 Inf -4.519 0.0001
StepF5 - StepF4 0.01161 0.0246 Inf 0.472 1.0000
StepF6 - StepF5 0.00308 0.0246 Inf 0.125 1.0000
StepF7 - StepF6 -0.06334 0.0246 Inf -2.580 0.0861
StepF8 - StepF7 -0.00371 0.0245 Inf -0.151 1.0000
StepF9 - StepF8 0.01811 0.0245 Inf 0.738 1.0000
StepF10 - StepF9 0.00930 0.0245 Inf 0.379 1.0000
StepF11 - StepF10 -0.04772 0.0245 Inf -1.945 0.3628
StepF12 - StepF11 -0.06370 0.0246 Inf -2.591 0.0861
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.17832 0.0249 Inf 7.153 <.0001
StepF3 - StepF2 -0.02300 0.0245 Inf -0.937 1.0000
StepF4 - StepF3 -0.11088 0.0245 Inf -4.519 0.0001
StepF5 - StepF4 0.01161 0.0246 Inf 0.472 1.0000
StepF6 - StepF5 0.00308 0.0246 Inf 0.125 1.0000
StepF7 - StepF6 -0.06334 0.0246 Inf -2.580 0.0861
StepF8 - StepF7 -0.00371 0.0245 Inf -0.151 1.0000
StepF9 - StepF8 0.01811 0.0245 Inf 0.738 1.0000
StepF10 - StepF9 0.00930 0.0245 Inf 0.379 1.0000
StepF11 - StepF10 -0.04772 0.0245 Inf -1.945 0.3628
StepF12 - StepF11 -0.06370 0.0246 Inf -2.591 0.0861
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests Warning: Some predictor variables are on very different scales: consider
rescalingWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 25.8378 (tol = 0.002, component 1)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?Warning: Some predictor variables are on very different scales: consider
rescaling
[Optional] Random-slope model for rt_ms | subject (summary):
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ StepF + Accuracy + rt_ms + (1 + rt_ms | subject) + (1 |
trial_id)
Data: dd
REML criterion at convergence: 13994.3
Scaled residuals:
Min 1Q Median 3Q Max
-3.2682 -0.4862 -0.1224 0.3220 11.8592
Random effects:
Groups Name Variance Std.Dev. Corr
trial_id (Intercept) 9.590e-02 0.3096759
subject (Intercept) 2.627e-01 0.5125615
rt_ms 3.753e-08 0.0001937 -0.97
Residual 2.243e-01 0.4735623
Number of obs: 9249, groups: trial_id, 771; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 7.191e-01 1.218e-01 1.962e+00 5.904 0.028770 *
StepF1 -2.798e-02 1.685e-02 7.491e+03 -1.660 0.096902 .
StepF2 1.429e-01 1.638e-02 8.553e+03 8.723 < 2e-16 ***
StepF3 1.183e-01 1.640e-02 8.554e+03 7.216 5.81e-13 ***
StepF4 8.386e-03 1.641e-02 8.559e+03 0.511 0.609237
StepF5 2.604e-02 1.636e-02 8.544e+03 1.592 0.111525
StepF6 2.625e-02 1.638e-02 8.428e+03 1.603 0.109073
StepF7 -3.484e-02 1.637e-02 8.514e+03 -2.128 0.033357 *
StepF8 -4.389e-02 1.636e-02 8.532e+03 -2.683 0.007320 **
StepF9 -2.494e-02 1.636e-02 8.565e+03 -1.525 0.127346
StepF10 -1.331e-02 1.637e-02 8.517e+03 -0.813 0.416117
StepF11 -5.684e-02 1.636e-02 8.567e+03 -3.475 0.000514 ***
Accuracy1 -1.438e-02 1.353e-02 4.526e+02 -1.063 0.288343
rt_ms -8.814e-05 4.818e-05 2.901e+00 -1.829 0.167904
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 14 > 12.
Use print(summary(m_rs), correlation=TRUE) or
vcov(summary(m_rs)) if you need itfit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 25.8378 (tol = 0.002, component 1)
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?
Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
==============================
TRAINING (rt_ms) | Block 2 (12 steps) | Axis Y
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 127.5802 11 < 2.2e-16 ***
Accuracy 3.3668 1 0.066525 .
rt_ms 9.9847 1 0.001578 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.795 0.0850 Inf 0.628 0.961
2 0.770 0.0848 Inf 0.604 0.936
3 0.863 0.0848 Inf 0.697 1.030
4 0.762 0.0847 Inf 0.596 0.928
5 0.713 0.0848 Inf 0.547 0.879
6 0.725 0.0848 Inf 0.559 0.892
7 0.808 0.0848 Inf 0.642 0.974
8 0.681 0.0848 Inf 0.515 0.847
9 0.704 0.0848 Inf 0.538 0.870
10 0.719 0.0848 Inf 0.553 0.885
11 0.679 0.0848 Inf 0.513 0.845
12 0.608 0.0848 Inf 0.442 0.774
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.840 0.0839 Inf 0.675 1.004
2 0.815 0.0838 Inf 0.651 0.980
3 0.908 0.0839 Inf 0.744 1.073
4 0.807 0.0839 Inf 0.643 0.971
5 0.758 0.0838 Inf 0.594 0.922
6 0.770 0.0838 Inf 0.606 0.935
7 0.853 0.0838 Inf 0.688 1.017
8 0.726 0.0838 Inf 0.562 0.890
9 0.749 0.0838 Inf 0.584 0.913
10 0.764 0.0838 Inf 0.600 0.928
11 0.724 0.0838 Inf 0.559 0.888
12 0.653 0.0838 Inf 0.489 0.817
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.02441 0.0288 Inf 0.848 0.9995
StepF1 - StepF3 -0.06884 0.0289 Inf -2.383 0.4172
StepF1 - StepF4 0.03256 0.0289 Inf 1.126 0.9936
StepF1 - StepF5 0.08151 0.0286 Inf 2.850 0.1594
StepF1 - StepF6 0.06915 0.0288 Inf 2.405 0.4022
StepF1 - StepF7 -0.01313 0.0286 Inf -0.459 1.0000
StepF1 - StepF8 0.11374 0.0286 Inf 3.972 0.0041
StepF1 - StepF9 0.09081 0.0286 Inf 3.172 0.0667
StepF1 - StepF10 0.07549 0.0287 Inf 2.630 0.2624
StepF1 - StepF11 0.11581 0.0287 Inf 4.028 0.0033
StepF1 - StepF12 0.18653 0.0286 Inf 6.522 <.0001
StepF2 - StepF3 -0.09325 0.0283 Inf -3.290 0.0467
StepF2 - StepF4 0.00815 0.0283 Inf 0.288 1.0000
StepF2 - StepF5 0.05711 0.0284 Inf 2.013 0.6845
StepF2 - StepF6 0.04475 0.0283 Inf 1.579 0.9170
StepF2 - StepF7 -0.03753 0.0284 Inf -1.323 0.9763
StepF2 - StepF8 0.08933 0.0284 Inf 3.150 0.0712
StepF2 - StepF9 0.06640 0.0284 Inf 2.342 0.4467
StepF2 - StepF10 0.05108 0.0283 Inf 1.802 0.8173
StepF2 - StepF11 0.09140 0.0283 Inf 3.225 0.0570
StepF2 - StepF12 0.16212 0.0284 Inf 5.707 <.0001
StepF3 - StepF4 0.10140 0.0283 Inf 3.578 0.0180
StepF3 - StepF5 0.15035 0.0284 Inf 5.296 <.0001
StepF3 - StepF6 0.13800 0.0283 Inf 4.868 0.0001
StepF3 - StepF7 0.05572 0.0284 Inf 1.962 0.7193
StepF3 - StepF8 0.18258 0.0284 Inf 6.434 <.0001
StepF3 - StepF9 0.15965 0.0284 Inf 5.625 <.0001
StepF3 - StepF10 0.14433 0.0284 Inf 5.089 <.0001
StepF3 - StepF11 0.18465 0.0283 Inf 6.513 <.0001
StepF3 - StepF12 0.25537 0.0284 Inf 8.980 <.0001
StepF4 - StepF5 0.04895 0.0284 Inf 1.723 0.8578
StepF4 - StepF6 0.03659 0.0284 Inf 1.291 0.9805
StepF4 - StepF7 -0.04569 0.0284 Inf -1.608 0.9066
StepF4 - StepF8 0.08118 0.0284 Inf 2.859 0.1557
StepF4 - StepF9 0.05825 0.0284 Inf 2.052 0.6576
StepF4 - StepF10 0.04293 0.0284 Inf 1.513 0.9374
StepF4 - StepF11 0.08324 0.0284 Inf 2.936 0.1284
StepF4 - StepF12 0.15397 0.0285 Inf 5.412 <.0001
StepF5 - StepF6 -0.01236 0.0284 Inf -0.436 1.0000
StepF5 - StepF7 -0.09464 0.0283 Inf -3.339 0.0400
StepF5 - StepF8 0.03222 0.0283 Inf 1.137 0.9930
StepF5 - StepF9 0.00930 0.0283 Inf 0.328 1.0000
StepF5 - StepF10 -0.00602 0.0283 Inf -0.213 1.0000
StepF5 - StepF11 0.03429 0.0284 Inf 1.209 0.9884
StepF5 - StepF12 0.10502 0.0284 Inf 3.702 0.0116
StepF6 - StepF7 -0.08228 0.0284 Inf -2.901 0.1402
StepF6 - StepF8 0.04458 0.0284 Inf 1.573 0.9191
StepF6 - StepF9 0.02166 0.0284 Inf 0.764 0.9998
StepF6 - StepF10 0.00633 0.0283 Inf 0.223 1.0000
StepF6 - StepF11 0.04665 0.0283 Inf 1.646 0.8919
StepF6 - StepF12 0.11737 0.0284 Inf 4.133 0.0021
StepF7 - StepF8 0.12686 0.0283 Inf 4.476 0.0005
StepF7 - StepF9 0.10394 0.0283 Inf 3.667 0.0131
StepF7 - StepF10 0.08861 0.0283 Inf 3.126 0.0764
StepF7 - StepF11 0.12893 0.0284 Inf 4.546 0.0003
StepF7 - StepF12 0.19965 0.0284 Inf 7.037 <.0001
StepF8 - StepF9 -0.02293 0.0283 Inf -0.809 0.9997
StepF8 - StepF10 -0.03825 0.0283 Inf -1.349 0.9725
StepF8 - StepF11 0.00207 0.0283 Inf 0.073 1.0000
StepF8 - StepF12 0.07279 0.0284 Inf 2.565 0.2995
StepF9 - StepF10 -0.01532 0.0283 Inf -0.541 1.0000
StepF9 - StepF11 0.02500 0.0284 Inf 0.882 0.9993
StepF9 - StepF12 0.09572 0.0284 Inf 3.374 0.0358
StepF10 - StepF11 0.04032 0.0283 Inf 1.423 0.9594
StepF10 - StepF12 0.11104 0.0284 Inf 3.912 0.0052
StepF11 - StepF12 0.07072 0.0284 Inf 2.491 0.3457
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.02441 0.0288 Inf 0.848 0.9995
StepF1 - StepF3 -0.06884 0.0289 Inf -2.383 0.4172
StepF1 - StepF4 0.03256 0.0289 Inf 1.126 0.9936
StepF1 - StepF5 0.08151 0.0286 Inf 2.850 0.1594
StepF1 - StepF6 0.06915 0.0288 Inf 2.405 0.4022
StepF1 - StepF7 -0.01313 0.0286 Inf -0.459 1.0000
StepF1 - StepF8 0.11374 0.0286 Inf 3.972 0.0041
StepF1 - StepF9 0.09081 0.0286 Inf 3.172 0.0667
StepF1 - StepF10 0.07549 0.0287 Inf 2.630 0.2624
StepF1 - StepF11 0.11581 0.0287 Inf 4.028 0.0033
StepF1 - StepF12 0.18653 0.0286 Inf 6.522 <.0001
StepF2 - StepF3 -0.09325 0.0283 Inf -3.290 0.0467
StepF2 - StepF4 0.00815 0.0283 Inf 0.288 1.0000
StepF2 - StepF5 0.05711 0.0284 Inf 2.013 0.6845
StepF2 - StepF6 0.04475 0.0283 Inf 1.579 0.9170
StepF2 - StepF7 -0.03753 0.0284 Inf -1.323 0.9763
StepF2 - StepF8 0.08933 0.0284 Inf 3.150 0.0712
StepF2 - StepF9 0.06640 0.0284 Inf 2.342 0.4467
StepF2 - StepF10 0.05108 0.0283 Inf 1.802 0.8173
StepF2 - StepF11 0.09140 0.0283 Inf 3.225 0.0570
StepF2 - StepF12 0.16212 0.0284 Inf 5.707 <.0001
StepF3 - StepF4 0.10140 0.0283 Inf 3.578 0.0180
StepF3 - StepF5 0.15035 0.0284 Inf 5.296 <.0001
StepF3 - StepF6 0.13800 0.0283 Inf 4.868 0.0001
StepF3 - StepF7 0.05572 0.0284 Inf 1.962 0.7193
StepF3 - StepF8 0.18258 0.0284 Inf 6.434 <.0001
StepF3 - StepF9 0.15965 0.0284 Inf 5.625 <.0001
StepF3 - StepF10 0.14433 0.0284 Inf 5.089 <.0001
StepF3 - StepF11 0.18465 0.0283 Inf 6.513 <.0001
StepF3 - StepF12 0.25537 0.0284 Inf 8.980 <.0001
StepF4 - StepF5 0.04895 0.0284 Inf 1.723 0.8578
StepF4 - StepF6 0.03659 0.0284 Inf 1.291 0.9805
StepF4 - StepF7 -0.04569 0.0284 Inf -1.608 0.9066
StepF4 - StepF8 0.08118 0.0284 Inf 2.859 0.1557
StepF4 - StepF9 0.05825 0.0284 Inf 2.052 0.6576
StepF4 - StepF10 0.04293 0.0284 Inf 1.513 0.9374
StepF4 - StepF11 0.08324 0.0284 Inf 2.936 0.1284
StepF4 - StepF12 0.15397 0.0285 Inf 5.412 <.0001
StepF5 - StepF6 -0.01236 0.0284 Inf -0.436 1.0000
StepF5 - StepF7 -0.09464 0.0283 Inf -3.339 0.0400
StepF5 - StepF8 0.03222 0.0283 Inf 1.137 0.9930
StepF5 - StepF9 0.00930 0.0283 Inf 0.328 1.0000
StepF5 - StepF10 -0.00602 0.0283 Inf -0.213 1.0000
StepF5 - StepF11 0.03429 0.0284 Inf 1.209 0.9884
StepF5 - StepF12 0.10502 0.0284 Inf 3.702 0.0116
StepF6 - StepF7 -0.08228 0.0284 Inf -2.901 0.1402
StepF6 - StepF8 0.04458 0.0284 Inf 1.573 0.9191
StepF6 - StepF9 0.02166 0.0284 Inf 0.764 0.9998
StepF6 - StepF10 0.00633 0.0283 Inf 0.223 1.0000
StepF6 - StepF11 0.04665 0.0283 Inf 1.646 0.8919
StepF6 - StepF12 0.11737 0.0284 Inf 4.133 0.0021
StepF7 - StepF8 0.12686 0.0283 Inf 4.476 0.0005
StepF7 - StepF9 0.10394 0.0283 Inf 3.667 0.0131
StepF7 - StepF10 0.08861 0.0283 Inf 3.126 0.0764
StepF7 - StepF11 0.12893 0.0284 Inf 4.546 0.0003
StepF7 - StepF12 0.19965 0.0284 Inf 7.037 <.0001
StepF8 - StepF9 -0.02293 0.0283 Inf -0.809 0.9997
StepF8 - StepF10 -0.03825 0.0283 Inf -1.349 0.9725
StepF8 - StepF11 0.00207 0.0283 Inf 0.073 1.0000
StepF8 - StepF12 0.07279 0.0284 Inf 2.565 0.2995
StepF9 - StepF10 -0.01532 0.0283 Inf -0.541 1.0000
StepF9 - StepF11 0.02500 0.0284 Inf 0.882 0.9993
StepF9 - StepF12 0.09572 0.0284 Inf 3.374 0.0358
StepF10 - StepF11 0.04032 0.0283 Inf 1.423 0.9594
StepF10 - StepF12 0.11104 0.0284 Inf 3.912 0.0052
StepF11 - StepF12 0.07072 0.0284 Inf 2.491 0.3457
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.0244 0.0288 Inf -0.848 1.0000
StepF3 - StepF2 0.0932 0.0283 Inf 3.290 0.0090
StepF4 - StepF3 -0.1014 0.0283 Inf -3.578 0.0035
StepF5 - StepF4 -0.0490 0.0284 Inf -1.723 0.5089
StepF6 - StepF5 0.0124 0.0284 Inf 0.436 1.0000
StepF7 - StepF6 0.0823 0.0284 Inf 2.901 0.0297
StepF8 - StepF7 -0.1269 0.0283 Inf -4.476 0.0001
StepF9 - StepF8 0.0229 0.0283 Inf 0.809 1.0000
StepF10 - StepF9 0.0153 0.0283 Inf 0.541 1.0000
StepF11 - StepF10 -0.0403 0.0283 Inf -1.423 0.7744
StepF12 - StepF11 -0.0707 0.0284 Inf -2.491 0.0893
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.0244 0.0288 Inf -0.848 1.0000
StepF3 - StepF2 0.0932 0.0283 Inf 3.290 0.0090
StepF4 - StepF3 -0.1014 0.0283 Inf -3.578 0.0035
StepF5 - StepF4 -0.0490 0.0284 Inf -1.723 0.5089
StepF6 - StepF5 0.0124 0.0284 Inf 0.436 1.0000
StepF7 - StepF6 0.0823 0.0284 Inf 2.901 0.0297
StepF8 - StepF7 -0.1269 0.0283 Inf -4.476 0.0001
StepF9 - StepF8 0.0229 0.0283 Inf 0.809 1.0000
StepF10 - StepF9 0.0153 0.0283 Inf 0.541 1.0000
StepF11 - StepF10 -0.0403 0.0283 Inf -1.423 0.7744
StepF12 - StepF11 -0.0707 0.0284 Inf -2.491 0.0893
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests Warning: Some predictor variables are on very different scales: consider
rescalingWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
unable to evaluate scaled gradientWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge: degenerate Hessian with 1 negative eigenvaluesWarning: Some predictor variables are on very different scales: consider
rescalingWarning: Model failed to converge with 1 negative eigenvalue: -5.9e+00
[Optional] Random-slope model for rt_ms | subject (summary):
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ StepF + Accuracy + rt_ms + (1 + rt_ms | subject) + (1 |
trial_id)
Data: dd
REML criterion at convergence: 16514.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.4892 -0.4741 -0.1345 0.3114 21.0301
Random effects:
Groups Name Variance Std.Dev. Corr
trial_id (Intercept) 7.131e-02 0.2670403
subject (Intercept) 6.299e-01 0.7936873
rt_ms 5.677e-08 0.0002383 -0.83
Residual 3.054e-01 0.5525982
Number of obs: 9249, groups: trial_id, 771; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 7.873e-01 1.877e-01 8.557e+03 4.194 2.77e-05 ***
StepF1 5.954e-02 1.976e-02 8.440e+03 3.014 0.002588 **
StepF2 3.305e-02 1.913e-02 8.508e+03 1.727 0.084180 .
StepF3 1.224e-01 1.916e-02 8.511e+03 6.391 1.74e-10 ***
StepF4 2.154e-02 1.917e-02 8.511e+03 1.124 0.261092
StepF5 -2.396e-02 1.912e-02 8.507e+03 -1.254 0.210026
StepF6 -8.818e-03 1.917e-02 8.480e+03 -0.460 0.645492
StepF7 7.271e-02 1.914e-02 8.506e+03 3.800 0.000146 ***
StepF8 -5.865e-02 1.912e-02 8.500e+03 -3.067 0.002169 **
StepF9 -3.326e-02 1.910e-02 8.506e+03 -1.741 0.081678 .
StepF10 -1.192e-02 1.913e-02 8.506e+03 -0.623 0.533281
StepF11 -5.207e-02 1.911e-02 8.507e+03 -2.725 0.006436 **
Accuracy1 -2.237e-02 1.252e-02 7.270e+02 -1.786 0.074458 .
rt_ms -7.612e-05 5.937e-05 7.276e+00 -1.282 0.239149
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 14 > 12.
Use print(summary(m_rs), correlation=TRUE) or
vcov(summary(m_rs)) if you need itfit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
unable to evaluate scaled gradient
Model failed to converge: degenerate Hessian with 1 negative eigenvalues
==============================
TRAINING (rt_ms) | Block 2 (12 steps) | Axis Z
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 157.5865 11 < 2.2e-16 ***
Accuracy 4.1857 1 0.040767 *
rt_ms 7.6789 1 0.005587 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.39 0.141 Inf 1.110 1.66
2 1.61 0.141 Inf 1.338 1.89
3 1.58 0.141 Inf 1.306 1.86
4 1.46 0.141 Inf 1.181 1.73
5 1.48 0.141 Inf 1.203 1.75
6 1.35 0.141 Inf 1.071 1.62
7 1.41 0.141 Inf 1.138 1.69
8 1.33 0.141 Inf 1.051 1.60
9 1.33 0.141 Inf 1.056 1.61
10 1.35 0.141 Inf 1.071 1.62
11 1.31 0.141 Inf 1.035 1.59
12 1.12 0.141 Inf 0.849 1.40
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.49 0.139 Inf 1.217 1.76
2 1.72 0.138 Inf 1.444 1.99
3 1.68 0.139 Inf 1.412 1.96
4 1.56 0.139 Inf 1.287 1.83
5 1.58 0.138 Inf 1.309 1.85
6 1.45 0.138 Inf 1.177 1.72
7 1.52 0.138 Inf 1.244 1.79
8 1.43 0.138 Inf 1.158 1.70
9 1.43 0.138 Inf 1.163 1.71
10 1.45 0.138 Inf 1.177 1.72
11 1.41 0.138 Inf 1.142 1.68
12 1.23 0.138 Inf 0.955 1.50
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -2.27e-01 0.0494 Inf -4.595 0.0003
StepF1 - StepF3 -1.95e-01 0.0495 Inf -3.935 0.0048
StepF1 - StepF4 -6.98e-02 0.0496 Inf -1.407 0.9625
StepF1 - StepF5 -9.19e-02 0.0490 Inf -1.873 0.7760
StepF1 - StepF6 4.01e-02 0.0493 Inf 0.813 0.9997
StepF1 - StepF7 -2.69e-02 0.0490 Inf -0.548 1.0000
StepF1 - StepF8 5.99e-02 0.0491 Inf 1.220 0.9875
StepF1 - StepF9 5.48e-02 0.0491 Inf 1.116 0.9941
StepF1 - StepF10 4.01e-02 0.0492 Inf 0.815 0.9997
StepF1 - StepF11 7.57e-02 0.0493 Inf 1.536 0.9308
StepF1 - StepF12 2.62e-01 0.0490 Inf 5.346 <.0001
StepF2 - StepF3 3.20e-02 0.0486 Inf 0.657 1.0000
StepF2 - StepF4 1.57e-01 0.0486 Inf 3.231 0.0560
StepF2 - StepF5 1.35e-01 0.0486 Inf 2.775 0.1906
StepF2 - StepF6 2.67e-01 0.0486 Inf 5.493 <.0001
StepF2 - StepF7 2.00e-01 0.0486 Inf 4.112 0.0023
StepF2 - StepF8 2.87e-01 0.0486 Inf 5.898 <.0001
StepF2 - StepF9 2.82e-01 0.0486 Inf 5.792 <.0001
StepF2 - StepF10 2.67e-01 0.0486 Inf 5.493 <.0001
StepF2 - StepF11 3.03e-01 0.0486 Inf 6.227 <.0001
StepF2 - StepF12 4.89e-01 0.0487 Inf 10.040 <.0001
StepF3 - StepF4 1.25e-01 0.0486 Inf 2.574 0.2942
StepF3 - StepF5 1.03e-01 0.0487 Inf 2.116 0.6112
StepF3 - StepF6 2.35e-01 0.0486 Inf 4.834 0.0001
StepF3 - StepF7 1.68e-01 0.0487 Inf 3.451 0.0278
StepF3 - StepF8 2.55e-01 0.0487 Inf 5.237 <.0001
StepF3 - StepF9 2.50e-01 0.0487 Inf 5.131 <.0001
StepF3 - StepF10 2.35e-01 0.0486 Inf 4.833 0.0001
StepF3 - StepF11 2.71e-01 0.0486 Inf 5.567 <.0001
StepF3 - StepF12 4.57e-01 0.0488 Inf 9.374 <.0001
StepF4 - StepF5 -2.21e-02 0.0487 Inf -0.454 1.0000
StepF4 - StepF6 1.10e-01 0.0486 Inf 2.260 0.5057
StepF4 - StepF7 4.29e-02 0.0487 Inf 0.881 0.9993
StepF4 - StepF8 1.30e-01 0.0487 Inf 2.665 0.2441
StepF4 - StepF9 1.25e-01 0.0487 Inf 2.559 0.3034
StepF4 - StepF10 1.10e-01 0.0486 Inf 2.259 0.5061
StepF4 - StepF11 1.46e-01 0.0486 Inf 2.993 0.1104
StepF4 - StepF12 3.32e-01 0.0488 Inf 6.805 <.0001
StepF5 - StepF6 1.32e-01 0.0486 Inf 2.714 0.2191
StepF5 - StepF7 6.50e-02 0.0486 Inf 1.338 0.9742
StepF5 - StepF8 1.52e-01 0.0486 Inf 3.124 0.0768
StepF5 - StepF9 1.47e-01 0.0486 Inf 3.018 0.1032
StepF5 - StepF10 1.32e-01 0.0486 Inf 2.715 0.2184
StepF5 - StepF11 1.68e-01 0.0486 Inf 3.447 0.0281
StepF5 - StepF12 3.54e-01 0.0486 Inf 7.278 <.0001
StepF6 - StepF7 -6.69e-02 0.0486 Inf -1.376 0.9681
StepF6 - StepF8 1.98e-02 0.0486 Inf 0.408 1.0000
StepF6 - StepF9 1.47e-02 0.0486 Inf 0.303 1.0000
StepF6 - StepF10 2.19e-05 0.0486 Inf 0.000 1.0000
StepF6 - StepF11 3.57e-02 0.0486 Inf 0.734 0.9999
StepF6 - StepF12 2.22e-01 0.0487 Inf 4.562 0.0003
StepF7 - StepF8 8.68e-02 0.0486 Inf 1.786 0.8263
StepF7 - StepF9 8.16e-02 0.0486 Inf 1.680 0.8776
StepF7 - StepF10 6.70e-02 0.0486 Inf 1.377 0.9679
StepF7 - StepF11 1.03e-01 0.0486 Inf 2.110 0.6157
StepF7 - StepF12 2.89e-01 0.0486 Inf 5.942 <.0001
StepF8 - StepF9 -5.13e-03 0.0486 Inf -0.106 1.0000
StepF8 - StepF10 -1.98e-02 0.0486 Inf -0.408 1.0000
StepF8 - StepF11 1.58e-02 0.0486 Inf 0.325 1.0000
StepF8 - StepF12 2.02e-01 0.0486 Inf 4.157 0.0019
StepF9 - StepF10 -1.47e-02 0.0486 Inf -0.302 1.0000
StepF9 - StepF11 2.09e-02 0.0486 Inf 0.431 1.0000
StepF9 - StepF12 2.07e-01 0.0486 Inf 4.263 0.0012
StepF10 - StepF11 3.56e-02 0.0486 Inf 0.733 0.9999
StepF10 - StepF12 2.22e-01 0.0487 Inf 4.563 0.0003
StepF11 - StepF12 1.86e-01 0.0487 Inf 3.829 0.0072
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -2.27e-01 0.0494 Inf -4.595 0.0003
StepF1 - StepF3 -1.95e-01 0.0495 Inf -3.935 0.0048
StepF1 - StepF4 -6.98e-02 0.0496 Inf -1.407 0.9625
StepF1 - StepF5 -9.19e-02 0.0490 Inf -1.873 0.7760
StepF1 - StepF6 4.01e-02 0.0493 Inf 0.813 0.9997
StepF1 - StepF7 -2.69e-02 0.0490 Inf -0.548 1.0000
StepF1 - StepF8 5.99e-02 0.0491 Inf 1.220 0.9875
StepF1 - StepF9 5.48e-02 0.0491 Inf 1.116 0.9941
StepF1 - StepF10 4.01e-02 0.0492 Inf 0.815 0.9997
StepF1 - StepF11 7.57e-02 0.0493 Inf 1.536 0.9308
StepF1 - StepF12 2.62e-01 0.0490 Inf 5.346 <.0001
StepF2 - StepF3 3.20e-02 0.0486 Inf 0.657 1.0000
StepF2 - StepF4 1.57e-01 0.0486 Inf 3.231 0.0560
StepF2 - StepF5 1.35e-01 0.0486 Inf 2.775 0.1906
StepF2 - StepF6 2.67e-01 0.0486 Inf 5.493 <.0001
StepF2 - StepF7 2.00e-01 0.0486 Inf 4.112 0.0023
StepF2 - StepF8 2.87e-01 0.0486 Inf 5.898 <.0001
StepF2 - StepF9 2.82e-01 0.0486 Inf 5.792 <.0001
StepF2 - StepF10 2.67e-01 0.0486 Inf 5.493 <.0001
StepF2 - StepF11 3.03e-01 0.0486 Inf 6.227 <.0001
StepF2 - StepF12 4.89e-01 0.0487 Inf 10.040 <.0001
StepF3 - StepF4 1.25e-01 0.0486 Inf 2.574 0.2942
StepF3 - StepF5 1.03e-01 0.0487 Inf 2.116 0.6112
StepF3 - StepF6 2.35e-01 0.0486 Inf 4.834 0.0001
StepF3 - StepF7 1.68e-01 0.0487 Inf 3.451 0.0278
StepF3 - StepF8 2.55e-01 0.0487 Inf 5.237 <.0001
StepF3 - StepF9 2.50e-01 0.0487 Inf 5.131 <.0001
StepF3 - StepF10 2.35e-01 0.0486 Inf 4.833 0.0001
StepF3 - StepF11 2.71e-01 0.0486 Inf 5.567 <.0001
StepF3 - StepF12 4.57e-01 0.0488 Inf 9.374 <.0001
StepF4 - StepF5 -2.21e-02 0.0487 Inf -0.454 1.0000
StepF4 - StepF6 1.10e-01 0.0486 Inf 2.260 0.5057
StepF4 - StepF7 4.29e-02 0.0487 Inf 0.881 0.9993
StepF4 - StepF8 1.30e-01 0.0487 Inf 2.665 0.2441
StepF4 - StepF9 1.25e-01 0.0487 Inf 2.559 0.3034
StepF4 - StepF10 1.10e-01 0.0486 Inf 2.259 0.5061
StepF4 - StepF11 1.46e-01 0.0486 Inf 2.993 0.1104
StepF4 - StepF12 3.32e-01 0.0488 Inf 6.805 <.0001
StepF5 - StepF6 1.32e-01 0.0486 Inf 2.714 0.2191
StepF5 - StepF7 6.50e-02 0.0486 Inf 1.338 0.9742
StepF5 - StepF8 1.52e-01 0.0486 Inf 3.124 0.0768
StepF5 - StepF9 1.47e-01 0.0486 Inf 3.018 0.1032
StepF5 - StepF10 1.32e-01 0.0486 Inf 2.715 0.2184
StepF5 - StepF11 1.68e-01 0.0486 Inf 3.447 0.0281
StepF5 - StepF12 3.54e-01 0.0486 Inf 7.278 <.0001
StepF6 - StepF7 -6.69e-02 0.0486 Inf -1.376 0.9681
StepF6 - StepF8 1.98e-02 0.0486 Inf 0.408 1.0000
StepF6 - StepF9 1.47e-02 0.0486 Inf 0.303 1.0000
StepF6 - StepF10 2.19e-05 0.0486 Inf 0.000 1.0000
StepF6 - StepF11 3.57e-02 0.0486 Inf 0.734 0.9999
StepF6 - StepF12 2.22e-01 0.0487 Inf 4.562 0.0003
StepF7 - StepF8 8.68e-02 0.0486 Inf 1.786 0.8263
StepF7 - StepF9 8.16e-02 0.0486 Inf 1.680 0.8776
StepF7 - StepF10 6.70e-02 0.0486 Inf 1.377 0.9679
StepF7 - StepF11 1.03e-01 0.0486 Inf 2.110 0.6157
StepF7 - StepF12 2.89e-01 0.0486 Inf 5.942 <.0001
StepF8 - StepF9 -5.13e-03 0.0486 Inf -0.106 1.0000
StepF8 - StepF10 -1.98e-02 0.0486 Inf -0.408 1.0000
StepF8 - StepF11 1.58e-02 0.0486 Inf 0.325 1.0000
StepF8 - StepF12 2.02e-01 0.0486 Inf 4.157 0.0019
StepF9 - StepF10 -1.47e-02 0.0486 Inf -0.302 1.0000
StepF9 - StepF11 2.09e-02 0.0486 Inf 0.431 1.0000
StepF9 - StepF12 2.07e-01 0.0486 Inf 4.263 0.0012
StepF10 - StepF11 3.56e-02 0.0486 Inf 0.733 0.9999
StepF10 - StepF12 2.22e-01 0.0487 Inf 4.563 0.0003
StepF11 - StepF12 1.86e-01 0.0487 Inf 3.829 0.0072
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.22685 0.0494 Inf 4.595 <.0001
StepF3 - StepF2 -0.03195 0.0486 Inf -0.657 1.0000
StepF4 - StepF3 -0.12510 0.0486 Inf -2.574 0.0803
StepF5 - StepF4 0.02209 0.0487 Inf 0.454 1.0000
StepF6 - StepF5 -0.13195 0.0486 Inf -2.714 0.0598
StepF7 - StepF6 0.06693 0.0486 Inf 1.376 1.0000
StepF8 - StepF7 -0.08677 0.0486 Inf -1.786 0.5192
StepF9 - StepF8 0.00513 0.0486 Inf 0.106 1.0000
StepF10 - StepF9 0.01469 0.0486 Inf 0.302 1.0000
StepF11 - StepF10 -0.03563 0.0486 Inf -0.733 1.0000
StepF12 - StepF11 -0.18643 0.0487 Inf -3.829 0.0013
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.22685 0.0494 Inf 4.595 <.0001
StepF3 - StepF2 -0.03195 0.0486 Inf -0.657 1.0000
StepF4 - StepF3 -0.12510 0.0486 Inf -2.574 0.0803
StepF5 - StepF4 0.02209 0.0487 Inf 0.454 1.0000
StepF6 - StepF5 -0.13195 0.0486 Inf -2.714 0.0598
StepF7 - StepF6 0.06693 0.0486 Inf 1.376 1.0000
StepF8 - StepF7 -0.08677 0.0486 Inf -1.786 0.5192
StepF9 - StepF8 0.00513 0.0486 Inf 0.106 1.0000
StepF10 - StepF9 0.01469 0.0486 Inf 0.302 1.0000
StepF11 - StepF10 -0.03563 0.0486 Inf -0.733 1.0000
StepF12 - StepF11 -0.18643 0.0487 Inf -3.829 0.0013
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests Warning: Some predictor variables are on very different scales: consider
rescalingWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
unable to evaluate scaled gradientWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge: degenerate Hessian with 1 negative eigenvaluesWarning: Some predictor variables are on very different scales: consider
rescalingWarning: Model failed to converge with 1 negative eigenvalue: -6.4e+00
[Optional] Random-slope model for rt_ms | subject (summary):
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ StepF + Accuracy + rt_ms + (1 + rt_ms | subject) + (1 |
trial_id)
Data: dd
REML criterion at convergence: 26768.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.9363 -0.4888 -0.1076 0.3642 14.1517
Random effects:
Groups Name Variance Std.Dev. Corr
trial_id (Intercept) 3.969e-01 0.6300367
subject (Intercept) 2.720e+00 1.6492945
rt_ms 1.943e-07 0.0004408 -0.92
Residual 8.890e-01 0.9428564
Number of obs: 9249, groups: trial_id, 771; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.482e+00 3.900e-01 8.623e+03 3.800 0.000146 ***
StepF1 -6.105e-03 3.368e-02 8.320e+03 -0.181 0.856150
StepF2 2.186e-01 3.263e-02 8.558e+03 6.699 2.22e-11 ***
StepF3 1.812e-01 3.267e-02 8.559e+03 5.545 3.03e-08 ***
StepF4 6.003e-02 3.269e-02 8.560e+03 1.836 0.066340 .
StepF5 8.608e-02 3.260e-02 8.554e+03 2.640 0.008303 **
StepF6 -5.198e-02 3.267e-02 8.498e+03 -1.591 0.111651
StepF7 2.833e-02 3.263e-02 8.547e+03 0.868 0.385395
StepF8 -6.838e-02 3.261e-02 8.542e+03 -2.097 0.036021 *
StepF9 -6.429e-02 3.258e-02 8.558e+03 -1.973 0.048520 *
StepF10 -4.768e-02 3.262e-02 8.546e+03 -1.461 0.143943
StepF11 -7.445e-02 3.259e-02 8.559e+03 -2.284 0.022380 *
Accuracy1 -5.174e-02 2.753e-02 5.735e+02 -1.880 0.060654 .
rt_ms -1.040e-04 1.088e-04 7.159e+00 -0.956 0.370280
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 14 > 12.
Use print(summary(m_rs), correlation=TRUE) or
vcov(summary(m_rs)) if you need itfit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
unable to evaluate scaled gradient
Model failed to converge: degenerate Hessian with 1 negative eigenvalues.report_step_block_rtms(stepwise_18_rt, "3 (18 steps)")
==============================
TRAINING (rt_ms) | Block 3 (18 steps) | Axis X
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 92.4695 17 2.165e-12 ***
Accuracy 0.5914 1 0.4418734
rt_ms 11.2769 1 0.0007848 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.584 0.0494 Inf 0.488 0.681
2 0.608 0.0493 Inf 0.511 0.705
3 0.614 0.0493 Inf 0.517 0.711
4 0.583 0.0493 Inf 0.486 0.680
5 0.581 0.0493 Inf 0.485 0.678
6 0.597 0.0493 Inf 0.501 0.694
7 0.596 0.0493 Inf 0.499 0.692
8 0.566 0.0493 Inf 0.469 0.662
9 0.572 0.0493 Inf 0.475 0.668
10 0.571 0.0493 Inf 0.474 0.668
11 0.549 0.0493 Inf 0.453 0.646
12 0.501 0.0493 Inf 0.405 0.598
13 0.503 0.0494 Inf 0.406 0.600
14 0.554 0.0493 Inf 0.457 0.651
15 0.532 0.0493 Inf 0.435 0.629
16 0.537 0.0493 Inf 0.441 0.634
17 0.513 0.0493 Inf 0.416 0.609
18 0.525 0.0493 Inf 0.428 0.621
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.567 0.0491 Inf 0.471 0.664
2 0.591 0.0491 Inf 0.495 0.687
3 0.597 0.0491 Inf 0.501 0.693
4 0.566 0.0491 Inf 0.470 0.662
5 0.564 0.0491 Inf 0.468 0.660
6 0.580 0.0491 Inf 0.484 0.676
7 0.579 0.0491 Inf 0.483 0.675
8 0.549 0.0491 Inf 0.453 0.645
9 0.555 0.0491 Inf 0.458 0.651
10 0.554 0.0491 Inf 0.458 0.650
11 0.532 0.0491 Inf 0.436 0.629
12 0.484 0.0491 Inf 0.388 0.581
13 0.486 0.0491 Inf 0.390 0.582
14 0.537 0.0491 Inf 0.441 0.633
15 0.515 0.0491 Inf 0.419 0.611
16 0.520 0.0491 Inf 0.424 0.617
17 0.496 0.0491 Inf 0.399 0.592
18 0.508 0.0491 Inf 0.412 0.604
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.023631 0.0218 Inf -1.086 0.9998
StepF1 - StepF3 -0.029687 0.0218 Inf -1.362 0.9969
StepF1 - StepF4 0.001437 0.0218 Inf 0.066 1.0000
StepF1 - StepF5 0.003149 0.0217 Inf 0.145 1.0000
StepF1 - StepF6 -0.012797 0.0217 Inf -0.591 1.0000
StepF1 - StepF7 -0.011304 0.0217 Inf -0.521 1.0000
StepF1 - StepF8 0.018756 0.0217 Inf 0.865 1.0000
StepF1 - StepF9 0.012773 0.0216 Inf 0.591 1.0000
StepF1 - StepF10 0.013207 0.0216 Inf 0.611 1.0000
StepF1 - StepF11 0.034995 0.0217 Inf 1.616 0.9799
StepF1 - StepF12 0.083110 0.0217 Inf 3.831 0.0152
StepF1 - StepF13 0.081533 0.0216 Inf 3.783 0.0181
StepF1 - StepF14 0.030484 0.0216 Inf 1.409 0.9954
StepF1 - StepF15 0.052518 0.0216 Inf 2.426 0.5880
StepF1 - StepF16 0.047049 0.0217 Inf 2.166 0.7763
StepF1 - StepF17 0.071784 0.0217 Inf 3.309 0.0872
StepF1 - StepF18 0.059664 0.0216 Inf 2.757 0.3407
StepF2 - StepF3 -0.006056 0.0215 Inf -0.282 1.0000
StepF2 - StepF4 0.025068 0.0215 Inf 1.166 0.9996
StepF2 - StepF5 0.026780 0.0215 Inf 1.245 0.9990
StepF2 - StepF6 0.010834 0.0215 Inf 0.504 1.0000
StepF2 - StepF7 0.012327 0.0215 Inf 0.573 1.0000
StepF2 - StepF8 0.042387 0.0215 Inf 1.971 0.8832
StepF2 - StepF9 0.036405 0.0215 Inf 1.691 0.9686
StepF2 - StepF10 0.036839 0.0215 Inf 1.711 0.9649
StepF2 - StepF11 0.058627 0.0215 Inf 2.725 0.3625
StepF2 - StepF12 0.106741 0.0215 Inf 4.964 0.0001
StepF2 - StepF13 0.105164 0.0216 Inf 4.874 0.0002
StepF2 - StepF14 0.054115 0.0215 Inf 2.515 0.5188
StepF2 - StepF15 0.076149 0.0215 Inf 3.540 0.0423
StepF2 - StepF16 0.070681 0.0215 Inf 3.287 0.0928
StepF2 - StepF17 0.095416 0.0215 Inf 4.437 0.0013
StepF2 - StepF18 0.083296 0.0215 Inf 3.867 0.0133
StepF3 - StepF4 0.031124 0.0215 Inf 1.448 0.9938
StepF3 - StepF5 0.032836 0.0215 Inf 1.526 0.9889
StepF3 - StepF6 0.016890 0.0215 Inf 0.785 1.0000
StepF3 - StepF7 0.018383 0.0215 Inf 0.855 1.0000
StepF3 - StepF8 0.048443 0.0215 Inf 2.252 0.7181
StepF3 - StepF9 0.042460 0.0215 Inf 1.971 0.8833
StepF3 - StepF10 0.042894 0.0215 Inf 1.991 0.8739
StepF3 - StepF11 0.064682 0.0215 Inf 3.006 0.1972
StepF3 - StepF12 0.112797 0.0215 Inf 5.244 <.0001
StepF3 - StepF13 0.111220 0.0216 Inf 5.150 <.0001
StepF3 - StepF14 0.060171 0.0215 Inf 2.795 0.3160
StepF3 - StepF15 0.082205 0.0215 Inf 3.820 0.0159
StepF3 - StepF16 0.076736 0.0215 Inf 3.569 0.0385
StepF3 - StepF17 0.101471 0.0215 Inf 4.718 0.0003
StepF3 - StepF18 0.089352 0.0216 Inf 4.146 0.0044
StepF4 - StepF5 0.001712 0.0215 Inf 0.080 1.0000
StepF4 - StepF6 -0.014234 0.0215 Inf -0.662 1.0000
StepF4 - StepF7 -0.012741 0.0215 Inf -0.592 1.0000
StepF4 - StepF8 0.017319 0.0215 Inf 0.805 1.0000
StepF4 - StepF9 0.011336 0.0215 Inf 0.526 1.0000
StepF4 - StepF10 0.011770 0.0215 Inf 0.546 1.0000
StepF4 - StepF11 0.033558 0.0215 Inf 1.559 0.9861
StepF4 - StepF12 0.081673 0.0215 Inf 3.797 0.0172
StepF4 - StepF13 0.080096 0.0216 Inf 3.709 0.0237
StepF4 - StepF14 0.029047 0.0215 Inf 1.349 0.9973
StepF4 - StepF15 0.051081 0.0215 Inf 2.373 0.6284
StepF4 - StepF16 0.045612 0.0215 Inf 2.121 0.8042
StepF4 - StepF17 0.070347 0.0215 Inf 3.271 0.0974
StepF4 - StepF18 0.058227 0.0216 Inf 2.702 0.3788
StepF5 - StepF6 -0.015946 0.0215 Inf -0.742 1.0000
StepF5 - StepF7 -0.014453 0.0215 Inf -0.672 1.0000
StepF5 - StepF8 0.015607 0.0215 Inf 0.726 1.0000
StepF5 - StepF9 0.009625 0.0215 Inf 0.448 1.0000
StepF5 - StepF10 0.010058 0.0215 Inf 0.468 1.0000
StepF5 - StepF11 0.031847 0.0215 Inf 1.481 0.9920
StepF5 - StepF12 0.079961 0.0215 Inf 3.719 0.0228
StepF5 - StepF13 0.078384 0.0215 Inf 3.640 0.0302
StepF5 - StepF14 0.027335 0.0215 Inf 1.271 0.9987
StepF5 - StepF15 0.049369 0.0215 Inf 2.296 0.6863
StepF5 - StepF16 0.043901 0.0215 Inf 2.042 0.8489
StepF5 - StepF17 0.068636 0.0215 Inf 3.192 0.1214
StepF5 - StepF18 0.056516 0.0215 Inf 2.626 0.4337
StepF6 - StepF7 0.001493 0.0215 Inf 0.069 1.0000
StepF6 - StepF8 0.031553 0.0215 Inf 1.468 0.9928
StepF6 - StepF9 0.025571 0.0215 Inf 1.189 0.9994
StepF6 - StepF10 0.026005 0.0215 Inf 1.209 0.9993
StepF6 - StepF11 0.047793 0.0215 Inf 2.223 0.7384
StepF6 - StepF12 0.095907 0.0215 Inf 4.461 0.0011
StepF6 - StepF13 0.094330 0.0215 Inf 4.381 0.0016
StepF6 - StepF14 0.043281 0.0215 Inf 2.013 0.8635
StepF6 - StepF15 0.065315 0.0215 Inf 3.038 0.1820
StepF6 - StepF16 0.059847 0.0215 Inf 2.783 0.3235
StepF6 - StepF17 0.084582 0.0215 Inf 3.934 0.0103
StepF6 - StepF18 0.072462 0.0215 Inf 3.367 0.0731
StepF7 - StepF8 0.030060 0.0215 Inf 1.398 0.9958
StepF7 - StepF9 0.024077 0.0215 Inf 1.119 0.9997
StepF7 - StepF10 0.024511 0.0215 Inf 1.139 0.9997
StepF7 - StepF11 0.046299 0.0215 Inf 2.153 0.7846
StepF7 - StepF12 0.094414 0.0215 Inf 4.391 0.0015
StepF7 - StepF13 0.092837 0.0216 Inf 4.307 0.0022
StepF7 - StepF14 0.041788 0.0215 Inf 1.943 0.8954
StepF7 - StepF15 0.063822 0.0215 Inf 2.968 0.2158
StepF7 - StepF16 0.058353 0.0215 Inf 2.714 0.3702
StepF7 - StepF17 0.083088 0.0215 Inf 3.865 0.0134
StepF7 - StepF18 0.070969 0.0215 Inf 3.297 0.0903
StepF8 - StepF9 -0.005982 0.0215 Inf -0.278 1.0000
StepF8 - StepF10 -0.005549 0.0215 Inf -0.258 1.0000
StepF8 - StepF11 0.016240 0.0215 Inf 0.755 1.0000
StepF8 - StepF12 0.064354 0.0215 Inf 2.993 0.2031
StepF8 - StepF13 0.062777 0.0215 Inf 2.914 0.2444
StepF8 - StepF14 0.011728 0.0215 Inf 0.545 1.0000
StepF8 - StepF15 0.033762 0.0215 Inf 1.570 0.9850
StepF8 - StepF16 0.028293 0.0215 Inf 1.316 0.9980
StepF8 - StepF17 0.053028 0.0215 Inf 2.467 0.5563
StepF8 - StepF18 0.040909 0.0215 Inf 1.901 0.9120
StepF9 - StepF10 0.000434 0.0215 Inf 0.020 1.0000
StepF9 - StepF11 0.022222 0.0215 Inf 1.033 0.9999
StepF9 - StepF12 0.070336 0.0215 Inf 3.270 0.0976
StepF9 - StepF13 0.068759 0.0215 Inf 3.197 0.1200
StepF9 - StepF14 0.017710 0.0215 Inf 0.824 1.0000
StepF9 - StepF15 0.039745 0.0215 Inf 1.848 0.9301
StepF9 - StepF16 0.034276 0.0215 Inf 1.593 0.9826
StepF9 - StepF17 0.059011 0.0215 Inf 2.743 0.3502
StepF9 - StepF18 0.046891 0.0215 Inf 2.179 0.7676
StepF10 - StepF11 0.021788 0.0215 Inf 1.013 0.9999
StepF10 - StepF12 0.069902 0.0215 Inf 3.250 0.1034
StepF10 - StepF13 0.068326 0.0215 Inf 3.176 0.1270
StepF10 - StepF14 0.017276 0.0215 Inf 0.804 1.0000
StepF10 - StepF15 0.039311 0.0215 Inf 1.828 0.9363
StepF10 - StepF16 0.033842 0.0215 Inf 1.573 0.9848
StepF10 - StepF17 0.058577 0.0215 Inf 2.723 0.3639
StepF10 - StepF18 0.046457 0.0215 Inf 2.159 0.7806
StepF11 - StepF12 0.048114 0.0215 Inf 2.238 0.7282
StepF11 - StepF13 0.046537 0.0215 Inf 2.162 0.7790
StepF11 - StepF14 -0.004512 0.0215 Inf -0.210 1.0000
StepF11 - StepF15 0.017523 0.0215 Inf 0.815 1.0000
StepF11 - StepF16 0.012054 0.0215 Inf 0.561 1.0000
StepF11 - StepF17 0.036789 0.0215 Inf 1.711 0.9649
StepF11 - StepF18 0.024669 0.0215 Inf 1.147 0.9996
StepF12 - StepF13 -0.001577 0.0215 Inf -0.073 1.0000
StepF12 - StepF14 -0.052626 0.0215 Inf -2.447 0.5714
StepF12 - StepF15 -0.030592 0.0215 Inf -1.423 0.9949
StepF12 - StepF16 -0.036060 0.0215 Inf -1.677 0.9710
StepF12 - StepF17 -0.011325 0.0215 Inf -0.527 1.0000
StepF12 - StepF18 -0.023445 0.0215 Inf -1.089 0.9998
StepF13 - StepF14 -0.051049 0.0215 Inf -2.372 0.6292
StepF13 - StepF15 -0.029015 0.0215 Inf -1.348 0.9973
StepF13 - StepF16 -0.034483 0.0216 Inf -1.599 0.9819
StepF13 - StepF17 -0.009748 0.0215 Inf -0.452 1.0000
StepF13 - StepF18 -0.021868 0.0215 Inf -1.016 0.9999
StepF14 - StepF15 0.022034 0.0215 Inf 1.025 0.9999
StepF14 - StepF16 0.016566 0.0215 Inf 0.770 1.0000
StepF14 - StepF17 0.041301 0.0215 Inf 1.920 0.9045
StepF14 - StepF18 0.029181 0.0215 Inf 1.356 0.9971
StepF15 - StepF16 -0.005469 0.0215 Inf -0.254 1.0000
StepF15 - StepF17 0.019266 0.0215 Inf 0.896 1.0000
StepF15 - StepF18 0.007147 0.0215 Inf 0.332 1.0000
StepF16 - StepF17 0.024735 0.0215 Inf 1.150 0.9996
StepF16 - StepF18 0.012615 0.0215 Inf 0.586 1.0000
StepF17 - StepF18 -0.012120 0.0215 Inf -0.563 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.023631 0.0218 Inf -1.086 0.9998
StepF1 - StepF3 -0.029687 0.0218 Inf -1.362 0.9969
StepF1 - StepF4 0.001437 0.0218 Inf 0.066 1.0000
StepF1 - StepF5 0.003149 0.0217 Inf 0.145 1.0000
StepF1 - StepF6 -0.012797 0.0217 Inf -0.591 1.0000
StepF1 - StepF7 -0.011304 0.0217 Inf -0.521 1.0000
StepF1 - StepF8 0.018756 0.0217 Inf 0.865 1.0000
StepF1 - StepF9 0.012773 0.0216 Inf 0.591 1.0000
StepF1 - StepF10 0.013207 0.0216 Inf 0.611 1.0000
StepF1 - StepF11 0.034995 0.0217 Inf 1.616 0.9799
StepF1 - StepF12 0.083110 0.0217 Inf 3.831 0.0152
StepF1 - StepF13 0.081533 0.0216 Inf 3.783 0.0181
StepF1 - StepF14 0.030484 0.0216 Inf 1.409 0.9954
StepF1 - StepF15 0.052518 0.0216 Inf 2.426 0.5880
StepF1 - StepF16 0.047049 0.0217 Inf 2.166 0.7763
StepF1 - StepF17 0.071784 0.0217 Inf 3.309 0.0872
StepF1 - StepF18 0.059664 0.0216 Inf 2.757 0.3407
StepF2 - StepF3 -0.006056 0.0215 Inf -0.282 1.0000
StepF2 - StepF4 0.025068 0.0215 Inf 1.166 0.9996
StepF2 - StepF5 0.026780 0.0215 Inf 1.245 0.9990
StepF2 - StepF6 0.010834 0.0215 Inf 0.504 1.0000
StepF2 - StepF7 0.012327 0.0215 Inf 0.573 1.0000
StepF2 - StepF8 0.042387 0.0215 Inf 1.971 0.8832
StepF2 - StepF9 0.036405 0.0215 Inf 1.691 0.9686
StepF2 - StepF10 0.036839 0.0215 Inf 1.711 0.9649
StepF2 - StepF11 0.058627 0.0215 Inf 2.725 0.3625
StepF2 - StepF12 0.106741 0.0215 Inf 4.964 0.0001
StepF2 - StepF13 0.105164 0.0216 Inf 4.874 0.0002
StepF2 - StepF14 0.054115 0.0215 Inf 2.515 0.5188
StepF2 - StepF15 0.076149 0.0215 Inf 3.540 0.0423
StepF2 - StepF16 0.070681 0.0215 Inf 3.287 0.0928
StepF2 - StepF17 0.095416 0.0215 Inf 4.437 0.0013
StepF2 - StepF18 0.083296 0.0215 Inf 3.867 0.0133
StepF3 - StepF4 0.031124 0.0215 Inf 1.448 0.9938
StepF3 - StepF5 0.032836 0.0215 Inf 1.526 0.9889
StepF3 - StepF6 0.016890 0.0215 Inf 0.785 1.0000
StepF3 - StepF7 0.018383 0.0215 Inf 0.855 1.0000
StepF3 - StepF8 0.048443 0.0215 Inf 2.252 0.7181
StepF3 - StepF9 0.042460 0.0215 Inf 1.971 0.8833
StepF3 - StepF10 0.042894 0.0215 Inf 1.991 0.8739
StepF3 - StepF11 0.064682 0.0215 Inf 3.006 0.1972
StepF3 - StepF12 0.112797 0.0215 Inf 5.244 <.0001
StepF3 - StepF13 0.111220 0.0216 Inf 5.150 <.0001
StepF3 - StepF14 0.060171 0.0215 Inf 2.795 0.3160
StepF3 - StepF15 0.082205 0.0215 Inf 3.820 0.0159
StepF3 - StepF16 0.076736 0.0215 Inf 3.569 0.0385
StepF3 - StepF17 0.101471 0.0215 Inf 4.718 0.0003
StepF3 - StepF18 0.089352 0.0216 Inf 4.146 0.0044
StepF4 - StepF5 0.001712 0.0215 Inf 0.080 1.0000
StepF4 - StepF6 -0.014234 0.0215 Inf -0.662 1.0000
StepF4 - StepF7 -0.012741 0.0215 Inf -0.592 1.0000
StepF4 - StepF8 0.017319 0.0215 Inf 0.805 1.0000
StepF4 - StepF9 0.011336 0.0215 Inf 0.526 1.0000
StepF4 - StepF10 0.011770 0.0215 Inf 0.546 1.0000
StepF4 - StepF11 0.033558 0.0215 Inf 1.559 0.9861
StepF4 - StepF12 0.081673 0.0215 Inf 3.797 0.0172
StepF4 - StepF13 0.080096 0.0216 Inf 3.709 0.0237
StepF4 - StepF14 0.029047 0.0215 Inf 1.349 0.9973
StepF4 - StepF15 0.051081 0.0215 Inf 2.373 0.6284
StepF4 - StepF16 0.045612 0.0215 Inf 2.121 0.8042
StepF4 - StepF17 0.070347 0.0215 Inf 3.271 0.0974
StepF4 - StepF18 0.058227 0.0216 Inf 2.702 0.3788
StepF5 - StepF6 -0.015946 0.0215 Inf -0.742 1.0000
StepF5 - StepF7 -0.014453 0.0215 Inf -0.672 1.0000
StepF5 - StepF8 0.015607 0.0215 Inf 0.726 1.0000
StepF5 - StepF9 0.009625 0.0215 Inf 0.448 1.0000
StepF5 - StepF10 0.010058 0.0215 Inf 0.468 1.0000
StepF5 - StepF11 0.031847 0.0215 Inf 1.481 0.9920
StepF5 - StepF12 0.079961 0.0215 Inf 3.719 0.0228
StepF5 - StepF13 0.078384 0.0215 Inf 3.640 0.0302
StepF5 - StepF14 0.027335 0.0215 Inf 1.271 0.9987
StepF5 - StepF15 0.049369 0.0215 Inf 2.296 0.6863
StepF5 - StepF16 0.043901 0.0215 Inf 2.042 0.8489
StepF5 - StepF17 0.068636 0.0215 Inf 3.192 0.1214
StepF5 - StepF18 0.056516 0.0215 Inf 2.626 0.4337
StepF6 - StepF7 0.001493 0.0215 Inf 0.069 1.0000
StepF6 - StepF8 0.031553 0.0215 Inf 1.468 0.9928
StepF6 - StepF9 0.025571 0.0215 Inf 1.189 0.9994
StepF6 - StepF10 0.026005 0.0215 Inf 1.209 0.9993
StepF6 - StepF11 0.047793 0.0215 Inf 2.223 0.7384
StepF6 - StepF12 0.095907 0.0215 Inf 4.461 0.0011
StepF6 - StepF13 0.094330 0.0215 Inf 4.381 0.0016
StepF6 - StepF14 0.043281 0.0215 Inf 2.013 0.8635
StepF6 - StepF15 0.065315 0.0215 Inf 3.038 0.1820
StepF6 - StepF16 0.059847 0.0215 Inf 2.783 0.3235
StepF6 - StepF17 0.084582 0.0215 Inf 3.934 0.0103
StepF6 - StepF18 0.072462 0.0215 Inf 3.367 0.0731
StepF7 - StepF8 0.030060 0.0215 Inf 1.398 0.9958
StepF7 - StepF9 0.024077 0.0215 Inf 1.119 0.9997
StepF7 - StepF10 0.024511 0.0215 Inf 1.139 0.9997
StepF7 - StepF11 0.046299 0.0215 Inf 2.153 0.7846
StepF7 - StepF12 0.094414 0.0215 Inf 4.391 0.0015
StepF7 - StepF13 0.092837 0.0216 Inf 4.307 0.0022
StepF7 - StepF14 0.041788 0.0215 Inf 1.943 0.8954
StepF7 - StepF15 0.063822 0.0215 Inf 2.968 0.2158
StepF7 - StepF16 0.058353 0.0215 Inf 2.714 0.3702
StepF7 - StepF17 0.083088 0.0215 Inf 3.865 0.0134
StepF7 - StepF18 0.070969 0.0215 Inf 3.297 0.0903
StepF8 - StepF9 -0.005982 0.0215 Inf -0.278 1.0000
StepF8 - StepF10 -0.005549 0.0215 Inf -0.258 1.0000
StepF8 - StepF11 0.016240 0.0215 Inf 0.755 1.0000
StepF8 - StepF12 0.064354 0.0215 Inf 2.993 0.2031
StepF8 - StepF13 0.062777 0.0215 Inf 2.914 0.2444
StepF8 - StepF14 0.011728 0.0215 Inf 0.545 1.0000
StepF8 - StepF15 0.033762 0.0215 Inf 1.570 0.9850
StepF8 - StepF16 0.028293 0.0215 Inf 1.316 0.9980
StepF8 - StepF17 0.053028 0.0215 Inf 2.467 0.5563
StepF8 - StepF18 0.040909 0.0215 Inf 1.901 0.9120
StepF9 - StepF10 0.000434 0.0215 Inf 0.020 1.0000
StepF9 - StepF11 0.022222 0.0215 Inf 1.033 0.9999
StepF9 - StepF12 0.070336 0.0215 Inf 3.270 0.0976
StepF9 - StepF13 0.068759 0.0215 Inf 3.197 0.1200
StepF9 - StepF14 0.017710 0.0215 Inf 0.824 1.0000
StepF9 - StepF15 0.039745 0.0215 Inf 1.848 0.9301
StepF9 - StepF16 0.034276 0.0215 Inf 1.593 0.9826
StepF9 - StepF17 0.059011 0.0215 Inf 2.743 0.3502
StepF9 - StepF18 0.046891 0.0215 Inf 2.179 0.7676
StepF10 - StepF11 0.021788 0.0215 Inf 1.013 0.9999
StepF10 - StepF12 0.069902 0.0215 Inf 3.250 0.1034
StepF10 - StepF13 0.068326 0.0215 Inf 3.176 0.1270
StepF10 - StepF14 0.017276 0.0215 Inf 0.804 1.0000
StepF10 - StepF15 0.039311 0.0215 Inf 1.828 0.9363
StepF10 - StepF16 0.033842 0.0215 Inf 1.573 0.9848
StepF10 - StepF17 0.058577 0.0215 Inf 2.723 0.3639
StepF10 - StepF18 0.046457 0.0215 Inf 2.159 0.7806
StepF11 - StepF12 0.048114 0.0215 Inf 2.238 0.7282
StepF11 - StepF13 0.046537 0.0215 Inf 2.162 0.7790
StepF11 - StepF14 -0.004512 0.0215 Inf -0.210 1.0000
StepF11 - StepF15 0.017523 0.0215 Inf 0.815 1.0000
StepF11 - StepF16 0.012054 0.0215 Inf 0.561 1.0000
StepF11 - StepF17 0.036789 0.0215 Inf 1.711 0.9649
StepF11 - StepF18 0.024669 0.0215 Inf 1.147 0.9996
StepF12 - StepF13 -0.001577 0.0215 Inf -0.073 1.0000
StepF12 - StepF14 -0.052626 0.0215 Inf -2.447 0.5714
StepF12 - StepF15 -0.030592 0.0215 Inf -1.423 0.9949
StepF12 - StepF16 -0.036060 0.0215 Inf -1.677 0.9710
StepF12 - StepF17 -0.011325 0.0215 Inf -0.527 1.0000
StepF12 - StepF18 -0.023445 0.0215 Inf -1.089 0.9998
StepF13 - StepF14 -0.051049 0.0215 Inf -2.372 0.6292
StepF13 - StepF15 -0.029015 0.0215 Inf -1.348 0.9973
StepF13 - StepF16 -0.034483 0.0216 Inf -1.599 0.9819
StepF13 - StepF17 -0.009748 0.0215 Inf -0.452 1.0000
StepF13 - StepF18 -0.021868 0.0215 Inf -1.016 0.9999
StepF14 - StepF15 0.022034 0.0215 Inf 1.025 0.9999
StepF14 - StepF16 0.016566 0.0215 Inf 0.770 1.0000
StepF14 - StepF17 0.041301 0.0215 Inf 1.920 0.9045
StepF14 - StepF18 0.029181 0.0215 Inf 1.356 0.9971
StepF15 - StepF16 -0.005469 0.0215 Inf -0.254 1.0000
StepF15 - StepF17 0.019266 0.0215 Inf 0.896 1.0000
StepF15 - StepF18 0.007147 0.0215 Inf 0.332 1.0000
StepF16 - StepF17 0.024735 0.0215 Inf 1.150 0.9996
StepF16 - StepF18 0.012615 0.0215 Inf 0.586 1.0000
StepF17 - StepF18 -0.012120 0.0215 Inf -0.563 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.023631 0.0218 Inf 1.086 1.0000
StepF3 - StepF2 0.006056 0.0215 Inf 0.282 1.0000
StepF4 - StepF3 -0.031124 0.0215 Inf -1.448 1.0000
StepF5 - StepF4 -0.001712 0.0215 Inf -0.080 1.0000
StepF6 - StepF5 0.015946 0.0215 Inf 0.742 1.0000
StepF7 - StepF6 -0.001493 0.0215 Inf -0.069 1.0000
StepF8 - StepF7 -0.030060 0.0215 Inf -1.398 1.0000
StepF9 - StepF8 0.005982 0.0215 Inf 0.278 1.0000
StepF10 - StepF9 -0.000434 0.0215 Inf -0.020 1.0000
StepF11 - StepF10 -0.021788 0.0215 Inf -1.013 1.0000
StepF12 - StepF11 -0.048114 0.0215 Inf -2.238 0.4038
StepF13 - StepF12 0.001577 0.0215 Inf 0.073 1.0000
StepF14 - StepF13 0.051049 0.0215 Inf 2.372 0.3004
StepF15 - StepF14 -0.022034 0.0215 Inf -1.025 1.0000
StepF16 - StepF15 0.005469 0.0215 Inf 0.254 1.0000
StepF17 - StepF16 -0.024735 0.0215 Inf -1.150 1.0000
StepF18 - StepF17 0.012120 0.0215 Inf 0.563 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.023631 0.0218 Inf 1.086 1.0000
StepF3 - StepF2 0.006056 0.0215 Inf 0.282 1.0000
StepF4 - StepF3 -0.031124 0.0215 Inf -1.448 1.0000
StepF5 - StepF4 -0.001712 0.0215 Inf -0.080 1.0000
StepF6 - StepF5 0.015946 0.0215 Inf 0.742 1.0000
StepF7 - StepF6 -0.001493 0.0215 Inf -0.069 1.0000
StepF8 - StepF7 -0.030060 0.0215 Inf -1.398 1.0000
StepF9 - StepF8 0.005982 0.0215 Inf 0.278 1.0000
StepF10 - StepF9 -0.000434 0.0215 Inf -0.020 1.0000
StepF11 - StepF10 -0.021788 0.0215 Inf -1.013 1.0000
StepF12 - StepF11 -0.048114 0.0215 Inf -2.238 0.4038
StepF13 - StepF12 0.001577 0.0215 Inf 0.073 1.0000
StepF14 - StepF13 0.051049 0.0215 Inf 2.372 0.3004
StepF15 - StepF14 -0.022034 0.0215 Inf -1.025 1.0000
StepF16 - StepF15 0.005469 0.0215 Inf 0.254 1.0000
StepF17 - StepF16 -0.024735 0.0215 Inf -1.150 1.0000
StepF18 - StepF17 0.012120 0.0215 Inf 0.563 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests Warning: Some predictor variables are on very different scales: consider
rescalingboundary (singular) fit: see help('isSingular')Warning: Some predictor variables are on very different scales: consider
rescalingWarning: Model failed to converge with 1 negative eigenvalue: -1.1e+01
[Optional] Random-slope model for rt_ms | subject (summary):
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ StepF + Accuracy + rt_ms + (1 + rt_ms | subject) + (1 |
trial_id)
Data: dd
REML criterion at convergence: 14920.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.6156 -0.5363 -0.1601 0.3247 16.7623
Random effects:
Groups Name Variance Std.Dev. Corr
trial_id (Intercept) 1.359e-01 0.3686269
subject (Intercept) 2.027e-01 0.4502203
rt_ms 1.180e-08 0.0001086 -0.97
Residual 1.586e-01 0.3981910
Number of obs: 12778, groups: trial_id, 710; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 5.610e-01 1.072e-01 1.264e+04 5.233 1.7e-07 ***
StepF1 2.385e-02 1.481e-02 1.162e+04 1.610 0.107385
StepF2 4.793e-02 1.456e-02 1.204e+04 3.293 0.000994 ***
StepF3 5.324e-02 1.456e-02 1.205e+04 3.657 0.000256 ***
StepF4 2.260e-02 1.457e-02 1.205e+04 1.551 0.120822
StepF5 2.308e-02 1.454e-02 1.203e+04 1.587 0.112496
StepF6 3.723e-02 1.455e-02 1.195e+04 2.558 0.010535 *
StepF7 3.503e-02 1.454e-02 1.204e+04 2.409 0.015999 *
StepF8 6.311e-03 1.454e-02 1.204e+04 0.434 0.664173
StepF9 1.096e-02 1.454e-02 1.204e+04 0.754 0.450817
StepF10 8.176e-03 1.455e-02 1.194e+04 0.562 0.574300
StepF11 -1.159e-02 1.454e-02 1.204e+04 -0.797 0.425449
StepF12 -5.893e-02 1.454e-02 1.204e+04 -4.052 5.1e-05 ***
StepF13 -5.238e-02 1.461e-02 1.171e+04 -3.584 0.000340 ***
StepF14 -8.377e-03 1.454e-02 1.203e+04 -0.576 0.564481
StepF15 -2.940e-02 1.454e-02 1.204e+04 -2.022 0.043156 *
StepF16 -2.333e-02 1.454e-02 1.205e+04 -1.605 0.108527
StepF17 -4.853e-02 1.454e-02 1.204e+04 -3.338 0.000845 ***
Accuracy1 5.656e-03 1.613e-02 1.481e+02 0.351 0.726329
rt_ms -2.378e-05 2.723e-05 1.131e+01 -0.874 0.400558
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 20 > 12.
Use print(summary(m_rs), correlation=TRUE) or
vcov(summary(m_rs)) if you need itfit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')
==============================
TRAINING (rt_ms) | Block 3 (18 steps) | Axis Y
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 160.7794 17 < 2.2e-16 ***
Accuracy 0.7371 1 0.390580
rt_ms 7.7475 1 0.005379 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.600 0.0539 Inf 0.495 0.706
2 0.678 0.0538 Inf 0.573 0.784
3 0.696 0.0538 Inf 0.591 0.802
4 0.592 0.0538 Inf 0.487 0.698
5 0.603 0.0538 Inf 0.498 0.709
6 0.637 0.0538 Inf 0.532 0.743
7 0.660 0.0538 Inf 0.554 0.765
8 0.577 0.0538 Inf 0.472 0.683
9 0.604 0.0538 Inf 0.499 0.710
10 0.663 0.0538 Inf 0.558 0.769
11 0.654 0.0538 Inf 0.549 0.760
12 0.536 0.0538 Inf 0.430 0.641
13 0.554 0.0539 Inf 0.449 0.660
14 0.565 0.0538 Inf 0.459 0.670
15 0.601 0.0538 Inf 0.496 0.707
16 0.553 0.0538 Inf 0.447 0.658
17 0.569 0.0538 Inf 0.464 0.675
18 0.537 0.0538 Inf 0.431 0.642
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.579 0.0536 Inf 0.474 0.685
2 0.658 0.0536 Inf 0.553 0.763
3 0.676 0.0536 Inf 0.571 0.781
4 0.571 0.0536 Inf 0.466 0.676
5 0.583 0.0536 Inf 0.478 0.688
6 0.616 0.0536 Inf 0.512 0.721
7 0.639 0.0536 Inf 0.534 0.744
8 0.557 0.0536 Inf 0.452 0.661
9 0.583 0.0536 Inf 0.479 0.688
10 0.642 0.0536 Inf 0.537 0.747
11 0.634 0.0536 Inf 0.529 0.739
12 0.515 0.0536 Inf 0.410 0.620
13 0.534 0.0536 Inf 0.429 0.639
14 0.544 0.0536 Inf 0.439 0.649
15 0.580 0.0536 Inf 0.475 0.685
16 0.532 0.0536 Inf 0.427 0.637
17 0.548 0.0536 Inf 0.443 0.653
18 0.516 0.0536 Inf 0.411 0.621
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.078123 0.0231 Inf -3.376 0.0712
StepF1 - StepF3 -0.096071 0.0232 Inf -4.145 0.0044
StepF1 - StepF4 0.008114 0.0232 Inf 0.350 1.0000
StepF1 - StepF5 -0.003149 0.0231 Inf -0.137 1.0000
StepF1 - StepF6 -0.036994 0.0230 Inf -1.605 0.9812
StepF1 - StepF7 -0.059457 0.0231 Inf -2.575 0.4727
StepF1 - StepF8 0.022965 0.0231 Inf 0.996 0.9999
StepF1 - StepF9 -0.004007 0.0230 Inf -0.174 1.0000
StepF1 - StepF10 -0.062947 0.0230 Inf -2.738 0.3538
StepF1 - StepF11 -0.054225 0.0230 Inf -2.354 0.6429
StepF1 - StepF12 0.064256 0.0231 Inf 2.785 0.3225
StepF1 - StepF13 0.045772 0.0229 Inf 1.997 0.8713
StepF1 - StepF14 0.035721 0.0230 Inf 1.552 0.9867
StepF1 - StepF15 -0.000833 0.0230 Inf -0.036 1.0000
StepF1 - StepF16 0.047468 0.0231 Inf 2.055 0.8422
StepF1 - StepF17 0.031138 0.0231 Inf 1.349 0.9972
StepF1 - StepF18 0.063320 0.0230 Inf 2.751 0.3449
StepF2 - StepF3 -0.017948 0.0229 Inf -0.785 1.0000
StepF2 - StepF4 0.086237 0.0229 Inf 3.771 0.0190
StepF2 - StepF5 0.074973 0.0229 Inf 3.277 0.0955
StepF2 - StepF6 0.041129 0.0229 Inf 1.798 0.9450
StepF2 - StepF7 0.018666 0.0229 Inf 0.816 1.0000
StepF2 - StepF8 0.101088 0.0229 Inf 4.419 0.0014
StepF2 - StepF9 0.074116 0.0229 Inf 3.236 0.1074
StepF2 - StepF10 0.015176 0.0229 Inf 0.663 1.0000
StepF2 - StepF11 0.023898 0.0229 Inf 1.044 0.9999
StepF2 - StepF12 0.142379 0.0229 Inf 6.225 <.0001
StepF2 - StepF13 0.123895 0.0230 Inf 5.398 <.0001
StepF2 - StepF14 0.113844 0.0229 Inf 4.974 0.0001
StepF2 - StepF15 0.077290 0.0229 Inf 3.378 0.0709
StepF2 - StepF16 0.125591 0.0229 Inf 5.492 <.0001
StepF2 - StepF17 0.109261 0.0229 Inf 4.777 0.0003
StepF2 - StepF18 0.141443 0.0229 Inf 6.174 <.0001
StepF3 - StepF4 0.104185 0.0229 Inf 4.556 0.0007
StepF3 - StepF5 0.092921 0.0229 Inf 4.061 0.0062
StepF3 - StepF6 0.059077 0.0229 Inf 2.581 0.4676
StepF3 - StepF7 0.036614 0.0229 Inf 1.601 0.9817
StepF3 - StepF8 0.119036 0.0229 Inf 5.203 <.0001
StepF3 - StepF9 0.092064 0.0229 Inf 4.018 0.0074
StepF3 - StepF10 0.033124 0.0229 Inf 1.446 0.9939
StepF3 - StepF11 0.041846 0.0229 Inf 1.828 0.9364
StepF3 - StepF12 0.160326 0.0229 Inf 7.008 <.0001
StepF3 - StepF13 0.141843 0.0230 Inf 6.174 <.0001
StepF3 - StepF14 0.131791 0.0229 Inf 5.755 <.0001
StepF3 - StepF15 0.095238 0.0229 Inf 4.160 0.0042
StepF3 - StepF16 0.143539 0.0229 Inf 6.276 <.0001
StepF3 - StepF17 0.127209 0.0229 Inf 5.560 <.0001
StepF3 - StepF18 0.159391 0.0229 Inf 6.953 <.0001
StepF4 - StepF5 -0.011263 0.0229 Inf -0.492 1.0000
StepF4 - StepF6 -0.045108 0.0229 Inf -1.971 0.8833
StepF4 - StepF7 -0.067571 0.0229 Inf -2.954 0.2230
StepF4 - StepF8 0.014851 0.0229 Inf 0.649 1.0000
StepF4 - StepF9 -0.012121 0.0229 Inf -0.529 1.0000
StepF4 - StepF10 -0.071061 0.0229 Inf -3.102 0.1548
StepF4 - StepF11 -0.062339 0.0229 Inf -2.723 0.3639
StepF4 - StepF12 0.056142 0.0229 Inf 2.454 0.5661
StepF4 - StepF13 0.037658 0.0230 Inf 1.639 0.9768
StepF4 - StepF14 0.027607 0.0229 Inf 1.205 0.9993
StepF4 - StepF15 -0.008947 0.0229 Inf -0.391 1.0000
StepF4 - StepF16 0.039354 0.0229 Inf 1.721 0.9630
StepF4 - StepF17 0.023024 0.0229 Inf 1.006 0.9999
StepF4 - StepF18 0.055206 0.0229 Inf 2.408 0.6015
StepF5 - StepF6 -0.033844 0.0229 Inf -1.480 0.9921
StepF5 - StepF7 -0.056307 0.0229 Inf -2.462 0.5597
StepF5 - StepF8 0.026115 0.0229 Inf 1.142 0.9997
StepF5 - StepF9 -0.000857 0.0229 Inf -0.037 1.0000
StepF5 - StepF10 -0.059798 0.0229 Inf -2.614 0.4428
StepF5 - StepF11 -0.051075 0.0229 Inf -2.233 0.7312
StepF5 - StepF12 0.067405 0.0229 Inf 2.948 0.2263
StepF5 - StepF13 0.048922 0.0229 Inf 2.136 0.7955
StepF5 - StepF14 0.038870 0.0229 Inf 1.700 0.9671
StepF5 - StepF15 0.002316 0.0229 Inf 0.101 1.0000
StepF5 - StepF16 0.050618 0.0229 Inf 2.213 0.7451
StepF5 - StepF17 0.034288 0.0229 Inf 1.499 0.9908
StepF5 - StepF18 0.066470 0.0229 Inf 2.904 0.2501
StepF6 - StepF7 -0.022463 0.0229 Inf -0.982 1.0000
StepF6 - StepF8 0.059959 0.0229 Inf 2.622 0.4370
StepF6 - StepF9 0.032987 0.0229 Inf 1.442 0.9940
StepF6 - StepF10 -0.025953 0.0229 Inf -1.135 0.9997
StepF6 - StepF11 -0.017231 0.0229 Inf -0.753 1.0000
StepF6 - StepF12 0.101249 0.0229 Inf 4.427 0.0013
StepF6 - StepF13 0.082766 0.0229 Inf 3.614 0.0330
StepF6 - StepF14 0.072714 0.0229 Inf 3.179 0.1258
StepF6 - StepF15 0.036161 0.0229 Inf 1.581 0.9839
StepF6 - StepF16 0.084462 0.0229 Inf 3.693 0.0251
StepF6 - StepF17 0.068132 0.0229 Inf 2.979 0.2101
StepF6 - StepF18 0.100314 0.0229 Inf 4.383 0.0016
StepF7 - StepF8 0.082422 0.0229 Inf 3.604 0.0341
StepF7 - StepF9 0.055450 0.0229 Inf 2.423 0.5902
StepF7 - StepF10 -0.003490 0.0229 Inf -0.153 1.0000
StepF7 - StepF11 0.005232 0.0229 Inf 0.229 1.0000
StepF7 - StepF12 0.123713 0.0229 Inf 5.410 <.0001
StepF7 - StepF13 0.105229 0.0229 Inf 4.590 0.0006
StepF7 - StepF14 0.095177 0.0229 Inf 4.160 0.0042
StepF7 - StepF15 0.058624 0.0229 Inf 2.563 0.4816
StepF7 - StepF16 0.106925 0.0229 Inf 4.676 0.0004
StepF7 - StepF17 0.090595 0.0229 Inf 3.962 0.0092
StepF7 - StepF18 0.122777 0.0229 Inf 5.362 <.0001
StepF8 - StepF9 -0.026972 0.0229 Inf -1.179 0.9995
StepF8 - StepF10 -0.085912 0.0229 Inf -3.755 0.0201
StepF8 - StepF11 -0.077190 0.0229 Inf -3.375 0.0714
StepF8 - StepF12 0.041290 0.0229 Inf 1.806 0.9428
StepF8 - StepF13 0.022807 0.0229 Inf 0.995 0.9999
StepF8 - StepF14 0.012755 0.0229 Inf 0.558 1.0000
StepF8 - StepF15 -0.023798 0.0229 Inf -1.041 0.9999
StepF8 - StepF16 0.024503 0.0229 Inf 1.071 0.9999
StepF8 - StepF17 0.008173 0.0229 Inf 0.357 1.0000
StepF8 - StepF18 0.040355 0.0229 Inf 1.763 0.9538
StepF9 - StepF10 -0.058940 0.0229 Inf -2.577 0.4705
StepF9 - StepF11 -0.050218 0.0229 Inf -2.196 0.7570
StepF9 - StepF12 0.068262 0.0229 Inf 2.983 0.2080
StepF9 - StepF13 0.049779 0.0229 Inf 2.176 0.7701
StepF9 - StepF14 0.039727 0.0229 Inf 1.737 0.9596
StepF9 - StepF15 0.003174 0.0229 Inf 0.139 1.0000
StepF9 - StepF16 0.051475 0.0229 Inf 2.249 0.7204
StepF9 - StepF17 0.035145 0.0229 Inf 1.536 0.9881
StepF9 - StepF18 0.067327 0.0229 Inf 2.942 0.2293
StepF10 - StepF11 0.008723 0.0229 Inf 0.381 1.0000
StepF10 - StepF12 0.127203 0.0229 Inf 5.560 <.0001
StepF10 - StepF13 0.108719 0.0229 Inf 4.751 0.0003
StepF10 - StepF14 0.098668 0.0229 Inf 4.315 0.0022
StepF10 - StepF15 0.062114 0.0229 Inf 2.716 0.3690
StepF10 - StepF16 0.110415 0.0229 Inf 4.824 0.0002
StepF10 - StepF17 0.094086 0.0229 Inf 4.112 0.0051
StepF10 - StepF18 0.126267 0.0229 Inf 5.517 <.0001
StepF11 - StepF12 0.118480 0.0229 Inf 5.180 <.0001
StepF11 - StepF13 0.099997 0.0229 Inf 4.367 0.0017
StepF11 - StepF14 0.089945 0.0229 Inf 3.933 0.0103
StepF11 - StepF15 0.053392 0.0229 Inf 2.335 0.6577
StepF11 - StepF16 0.101693 0.0229 Inf 4.446 0.0012
StepF11 - StepF17 0.085363 0.0229 Inf 3.732 0.0218
StepF11 - StepF18 0.117545 0.0229 Inf 5.136 <.0001
StepF12 - StepF13 -0.018483 0.0229 Inf -0.807 1.0000
StepF12 - StepF14 -0.028535 0.0229 Inf -1.247 0.9989
StepF12 - StepF15 -0.065089 0.0229 Inf -2.846 0.2840
StepF12 - StepF16 -0.016788 0.0229 Inf -0.734 1.0000
StepF12 - StepF17 -0.033117 0.0229 Inf -1.448 0.9938
StepF12 - StepF18 -0.000936 0.0229 Inf -0.041 1.0000
StepF13 - StepF14 -0.010052 0.0229 Inf -0.439 1.0000
StepF13 - StepF15 -0.046605 0.0229 Inf -2.036 0.8522
StepF13 - StepF16 0.001696 0.0229 Inf 0.074 1.0000
StepF13 - StepF17 -0.014634 0.0229 Inf -0.639 1.0000
StepF13 - StepF18 0.017548 0.0229 Inf 0.766 1.0000
StepF14 - StepF15 -0.036554 0.0229 Inf -1.598 0.9820
StepF14 - StepF16 0.011747 0.0229 Inf 0.513 1.0000
StepF14 - StepF17 -0.004582 0.0229 Inf -0.200 1.0000
StepF14 - StepF18 0.027600 0.0229 Inf 1.206 0.9993
StepF15 - StepF16 0.048301 0.0229 Inf 2.111 0.8100
StepF15 - StepF17 0.031971 0.0229 Inf 1.398 0.9958
StepF15 - StepF18 0.064153 0.0229 Inf 2.803 0.3106
StepF16 - StepF17 -0.016330 0.0229 Inf -0.714 1.0000
StepF16 - StepF18 0.015852 0.0229 Inf 0.692 1.0000
StepF17 - StepF18 0.032182 0.0229 Inf 1.406 0.9956
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.078123 0.0231 Inf -3.376 0.0712
StepF1 - StepF3 -0.096071 0.0232 Inf -4.145 0.0044
StepF1 - StepF4 0.008114 0.0232 Inf 0.350 1.0000
StepF1 - StepF5 -0.003149 0.0231 Inf -0.137 1.0000
StepF1 - StepF6 -0.036994 0.0230 Inf -1.605 0.9812
StepF1 - StepF7 -0.059457 0.0231 Inf -2.575 0.4727
StepF1 - StepF8 0.022965 0.0231 Inf 0.996 0.9999
StepF1 - StepF9 -0.004007 0.0230 Inf -0.174 1.0000
StepF1 - StepF10 -0.062947 0.0230 Inf -2.738 0.3538
StepF1 - StepF11 -0.054225 0.0230 Inf -2.354 0.6429
StepF1 - StepF12 0.064256 0.0231 Inf 2.785 0.3225
StepF1 - StepF13 0.045772 0.0229 Inf 1.997 0.8713
StepF1 - StepF14 0.035721 0.0230 Inf 1.552 0.9867
StepF1 - StepF15 -0.000833 0.0230 Inf -0.036 1.0000
StepF1 - StepF16 0.047468 0.0231 Inf 2.055 0.8422
StepF1 - StepF17 0.031138 0.0231 Inf 1.349 0.9972
StepF1 - StepF18 0.063320 0.0230 Inf 2.751 0.3449
StepF2 - StepF3 -0.017948 0.0229 Inf -0.785 1.0000
StepF2 - StepF4 0.086237 0.0229 Inf 3.771 0.0190
StepF2 - StepF5 0.074973 0.0229 Inf 3.277 0.0955
StepF2 - StepF6 0.041129 0.0229 Inf 1.798 0.9450
StepF2 - StepF7 0.018666 0.0229 Inf 0.816 1.0000
StepF2 - StepF8 0.101088 0.0229 Inf 4.419 0.0014
StepF2 - StepF9 0.074116 0.0229 Inf 3.236 0.1074
StepF2 - StepF10 0.015176 0.0229 Inf 0.663 1.0000
StepF2 - StepF11 0.023898 0.0229 Inf 1.044 0.9999
StepF2 - StepF12 0.142379 0.0229 Inf 6.225 <.0001
StepF2 - StepF13 0.123895 0.0230 Inf 5.398 <.0001
StepF2 - StepF14 0.113844 0.0229 Inf 4.974 0.0001
StepF2 - StepF15 0.077290 0.0229 Inf 3.378 0.0709
StepF2 - StepF16 0.125591 0.0229 Inf 5.492 <.0001
StepF2 - StepF17 0.109261 0.0229 Inf 4.777 0.0003
StepF2 - StepF18 0.141443 0.0229 Inf 6.174 <.0001
StepF3 - StepF4 0.104185 0.0229 Inf 4.556 0.0007
StepF3 - StepF5 0.092921 0.0229 Inf 4.061 0.0062
StepF3 - StepF6 0.059077 0.0229 Inf 2.581 0.4676
StepF3 - StepF7 0.036614 0.0229 Inf 1.601 0.9817
StepF3 - StepF8 0.119036 0.0229 Inf 5.203 <.0001
StepF3 - StepF9 0.092064 0.0229 Inf 4.018 0.0074
StepF3 - StepF10 0.033124 0.0229 Inf 1.446 0.9939
StepF3 - StepF11 0.041846 0.0229 Inf 1.828 0.9364
StepF3 - StepF12 0.160326 0.0229 Inf 7.008 <.0001
StepF3 - StepF13 0.141843 0.0230 Inf 6.174 <.0001
StepF3 - StepF14 0.131791 0.0229 Inf 5.755 <.0001
StepF3 - StepF15 0.095238 0.0229 Inf 4.160 0.0042
StepF3 - StepF16 0.143539 0.0229 Inf 6.276 <.0001
StepF3 - StepF17 0.127209 0.0229 Inf 5.560 <.0001
StepF3 - StepF18 0.159391 0.0229 Inf 6.953 <.0001
StepF4 - StepF5 -0.011263 0.0229 Inf -0.492 1.0000
StepF4 - StepF6 -0.045108 0.0229 Inf -1.971 0.8833
StepF4 - StepF7 -0.067571 0.0229 Inf -2.954 0.2230
StepF4 - StepF8 0.014851 0.0229 Inf 0.649 1.0000
StepF4 - StepF9 -0.012121 0.0229 Inf -0.529 1.0000
StepF4 - StepF10 -0.071061 0.0229 Inf -3.102 0.1548
StepF4 - StepF11 -0.062339 0.0229 Inf -2.723 0.3639
StepF4 - StepF12 0.056142 0.0229 Inf 2.454 0.5661
StepF4 - StepF13 0.037658 0.0230 Inf 1.639 0.9768
StepF4 - StepF14 0.027607 0.0229 Inf 1.205 0.9993
StepF4 - StepF15 -0.008947 0.0229 Inf -0.391 1.0000
StepF4 - StepF16 0.039354 0.0229 Inf 1.721 0.9630
StepF4 - StepF17 0.023024 0.0229 Inf 1.006 0.9999
StepF4 - StepF18 0.055206 0.0229 Inf 2.408 0.6015
StepF5 - StepF6 -0.033844 0.0229 Inf -1.480 0.9921
StepF5 - StepF7 -0.056307 0.0229 Inf -2.462 0.5597
StepF5 - StepF8 0.026115 0.0229 Inf 1.142 0.9997
StepF5 - StepF9 -0.000857 0.0229 Inf -0.037 1.0000
StepF5 - StepF10 -0.059798 0.0229 Inf -2.614 0.4428
StepF5 - StepF11 -0.051075 0.0229 Inf -2.233 0.7312
StepF5 - StepF12 0.067405 0.0229 Inf 2.948 0.2263
StepF5 - StepF13 0.048922 0.0229 Inf 2.136 0.7955
StepF5 - StepF14 0.038870 0.0229 Inf 1.700 0.9671
StepF5 - StepF15 0.002316 0.0229 Inf 0.101 1.0000
StepF5 - StepF16 0.050618 0.0229 Inf 2.213 0.7451
StepF5 - StepF17 0.034288 0.0229 Inf 1.499 0.9908
StepF5 - StepF18 0.066470 0.0229 Inf 2.904 0.2501
StepF6 - StepF7 -0.022463 0.0229 Inf -0.982 1.0000
StepF6 - StepF8 0.059959 0.0229 Inf 2.622 0.4370
StepF6 - StepF9 0.032987 0.0229 Inf 1.442 0.9940
StepF6 - StepF10 -0.025953 0.0229 Inf -1.135 0.9997
StepF6 - StepF11 -0.017231 0.0229 Inf -0.753 1.0000
StepF6 - StepF12 0.101249 0.0229 Inf 4.427 0.0013
StepF6 - StepF13 0.082766 0.0229 Inf 3.614 0.0330
StepF6 - StepF14 0.072714 0.0229 Inf 3.179 0.1258
StepF6 - StepF15 0.036161 0.0229 Inf 1.581 0.9839
StepF6 - StepF16 0.084462 0.0229 Inf 3.693 0.0251
StepF6 - StepF17 0.068132 0.0229 Inf 2.979 0.2101
StepF6 - StepF18 0.100314 0.0229 Inf 4.383 0.0016
StepF7 - StepF8 0.082422 0.0229 Inf 3.604 0.0341
StepF7 - StepF9 0.055450 0.0229 Inf 2.423 0.5902
StepF7 - StepF10 -0.003490 0.0229 Inf -0.153 1.0000
StepF7 - StepF11 0.005232 0.0229 Inf 0.229 1.0000
StepF7 - StepF12 0.123713 0.0229 Inf 5.410 <.0001
StepF7 - StepF13 0.105229 0.0229 Inf 4.590 0.0006
StepF7 - StepF14 0.095177 0.0229 Inf 4.160 0.0042
StepF7 - StepF15 0.058624 0.0229 Inf 2.563 0.4816
StepF7 - StepF16 0.106925 0.0229 Inf 4.676 0.0004
StepF7 - StepF17 0.090595 0.0229 Inf 3.962 0.0092
StepF7 - StepF18 0.122777 0.0229 Inf 5.362 <.0001
StepF8 - StepF9 -0.026972 0.0229 Inf -1.179 0.9995
StepF8 - StepF10 -0.085912 0.0229 Inf -3.755 0.0201
StepF8 - StepF11 -0.077190 0.0229 Inf -3.375 0.0714
StepF8 - StepF12 0.041290 0.0229 Inf 1.806 0.9428
StepF8 - StepF13 0.022807 0.0229 Inf 0.995 0.9999
StepF8 - StepF14 0.012755 0.0229 Inf 0.558 1.0000
StepF8 - StepF15 -0.023798 0.0229 Inf -1.041 0.9999
StepF8 - StepF16 0.024503 0.0229 Inf 1.071 0.9999
StepF8 - StepF17 0.008173 0.0229 Inf 0.357 1.0000
StepF8 - StepF18 0.040355 0.0229 Inf 1.763 0.9538
StepF9 - StepF10 -0.058940 0.0229 Inf -2.577 0.4705
StepF9 - StepF11 -0.050218 0.0229 Inf -2.196 0.7570
StepF9 - StepF12 0.068262 0.0229 Inf 2.983 0.2080
StepF9 - StepF13 0.049779 0.0229 Inf 2.176 0.7701
StepF9 - StepF14 0.039727 0.0229 Inf 1.737 0.9596
StepF9 - StepF15 0.003174 0.0229 Inf 0.139 1.0000
StepF9 - StepF16 0.051475 0.0229 Inf 2.249 0.7204
StepF9 - StepF17 0.035145 0.0229 Inf 1.536 0.9881
StepF9 - StepF18 0.067327 0.0229 Inf 2.942 0.2293
StepF10 - StepF11 0.008723 0.0229 Inf 0.381 1.0000
StepF10 - StepF12 0.127203 0.0229 Inf 5.560 <.0001
StepF10 - StepF13 0.108719 0.0229 Inf 4.751 0.0003
StepF10 - StepF14 0.098668 0.0229 Inf 4.315 0.0022
StepF10 - StepF15 0.062114 0.0229 Inf 2.716 0.3690
StepF10 - StepF16 0.110415 0.0229 Inf 4.824 0.0002
StepF10 - StepF17 0.094086 0.0229 Inf 4.112 0.0051
StepF10 - StepF18 0.126267 0.0229 Inf 5.517 <.0001
StepF11 - StepF12 0.118480 0.0229 Inf 5.180 <.0001
StepF11 - StepF13 0.099997 0.0229 Inf 4.367 0.0017
StepF11 - StepF14 0.089945 0.0229 Inf 3.933 0.0103
StepF11 - StepF15 0.053392 0.0229 Inf 2.335 0.6577
StepF11 - StepF16 0.101693 0.0229 Inf 4.446 0.0012
StepF11 - StepF17 0.085363 0.0229 Inf 3.732 0.0218
StepF11 - StepF18 0.117545 0.0229 Inf 5.136 <.0001
StepF12 - StepF13 -0.018483 0.0229 Inf -0.807 1.0000
StepF12 - StepF14 -0.028535 0.0229 Inf -1.247 0.9989
StepF12 - StepF15 -0.065089 0.0229 Inf -2.846 0.2840
StepF12 - StepF16 -0.016788 0.0229 Inf -0.734 1.0000
StepF12 - StepF17 -0.033117 0.0229 Inf -1.448 0.9938
StepF12 - StepF18 -0.000936 0.0229 Inf -0.041 1.0000
StepF13 - StepF14 -0.010052 0.0229 Inf -0.439 1.0000
StepF13 - StepF15 -0.046605 0.0229 Inf -2.036 0.8522
StepF13 - StepF16 0.001696 0.0229 Inf 0.074 1.0000
StepF13 - StepF17 -0.014634 0.0229 Inf -0.639 1.0000
StepF13 - StepF18 0.017548 0.0229 Inf 0.766 1.0000
StepF14 - StepF15 -0.036554 0.0229 Inf -1.598 0.9820
StepF14 - StepF16 0.011747 0.0229 Inf 0.513 1.0000
StepF14 - StepF17 -0.004582 0.0229 Inf -0.200 1.0000
StepF14 - StepF18 0.027600 0.0229 Inf 1.206 0.9993
StepF15 - StepF16 0.048301 0.0229 Inf 2.111 0.8100
StepF15 - StepF17 0.031971 0.0229 Inf 1.398 0.9958
StepF15 - StepF18 0.064153 0.0229 Inf 2.803 0.3106
StepF16 - StepF17 -0.016330 0.0229 Inf -0.714 1.0000
StepF16 - StepF18 0.015852 0.0229 Inf 0.692 1.0000
StepF17 - StepF18 0.032182 0.0229 Inf 1.406 0.9956
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.07812 0.0231 Inf 3.376 0.0103
StepF3 - StepF2 0.01795 0.0229 Inf 0.785 1.0000
StepF4 - StepF3 -0.10418 0.0229 Inf -4.556 0.0001
StepF5 - StepF4 0.01126 0.0229 Inf 0.492 1.0000
StepF6 - StepF5 0.03384 0.0229 Inf 1.480 1.0000
StepF7 - StepF6 0.02246 0.0229 Inf 0.982 1.0000
StepF8 - StepF7 -0.08242 0.0229 Inf -3.604 0.0047
StepF9 - StepF8 0.02697 0.0229 Inf 1.179 1.0000
StepF10 - StepF9 0.05894 0.0229 Inf 2.577 0.1294
StepF11 - StepF10 -0.00872 0.0229 Inf -0.381 1.0000
StepF12 - StepF11 -0.11848 0.0229 Inf -5.180 <.0001
StepF13 - StepF12 0.01848 0.0229 Inf 0.807 1.0000
StepF14 - StepF13 0.01005 0.0229 Inf 0.439 1.0000
StepF15 - StepF14 0.03655 0.0229 Inf 1.598 1.0000
StepF16 - StepF15 -0.04830 0.0229 Inf -2.111 0.4168
StepF17 - StepF16 0.01633 0.0229 Inf 0.714 1.0000
StepF18 - StepF17 -0.03218 0.0229 Inf -1.406 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.07812 0.0231 Inf 3.376 0.0103
StepF3 - StepF2 0.01795 0.0229 Inf 0.785 1.0000
StepF4 - StepF3 -0.10418 0.0229 Inf -4.556 0.0001
StepF5 - StepF4 0.01126 0.0229 Inf 0.492 1.0000
StepF6 - StepF5 0.03384 0.0229 Inf 1.480 1.0000
StepF7 - StepF6 0.02246 0.0229 Inf 0.982 1.0000
StepF8 - StepF7 -0.08242 0.0229 Inf -3.604 0.0047
StepF9 - StepF8 0.02697 0.0229 Inf 1.179 1.0000
StepF10 - StepF9 0.05894 0.0229 Inf 2.577 0.1294
StepF11 - StepF10 -0.00872 0.0229 Inf -0.381 1.0000
StepF12 - StepF11 -0.11848 0.0229 Inf -5.180 <.0001
StepF13 - StepF12 0.01848 0.0229 Inf 0.807 1.0000
StepF14 - StepF13 0.01005 0.0229 Inf 0.439 1.0000
StepF15 - StepF14 0.03655 0.0229 Inf 1.598 1.0000
StepF16 - StepF15 -0.04830 0.0229 Inf -2.111 0.4168
StepF17 - StepF16 0.01633 0.0229 Inf 0.714 1.0000
StepF18 - StepF17 -0.03218 0.0229 Inf -1.406 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests Warning: Some predictor variables are on very different scales: consider
rescalingWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 25.3913 (tol = 0.002, component 1)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?;Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?Warning: Some predictor variables are on very different scales: consider
rescaling
[Optional] Random-slope model for rt_ms | subject (summary):
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ StepF + Accuracy + rt_ms + (1 + rt_ms | subject) + (1 |
trial_id)
Data: dd
REML criterion at convergence: 16387.1
Scaled residuals:
Min 1Q Median 3Q Max
-2.8335 -0.5400 -0.1679 0.3267 8.8004
Random effects:
Groups Name Variance Std.Dev. Corr
trial_id (Intercept) 8.846e-02 2.974e-01
subject (Intercept) 1.282e-01 3.581e-01
rt_ms 5.272e-09 7.261e-05 -0.48
Residual 1.830e-01 4.277e-01
Number of obs: 12778, groups: trial_id, 710; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 6.022e-01 8.544e-02 1.042e+00 7.048 0.083379 .
StepF1 -6.875e-03 1.596e-02 1.177e+04 -0.431 0.666585
StepF2 7.386e-02 1.565e-02 1.209e+04 4.720 2.38e-06 ***
StepF3 9.144e-02 1.565e-02 1.210e+04 5.845 5.21e-09 ***
StepF4 -1.229e-02 1.565e-02 1.209e+04 -0.785 0.432394
StepF5 -1.289e-03 1.564e-02 1.208e+04 -0.082 0.934273
StepF6 3.251e-02 1.566e-02 1.195e+04 2.076 0.037954 *
StepF7 5.552e-02 1.563e-02 1.209e+04 3.553 0.000383 ***
StepF8 -2.692e-02 1.562e-02 1.209e+04 -1.723 0.084890 .
StepF9 3.344e-04 1.563e-02 1.209e+04 0.021 0.982931
StepF10 5.772e-02 1.566e-02 1.188e+04 3.685 0.000230 ***
StepF11 5.003e-02 1.563e-02 1.209e+04 3.202 0.001370 **
StepF12 -6.877e-02 1.563e-02 1.209e+04 -4.400 1.09e-05 ***
StepF13 -4.553e-02 1.574e-02 1.170e+04 -2.892 0.003835 **
StepF14 -4.147e-02 1.563e-02 1.204e+04 -2.653 0.007991 **
StepF15 -4.535e-03 1.562e-02 1.209e+04 -0.290 0.771607
StepF16 -5.105e-02 1.562e-02 1.209e+04 -3.268 0.001086 **
StepF17 -3.476e-02 1.562e-02 1.209e+04 -2.225 0.026111 *
Accuracy1 1.057e-02 1.350e-02 4.987e+02 0.783 0.434017
rt_ms -1.715e-05 2.029e-05 7.129e+00 -0.845 0.425492
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 20 > 12.
Use print(summary(m_rs), correlation=TRUE) or
vcov(summary(m_rs)) if you need itfit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
Model failed to converge with max|grad| = 25.3913 (tol = 0.002, component 1)
Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?
Model is nearly unidentifiable: large eigenvalue ratio
- Rescale variables?
==============================
TRAINING (rt_ms) | Block 3 (18 steps) | Axis Z
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 150.4538 17 < 2e-16 ***
Accuracy 1.1998 1 0.27335
rt_ms 2.8769 1 0.08986 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.23 0.124 Inf 0.983 1.47
2 1.38 0.124 Inf 1.139 1.62
3 1.39 0.124 Inf 1.149 1.63
4 1.25 0.124 Inf 1.006 1.49
5 1.27 0.124 Inf 1.028 1.51
6 1.29 0.124 Inf 1.052 1.54
7 1.30 0.124 Inf 1.060 1.54
8 1.29 0.124 Inf 1.051 1.54
9 1.25 0.124 Inf 1.004 1.49
10 1.29 0.124 Inf 1.048 1.53
11 1.25 0.124 Inf 1.006 1.49
12 1.13 0.124 Inf 0.888 1.37
13 1.20 0.124 Inf 0.956 1.44
14 1.24 0.124 Inf 1.002 1.49
15 1.19 0.124 Inf 0.951 1.44
16 1.13 0.124 Inf 0.889 1.37
17 1.11 0.124 Inf 0.865 1.35
18 1.08 0.124 Inf 0.839 1.32
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.16 0.123 Inf 0.923 1.40
2 1.32 0.123 Inf 1.079 1.56
3 1.33 0.123 Inf 1.088 1.57
4 1.19 0.123 Inf 0.946 1.43
5 1.21 0.123 Inf 0.967 1.45
6 1.23 0.123 Inf 0.992 1.47
7 1.24 0.123 Inf 1.000 1.48
8 1.23 0.123 Inf 0.990 1.47
9 1.18 0.123 Inf 0.944 1.43
10 1.23 0.123 Inf 0.988 1.47
11 1.19 0.123 Inf 0.945 1.43
12 1.07 0.123 Inf 0.828 1.31
13 1.14 0.123 Inf 0.895 1.38
14 1.18 0.123 Inf 0.941 1.42
15 1.13 0.123 Inf 0.891 1.37
16 1.07 0.123 Inf 0.828 1.31
17 1.04 0.123 Inf 0.804 1.29
18 1.02 0.123 Inf 0.779 1.26
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.156268 0.0415 Inf -3.763 0.0195
StepF1 - StepF3 -0.165700 0.0416 Inf -3.983 0.0085
StepF1 - StepF4 -0.023194 0.0416 Inf -0.558 1.0000
StepF1 - StepF5 -0.044502 0.0414 Inf -1.076 0.9998
StepF1 - StepF6 -0.068933 0.0414 Inf -1.667 0.9727
StepF1 - StepF7 -0.077152 0.0414 Inf -1.861 0.9259
StepF1 - StepF8 -0.067784 0.0414 Inf -1.637 0.9771
StepF1 - StepF9 -0.021047 0.0412 Inf -0.510 1.0000
StepF1 - StepF10 -0.065258 0.0413 Inf -1.582 0.9838
StepF1 - StepF11 -0.022380 0.0413 Inf -0.541 1.0000
StepF1 - StepF12 0.095021 0.0414 Inf 2.295 0.6876
StepF1 - StepF13 0.027432 0.0411 Inf 0.667 1.0000
StepF1 - StepF14 -0.018450 0.0413 Inf -0.447 1.0000
StepF1 - StepF15 0.031790 0.0413 Inf 0.769 1.0000
StepF1 - StepF16 0.094519 0.0415 Inf 2.279 0.6985
StepF1 - StepF17 0.118663 0.0414 Inf 2.865 0.2725
StepF1 - StepF18 0.143970 0.0413 Inf 3.485 0.0505
StepF2 - StepF3 -0.009432 0.0410 Inf -0.230 1.0000
StepF2 - StepF4 0.133074 0.0410 Inf 3.242 0.1056
StepF2 - StepF5 0.111766 0.0411 Inf 2.722 0.3644
StepF2 - StepF6 0.087335 0.0411 Inf 2.127 0.8006
StepF2 - StepF7 0.079116 0.0410 Inf 1.928 0.9017
StepF2 - StepF8 0.088484 0.0410 Inf 2.156 0.7830
StepF2 - StepF9 0.135221 0.0411 Inf 3.290 0.0920
StepF2 - StepF10 0.091011 0.0411 Inf 2.215 0.7440
StepF2 - StepF11 0.133888 0.0411 Inf 3.260 0.1003
StepF2 - StepF12 0.251290 0.0410 Inf 6.122 <.0001
StepF2 - StepF13 0.183700 0.0412 Inf 4.460 0.0011
StepF2 - StepF14 0.137818 0.0411 Inf 3.355 0.0759
StepF2 - StepF15 0.188058 0.0411 Inf 4.579 0.0007
StepF2 - StepF16 0.250787 0.0410 Inf 6.111 <.0001
StepF2 - StepF17 0.274931 0.0410 Inf 6.698 <.0001
StepF2 - StepF18 0.300238 0.0411 Inf 7.302 <.0001
StepF3 - StepF4 0.142506 0.0410 Inf 3.472 0.0527
StepF3 - StepF5 0.121198 0.0411 Inf 2.951 0.2244
StepF3 - StepF6 0.096767 0.0411 Inf 2.356 0.6417
StepF3 - StepF7 0.088548 0.0411 Inf 2.157 0.7821
StepF3 - StepF8 0.097916 0.0411 Inf 2.385 0.6198
StepF3 - StepF9 0.144653 0.0411 Inf 3.518 0.0455
StepF3 - StepF10 0.100442 0.0411 Inf 2.443 0.5747
StepF3 - StepF11 0.143320 0.0411 Inf 3.489 0.0500
StepF3 - StepF12 0.260721 0.0411 Inf 6.350 <.0001
StepF3 - StepF13 0.193132 0.0412 Inf 4.684 0.0004
StepF3 - StepF14 0.147250 0.0411 Inf 3.583 0.0367
StepF3 - StepF15 0.197490 0.0411 Inf 4.807 0.0002
StepF3 - StepF16 0.260219 0.0410 Inf 6.339 <.0001
StepF3 - StepF17 0.284363 0.0411 Inf 6.926 <.0001
StepF3 - StepF18 0.309670 0.0411 Inf 7.528 <.0001
StepF4 - StepF5 -0.021308 0.0411 Inf -0.519 1.0000
StepF4 - StepF6 -0.045738 0.0411 Inf -1.114 0.9998
StepF4 - StepF7 -0.053958 0.0411 Inf -1.314 0.9980
StepF4 - StepF8 -0.044590 0.0411 Inf -1.086 0.9998
StepF4 - StepF9 0.002147 0.0411 Inf 0.052 1.0000
StepF4 - StepF10 -0.042063 0.0411 Inf -1.023 0.9999
StepF4 - StepF11 0.000814 0.0411 Inf 0.020 1.0000
StepF4 - StepF12 0.118216 0.0411 Inf 2.879 0.2642
StepF4 - StepF13 0.050627 0.0412 Inf 1.228 0.9991
StepF4 - StepF14 0.004744 0.0411 Inf 0.115 1.0000
StepF4 - StepF15 0.054984 0.0411 Inf 1.338 0.9975
StepF4 - StepF16 0.117714 0.0410 Inf 2.868 0.2709
StepF4 - StepF17 0.141857 0.0411 Inf 3.455 0.0557
StepF4 - StepF18 0.167164 0.0411 Inf 4.064 0.0062
StepF5 - StepF6 -0.024431 0.0410 Inf -0.595 1.0000
StepF5 - StepF7 -0.032650 0.0410 Inf -0.796 1.0000
StepF5 - StepF8 -0.023282 0.0410 Inf -0.567 1.0000
StepF5 - StepF9 0.023455 0.0411 Inf 0.571 1.0000
StepF5 - StepF10 -0.020755 0.0411 Inf -0.506 1.0000
StepF5 - StepF11 0.022122 0.0410 Inf 0.539 1.0000
StepF5 - StepF12 0.139524 0.0410 Inf 3.400 0.0662
StepF5 - StepF13 0.071934 0.0411 Inf 1.750 0.9568
StepF5 - StepF14 0.026052 0.0410 Inf 0.635 1.0000
StepF5 - StepF15 0.076292 0.0410 Inf 1.859 0.9267
StepF5 - StepF16 0.139021 0.0410 Inf 3.387 0.0688
StepF5 - StepF17 0.163165 0.0410 Inf 3.976 0.0088
StepF5 - StepF18 0.188472 0.0411 Inf 4.588 0.0006
StepF6 - StepF7 -0.008219 0.0410 Inf -0.200 1.0000
StepF6 - StepF8 0.001149 0.0410 Inf 0.028 1.0000
StepF6 - StepF9 0.047885 0.0411 Inf 1.167 0.9996
StepF6 - StepF10 0.003675 0.0410 Inf 0.090 1.0000
StepF6 - StepF11 0.046552 0.0410 Inf 1.134 0.9997
StepF6 - StepF12 0.163954 0.0410 Inf 3.995 0.0081
StepF6 - StepF13 0.096365 0.0411 Inf 2.345 0.6503
StepF6 - StepF14 0.050483 0.0410 Inf 1.230 0.9991
StepF6 - StepF15 0.100723 0.0410 Inf 2.454 0.5658
StepF6 - StepF16 0.163452 0.0410 Inf 3.982 0.0085
StepF6 - StepF17 0.187596 0.0410 Inf 4.571 0.0007
StepF6 - StepF18 0.212903 0.0411 Inf 5.183 <.0001
StepF7 - StepF8 0.009368 0.0410 Inf 0.228 1.0000
StepF7 - StepF9 0.056105 0.0411 Inf 1.366 0.9968
StepF7 - StepF10 0.011894 0.0411 Inf 0.290 1.0000
StepF7 - StepF11 0.054772 0.0410 Inf 1.334 0.9976
StepF7 - StepF12 0.172173 0.0410 Inf 4.195 0.0036
StepF7 - StepF13 0.104584 0.0411 Inf 2.542 0.4978
StepF7 - StepF14 0.058702 0.0411 Inf 1.430 0.9946
StepF7 - StepF15 0.108942 0.0410 Inf 2.654 0.4133
StepF7 - StepF16 0.171671 0.0410 Inf 4.183 0.0038
StepF7 - StepF17 0.195815 0.0410 Inf 4.771 0.0003
StepF7 - StepF18 0.221122 0.0411 Inf 5.381 <.0001
StepF8 - StepF9 0.046736 0.0411 Inf 1.138 0.9997
StepF8 - StepF10 0.002526 0.0411 Inf 0.062 1.0000
StepF8 - StepF11 0.045404 0.0410 Inf 1.106 0.9998
StepF8 - StepF12 0.162805 0.0410 Inf 3.967 0.0091
StepF8 - StepF13 0.095216 0.0411 Inf 2.315 0.6721
StepF8 - StepF14 0.049334 0.0410 Inf 1.202 0.9993
StepF8 - StepF15 0.099574 0.0410 Inf 2.426 0.5877
StepF8 - StepF16 0.162303 0.0410 Inf 3.955 0.0095
StepF8 - StepF17 0.186447 0.0410 Inf 4.543 0.0008
StepF8 - StepF18 0.211754 0.0411 Inf 5.154 <.0001
StepF9 - StepF10 -0.044210 0.0410 Inf -1.077 0.9998
StepF9 - StepF11 -0.001333 0.0410 Inf -0.032 1.0000
StepF9 - StepF12 0.116069 0.0411 Inf 2.827 0.2958
StepF9 - StepF13 0.048480 0.0411 Inf 1.181 0.9995
StepF9 - StepF14 0.002597 0.0410 Inf 0.063 1.0000
StepF9 - StepF15 0.052837 0.0410 Inf 1.287 0.9984
StepF9 - StepF16 0.115567 0.0411 Inf 2.813 0.3041
StepF9 - StepF17 0.139710 0.0411 Inf 3.402 0.0657
StepF9 - StepF18 0.165017 0.0411 Inf 4.018 0.0074
StepF10 - StepF11 0.042877 0.0410 Inf 1.045 0.9999
StepF10 - StepF12 0.160279 0.0411 Inf 3.904 0.0116
StepF10 - StepF13 0.092690 0.0411 Inf 2.257 0.7145
StepF10 - StepF14 0.046807 0.0410 Inf 1.141 0.9997
StepF10 - StepF15 0.097048 0.0410 Inf 2.365 0.6352
StepF10 - StepF16 0.159777 0.0411 Inf 3.890 0.0122
StepF10 - StepF17 0.183920 0.0411 Inf 4.479 0.0010
StepF10 - StepF18 0.209228 0.0411 Inf 5.094 0.0001
StepF11 - StepF12 0.117402 0.0410 Inf 2.860 0.2753
StepF11 - StepF13 0.049813 0.0411 Inf 1.212 0.9993
StepF11 - StepF14 0.003930 0.0410 Inf 0.096 1.0000
StepF11 - StepF15 0.054170 0.0410 Inf 1.320 0.9979
StepF11 - StepF16 0.116900 0.0411 Inf 2.848 0.2829
StepF11 - StepF17 0.141043 0.0410 Inf 3.436 0.0591
StepF11 - StepF18 0.166350 0.0411 Inf 4.050 0.0065
StepF12 - StepF13 -0.067589 0.0411 Inf -1.643 0.9762
StepF12 - StepF14 -0.113471 0.0411 Inf -2.764 0.3360
StepF12 - StepF15 -0.063231 0.0410 Inf -1.541 0.9877
StepF12 - StepF16 -0.000502 0.0410 Inf -0.012 1.0000
StepF12 - StepF17 0.023641 0.0410 Inf 0.576 1.0000
StepF12 - StepF18 0.048949 0.0411 Inf 1.191 0.9994
StepF13 - StepF14 -0.045882 0.0411 Inf -1.117 0.9997
StepF13 - StepF15 0.004358 0.0411 Inf 0.106 1.0000
StepF13 - StepF16 0.067087 0.0412 Inf 1.630 0.9781
StepF13 - StepF17 0.091231 0.0411 Inf 2.218 0.7418
StepF13 - StepF18 0.116538 0.0411 Inf 2.835 0.2903
StepF14 - StepF15 0.050240 0.0410 Inf 1.224 0.9992
StepF14 - StepF16 0.112969 0.0411 Inf 2.751 0.3447
StepF14 - StepF17 0.137113 0.0411 Inf 3.340 0.0794
StepF14 - StepF18 0.162420 0.0411 Inf 3.955 0.0095
StepF15 - StepF16 0.062729 0.0411 Inf 1.528 0.9888
StepF15 - StepF17 0.086873 0.0410 Inf 2.117 0.8070
StepF15 - StepF18 0.112180 0.0411 Inf 2.731 0.3583
StepF16 - StepF17 0.024143 0.0410 Inf 0.588 1.0000
StepF16 - StepF18 0.049451 0.0411 Inf 1.203 0.9993
StepF17 - StepF18 0.025307 0.0411 Inf 0.616 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.156268 0.0415 Inf -3.763 0.0195
StepF1 - StepF3 -0.165700 0.0416 Inf -3.983 0.0085
StepF1 - StepF4 -0.023194 0.0416 Inf -0.558 1.0000
StepF1 - StepF5 -0.044502 0.0414 Inf -1.076 0.9998
StepF1 - StepF6 -0.068933 0.0414 Inf -1.667 0.9727
StepF1 - StepF7 -0.077152 0.0414 Inf -1.861 0.9259
StepF1 - StepF8 -0.067784 0.0414 Inf -1.637 0.9771
StepF1 - StepF9 -0.021047 0.0412 Inf -0.510 1.0000
StepF1 - StepF10 -0.065258 0.0413 Inf -1.582 0.9838
StepF1 - StepF11 -0.022380 0.0413 Inf -0.541 1.0000
StepF1 - StepF12 0.095021 0.0414 Inf 2.295 0.6876
StepF1 - StepF13 0.027432 0.0411 Inf 0.667 1.0000
StepF1 - StepF14 -0.018450 0.0413 Inf -0.447 1.0000
StepF1 - StepF15 0.031790 0.0413 Inf 0.769 1.0000
StepF1 - StepF16 0.094519 0.0415 Inf 2.279 0.6985
StepF1 - StepF17 0.118663 0.0414 Inf 2.865 0.2725
StepF1 - StepF18 0.143970 0.0413 Inf 3.485 0.0505
StepF2 - StepF3 -0.009432 0.0410 Inf -0.230 1.0000
StepF2 - StepF4 0.133074 0.0410 Inf 3.242 0.1056
StepF2 - StepF5 0.111766 0.0411 Inf 2.722 0.3644
StepF2 - StepF6 0.087335 0.0411 Inf 2.127 0.8006
StepF2 - StepF7 0.079116 0.0410 Inf 1.928 0.9017
StepF2 - StepF8 0.088484 0.0410 Inf 2.156 0.7830
StepF2 - StepF9 0.135221 0.0411 Inf 3.290 0.0920
StepF2 - StepF10 0.091011 0.0411 Inf 2.215 0.7440
StepF2 - StepF11 0.133888 0.0411 Inf 3.260 0.1003
StepF2 - StepF12 0.251290 0.0410 Inf 6.122 <.0001
StepF2 - StepF13 0.183700 0.0412 Inf 4.460 0.0011
StepF2 - StepF14 0.137818 0.0411 Inf 3.355 0.0759
StepF2 - StepF15 0.188058 0.0411 Inf 4.579 0.0007
StepF2 - StepF16 0.250787 0.0410 Inf 6.111 <.0001
StepF2 - StepF17 0.274931 0.0410 Inf 6.698 <.0001
StepF2 - StepF18 0.300238 0.0411 Inf 7.302 <.0001
StepF3 - StepF4 0.142506 0.0410 Inf 3.472 0.0527
StepF3 - StepF5 0.121198 0.0411 Inf 2.951 0.2244
StepF3 - StepF6 0.096767 0.0411 Inf 2.356 0.6417
StepF3 - StepF7 0.088548 0.0411 Inf 2.157 0.7821
StepF3 - StepF8 0.097916 0.0411 Inf 2.385 0.6198
StepF3 - StepF9 0.144653 0.0411 Inf 3.518 0.0455
StepF3 - StepF10 0.100442 0.0411 Inf 2.443 0.5747
StepF3 - StepF11 0.143320 0.0411 Inf 3.489 0.0500
StepF3 - StepF12 0.260721 0.0411 Inf 6.350 <.0001
StepF3 - StepF13 0.193132 0.0412 Inf 4.684 0.0004
StepF3 - StepF14 0.147250 0.0411 Inf 3.583 0.0367
StepF3 - StepF15 0.197490 0.0411 Inf 4.807 0.0002
StepF3 - StepF16 0.260219 0.0410 Inf 6.339 <.0001
StepF3 - StepF17 0.284363 0.0411 Inf 6.926 <.0001
StepF3 - StepF18 0.309670 0.0411 Inf 7.528 <.0001
StepF4 - StepF5 -0.021308 0.0411 Inf -0.519 1.0000
StepF4 - StepF6 -0.045738 0.0411 Inf -1.114 0.9998
StepF4 - StepF7 -0.053958 0.0411 Inf -1.314 0.9980
StepF4 - StepF8 -0.044590 0.0411 Inf -1.086 0.9998
StepF4 - StepF9 0.002147 0.0411 Inf 0.052 1.0000
StepF4 - StepF10 -0.042063 0.0411 Inf -1.023 0.9999
StepF4 - StepF11 0.000814 0.0411 Inf 0.020 1.0000
StepF4 - StepF12 0.118216 0.0411 Inf 2.879 0.2642
StepF4 - StepF13 0.050627 0.0412 Inf 1.228 0.9991
StepF4 - StepF14 0.004744 0.0411 Inf 0.115 1.0000
StepF4 - StepF15 0.054984 0.0411 Inf 1.338 0.9975
StepF4 - StepF16 0.117714 0.0410 Inf 2.868 0.2709
StepF4 - StepF17 0.141857 0.0411 Inf 3.455 0.0557
StepF4 - StepF18 0.167164 0.0411 Inf 4.064 0.0062
StepF5 - StepF6 -0.024431 0.0410 Inf -0.595 1.0000
StepF5 - StepF7 -0.032650 0.0410 Inf -0.796 1.0000
StepF5 - StepF8 -0.023282 0.0410 Inf -0.567 1.0000
StepF5 - StepF9 0.023455 0.0411 Inf 0.571 1.0000
StepF5 - StepF10 -0.020755 0.0411 Inf -0.506 1.0000
StepF5 - StepF11 0.022122 0.0410 Inf 0.539 1.0000
StepF5 - StepF12 0.139524 0.0410 Inf 3.400 0.0662
StepF5 - StepF13 0.071934 0.0411 Inf 1.750 0.9568
StepF5 - StepF14 0.026052 0.0410 Inf 0.635 1.0000
StepF5 - StepF15 0.076292 0.0410 Inf 1.859 0.9267
StepF5 - StepF16 0.139021 0.0410 Inf 3.387 0.0688
StepF5 - StepF17 0.163165 0.0410 Inf 3.976 0.0088
StepF5 - StepF18 0.188472 0.0411 Inf 4.588 0.0006
StepF6 - StepF7 -0.008219 0.0410 Inf -0.200 1.0000
StepF6 - StepF8 0.001149 0.0410 Inf 0.028 1.0000
StepF6 - StepF9 0.047885 0.0411 Inf 1.167 0.9996
StepF6 - StepF10 0.003675 0.0410 Inf 0.090 1.0000
StepF6 - StepF11 0.046552 0.0410 Inf 1.134 0.9997
StepF6 - StepF12 0.163954 0.0410 Inf 3.995 0.0081
StepF6 - StepF13 0.096365 0.0411 Inf 2.345 0.6503
StepF6 - StepF14 0.050483 0.0410 Inf 1.230 0.9991
StepF6 - StepF15 0.100723 0.0410 Inf 2.454 0.5658
StepF6 - StepF16 0.163452 0.0410 Inf 3.982 0.0085
StepF6 - StepF17 0.187596 0.0410 Inf 4.571 0.0007
StepF6 - StepF18 0.212903 0.0411 Inf 5.183 <.0001
StepF7 - StepF8 0.009368 0.0410 Inf 0.228 1.0000
StepF7 - StepF9 0.056105 0.0411 Inf 1.366 0.9968
StepF7 - StepF10 0.011894 0.0411 Inf 0.290 1.0000
StepF7 - StepF11 0.054772 0.0410 Inf 1.334 0.9976
StepF7 - StepF12 0.172173 0.0410 Inf 4.195 0.0036
StepF7 - StepF13 0.104584 0.0411 Inf 2.542 0.4978
StepF7 - StepF14 0.058702 0.0411 Inf 1.430 0.9946
StepF7 - StepF15 0.108942 0.0410 Inf 2.654 0.4133
StepF7 - StepF16 0.171671 0.0410 Inf 4.183 0.0038
StepF7 - StepF17 0.195815 0.0410 Inf 4.771 0.0003
StepF7 - StepF18 0.221122 0.0411 Inf 5.381 <.0001
StepF8 - StepF9 0.046736 0.0411 Inf 1.138 0.9997
StepF8 - StepF10 0.002526 0.0411 Inf 0.062 1.0000
StepF8 - StepF11 0.045404 0.0410 Inf 1.106 0.9998
StepF8 - StepF12 0.162805 0.0410 Inf 3.967 0.0091
StepF8 - StepF13 0.095216 0.0411 Inf 2.315 0.6721
StepF8 - StepF14 0.049334 0.0410 Inf 1.202 0.9993
StepF8 - StepF15 0.099574 0.0410 Inf 2.426 0.5877
StepF8 - StepF16 0.162303 0.0410 Inf 3.955 0.0095
StepF8 - StepF17 0.186447 0.0410 Inf 4.543 0.0008
StepF8 - StepF18 0.211754 0.0411 Inf 5.154 <.0001
StepF9 - StepF10 -0.044210 0.0410 Inf -1.077 0.9998
StepF9 - StepF11 -0.001333 0.0410 Inf -0.032 1.0000
StepF9 - StepF12 0.116069 0.0411 Inf 2.827 0.2958
StepF9 - StepF13 0.048480 0.0411 Inf 1.181 0.9995
StepF9 - StepF14 0.002597 0.0410 Inf 0.063 1.0000
StepF9 - StepF15 0.052837 0.0410 Inf 1.287 0.9984
StepF9 - StepF16 0.115567 0.0411 Inf 2.813 0.3041
StepF9 - StepF17 0.139710 0.0411 Inf 3.402 0.0657
StepF9 - StepF18 0.165017 0.0411 Inf 4.018 0.0074
StepF10 - StepF11 0.042877 0.0410 Inf 1.045 0.9999
StepF10 - StepF12 0.160279 0.0411 Inf 3.904 0.0116
StepF10 - StepF13 0.092690 0.0411 Inf 2.257 0.7145
StepF10 - StepF14 0.046807 0.0410 Inf 1.141 0.9997
StepF10 - StepF15 0.097048 0.0410 Inf 2.365 0.6352
StepF10 - StepF16 0.159777 0.0411 Inf 3.890 0.0122
StepF10 - StepF17 0.183920 0.0411 Inf 4.479 0.0010
StepF10 - StepF18 0.209228 0.0411 Inf 5.094 0.0001
StepF11 - StepF12 0.117402 0.0410 Inf 2.860 0.2753
StepF11 - StepF13 0.049813 0.0411 Inf 1.212 0.9993
StepF11 - StepF14 0.003930 0.0410 Inf 0.096 1.0000
StepF11 - StepF15 0.054170 0.0410 Inf 1.320 0.9979
StepF11 - StepF16 0.116900 0.0411 Inf 2.848 0.2829
StepF11 - StepF17 0.141043 0.0410 Inf 3.436 0.0591
StepF11 - StepF18 0.166350 0.0411 Inf 4.050 0.0065
StepF12 - StepF13 -0.067589 0.0411 Inf -1.643 0.9762
StepF12 - StepF14 -0.113471 0.0411 Inf -2.764 0.3360
StepF12 - StepF15 -0.063231 0.0410 Inf -1.541 0.9877
StepF12 - StepF16 -0.000502 0.0410 Inf -0.012 1.0000
StepF12 - StepF17 0.023641 0.0410 Inf 0.576 1.0000
StepF12 - StepF18 0.048949 0.0411 Inf 1.191 0.9994
StepF13 - StepF14 -0.045882 0.0411 Inf -1.117 0.9997
StepF13 - StepF15 0.004358 0.0411 Inf 0.106 1.0000
StepF13 - StepF16 0.067087 0.0412 Inf 1.630 0.9781
StepF13 - StepF17 0.091231 0.0411 Inf 2.218 0.7418
StepF13 - StepF18 0.116538 0.0411 Inf 2.835 0.2903
StepF14 - StepF15 0.050240 0.0410 Inf 1.224 0.9992
StepF14 - StepF16 0.112969 0.0411 Inf 2.751 0.3447
StepF14 - StepF17 0.137113 0.0411 Inf 3.340 0.0794
StepF14 - StepF18 0.162420 0.0411 Inf 3.955 0.0095
StepF15 - StepF16 0.062729 0.0411 Inf 1.528 0.9888
StepF15 - StepF17 0.086873 0.0410 Inf 2.117 0.8070
StepF15 - StepF18 0.112180 0.0411 Inf 2.731 0.3583
StepF16 - StepF17 0.024143 0.0410 Inf 0.588 1.0000
StepF16 - StepF18 0.049451 0.0411 Inf 1.203 0.9993
StepF17 - StepF18 0.025307 0.0411 Inf 0.616 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.15627 0.0415 Inf 3.763 0.0029
StepF3 - StepF2 0.00943 0.0410 Inf 0.230 1.0000
StepF4 - StepF3 -0.14251 0.0410 Inf -3.472 0.0083
StepF5 - StepF4 0.02131 0.0411 Inf 0.519 1.0000
StepF6 - StepF5 0.02443 0.0410 Inf 0.595 1.0000
StepF7 - StepF6 0.00822 0.0410 Inf 0.200 1.0000
StepF8 - StepF7 -0.00937 0.0410 Inf -0.228 1.0000
StepF9 - StepF8 -0.04674 0.0411 Inf -1.138 1.0000
StepF10 - StepF9 0.04421 0.0410 Inf 1.077 1.0000
StepF11 - StepF10 -0.04288 0.0410 Inf -1.045 1.0000
StepF12 - StepF11 -0.11740 0.0410 Inf -2.860 0.0635
StepF13 - StepF12 0.06759 0.0411 Inf 1.643 1.0000
StepF14 - StepF13 0.04588 0.0411 Inf 1.117 1.0000
StepF15 - StepF14 -0.05024 0.0410 Inf -1.224 1.0000
StepF16 - StepF15 -0.06273 0.0411 Inf -1.528 1.0000
StepF17 - StepF16 -0.02414 0.0410 Inf -0.588 1.0000
StepF18 - StepF17 -0.02531 0.0411 Inf -0.616 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.15627 0.0415 Inf 3.763 0.0029
StepF3 - StepF2 0.00943 0.0410 Inf 0.230 1.0000
StepF4 - StepF3 -0.14251 0.0410 Inf -3.472 0.0083
StepF5 - StepF4 0.02131 0.0411 Inf 0.519 1.0000
StepF6 - StepF5 0.02443 0.0410 Inf 0.595 1.0000
StepF7 - StepF6 0.00822 0.0410 Inf 0.200 1.0000
StepF8 - StepF7 -0.00937 0.0410 Inf -0.228 1.0000
StepF9 - StepF8 -0.04674 0.0411 Inf -1.138 1.0000
StepF10 - StepF9 0.04421 0.0410 Inf 1.077 1.0000
StepF11 - StepF10 -0.04288 0.0410 Inf -1.045 1.0000
StepF12 - StepF11 -0.11740 0.0410 Inf -2.860 0.0635
StepF13 - StepF12 0.06759 0.0411 Inf 1.643 1.0000
StepF14 - StepF13 0.04588 0.0411 Inf 1.117 1.0000
StepF15 - StepF14 -0.05024 0.0410 Inf -1.224 1.0000
StepF16 - StepF15 -0.06273 0.0411 Inf -1.528 1.0000
StepF17 - StepF16 -0.02414 0.0410 Inf -0.588 1.0000
StepF18 - StepF17 -0.02531 0.0411 Inf -0.616 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests Warning: Some predictor variables are on very different scales: consider
rescalingboundary (singular) fit: see help('isSingular')Warning: Some predictor variables are on very different scales: consider
rescaling
[Optional] Random-slope model for rt_ms | subject (summary):
Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: RMS ~ StepF + Accuracy + rt_ms + (1 + rt_ms | subject) + (1 |
trial_id)
Data: dd
REML criterion at convergence: 31780.8
Scaled residuals:
Min 1Q Median 3Q Max
-4.1293 -0.5343 -0.1401 0.3573 10.2860
Random effects:
Groups Name Variance Std.Dev. Corr
trial_id (Intercept) 7.621e-01 0.8729682
subject (Intercept) 5.467e-01 0.7393984
rt_ms 5.013e-09 0.0000708 0.13
Residual 5.816e-01 0.7626053
Number of obs: 12778, groups: trial_id, 710; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 1.217e+00 1.778e-01 2.808e+00 6.848 0.007850 **
StepF1 -1.420e-02 2.839e-02 1.121e+04 -0.500 0.616944
StepF2 1.458e-01 2.788e-02 1.209e+04 5.231 1.71e-07 ***
StepF3 1.537e-01 2.789e-02 1.211e+04 5.513 3.59e-08 ***
StepF4 1.187e-02 2.790e-02 1.211e+04 0.425 0.670563
StepF5 3.235e-02 2.785e-02 1.207e+04 1.161 0.245530
StepF6 5.717e-02 2.788e-02 1.188e+04 2.050 0.040344 *
StepF7 6.646e-02 2.785e-02 1.211e+04 2.386 0.017037 *
StepF8 5.701e-02 2.784e-02 1.211e+04 2.048 0.040611 *
StepF9 7.957e-03 2.785e-02 1.210e+04 0.286 0.775095
StepF10 5.199e-02 2.788e-02 1.183e+04 1.865 0.062217 .
StepF11 1.192e-02 2.785e-02 1.209e+04 0.428 0.668550
StepF12 -1.072e-01 2.785e-02 1.210e+04 -3.849 0.000119 ***
StepF13 -4.033e-02 2.800e-02 1.129e+04 -1.440 0.149870
StepF14 5.934e-03 2.785e-02 1.207e+04 0.213 0.831252
StepF15 -4.611e-02 2.784e-02 1.210e+04 -1.656 0.097675 .
StepF16 -1.070e-01 2.785e-02 1.211e+04 -3.841 0.000123 ***
StepF17 -1.302e-01 2.784e-02 1.211e+04 -4.677 2.94e-06 ***
Accuracy1 3.140e-02 3.820e-02 2.279e+02 0.822 0.411928
rt_ms -1.741e-05 2.462e-05 7.707e+00 -0.707 0.500246
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 20 > 12.
Use print(summary(m_rs), correlation=TRUE) or
vcov(summary(m_rs)) if you need itfit warnings:
Some predictor variables are on very different scales: consider rescaling
optimizer (nloptwrap) convergence code: 0 (OK)
boundary (singular) fit: see help('isSingular')# ---- RUN: TEST (Blocks 4–5) ----
# Block 4
.report_step_test_rtms(sw_b4_6_rt, "4", "6 steps")
==============================
TEST (rt_ms) | Block 4 | 6 steps | Axis X
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 39.8252 5 1.62e-07 ***
Accuracy 0.5446 1 0.46053
rt_ms 4.5293 1 0.03332 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.709 0.0802 Inf 0.552 0.866
2 0.814 0.0793 Inf 0.659 0.969
3 0.804 0.0793 Inf 0.649 0.960
4 0.752 0.0793 Inf 0.597 0.908
5 0.621 0.0793 Inf 0.465 0.776
6 0.653 0.0793 Inf 0.497 0.808
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.672 0.0719 Inf 0.531 0.813
2 0.777 0.0712 Inf 0.637 0.916
3 0.767 0.0712 Inf 0.627 0.907
4 0.715 0.0713 Inf 0.575 0.855
5 0.583 0.0712 Inf 0.444 0.723
6 0.616 0.0712 Inf 0.476 0.755
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.10484 0.0416 Inf -2.523 0.1173
StepF1 - StepF3 -0.09510 0.0419 Inf -2.270 0.2065
StepF1 - StepF4 -0.04331 0.0423 Inf -1.023 0.9104
StepF1 - StepF5 0.08858 0.0418 Inf 2.118 0.2780
StepF1 - StepF6 0.05637 0.0410 Inf 1.374 0.7425
StepF2 - StepF3 0.00974 0.0396 Inf 0.246 0.9999
StepF2 - StepF4 0.06154 0.0396 Inf 1.553 0.6297
StepF2 - StepF5 0.19342 0.0396 Inf 4.889 <.0001
StepF2 - StepF6 0.16121 0.0396 Inf 4.071 0.0007
StepF3 - StepF4 0.05180 0.0396 Inf 1.309 0.7803
StepF3 - StepF5 0.18368 0.0396 Inf 4.644 0.0001
StepF3 - StepF6 0.15147 0.0397 Inf 3.819 0.0019
StepF4 - StepF5 0.13189 0.0396 Inf 3.332 0.0111
StepF4 - StepF6 0.09968 0.0398 Inf 2.506 0.1223
StepF5 - StepF6 -0.03221 0.0396 Inf -0.812 0.9654
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.10484 0.0416 Inf -2.523 0.1173
StepF1 - StepF3 -0.09510 0.0419 Inf -2.270 0.2065
StepF1 - StepF4 -0.04331 0.0423 Inf -1.023 0.9104
StepF1 - StepF5 0.08858 0.0418 Inf 2.118 0.2780
StepF1 - StepF6 0.05637 0.0410 Inf 1.374 0.7425
StepF2 - StepF3 0.00974 0.0396 Inf 0.246 0.9999
StepF2 - StepF4 0.06154 0.0396 Inf 1.553 0.6297
StepF2 - StepF5 0.19342 0.0396 Inf 4.889 <.0001
StepF2 - StepF6 0.16121 0.0396 Inf 4.071 0.0007
StepF3 - StepF4 0.05180 0.0396 Inf 1.309 0.7803
StepF3 - StepF5 0.18368 0.0396 Inf 4.644 0.0001
StepF3 - StepF6 0.15147 0.0397 Inf 3.819 0.0019
StepF4 - StepF5 0.13189 0.0396 Inf 3.332 0.0111
StepF4 - StepF6 0.09968 0.0398 Inf 2.506 0.1223
StepF5 - StepF6 -0.03221 0.0396 Inf -0.812 0.9654
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.10484 0.0416 Inf 2.523 0.0465
StepF3 - StepF2 -0.00974 0.0396 Inf -0.246 0.8332
StepF4 - StepF3 -0.05180 0.0396 Inf -1.309 0.5717
StepF5 - StepF4 -0.13189 0.0396 Inf -3.332 0.0043
StepF6 - StepF5 0.03221 0.0396 Inf 0.812 0.8332
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.10484 0.0416 Inf 2.523 0.0465
StepF3 - StepF2 -0.00974 0.0396 Inf -0.246 0.8332
StepF4 - StepF3 -0.05180 0.0396 Inf -1.309 0.5717
StepF5 - StepF4 -0.13189 0.0396 Inf -3.332 0.0043
StepF6 - StepF5 0.03221 0.0396 Inf 0.812 0.8332
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests
==============================
TEST (rt_ms) | Block 4 | 6 steps | Axis Y
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 38.6464 5 2.798e-07 ***
Accuracy 0.0116 1 0.9142
rt_ms 1.6224 1 0.2028
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.713 0.0954 Inf 0.525 0.900
2 0.899 0.0944 Inf 0.714 1.084
3 0.874 0.0945 Inf 0.689 1.060
4 0.753 0.0945 Inf 0.568 0.939
5 0.683 0.0945 Inf 0.498 0.869
6 0.752 0.0945 Inf 0.567 0.937
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.707 0.0874 Inf 0.535 0.878
2 0.893 0.0867 Inf 0.723 1.063
3 0.869 0.0867 Inf 0.699 1.039
4 0.748 0.0868 Inf 0.577 0.918
5 0.678 0.0867 Inf 0.508 0.848
6 0.746 0.0866 Inf 0.576 0.916
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.18636 0.0464 Inf -4.020 0.0008
StepF1 - StepF3 -0.16190 0.0468 Inf -3.463 0.0071
StepF1 - StepF4 -0.04086 0.0472 Inf -0.865 0.9548
StepF1 - StepF5 0.02909 0.0467 Inf 0.623 0.9894
StepF1 - StepF6 -0.03921 0.0458 Inf -0.857 0.9566
StepF2 - StepF3 0.02446 0.0442 Inf 0.554 0.9938
StepF2 - StepF4 0.14551 0.0442 Inf 3.291 0.0128
StepF2 - StepF5 0.21545 0.0441 Inf 4.880 <.0001
StepF2 - StepF6 0.14716 0.0442 Inf 3.330 0.0112
StepF3 - StepF4 0.12105 0.0442 Inf 2.741 0.0674
StepF3 - StepF5 0.19099 0.0441 Inf 4.327 0.0002
StepF3 - StepF6 0.12270 0.0443 Inf 2.772 0.0620
StepF4 - StepF5 0.06995 0.0442 Inf 1.584 0.6095
StepF4 - StepF6 0.00165 0.0444 Inf 0.037 1.0000
StepF5 - StepF6 -0.06830 0.0442 Inf -1.544 0.6359
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.18636 0.0464 Inf -4.020 0.0008
StepF1 - StepF3 -0.16190 0.0468 Inf -3.463 0.0071
StepF1 - StepF4 -0.04086 0.0472 Inf -0.865 0.9548
StepF1 - StepF5 0.02909 0.0467 Inf 0.623 0.9894
StepF1 - StepF6 -0.03921 0.0458 Inf -0.857 0.9566
StepF2 - StepF3 0.02446 0.0442 Inf 0.554 0.9938
StepF2 - StepF4 0.14551 0.0442 Inf 3.291 0.0128
StepF2 - StepF5 0.21545 0.0441 Inf 4.880 <.0001
StepF2 - StepF6 0.14716 0.0442 Inf 3.330 0.0112
StepF3 - StepF4 0.12105 0.0442 Inf 2.741 0.0674
StepF3 - StepF5 0.19099 0.0441 Inf 4.327 0.0002
StepF3 - StepF6 0.12270 0.0443 Inf 2.772 0.0620
StepF4 - StepF5 0.06995 0.0442 Inf 1.584 0.6095
StepF4 - StepF6 0.00165 0.0444 Inf 0.037 1.0000
StepF5 - StepF6 -0.06830 0.0442 Inf -1.544 0.6359
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1864 0.0464 Inf 4.020 0.0003
StepF3 - StepF2 -0.0245 0.0442 Inf -0.554 0.5796
StepF4 - StepF3 -0.1210 0.0442 Inf -2.741 0.0245
StepF5 - StepF4 -0.0699 0.0442 Inf -1.584 0.3399
StepF6 - StepF5 0.0683 0.0442 Inf 1.544 0.3399
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1864 0.0464 Inf 4.020 0.0003
StepF3 - StepF2 -0.0245 0.0442 Inf -0.554 0.5796
StepF4 - StepF3 -0.1210 0.0442 Inf -2.741 0.0245
StepF5 - StepF4 -0.0699 0.0442 Inf -1.584 0.3399
StepF6 - StepF5 0.0683 0.0442 Inf 1.544 0.3399
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests
==============================
TEST (rt_ms) | Block 4 | 6 steps | Axis Z
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 45.7962 5 9.992e-09 ***
Accuracy 0.1530 1 0.6957
rt_ms 0.3747 1 0.5405
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.41 0.176 Inf 1.060 1.75
2 1.66 0.174 Inf 1.315 2.00
3 1.71 0.174 Inf 1.372 2.06
4 1.57 0.175 Inf 1.226 1.91
5 1.33 0.174 Inf 0.985 1.67
6 1.32 0.174 Inf 0.981 1.66
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.45 0.156 Inf 1.144 1.76
2 1.70 0.155 Inf 1.398 2.01
3 1.76 0.155 Inf 1.456 2.06
4 1.61 0.155 Inf 1.309 1.92
5 1.37 0.155 Inf 1.069 1.68
6 1.37 0.155 Inf 1.064 1.67
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.2510 0.0828 Inf -3.032 0.0293
StepF1 - StepF3 -0.3087 0.0835 Inf -3.698 0.0030
StepF1 - StepF4 -0.1627 0.0844 Inf -1.928 0.3846
StepF1 - StepF5 0.0784 0.0833 Inf 0.941 0.9360
StepF1 - StepF6 0.0828 0.0817 Inf 1.014 0.9136
StepF2 - StepF3 -0.0577 0.0787 Inf -0.733 0.9779
StepF2 - StepF4 0.0883 0.0788 Inf 1.120 0.8733
StepF2 - StepF5 0.3293 0.0787 Inf 4.183 0.0004
StepF2 - StepF6 0.3337 0.0788 Inf 4.236 0.0003
StepF3 - StepF4 0.1460 0.0787 Inf 1.855 0.4305
StepF3 - StepF5 0.3871 0.0787 Inf 4.918 <.0001
StepF3 - StepF6 0.3915 0.0789 Inf 4.960 <.0001
StepF4 - StepF5 0.2410 0.0788 Inf 3.060 0.0269
StepF4 - StepF6 0.2454 0.0792 Inf 3.100 0.0237
StepF5 - StepF6 0.0044 0.0789 Inf 0.056 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.2510 0.0828 Inf -3.032 0.0293
StepF1 - StepF3 -0.3087 0.0835 Inf -3.698 0.0030
StepF1 - StepF4 -0.1627 0.0844 Inf -1.928 0.3846
StepF1 - StepF5 0.0784 0.0833 Inf 0.941 0.9360
StepF1 - StepF6 0.0828 0.0817 Inf 1.014 0.9136
StepF2 - StepF3 -0.0577 0.0787 Inf -0.733 0.9779
StepF2 - StepF4 0.0883 0.0788 Inf 1.120 0.8733
StepF2 - StepF5 0.3293 0.0787 Inf 4.183 0.0004
StepF2 - StepF6 0.3337 0.0788 Inf 4.236 0.0003
StepF3 - StepF4 0.1460 0.0787 Inf 1.855 0.4305
StepF3 - StepF5 0.3871 0.0787 Inf 4.918 <.0001
StepF3 - StepF6 0.3915 0.0789 Inf 4.960 <.0001
StepF4 - StepF5 0.2410 0.0788 Inf 3.060 0.0269
StepF4 - StepF6 0.2454 0.0792 Inf 3.100 0.0237
StepF5 - StepF6 0.0044 0.0789 Inf 0.056 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2510 0.0828 Inf 3.032 0.0111
StepF3 - StepF2 0.0577 0.0787 Inf 0.733 0.9267
StepF4 - StepF3 -0.1460 0.0787 Inf -1.855 0.1910
StepF5 - StepF4 -0.2410 0.0788 Inf -3.060 0.0111
StepF6 - StepF5 -0.0044 0.0789 Inf -0.056 0.9555
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2510 0.0828 Inf 3.032 0.0111
StepF3 - StepF2 0.0577 0.0787 Inf 0.733 0.9267
StepF4 - StepF3 -0.1460 0.0787 Inf -1.855 0.1910
StepF5 - StepF4 -0.2410 0.0788 Inf -3.060 0.0111
StepF6 - StepF5 -0.0044 0.0789 Inf -0.056 0.9555
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests .report_step_test_rtms(sw_b4_12_rt, "4", "12 steps")
==============================
TEST (rt_ms) | Block 4 | 12 steps | Axis X
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 78.1244 11 3.398e-12 ***
Accuracy 0.1995 1 0.6551
rt_ms 0.0047 1 0.9453
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.696 0.0760 Inf 0.547 0.845
2 0.786 0.0758 Inf 0.637 0.934
3 0.783 0.0758 Inf 0.635 0.932
4 0.716 0.0758 Inf 0.568 0.865
5 0.678 0.0758 Inf 0.529 0.826
6 0.763 0.0758 Inf 0.615 0.912
7 0.724 0.0759 Inf 0.576 0.873
8 0.708 0.0758 Inf 0.559 0.856
9 0.678 0.0758 Inf 0.529 0.826
10 0.628 0.0758 Inf 0.479 0.776
11 0.641 0.0758 Inf 0.493 0.790
12 0.538 0.0758 Inf 0.389 0.686
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.680 0.0724 Inf 0.538 0.822
2 0.770 0.0722 Inf 0.629 0.912
3 0.768 0.0723 Inf 0.626 0.910
4 0.701 0.0723 Inf 0.559 0.843
5 0.662 0.0722 Inf 0.521 0.804
6 0.748 0.0722 Inf 0.606 0.889
7 0.709 0.0723 Inf 0.567 0.851
8 0.692 0.0722 Inf 0.551 0.834
9 0.662 0.0722 Inf 0.521 0.804
10 0.612 0.0722 Inf 0.471 0.754
11 0.626 0.0722 Inf 0.484 0.767
12 0.522 0.0722 Inf 0.381 0.664
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -9.00e-02 0.0379 Inf -2.374 0.4238
StepF1 - StepF3 -8.76e-02 0.0380 Inf -2.303 0.4743
StepF1 - StepF4 -2.07e-02 0.0380 Inf -0.545 1.0000
StepF1 - StepF5 1.79e-02 0.0379 Inf 0.474 1.0000
StepF1 - StepF6 -6.75e-02 0.0378 Inf -1.786 0.8261
StepF1 - StepF7 -2.87e-02 0.0375 Inf -0.764 0.9998
StepF1 - StepF8 -1.19e-02 0.0378 Inf -0.315 1.0000
StepF1 - StepF9 1.79e-02 0.0377 Inf 0.475 1.0000
StepF1 - StepF10 6.80e-02 0.0378 Inf 1.799 0.8192
StepF1 - StepF11 5.44e-02 0.0377 Inf 1.443 0.9550
StepF1 - StepF12 1.58e-01 0.0377 Inf 4.184 0.0017
StepF2 - StepF3 2.36e-03 0.0375 Inf 0.063 1.0000
StepF2 - StepF4 6.92e-02 0.0374 Inf 1.849 0.7907
StepF2 - StepF5 1.08e-01 0.0374 Inf 2.882 0.1473
StepF2 - StepF6 2.24e-02 0.0374 Inf 0.599 1.0000
StepF2 - StepF7 6.13e-02 0.0376 Inf 1.631 0.8981
StepF2 - StepF8 7.80e-02 0.0374 Inf 2.084 0.6344
StepF2 - StepF9 1.08e-01 0.0375 Inf 2.879 0.1485
StepF2 - StepF10 1.58e-01 0.0374 Inf 4.219 0.0015
StepF2 - StepF11 1.44e-01 0.0375 Inf 3.854 0.0065
StepF2 - StepF12 2.48e-01 0.0375 Inf 6.614 <.0001
StepF3 - StepF4 6.69e-02 0.0374 Inf 1.786 0.8259
StepF3 - StepF5 1.06e-01 0.0375 Inf 2.818 0.1722
StepF3 - StepF6 2.01e-02 0.0375 Inf 0.536 1.0000
StepF3 - StepF7 5.89e-02 0.0377 Inf 1.564 0.9219
StepF3 - StepF8 7.57e-02 0.0375 Inf 2.020 0.6801
StepF3 - StepF9 1.05e-01 0.0375 Inf 2.812 0.1745
StepF3 - StepF10 1.56e-01 0.0375 Inf 4.154 0.0019
StepF3 - StepF11 1.42e-01 0.0375 Inf 3.787 0.0084
StepF3 - StepF12 2.45e-01 0.0375 Inf 6.544 <.0001
StepF4 - StepF5 3.87e-02 0.0374 Inf 1.032 0.9970
StepF4 - StepF6 -4.68e-02 0.0375 Inf -1.250 0.9848
StepF4 - StepF7 -7.95e-03 0.0377 Inf -0.211 1.0000
StepF4 - StepF8 8.80e-03 0.0375 Inf 0.235 1.0000
StepF4 - StepF9 3.86e-02 0.0375 Inf 1.030 0.9971
StepF4 - StepF10 8.88e-02 0.0375 Inf 2.369 0.4271
StepF4 - StepF11 7.51e-02 0.0375 Inf 2.005 0.6905
StepF4 - StepF12 1.79e-01 0.0375 Inf 4.762 0.0001
StepF5 - StepF6 -8.55e-02 0.0374 Inf -2.283 0.4889
StepF5 - StepF7 -4.66e-02 0.0376 Inf -1.240 0.9857
StepF5 - StepF8 -2.99e-02 0.0374 Inf -0.797 0.9997
StepF5 - StepF9 -5.32e-05 0.0375 Inf -0.001 1.0000
StepF5 - StepF10 5.01e-02 0.0374 Inf 1.338 0.9742
StepF5 - StepF11 3.65e-02 0.0375 Inf 0.974 0.9982
StepF5 - StepF12 1.40e-01 0.0375 Inf 3.734 0.0103
StepF6 - StepF7 3.89e-02 0.0375 Inf 1.035 0.9969
StepF6 - StepF8 5.56e-02 0.0374 Inf 1.485 0.9449
StepF6 - StepF9 8.54e-02 0.0375 Inf 2.281 0.4903
StepF6 - StepF10 1.36e-01 0.0374 Inf 3.621 0.0155
StepF6 - StepF11 1.22e-01 0.0374 Inf 3.257 0.0517
StepF6 - StepF12 2.25e-01 0.0374 Inf 6.018 <.0001
StepF7 - StepF8 1.68e-02 0.0375 Inf 0.446 1.0000
StepF7 - StepF9 4.66e-02 0.0375 Inf 1.242 0.9856
StepF7 - StepF10 9.67e-02 0.0376 Inf 2.575 0.2938
StepF7 - StepF11 8.31e-02 0.0375 Inf 2.216 0.5378
StepF7 - StepF12 1.86e-01 0.0375 Inf 4.973 <.0001
StepF8 - StepF9 2.98e-02 0.0374 Inf 0.796 0.9997
StepF8 - StepF10 7.99e-02 0.0374 Inf 2.135 0.5972
StepF8 - StepF11 6.63e-02 0.0374 Inf 1.772 0.8336
StepF8 - StepF12 1.70e-01 0.0374 Inf 4.533 0.0004
StepF9 - StepF10 5.01e-02 0.0375 Inf 1.339 0.9741
StepF9 - StepF11 3.65e-02 0.0374 Inf 0.976 0.9982
StepF9 - StepF12 1.40e-01 0.0374 Inf 3.737 0.0101
StepF10 - StepF11 -1.36e-02 0.0374 Inf -0.363 1.0000
StepF10 - StepF12 8.98e-02 0.0374 Inf 2.397 0.4076
StepF11 - StepF12 1.03e-01 0.0374 Inf 2.761 0.1968
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -9.00e-02 0.0379 Inf -2.374 0.4238
StepF1 - StepF3 -8.76e-02 0.0380 Inf -2.303 0.4743
StepF1 - StepF4 -2.07e-02 0.0380 Inf -0.545 1.0000
StepF1 - StepF5 1.79e-02 0.0379 Inf 0.474 1.0000
StepF1 - StepF6 -6.75e-02 0.0378 Inf -1.786 0.8261
StepF1 - StepF7 -2.87e-02 0.0375 Inf -0.764 0.9998
StepF1 - StepF8 -1.19e-02 0.0378 Inf -0.315 1.0000
StepF1 - StepF9 1.79e-02 0.0377 Inf 0.475 1.0000
StepF1 - StepF10 6.80e-02 0.0378 Inf 1.799 0.8192
StepF1 - StepF11 5.44e-02 0.0377 Inf 1.443 0.9550
StepF1 - StepF12 1.58e-01 0.0377 Inf 4.184 0.0017
StepF2 - StepF3 2.36e-03 0.0375 Inf 0.063 1.0000
StepF2 - StepF4 6.92e-02 0.0374 Inf 1.849 0.7907
StepF2 - StepF5 1.08e-01 0.0374 Inf 2.882 0.1473
StepF2 - StepF6 2.24e-02 0.0374 Inf 0.599 1.0000
StepF2 - StepF7 6.13e-02 0.0376 Inf 1.631 0.8981
StepF2 - StepF8 7.80e-02 0.0374 Inf 2.084 0.6344
StepF2 - StepF9 1.08e-01 0.0375 Inf 2.879 0.1485
StepF2 - StepF10 1.58e-01 0.0374 Inf 4.219 0.0015
StepF2 - StepF11 1.44e-01 0.0375 Inf 3.854 0.0065
StepF2 - StepF12 2.48e-01 0.0375 Inf 6.614 <.0001
StepF3 - StepF4 6.69e-02 0.0374 Inf 1.786 0.8259
StepF3 - StepF5 1.06e-01 0.0375 Inf 2.818 0.1722
StepF3 - StepF6 2.01e-02 0.0375 Inf 0.536 1.0000
StepF3 - StepF7 5.89e-02 0.0377 Inf 1.564 0.9219
StepF3 - StepF8 7.57e-02 0.0375 Inf 2.020 0.6801
StepF3 - StepF9 1.05e-01 0.0375 Inf 2.812 0.1745
StepF3 - StepF10 1.56e-01 0.0375 Inf 4.154 0.0019
StepF3 - StepF11 1.42e-01 0.0375 Inf 3.787 0.0084
StepF3 - StepF12 2.45e-01 0.0375 Inf 6.544 <.0001
StepF4 - StepF5 3.87e-02 0.0374 Inf 1.032 0.9970
StepF4 - StepF6 -4.68e-02 0.0375 Inf -1.250 0.9848
StepF4 - StepF7 -7.95e-03 0.0377 Inf -0.211 1.0000
StepF4 - StepF8 8.80e-03 0.0375 Inf 0.235 1.0000
StepF4 - StepF9 3.86e-02 0.0375 Inf 1.030 0.9971
StepF4 - StepF10 8.88e-02 0.0375 Inf 2.369 0.4271
StepF4 - StepF11 7.51e-02 0.0375 Inf 2.005 0.6905
StepF4 - StepF12 1.79e-01 0.0375 Inf 4.762 0.0001
StepF5 - StepF6 -8.55e-02 0.0374 Inf -2.283 0.4889
StepF5 - StepF7 -4.66e-02 0.0376 Inf -1.240 0.9857
StepF5 - StepF8 -2.99e-02 0.0374 Inf -0.797 0.9997
StepF5 - StepF9 -5.32e-05 0.0375 Inf -0.001 1.0000
StepF5 - StepF10 5.01e-02 0.0374 Inf 1.338 0.9742
StepF5 - StepF11 3.65e-02 0.0375 Inf 0.974 0.9982
StepF5 - StepF12 1.40e-01 0.0375 Inf 3.734 0.0103
StepF6 - StepF7 3.89e-02 0.0375 Inf 1.035 0.9969
StepF6 - StepF8 5.56e-02 0.0374 Inf 1.485 0.9449
StepF6 - StepF9 8.54e-02 0.0375 Inf 2.281 0.4903
StepF6 - StepF10 1.36e-01 0.0374 Inf 3.621 0.0155
StepF6 - StepF11 1.22e-01 0.0374 Inf 3.257 0.0517
StepF6 - StepF12 2.25e-01 0.0374 Inf 6.018 <.0001
StepF7 - StepF8 1.68e-02 0.0375 Inf 0.446 1.0000
StepF7 - StepF9 4.66e-02 0.0375 Inf 1.242 0.9856
StepF7 - StepF10 9.67e-02 0.0376 Inf 2.575 0.2938
StepF7 - StepF11 8.31e-02 0.0375 Inf 2.216 0.5378
StepF7 - StepF12 1.86e-01 0.0375 Inf 4.973 <.0001
StepF8 - StepF9 2.98e-02 0.0374 Inf 0.796 0.9997
StepF8 - StepF10 7.99e-02 0.0374 Inf 2.135 0.5972
StepF8 - StepF11 6.63e-02 0.0374 Inf 1.772 0.8336
StepF8 - StepF12 1.70e-01 0.0374 Inf 4.533 0.0004
StepF9 - StepF10 5.01e-02 0.0375 Inf 1.339 0.9741
StepF9 - StepF11 3.65e-02 0.0374 Inf 0.976 0.9982
StepF9 - StepF12 1.40e-01 0.0374 Inf 3.737 0.0101
StepF10 - StepF11 -1.36e-02 0.0374 Inf -0.363 1.0000
StepF10 - StepF12 8.98e-02 0.0374 Inf 2.397 0.4076
StepF11 - StepF12 1.03e-01 0.0374 Inf 2.761 0.1968
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.08996 0.0379 Inf 2.374 0.1759
StepF3 - StepF2 -0.00236 0.0375 Inf -0.063 1.0000
StepF4 - StepF3 -0.06688 0.0374 Inf -1.786 0.5925
StepF5 - StepF4 -0.03866 0.0374 Inf -1.032 1.0000
StepF6 - StepF5 0.08548 0.0374 Inf 2.283 0.2019
StepF7 - StepF6 -0.03887 0.0375 Inf -1.035 1.0000
StepF8 - StepF7 -0.01675 0.0375 Inf -0.446 1.0000
StepF9 - StepF8 -0.02981 0.0374 Inf -0.796 1.0000
StepF10 - StepF9 -0.05014 0.0375 Inf -1.339 1.0000
StepF11 - StepF10 0.01360 0.0374 Inf 0.363 1.0000
StepF12 - StepF11 -0.10338 0.0374 Inf -2.761 0.0633
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.08996 0.0379 Inf 2.374 0.1759
StepF3 - StepF2 -0.00236 0.0375 Inf -0.063 1.0000
StepF4 - StepF3 -0.06688 0.0374 Inf -1.786 0.5925
StepF5 - StepF4 -0.03866 0.0374 Inf -1.032 1.0000
StepF6 - StepF5 0.08548 0.0374 Inf 2.283 0.2019
StepF7 - StepF6 -0.03887 0.0375 Inf -1.035 1.0000
StepF8 - StepF7 -0.01675 0.0375 Inf -0.446 1.0000
StepF9 - StepF8 -0.02981 0.0374 Inf -0.796 1.0000
StepF10 - StepF9 -0.05014 0.0375 Inf -1.339 1.0000
StepF11 - StepF10 0.01360 0.0374 Inf 0.363 1.0000
StepF12 - StepF11 -0.10338 0.0374 Inf -2.761 0.0633
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests
==============================
TEST (rt_ms) | Block 4 | 12 steps | Axis Y
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 111.7950 11 <2e-16 ***
Accuracy 0.3352 1 0.5626
rt_ms 0.5232 1 0.4695
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.721 0.0909 Inf 0.543 0.900
2 0.880 0.0907 Inf 0.702 1.057
3 0.941 0.0907 Inf 0.763 1.119
4 0.781 0.0907 Inf 0.603 0.958
5 0.739 0.0907 Inf 0.561 0.917
6 0.825 0.0907 Inf 0.648 1.003
7 0.799 0.0908 Inf 0.621 0.977
8 0.706 0.0907 Inf 0.528 0.884
9 0.710 0.0907 Inf 0.532 0.887
10 0.729 0.0907 Inf 0.551 0.906
11 0.695 0.0907 Inf 0.517 0.873
12 0.557 0.0907 Inf 0.379 0.735
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.697 0.0865 Inf 0.528 0.867
2 0.856 0.0863 Inf 0.686 1.025
3 0.917 0.0864 Inf 0.747 1.086
4 0.757 0.0864 Inf 0.587 0.926
5 0.715 0.0863 Inf 0.546 0.884
6 0.801 0.0863 Inf 0.632 0.971
7 0.775 0.0864 Inf 0.606 0.945
8 0.682 0.0863 Inf 0.513 0.851
9 0.686 0.0863 Inf 0.516 0.855
10 0.704 0.0863 Inf 0.535 0.874
11 0.671 0.0863 Inf 0.502 0.840
12 0.533 0.0863 Inf 0.363 0.702
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.15828 0.0442 Inf -3.584 0.0176
StepF1 - StepF3 -0.21937 0.0443 Inf -4.948 <.0001
StepF1 - StepF4 -0.05930 0.0443 Inf -1.339 0.9741
StepF1 - StepF5 -0.01758 0.0441 Inf -0.398 1.0000
StepF1 - StepF6 -0.10412 0.0441 Inf -2.362 0.4322
StepF1 - StepF7 -0.07795 0.0437 Inf -1.782 0.8281
StepF1 - StepF8 0.01523 0.0440 Inf 0.346 1.0000
StepF1 - StepF9 0.01175 0.0439 Inf 0.267 1.0000
StepF1 - StepF10 -0.00716 0.0441 Inf -0.162 1.0000
StepF1 - StepF11 0.02639 0.0440 Inf 0.600 1.0000
StepF1 - StepF12 0.16461 0.0440 Inf 3.744 0.0099
StepF2 - StepF3 -0.06110 0.0437 Inf -1.400 0.9639
StepF2 - StepF4 0.09897 0.0436 Inf 2.267 0.5001
StepF2 - StepF5 0.14069 0.0436 Inf 3.224 0.0572
StepF2 - StepF6 0.05415 0.0436 Inf 1.241 0.9857
StepF2 - StepF7 0.08033 0.0438 Inf 1.833 0.7997
StepF2 - StepF8 0.17350 0.0436 Inf 3.975 0.0041
StepF2 - StepF9 0.17002 0.0437 Inf 3.893 0.0056
StepF2 - StepF10 0.15111 0.0436 Inf 3.462 0.0267
StepF2 - StepF11 0.18467 0.0437 Inf 4.229 0.0014
StepF2 - StepF12 0.32289 0.0437 Inf 7.395 <.0001
StepF3 - StepF4 0.16007 0.0436 Inf 3.668 0.0131
StepF3 - StepF5 0.20179 0.0437 Inf 4.622 0.0002
StepF3 - StepF6 0.11525 0.0437 Inf 2.639 0.2577
StepF3 - StepF7 0.14142 0.0439 Inf 3.220 0.0578
StepF3 - StepF8 0.23460 0.0437 Inf 5.371 <.0001
StepF3 - StepF9 0.23112 0.0437 Inf 5.286 <.0001
StepF3 - StepF10 0.21221 0.0437 Inf 4.860 0.0001
StepF3 - StepF11 0.24576 0.0437 Inf 5.622 <.0001
StepF3 - StepF12 0.38399 0.0437 Inf 8.785 <.0001
StepF4 - StepF5 0.04172 0.0437 Inf 0.956 0.9985
StepF4 - StepF6 -0.04482 0.0437 Inf -1.026 0.9971
StepF4 - StepF7 -0.01864 0.0439 Inf -0.425 1.0000
StepF4 - StepF8 0.07453 0.0437 Inf 1.707 0.8657
StepF4 - StepF9 0.07105 0.0437 Inf 1.626 0.9000
StepF4 - StepF10 0.05214 0.0437 Inf 1.194 0.9895
StepF4 - StepF11 0.08570 0.0437 Inf 1.961 0.7200
StepF4 - StepF12 0.22392 0.0437 Inf 5.124 <.0001
StepF5 - StepF6 -0.08654 0.0436 Inf -1.983 0.7054
StepF5 - StepF7 -0.06037 0.0438 Inf -1.378 0.9678
StepF5 - StepF8 0.03281 0.0436 Inf 0.752 0.9998
StepF5 - StepF9 0.02933 0.0437 Inf 0.672 1.0000
StepF5 - StepF10 0.01042 0.0436 Inf 0.239 1.0000
StepF5 - StepF11 0.04397 0.0437 Inf 1.007 0.9976
StepF5 - StepF12 0.18220 0.0437 Inf 4.173 0.0018
StepF6 - StepF7 0.02617 0.0438 Inf 0.598 1.0000
StepF6 - StepF8 0.11935 0.0436 Inf 2.735 0.2091
StepF6 - StepF9 0.11587 0.0437 Inf 2.654 0.2495
StepF6 - StepF10 0.09696 0.0436 Inf 2.222 0.5336
StepF6 - StepF11 0.13051 0.0436 Inf 2.990 0.1113
StepF6 - StepF12 0.26874 0.0436 Inf 6.157 <.0001
StepF7 - StepF8 0.09318 0.0437 Inf 2.130 0.6010
StepF7 - StepF9 0.08970 0.0437 Inf 2.053 0.6569
StepF7 - StepF10 0.07079 0.0438 Inf 1.617 0.9032
StepF7 - StepF11 0.10434 0.0437 Inf 2.387 0.4147
StepF7 - StepF12 0.24256 0.0437 Inf 5.549 <.0001
StepF8 - StepF9 -0.00348 0.0436 Inf -0.080 1.0000
StepF8 - StepF10 -0.02239 0.0436 Inf -0.513 1.0000
StepF8 - StepF11 0.01116 0.0436 Inf 0.256 1.0000
StepF8 - StepF12 0.14939 0.0436 Inf 3.423 0.0305
StepF9 - StepF10 -0.01891 0.0437 Inf -0.433 1.0000
StepF9 - StepF11 0.01464 0.0436 Inf 0.336 1.0000
StepF9 - StepF12 0.15287 0.0436 Inf 3.503 0.0233
StepF10 - StepF11 0.03355 0.0437 Inf 0.769 0.9998
StepF10 - StepF12 0.17178 0.0437 Inf 3.935 0.0047
StepF11 - StepF12 0.13822 0.0436 Inf 3.167 0.0677
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.15828 0.0442 Inf -3.584 0.0176
StepF1 - StepF3 -0.21937 0.0443 Inf -4.948 <.0001
StepF1 - StepF4 -0.05930 0.0443 Inf -1.339 0.9741
StepF1 - StepF5 -0.01758 0.0441 Inf -0.398 1.0000
StepF1 - StepF6 -0.10412 0.0441 Inf -2.362 0.4322
StepF1 - StepF7 -0.07795 0.0437 Inf -1.782 0.8281
StepF1 - StepF8 0.01523 0.0440 Inf 0.346 1.0000
StepF1 - StepF9 0.01175 0.0439 Inf 0.267 1.0000
StepF1 - StepF10 -0.00716 0.0441 Inf -0.162 1.0000
StepF1 - StepF11 0.02639 0.0440 Inf 0.600 1.0000
StepF1 - StepF12 0.16461 0.0440 Inf 3.744 0.0099
StepF2 - StepF3 -0.06110 0.0437 Inf -1.400 0.9639
StepF2 - StepF4 0.09897 0.0436 Inf 2.267 0.5001
StepF2 - StepF5 0.14069 0.0436 Inf 3.224 0.0572
StepF2 - StepF6 0.05415 0.0436 Inf 1.241 0.9857
StepF2 - StepF7 0.08033 0.0438 Inf 1.833 0.7997
StepF2 - StepF8 0.17350 0.0436 Inf 3.975 0.0041
StepF2 - StepF9 0.17002 0.0437 Inf 3.893 0.0056
StepF2 - StepF10 0.15111 0.0436 Inf 3.462 0.0267
StepF2 - StepF11 0.18467 0.0437 Inf 4.229 0.0014
StepF2 - StepF12 0.32289 0.0437 Inf 7.395 <.0001
StepF3 - StepF4 0.16007 0.0436 Inf 3.668 0.0131
StepF3 - StepF5 0.20179 0.0437 Inf 4.622 0.0002
StepF3 - StepF6 0.11525 0.0437 Inf 2.639 0.2577
StepF3 - StepF7 0.14142 0.0439 Inf 3.220 0.0578
StepF3 - StepF8 0.23460 0.0437 Inf 5.371 <.0001
StepF3 - StepF9 0.23112 0.0437 Inf 5.286 <.0001
StepF3 - StepF10 0.21221 0.0437 Inf 4.860 0.0001
StepF3 - StepF11 0.24576 0.0437 Inf 5.622 <.0001
StepF3 - StepF12 0.38399 0.0437 Inf 8.785 <.0001
StepF4 - StepF5 0.04172 0.0437 Inf 0.956 0.9985
StepF4 - StepF6 -0.04482 0.0437 Inf -1.026 0.9971
StepF4 - StepF7 -0.01864 0.0439 Inf -0.425 1.0000
StepF4 - StepF8 0.07453 0.0437 Inf 1.707 0.8657
StepF4 - StepF9 0.07105 0.0437 Inf 1.626 0.9000
StepF4 - StepF10 0.05214 0.0437 Inf 1.194 0.9895
StepF4 - StepF11 0.08570 0.0437 Inf 1.961 0.7200
StepF4 - StepF12 0.22392 0.0437 Inf 5.124 <.0001
StepF5 - StepF6 -0.08654 0.0436 Inf -1.983 0.7054
StepF5 - StepF7 -0.06037 0.0438 Inf -1.378 0.9678
StepF5 - StepF8 0.03281 0.0436 Inf 0.752 0.9998
StepF5 - StepF9 0.02933 0.0437 Inf 0.672 1.0000
StepF5 - StepF10 0.01042 0.0436 Inf 0.239 1.0000
StepF5 - StepF11 0.04397 0.0437 Inf 1.007 0.9976
StepF5 - StepF12 0.18220 0.0437 Inf 4.173 0.0018
StepF6 - StepF7 0.02617 0.0438 Inf 0.598 1.0000
StepF6 - StepF8 0.11935 0.0436 Inf 2.735 0.2091
StepF6 - StepF9 0.11587 0.0437 Inf 2.654 0.2495
StepF6 - StepF10 0.09696 0.0436 Inf 2.222 0.5336
StepF6 - StepF11 0.13051 0.0436 Inf 2.990 0.1113
StepF6 - StepF12 0.26874 0.0436 Inf 6.157 <.0001
StepF7 - StepF8 0.09318 0.0437 Inf 2.130 0.6010
StepF7 - StepF9 0.08970 0.0437 Inf 2.053 0.6569
StepF7 - StepF10 0.07079 0.0438 Inf 1.617 0.9032
StepF7 - StepF11 0.10434 0.0437 Inf 2.387 0.4147
StepF7 - StepF12 0.24256 0.0437 Inf 5.549 <.0001
StepF8 - StepF9 -0.00348 0.0436 Inf -0.080 1.0000
StepF8 - StepF10 -0.02239 0.0436 Inf -0.513 1.0000
StepF8 - StepF11 0.01116 0.0436 Inf 0.256 1.0000
StepF8 - StepF12 0.14939 0.0436 Inf 3.423 0.0305
StepF9 - StepF10 -0.01891 0.0437 Inf -0.433 1.0000
StepF9 - StepF11 0.01464 0.0436 Inf 0.336 1.0000
StepF9 - StepF12 0.15287 0.0436 Inf 3.503 0.0233
StepF10 - StepF11 0.03355 0.0437 Inf 0.769 0.9998
StepF10 - StepF12 0.17178 0.0437 Inf 3.935 0.0047
StepF11 - StepF12 0.13822 0.0436 Inf 3.167 0.0677
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.15828 0.0442 Inf 3.584 0.0034
StepF3 - StepF2 0.06110 0.0437 Inf 1.400 0.9698
StepF4 - StepF3 -0.16007 0.0436 Inf -3.668 0.0027
StepF5 - StepF4 -0.04172 0.0437 Inf -0.956 1.0000
StepF6 - StepF5 0.08654 0.0436 Inf 1.983 0.3317
StepF7 - StepF6 -0.02617 0.0438 Inf -0.598 1.0000
StepF8 - StepF7 -0.09318 0.0437 Inf -2.130 0.2654
StepF9 - StepF8 0.00348 0.0436 Inf 0.080 1.0000
StepF10 - StepF9 0.01891 0.0437 Inf 0.433 1.0000
StepF11 - StepF10 -0.03355 0.0437 Inf -0.769 1.0000
StepF12 - StepF11 -0.13822 0.0436 Inf -3.167 0.0139
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.15828 0.0442 Inf 3.584 0.0034
StepF3 - StepF2 0.06110 0.0437 Inf 1.400 0.9698
StepF4 - StepF3 -0.16007 0.0436 Inf -3.668 0.0027
StepF5 - StepF4 -0.04172 0.0437 Inf -0.956 1.0000
StepF6 - StepF5 0.08654 0.0436 Inf 1.983 0.3317
StepF7 - StepF6 -0.02617 0.0438 Inf -0.598 1.0000
StepF8 - StepF7 -0.09318 0.0437 Inf -2.130 0.2654
StepF9 - StepF8 0.00348 0.0436 Inf 0.080 1.0000
StepF10 - StepF9 0.01891 0.0437 Inf 0.433 1.0000
StepF11 - StepF10 -0.03355 0.0437 Inf -0.769 1.0000
StepF12 - StepF11 -0.13822 0.0436 Inf -3.167 0.0139
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests
==============================
TEST (rt_ms) | Block 4 | 12 steps | Axis Z
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 114.6293 11 <2e-16 ***
Accuracy 0.1693 1 0.6807
rt_ms 0.0561 1 0.8128
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.45 0.162 Inf 1.137 1.77
2 1.67 0.161 Inf 1.357 1.99
3 1.70 0.161 Inf 1.384 2.02
4 1.55 0.161 Inf 1.234 1.87
5 1.41 0.161 Inf 1.089 1.72
6 1.46 0.161 Inf 1.140 1.77
7 1.50 0.161 Inf 1.182 1.81
8 1.39 0.161 Inf 1.076 1.71
9 1.28 0.161 Inf 0.959 1.59
10 1.34 0.161 Inf 1.021 1.65
11 1.34 0.161 Inf 1.023 1.66
12 1.10 0.161 Inf 0.788 1.42
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.49 0.152 Inf 1.191 1.78
2 1.71 0.151 Inf 1.411 2.00
3 1.73 0.151 Inf 1.437 2.03
4 1.58 0.151 Inf 1.287 1.88
5 1.44 0.151 Inf 1.143 1.74
6 1.49 0.151 Inf 1.194 1.79
7 1.53 0.151 Inf 1.236 1.83
8 1.43 0.151 Inf 1.130 1.72
9 1.31 0.151 Inf 1.013 1.61
10 1.37 0.151 Inf 1.074 1.67
11 1.37 0.151 Inf 1.077 1.67
12 1.14 0.151 Inf 0.842 1.44
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.21979 0.0733 Inf -3.000 0.1083
StepF1 - StepF3 -0.24635 0.0735 Inf -3.350 0.0387
StepF1 - StepF4 -0.09634 0.0735 Inf -1.311 0.9779
StepF1 - StepF5 0.04827 0.0732 Inf 0.659 1.0000
StepF1 - StepF6 -0.00274 0.0731 Inf -0.037 1.0000
StepF1 - StepF7 -0.04477 0.0725 Inf -0.617 1.0000
StepF1 - StepF8 0.06103 0.0731 Inf 0.835 0.9996
StepF1 - StepF9 0.17782 0.0729 Inf 2.439 0.3792
StepF1 - StepF10 0.11655 0.0731 Inf 1.594 0.9119
StepF1 - StepF11 0.11395 0.0729 Inf 1.563 0.9224
StepF1 - StepF12 0.34931 0.0729 Inf 4.790 0.0001
StepF2 - StepF3 -0.02656 0.0724 Inf -0.367 1.0000
StepF2 - StepF4 0.12345 0.0724 Inf 1.705 0.8663
StepF2 - StepF5 0.26806 0.0724 Inf 3.703 0.0115
StepF2 - StepF6 0.21705 0.0724 Inf 2.999 0.1088
StepF2 - StepF7 0.17502 0.0727 Inf 2.409 0.4000
StepF2 - StepF8 0.28083 0.0724 Inf 3.879 0.0059
StepF2 - StepF9 0.39761 0.0724 Inf 5.490 <.0001
StepF2 - StepF10 0.33634 0.0724 Inf 4.647 0.0002
StepF2 - StepF11 0.33374 0.0724 Inf 4.608 0.0003
StepF2 - StepF12 0.56910 0.0724 Inf 7.859 <.0001
StepF3 - StepF4 0.15001 0.0724 Inf 2.073 0.6426
StepF3 - StepF5 0.29462 0.0724 Inf 4.069 0.0028
StepF3 - StepF6 0.24361 0.0724 Inf 3.363 0.0370
StepF3 - StepF7 0.20158 0.0728 Inf 2.767 0.1940
StepF3 - StepF8 0.30739 0.0724 Inf 4.243 0.0013
StepF3 - StepF9 0.42417 0.0725 Inf 5.850 <.0001
StepF3 - StepF10 0.36290 0.0724 Inf 5.011 <.0001
StepF3 - StepF11 0.36030 0.0725 Inf 4.970 <.0001
StepF3 - StepF12 0.59566 0.0725 Inf 8.217 <.0001
StepF4 - StepF5 0.14461 0.0724 Inf 1.997 0.6954
StepF4 - StepF6 0.09360 0.0724 Inf 1.293 0.9802
StepF4 - StepF7 0.05157 0.0728 Inf 0.708 0.9999
StepF4 - StepF8 0.15738 0.0724 Inf 2.173 0.5696
StepF4 - StepF9 0.27416 0.0725 Inf 3.782 0.0086
StepF4 - StepF10 0.21289 0.0724 Inf 2.940 0.1270
StepF4 - StepF11 0.21029 0.0725 Inf 2.902 0.1401
StepF4 - StepF12 0.44565 0.0725 Inf 6.149 <.0001
StepF5 - StepF6 -0.05101 0.0724 Inf -0.705 0.9999
StepF5 - StepF7 -0.09304 0.0726 Inf -1.281 0.9816
StepF5 - StepF8 0.01277 0.0724 Inf 0.176 1.0000
StepF5 - StepF9 0.12955 0.0724 Inf 1.789 0.8245
StepF5 - StepF10 0.06828 0.0724 Inf 0.943 0.9987
StepF5 - StepF11 0.06568 0.0724 Inf 0.907 0.9991
StepF5 - StepF12 0.30104 0.0724 Inf 4.157 0.0019
StepF6 - StepF7 -0.04203 0.0726 Inf -0.579 1.0000
StepF6 - StepF8 0.06378 0.0724 Inf 0.881 0.9993
StepF6 - StepF9 0.18056 0.0724 Inf 2.494 0.3435
StepF6 - StepF10 0.11929 0.0724 Inf 1.648 0.8911
StepF6 - StepF11 0.11669 0.0724 Inf 1.612 0.9052
StepF6 - StepF12 0.35205 0.0724 Inf 4.863 0.0001
StepF7 - StepF8 0.10581 0.0726 Inf 1.458 0.9515
StepF7 - StepF9 0.22259 0.0725 Inf 3.071 0.0892
StepF7 - StepF10 0.16132 0.0726 Inf 2.222 0.5333
StepF7 - StepF11 0.15872 0.0725 Inf 2.190 0.5573
StepF7 - StepF12 0.39408 0.0725 Inf 5.436 <.0001
StepF8 - StepF9 0.11678 0.0724 Inf 1.613 0.9047
StepF8 - StepF10 0.05551 0.0724 Inf 0.767 0.9998
StepF8 - StepF11 0.05292 0.0724 Inf 0.731 0.9999
StepF8 - StepF12 0.28827 0.0724 Inf 3.982 0.0039
StepF9 - StepF10 -0.06127 0.0724 Inf -0.846 0.9995
StepF9 - StepF11 -0.06387 0.0724 Inf -0.882 0.9993
StepF9 - StepF12 0.17149 0.0724 Inf 2.369 0.4272
StepF10 - StepF11 -0.00260 0.0724 Inf -0.036 1.0000
StepF10 - StepF12 0.23276 0.0724 Inf 3.215 0.0587
StepF11 - StepF12 0.23536 0.0724 Inf 3.252 0.0526
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.21979 0.0733 Inf -3.000 0.1083
StepF1 - StepF3 -0.24635 0.0735 Inf -3.350 0.0387
StepF1 - StepF4 -0.09634 0.0735 Inf -1.311 0.9779
StepF1 - StepF5 0.04827 0.0732 Inf 0.659 1.0000
StepF1 - StepF6 -0.00274 0.0731 Inf -0.037 1.0000
StepF1 - StepF7 -0.04477 0.0725 Inf -0.617 1.0000
StepF1 - StepF8 0.06103 0.0731 Inf 0.835 0.9996
StepF1 - StepF9 0.17782 0.0729 Inf 2.439 0.3792
StepF1 - StepF10 0.11655 0.0731 Inf 1.594 0.9119
StepF1 - StepF11 0.11395 0.0729 Inf 1.563 0.9224
StepF1 - StepF12 0.34931 0.0729 Inf 4.790 0.0001
StepF2 - StepF3 -0.02656 0.0724 Inf -0.367 1.0000
StepF2 - StepF4 0.12345 0.0724 Inf 1.705 0.8663
StepF2 - StepF5 0.26806 0.0724 Inf 3.703 0.0115
StepF2 - StepF6 0.21705 0.0724 Inf 2.999 0.1088
StepF2 - StepF7 0.17502 0.0727 Inf 2.409 0.4000
StepF2 - StepF8 0.28083 0.0724 Inf 3.879 0.0059
StepF2 - StepF9 0.39761 0.0724 Inf 5.490 <.0001
StepF2 - StepF10 0.33634 0.0724 Inf 4.647 0.0002
StepF2 - StepF11 0.33374 0.0724 Inf 4.608 0.0003
StepF2 - StepF12 0.56910 0.0724 Inf 7.859 <.0001
StepF3 - StepF4 0.15001 0.0724 Inf 2.073 0.6426
StepF3 - StepF5 0.29462 0.0724 Inf 4.069 0.0028
StepF3 - StepF6 0.24361 0.0724 Inf 3.363 0.0370
StepF3 - StepF7 0.20158 0.0728 Inf 2.767 0.1940
StepF3 - StepF8 0.30739 0.0724 Inf 4.243 0.0013
StepF3 - StepF9 0.42417 0.0725 Inf 5.850 <.0001
StepF3 - StepF10 0.36290 0.0724 Inf 5.011 <.0001
StepF3 - StepF11 0.36030 0.0725 Inf 4.970 <.0001
StepF3 - StepF12 0.59566 0.0725 Inf 8.217 <.0001
StepF4 - StepF5 0.14461 0.0724 Inf 1.997 0.6954
StepF4 - StepF6 0.09360 0.0724 Inf 1.293 0.9802
StepF4 - StepF7 0.05157 0.0728 Inf 0.708 0.9999
StepF4 - StepF8 0.15738 0.0724 Inf 2.173 0.5696
StepF4 - StepF9 0.27416 0.0725 Inf 3.782 0.0086
StepF4 - StepF10 0.21289 0.0724 Inf 2.940 0.1270
StepF4 - StepF11 0.21029 0.0725 Inf 2.902 0.1401
StepF4 - StepF12 0.44565 0.0725 Inf 6.149 <.0001
StepF5 - StepF6 -0.05101 0.0724 Inf -0.705 0.9999
StepF5 - StepF7 -0.09304 0.0726 Inf -1.281 0.9816
StepF5 - StepF8 0.01277 0.0724 Inf 0.176 1.0000
StepF5 - StepF9 0.12955 0.0724 Inf 1.789 0.8245
StepF5 - StepF10 0.06828 0.0724 Inf 0.943 0.9987
StepF5 - StepF11 0.06568 0.0724 Inf 0.907 0.9991
StepF5 - StepF12 0.30104 0.0724 Inf 4.157 0.0019
StepF6 - StepF7 -0.04203 0.0726 Inf -0.579 1.0000
StepF6 - StepF8 0.06378 0.0724 Inf 0.881 0.9993
StepF6 - StepF9 0.18056 0.0724 Inf 2.494 0.3435
StepF6 - StepF10 0.11929 0.0724 Inf 1.648 0.8911
StepF6 - StepF11 0.11669 0.0724 Inf 1.612 0.9052
StepF6 - StepF12 0.35205 0.0724 Inf 4.863 0.0001
StepF7 - StepF8 0.10581 0.0726 Inf 1.458 0.9515
StepF7 - StepF9 0.22259 0.0725 Inf 3.071 0.0892
StepF7 - StepF10 0.16132 0.0726 Inf 2.222 0.5333
StepF7 - StepF11 0.15872 0.0725 Inf 2.190 0.5573
StepF7 - StepF12 0.39408 0.0725 Inf 5.436 <.0001
StepF8 - StepF9 0.11678 0.0724 Inf 1.613 0.9047
StepF8 - StepF10 0.05551 0.0724 Inf 0.767 0.9998
StepF8 - StepF11 0.05292 0.0724 Inf 0.731 0.9999
StepF8 - StepF12 0.28827 0.0724 Inf 3.982 0.0039
StepF9 - StepF10 -0.06127 0.0724 Inf -0.846 0.9995
StepF9 - StepF11 -0.06387 0.0724 Inf -0.882 0.9993
StepF9 - StepF12 0.17149 0.0724 Inf 2.369 0.4272
StepF10 - StepF11 -0.00260 0.0724 Inf -0.036 1.0000
StepF10 - StepF12 0.23276 0.0724 Inf 3.215 0.0587
StepF11 - StepF12 0.23536 0.0724 Inf 3.252 0.0526
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2198 0.0733 Inf 3.000 0.0270
StepF3 - StepF2 0.0266 0.0724 Inf 0.367 1.0000
StepF4 - StepF3 -0.1500 0.0724 Inf -2.073 0.3440
StepF5 - StepF4 -0.1446 0.0724 Inf -1.997 0.3662
StepF6 - StepF5 0.0510 0.0724 Inf 0.705 1.0000
StepF7 - StepF6 0.0420 0.0726 Inf 0.579 1.0000
StepF8 - StepF7 -0.1058 0.0726 Inf -1.458 0.8685
StepF9 - StepF8 -0.1168 0.0724 Inf -1.613 0.7468
StepF10 - StepF9 0.0613 0.0724 Inf 0.846 1.0000
StepF11 - StepF10 0.0026 0.0724 Inf 0.036 1.0000
StepF12 - StepF11 -0.2354 0.0724 Inf -3.252 0.0126
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.2198 0.0733 Inf 3.000 0.0270
StepF3 - StepF2 0.0266 0.0724 Inf 0.367 1.0000
StepF4 - StepF3 -0.1500 0.0724 Inf -2.073 0.3440
StepF5 - StepF4 -0.1446 0.0724 Inf -1.997 0.3662
StepF6 - StepF5 0.0510 0.0724 Inf 0.705 1.0000
StepF7 - StepF6 0.0420 0.0726 Inf 0.579 1.0000
StepF8 - StepF7 -0.1058 0.0726 Inf -1.458 0.8685
StepF9 - StepF8 -0.1168 0.0724 Inf -1.613 0.7468
StepF10 - StepF9 0.0613 0.0724 Inf 0.846 1.0000
StepF11 - StepF10 0.0026 0.0724 Inf 0.036 1.0000
StepF12 - StepF11 -0.2354 0.0724 Inf -3.252 0.0126
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests .report_step_test_rtms(sw_b4_18_rt, "4", "18 steps")
==============================
TEST (rt_ms) | Block 4 | 18 steps | Axis X
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 138.8729 17 < 2e-16 ***
Accuracy 0.0433 1 0.83507
rt_ms 2.8101 1 0.09367 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.699 0.0766 Inf 0.549 0.849
2 0.797 0.0765 Inf 0.647 0.947
3 0.768 0.0765 Inf 0.618 0.918
4 0.761 0.0765 Inf 0.611 0.911
5 0.699 0.0765 Inf 0.549 0.849
6 0.814 0.0765 Inf 0.664 0.964
7 0.686 0.0765 Inf 0.536 0.836
8 0.674 0.0765 Inf 0.524 0.824
9 0.743 0.0765 Inf 0.593 0.893
10 0.659 0.0765 Inf 0.509 0.809
11 0.693 0.0765 Inf 0.543 0.843
12 0.598 0.0765 Inf 0.448 0.748
13 0.654 0.0765 Inf 0.504 0.804
14 0.655 0.0765 Inf 0.505 0.805
15 0.690 0.0765 Inf 0.540 0.840
16 0.645 0.0765 Inf 0.495 0.795
17 0.526 0.0765 Inf 0.376 0.676
18 0.533 0.0766 Inf 0.383 0.683
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.706 0.0734 Inf 0.562 0.850
2 0.805 0.0733 Inf 0.661 0.948
3 0.775 0.0733 Inf 0.631 0.919
4 0.768 0.0733 Inf 0.624 0.912
5 0.706 0.0733 Inf 0.562 0.850
6 0.821 0.0733 Inf 0.678 0.965
7 0.694 0.0733 Inf 0.550 0.837
8 0.682 0.0733 Inf 0.538 0.825
9 0.751 0.0733 Inf 0.607 0.894
10 0.666 0.0733 Inf 0.522 0.810
11 0.700 0.0733 Inf 0.556 0.844
12 0.605 0.0733 Inf 0.462 0.749
13 0.661 0.0733 Inf 0.518 0.805
14 0.662 0.0733 Inf 0.518 0.806
15 0.697 0.0733 Inf 0.553 0.841
16 0.652 0.0733 Inf 0.509 0.796
17 0.533 0.0733 Inf 0.390 0.677
18 0.540 0.0733 Inf 0.396 0.684
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.098301 0.0393 Inf -2.504 0.5274
StepF1 - StepF3 -0.068741 0.0393 Inf -1.750 0.9568
StepF1 - StepF4 -0.061631 0.0392 Inf -1.571 0.9850
StepF1 - StepF5 0.000349 0.0392 Inf 0.009 1.0000
StepF1 - StepF6 -0.115007 0.0392 Inf -2.933 0.2339
StepF1 - StepF7 0.012692 0.0391 Inf 0.325 1.0000
StepF1 - StepF8 0.024574 0.0391 Inf 0.628 1.0000
StepF1 - StepF9 -0.044322 0.0391 Inf -1.134 0.9997
StepF1 - StepF10 0.040175 0.0392 Inf 1.026 0.9999
StepF1 - StepF11 0.006158 0.0392 Inf 0.157 1.0000
StepF1 - StepF12 0.101007 0.0392 Inf 2.576 0.4716
StepF1 - StepF13 0.044962 0.0391 Inf 1.150 0.9996
StepF1 - StepF14 0.044310 0.0391 Inf 1.132 0.9997
StepF1 - StepF15 0.009254 0.0392 Inf 0.236 1.0000
StepF1 - StepF16 0.053938 0.0392 Inf 1.376 0.9965
StepF1 - StepF17 0.172964 0.0392 Inf 4.412 0.0014
StepF1 - StepF18 0.166314 0.0390 Inf 4.260 0.0027
StepF2 - StepF3 0.029560 0.0390 Inf 0.758 1.0000
StepF2 - StepF4 0.036670 0.0390 Inf 0.940 1.0000
StepF2 - StepF5 0.098650 0.0390 Inf 2.528 0.5085
StepF2 - StepF6 -0.016706 0.0390 Inf -0.428 1.0000
StepF2 - StepF7 0.110993 0.0391 Inf 2.842 0.2862
StepF2 - StepF8 0.122876 0.0390 Inf 3.148 0.1372
StepF2 - StepF9 0.053979 0.0391 Inf 1.381 0.9964
StepF2 - StepF10 0.138476 0.0390 Inf 3.548 0.0412
StepF2 - StepF11 0.104459 0.0390 Inf 2.677 0.3968
StepF2 - StepF12 0.199308 0.0390 Inf 5.108 <.0001
StepF2 - StepF13 0.143263 0.0391 Inf 3.667 0.0274
StepF2 - StepF14 0.142611 0.0390 Inf 3.653 0.0289
StepF2 - StepF15 0.107555 0.0390 Inf 2.757 0.3411
StepF2 - StepF16 0.152239 0.0390 Inf 3.901 0.0117
StepF2 - StepF17 0.271265 0.0390 Inf 6.952 <.0001
StepF2 - StepF18 0.264615 0.0391 Inf 6.763 <.0001
StepF3 - StepF4 0.007110 0.0390 Inf 0.182 1.0000
StepF3 - StepF5 0.069090 0.0390 Inf 1.770 0.9520
StepF3 - StepF6 -0.046266 0.0390 Inf -1.186 0.9995
StepF3 - StepF7 0.081433 0.0391 Inf 2.085 0.8255
StepF3 - StepF8 0.093315 0.0390 Inf 2.390 0.6158
StepF3 - StepF9 0.024419 0.0391 Inf 0.625 1.0000
StepF3 - StepF10 0.108915 0.0390 Inf 2.790 0.3190
StepF3 - StepF11 0.074898 0.0390 Inf 1.919 0.9051
StepF3 - StepF12 0.169747 0.0390 Inf 4.350 0.0019
StepF3 - StepF13 0.113702 0.0391 Inf 2.910 0.2468
StepF3 - StepF14 0.113051 0.0390 Inf 2.895 0.2551
StepF3 - StepF15 0.077995 0.0390 Inf 1.999 0.8704
StepF3 - StepF16 0.122679 0.0390 Inf 3.144 0.1386
StepF3 - StepF17 0.241704 0.0390 Inf 6.194 <.0001
StepF3 - StepF18 0.235055 0.0391 Inf 6.005 <.0001
StepF4 - StepF5 0.061980 0.0390 Inf 1.588 0.9831
StepF4 - StepF6 -0.053376 0.0390 Inf -1.368 0.9968
StepF4 - StepF7 0.074323 0.0390 Inf 1.904 0.9110
StepF4 - StepF8 0.086205 0.0390 Inf 2.209 0.7483
StepF4 - StepF9 0.017309 0.0391 Inf 0.443 1.0000
StepF4 - StepF10 0.101805 0.0390 Inf 2.609 0.4468
StepF4 - StepF11 0.067788 0.0390 Inf 1.737 0.9596
StepF4 - StepF12 0.162638 0.0390 Inf 4.168 0.0040
StepF4 - StepF13 0.106592 0.0391 Inf 2.729 0.3597
StepF4 - StepF14 0.105941 0.0390 Inf 2.714 0.3703
StepF4 - StepF15 0.070885 0.0390 Inf 1.817 0.9397
StepF4 - StepF16 0.115569 0.0390 Inf 2.962 0.2189
StepF4 - StepF17 0.234594 0.0390 Inf 6.013 <.0001
StepF4 - StepF18 0.227945 0.0391 Inf 5.828 <.0001
StepF5 - StepF6 -0.115356 0.0390 Inf -2.957 0.2216
StepF5 - StepF7 0.012343 0.0390 Inf 0.316 1.0000
StepF5 - StepF8 0.024226 0.0390 Inf 0.621 1.0000
StepF5 - StepF9 -0.044671 0.0390 Inf -1.144 0.9997
StepF5 - StepF10 0.039826 0.0390 Inf 1.021 0.9999
StepF5 - StepF11 0.005809 0.0390 Inf 0.149 1.0000
StepF5 - StepF12 0.100658 0.0390 Inf 2.580 0.4687
StepF5 - StepF13 0.044613 0.0390 Inf 1.143 0.9997
StepF5 - StepF14 0.043961 0.0390 Inf 1.127 0.9997
StepF5 - StepF15 0.008905 0.0390 Inf 0.228 1.0000
StepF5 - StepF16 0.053589 0.0390 Inf 1.374 0.9966
StepF5 - StepF17 0.172615 0.0390 Inf 4.424 0.0013
StepF5 - StepF18 0.165965 0.0391 Inf 4.247 0.0029
StepF6 - StepF7 0.127699 0.0390 Inf 3.272 0.0971
StepF6 - StepF8 0.139581 0.0390 Inf 3.577 0.0374
StepF6 - StepF9 0.070685 0.0391 Inf 1.810 0.9416
StepF6 - StepF10 0.155182 0.0390 Inf 3.977 0.0087
StepF6 - StepF11 0.121165 0.0390 Inf 3.105 0.1533
StepF6 - StepF12 0.216014 0.0390 Inf 5.537 <.0001
StepF6 - StepF13 0.159968 0.0390 Inf 4.097 0.0054
StepF6 - StepF14 0.159317 0.0390 Inf 4.082 0.0057
StepF6 - StepF15 0.124261 0.0390 Inf 3.185 0.1240
StepF6 - StepF16 0.168945 0.0390 Inf 4.330 0.0020
StepF6 - StepF17 0.287970 0.0390 Inf 7.381 <.0001
StepF6 - StepF18 0.281321 0.0391 Inf 7.197 <.0001
StepF7 - StepF8 0.011882 0.0390 Inf 0.305 1.0000
StepF7 - StepF9 -0.057014 0.0390 Inf -1.461 0.9931
StepF7 - StepF10 0.027483 0.0390 Inf 0.704 1.0000
StepF7 - StepF11 -0.006534 0.0390 Inf -0.167 1.0000
StepF7 - StepF12 0.088315 0.0390 Inf 2.263 0.7107
StepF7 - StepF13 0.032269 0.0390 Inf 0.827 1.0000
StepF7 - StepF14 0.031618 0.0390 Inf 0.810 1.0000
StepF7 - StepF15 -0.003438 0.0390 Inf -0.088 1.0000
StepF7 - StepF16 0.041246 0.0390 Inf 1.057 0.9999
StepF7 - StepF17 0.160272 0.0390 Inf 4.106 0.0052
StepF7 - StepF18 0.153622 0.0390 Inf 3.935 0.0102
StepF8 - StepF9 -0.068897 0.0390 Inf -1.765 0.9532
StepF8 - StepF10 0.015600 0.0390 Inf 0.400 1.0000
StepF8 - StepF11 -0.018417 0.0390 Inf -0.472 1.0000
StepF8 - StepF12 0.076432 0.0390 Inf 1.959 0.8887
StepF8 - StepF13 0.020387 0.0390 Inf 0.522 1.0000
StepF8 - StepF14 0.019735 0.0390 Inf 0.506 1.0000
StepF8 - StepF15 -0.015321 0.0390 Inf -0.393 1.0000
StepF8 - StepF16 0.029364 0.0390 Inf 0.752 1.0000
StepF8 - StepF17 0.148389 0.0390 Inf 3.802 0.0169
StepF8 - StepF18 0.141739 0.0390 Inf 3.630 0.0312
StepF9 - StepF10 0.084497 0.0390 Inf 2.165 0.7772
StepF9 - StepF11 0.050480 0.0390 Inf 1.293 0.9984
StepF9 - StepF12 0.145329 0.0391 Inf 3.721 0.0227
StepF9 - StepF13 0.089284 0.0390 Inf 2.288 0.6921
StepF9 - StepF14 0.088632 0.0390 Inf 2.271 0.7045
StepF9 - StepF15 0.053576 0.0391 Inf 1.372 0.9967
StepF9 - StepF16 0.098260 0.0390 Inf 2.516 0.5175
StepF9 - StepF17 0.217286 0.0391 Inf 5.564 <.0001
StepF9 - StepF18 0.210636 0.0390 Inf 5.398 <.0001
StepF10 - StepF11 -0.034017 0.0390 Inf -0.872 1.0000
StepF10 - StepF12 0.060832 0.0390 Inf 1.559 0.9861
StepF10 - StepF13 0.004787 0.0390 Inf 0.123 1.0000
StepF10 - StepF14 0.004135 0.0390 Inf 0.106 1.0000
StepF10 - StepF15 -0.030921 0.0390 Inf -0.792 1.0000
StepF10 - StepF16 0.013763 0.0390 Inf 0.353 1.0000
StepF10 - StepF17 0.132789 0.0390 Inf 3.403 0.0655
StepF10 - StepF18 0.126139 0.0391 Inf 3.229 0.1096
StepF11 - StepF12 0.094849 0.0390 Inf 2.431 0.5840
StepF11 - StepF13 0.038804 0.0390 Inf 0.994 0.9999
StepF11 - StepF14 0.038152 0.0390 Inf 0.978 1.0000
StepF11 - StepF15 0.003096 0.0390 Inf 0.079 1.0000
StepF11 - StepF16 0.047781 0.0390 Inf 1.225 0.9992
StepF11 - StepF17 0.166806 0.0390 Inf 4.275 0.0026
StepF11 - StepF18 0.160156 0.0391 Inf 4.099 0.0053
StepF12 - StepF13 -0.056045 0.0390 Inf -1.435 0.9943
StepF12 - StepF14 -0.056697 0.0390 Inf -1.453 0.9935
StepF12 - StepF15 -0.091753 0.0390 Inf -2.352 0.6450
StepF12 - StepF16 -0.047069 0.0390 Inf -1.206 0.9993
StepF12 - StepF17 0.071957 0.0390 Inf 1.844 0.9314
StepF12 - StepF18 0.065307 0.0391 Inf 1.671 0.9721
StepF13 - StepF14 -0.000652 0.0390 Inf -0.017 1.0000
StepF13 - StepF15 -0.035708 0.0390 Inf -0.914 1.0000
StepF13 - StepF16 0.008977 0.0390 Inf 0.230 1.0000
StepF13 - StepF17 0.128002 0.0390 Inf 3.279 0.0952
StepF13 - StepF18 0.121352 0.0390 Inf 3.109 0.1518
StepF14 - StepF15 -0.035056 0.0390 Inf -0.898 1.0000
StepF14 - StepF16 0.009628 0.0390 Inf 0.247 1.0000
StepF14 - StepF17 0.128654 0.0390 Inf 3.297 0.0903
StepF14 - StepF18 0.122004 0.0390 Inf 3.125 0.1458
StepF15 - StepF16 0.044684 0.0390 Inf 1.145 0.9997
StepF15 - StepF17 0.163710 0.0390 Inf 4.196 0.0036
StepF15 - StepF18 0.157060 0.0391 Inf 4.017 0.0074
StepF16 - StepF17 0.119025 0.0390 Inf 3.051 0.1764
StepF16 - StepF18 0.112376 0.0391 Inf 2.875 0.2665
StepF17 - StepF18 -0.006650 0.0391 Inf -0.170 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.098301 0.0393 Inf -2.504 0.5274
StepF1 - StepF3 -0.068741 0.0393 Inf -1.750 0.9568
StepF1 - StepF4 -0.061631 0.0392 Inf -1.571 0.9850
StepF1 - StepF5 0.000349 0.0392 Inf 0.009 1.0000
StepF1 - StepF6 -0.115007 0.0392 Inf -2.933 0.2339
StepF1 - StepF7 0.012692 0.0391 Inf 0.325 1.0000
StepF1 - StepF8 0.024574 0.0391 Inf 0.628 1.0000
StepF1 - StepF9 -0.044322 0.0391 Inf -1.134 0.9997
StepF1 - StepF10 0.040175 0.0392 Inf 1.026 0.9999
StepF1 - StepF11 0.006158 0.0392 Inf 0.157 1.0000
StepF1 - StepF12 0.101007 0.0392 Inf 2.576 0.4716
StepF1 - StepF13 0.044962 0.0391 Inf 1.150 0.9996
StepF1 - StepF14 0.044310 0.0391 Inf 1.132 0.9997
StepF1 - StepF15 0.009254 0.0392 Inf 0.236 1.0000
StepF1 - StepF16 0.053938 0.0392 Inf 1.376 0.9965
StepF1 - StepF17 0.172964 0.0392 Inf 4.412 0.0014
StepF1 - StepF18 0.166314 0.0390 Inf 4.260 0.0027
StepF2 - StepF3 0.029560 0.0390 Inf 0.758 1.0000
StepF2 - StepF4 0.036670 0.0390 Inf 0.940 1.0000
StepF2 - StepF5 0.098650 0.0390 Inf 2.528 0.5085
StepF2 - StepF6 -0.016706 0.0390 Inf -0.428 1.0000
StepF2 - StepF7 0.110993 0.0391 Inf 2.842 0.2862
StepF2 - StepF8 0.122876 0.0390 Inf 3.148 0.1372
StepF2 - StepF9 0.053979 0.0391 Inf 1.381 0.9964
StepF2 - StepF10 0.138476 0.0390 Inf 3.548 0.0412
StepF2 - StepF11 0.104459 0.0390 Inf 2.677 0.3968
StepF2 - StepF12 0.199308 0.0390 Inf 5.108 <.0001
StepF2 - StepF13 0.143263 0.0391 Inf 3.667 0.0274
StepF2 - StepF14 0.142611 0.0390 Inf 3.653 0.0289
StepF2 - StepF15 0.107555 0.0390 Inf 2.757 0.3411
StepF2 - StepF16 0.152239 0.0390 Inf 3.901 0.0117
StepF2 - StepF17 0.271265 0.0390 Inf 6.952 <.0001
StepF2 - StepF18 0.264615 0.0391 Inf 6.763 <.0001
StepF3 - StepF4 0.007110 0.0390 Inf 0.182 1.0000
StepF3 - StepF5 0.069090 0.0390 Inf 1.770 0.9520
StepF3 - StepF6 -0.046266 0.0390 Inf -1.186 0.9995
StepF3 - StepF7 0.081433 0.0391 Inf 2.085 0.8255
StepF3 - StepF8 0.093315 0.0390 Inf 2.390 0.6158
StepF3 - StepF9 0.024419 0.0391 Inf 0.625 1.0000
StepF3 - StepF10 0.108915 0.0390 Inf 2.790 0.3190
StepF3 - StepF11 0.074898 0.0390 Inf 1.919 0.9051
StepF3 - StepF12 0.169747 0.0390 Inf 4.350 0.0019
StepF3 - StepF13 0.113702 0.0391 Inf 2.910 0.2468
StepF3 - StepF14 0.113051 0.0390 Inf 2.895 0.2551
StepF3 - StepF15 0.077995 0.0390 Inf 1.999 0.8704
StepF3 - StepF16 0.122679 0.0390 Inf 3.144 0.1386
StepF3 - StepF17 0.241704 0.0390 Inf 6.194 <.0001
StepF3 - StepF18 0.235055 0.0391 Inf 6.005 <.0001
StepF4 - StepF5 0.061980 0.0390 Inf 1.588 0.9831
StepF4 - StepF6 -0.053376 0.0390 Inf -1.368 0.9968
StepF4 - StepF7 0.074323 0.0390 Inf 1.904 0.9110
StepF4 - StepF8 0.086205 0.0390 Inf 2.209 0.7483
StepF4 - StepF9 0.017309 0.0391 Inf 0.443 1.0000
StepF4 - StepF10 0.101805 0.0390 Inf 2.609 0.4468
StepF4 - StepF11 0.067788 0.0390 Inf 1.737 0.9596
StepF4 - StepF12 0.162638 0.0390 Inf 4.168 0.0040
StepF4 - StepF13 0.106592 0.0391 Inf 2.729 0.3597
StepF4 - StepF14 0.105941 0.0390 Inf 2.714 0.3703
StepF4 - StepF15 0.070885 0.0390 Inf 1.817 0.9397
StepF4 - StepF16 0.115569 0.0390 Inf 2.962 0.2189
StepF4 - StepF17 0.234594 0.0390 Inf 6.013 <.0001
StepF4 - StepF18 0.227945 0.0391 Inf 5.828 <.0001
StepF5 - StepF6 -0.115356 0.0390 Inf -2.957 0.2216
StepF5 - StepF7 0.012343 0.0390 Inf 0.316 1.0000
StepF5 - StepF8 0.024226 0.0390 Inf 0.621 1.0000
StepF5 - StepF9 -0.044671 0.0390 Inf -1.144 0.9997
StepF5 - StepF10 0.039826 0.0390 Inf 1.021 0.9999
StepF5 - StepF11 0.005809 0.0390 Inf 0.149 1.0000
StepF5 - StepF12 0.100658 0.0390 Inf 2.580 0.4687
StepF5 - StepF13 0.044613 0.0390 Inf 1.143 0.9997
StepF5 - StepF14 0.043961 0.0390 Inf 1.127 0.9997
StepF5 - StepF15 0.008905 0.0390 Inf 0.228 1.0000
StepF5 - StepF16 0.053589 0.0390 Inf 1.374 0.9966
StepF5 - StepF17 0.172615 0.0390 Inf 4.424 0.0013
StepF5 - StepF18 0.165965 0.0391 Inf 4.247 0.0029
StepF6 - StepF7 0.127699 0.0390 Inf 3.272 0.0971
StepF6 - StepF8 0.139581 0.0390 Inf 3.577 0.0374
StepF6 - StepF9 0.070685 0.0391 Inf 1.810 0.9416
StepF6 - StepF10 0.155182 0.0390 Inf 3.977 0.0087
StepF6 - StepF11 0.121165 0.0390 Inf 3.105 0.1533
StepF6 - StepF12 0.216014 0.0390 Inf 5.537 <.0001
StepF6 - StepF13 0.159968 0.0390 Inf 4.097 0.0054
StepF6 - StepF14 0.159317 0.0390 Inf 4.082 0.0057
StepF6 - StepF15 0.124261 0.0390 Inf 3.185 0.1240
StepF6 - StepF16 0.168945 0.0390 Inf 4.330 0.0020
StepF6 - StepF17 0.287970 0.0390 Inf 7.381 <.0001
StepF6 - StepF18 0.281321 0.0391 Inf 7.197 <.0001
StepF7 - StepF8 0.011882 0.0390 Inf 0.305 1.0000
StepF7 - StepF9 -0.057014 0.0390 Inf -1.461 0.9931
StepF7 - StepF10 0.027483 0.0390 Inf 0.704 1.0000
StepF7 - StepF11 -0.006534 0.0390 Inf -0.167 1.0000
StepF7 - StepF12 0.088315 0.0390 Inf 2.263 0.7107
StepF7 - StepF13 0.032269 0.0390 Inf 0.827 1.0000
StepF7 - StepF14 0.031618 0.0390 Inf 0.810 1.0000
StepF7 - StepF15 -0.003438 0.0390 Inf -0.088 1.0000
StepF7 - StepF16 0.041246 0.0390 Inf 1.057 0.9999
StepF7 - StepF17 0.160272 0.0390 Inf 4.106 0.0052
StepF7 - StepF18 0.153622 0.0390 Inf 3.935 0.0102
StepF8 - StepF9 -0.068897 0.0390 Inf -1.765 0.9532
StepF8 - StepF10 0.015600 0.0390 Inf 0.400 1.0000
StepF8 - StepF11 -0.018417 0.0390 Inf -0.472 1.0000
StepF8 - StepF12 0.076432 0.0390 Inf 1.959 0.8887
StepF8 - StepF13 0.020387 0.0390 Inf 0.522 1.0000
StepF8 - StepF14 0.019735 0.0390 Inf 0.506 1.0000
StepF8 - StepF15 -0.015321 0.0390 Inf -0.393 1.0000
StepF8 - StepF16 0.029364 0.0390 Inf 0.752 1.0000
StepF8 - StepF17 0.148389 0.0390 Inf 3.802 0.0169
StepF8 - StepF18 0.141739 0.0390 Inf 3.630 0.0312
StepF9 - StepF10 0.084497 0.0390 Inf 2.165 0.7772
StepF9 - StepF11 0.050480 0.0390 Inf 1.293 0.9984
StepF9 - StepF12 0.145329 0.0391 Inf 3.721 0.0227
StepF9 - StepF13 0.089284 0.0390 Inf 2.288 0.6921
StepF9 - StepF14 0.088632 0.0390 Inf 2.271 0.7045
StepF9 - StepF15 0.053576 0.0391 Inf 1.372 0.9967
StepF9 - StepF16 0.098260 0.0390 Inf 2.516 0.5175
StepF9 - StepF17 0.217286 0.0391 Inf 5.564 <.0001
StepF9 - StepF18 0.210636 0.0390 Inf 5.398 <.0001
StepF10 - StepF11 -0.034017 0.0390 Inf -0.872 1.0000
StepF10 - StepF12 0.060832 0.0390 Inf 1.559 0.9861
StepF10 - StepF13 0.004787 0.0390 Inf 0.123 1.0000
StepF10 - StepF14 0.004135 0.0390 Inf 0.106 1.0000
StepF10 - StepF15 -0.030921 0.0390 Inf -0.792 1.0000
StepF10 - StepF16 0.013763 0.0390 Inf 0.353 1.0000
StepF10 - StepF17 0.132789 0.0390 Inf 3.403 0.0655
StepF10 - StepF18 0.126139 0.0391 Inf 3.229 0.1096
StepF11 - StepF12 0.094849 0.0390 Inf 2.431 0.5840
StepF11 - StepF13 0.038804 0.0390 Inf 0.994 0.9999
StepF11 - StepF14 0.038152 0.0390 Inf 0.978 1.0000
StepF11 - StepF15 0.003096 0.0390 Inf 0.079 1.0000
StepF11 - StepF16 0.047781 0.0390 Inf 1.225 0.9992
StepF11 - StepF17 0.166806 0.0390 Inf 4.275 0.0026
StepF11 - StepF18 0.160156 0.0391 Inf 4.099 0.0053
StepF12 - StepF13 -0.056045 0.0390 Inf -1.435 0.9943
StepF12 - StepF14 -0.056697 0.0390 Inf -1.453 0.9935
StepF12 - StepF15 -0.091753 0.0390 Inf -2.352 0.6450
StepF12 - StepF16 -0.047069 0.0390 Inf -1.206 0.9993
StepF12 - StepF17 0.071957 0.0390 Inf 1.844 0.9314
StepF12 - StepF18 0.065307 0.0391 Inf 1.671 0.9721
StepF13 - StepF14 -0.000652 0.0390 Inf -0.017 1.0000
StepF13 - StepF15 -0.035708 0.0390 Inf -0.914 1.0000
StepF13 - StepF16 0.008977 0.0390 Inf 0.230 1.0000
StepF13 - StepF17 0.128002 0.0390 Inf 3.279 0.0952
StepF13 - StepF18 0.121352 0.0390 Inf 3.109 0.1518
StepF14 - StepF15 -0.035056 0.0390 Inf -0.898 1.0000
StepF14 - StepF16 0.009628 0.0390 Inf 0.247 1.0000
StepF14 - StepF17 0.128654 0.0390 Inf 3.297 0.0903
StepF14 - StepF18 0.122004 0.0390 Inf 3.125 0.1458
StepF15 - StepF16 0.044684 0.0390 Inf 1.145 0.9997
StepF15 - StepF17 0.163710 0.0390 Inf 4.196 0.0036
StepF15 - StepF18 0.157060 0.0391 Inf 4.017 0.0074
StepF16 - StepF17 0.119025 0.0390 Inf 3.051 0.1764
StepF16 - StepF18 0.112376 0.0391 Inf 2.875 0.2665
StepF17 - StepF18 -0.006650 0.0391 Inf -0.170 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.098301 0.0393 Inf 2.504 0.1721
StepF3 - StepF2 -0.029560 0.0390 Inf -0.758 1.0000
StepF4 - StepF3 -0.007110 0.0390 Inf -0.182 1.0000
StepF5 - StepF4 -0.061980 0.0390 Inf -1.588 1.0000
StepF6 - StepF5 0.115356 0.0390 Inf 2.957 0.0467
StepF7 - StepF6 -0.127699 0.0390 Inf -3.272 0.0182
StepF8 - StepF7 -0.011882 0.0390 Inf -0.305 1.0000
StepF9 - StepF8 0.068897 0.0390 Inf 1.765 0.8525
StepF10 - StepF9 -0.084497 0.0390 Inf -2.165 0.3649
StepF11 - StepF10 0.034017 0.0390 Inf 0.872 1.0000
StepF12 - StepF11 -0.094849 0.0390 Inf -2.431 0.1958
StepF13 - StepF12 0.056045 0.0390 Inf 1.435 1.0000
StepF14 - StepF13 0.000652 0.0390 Inf 0.017 1.0000
StepF15 - StepF14 0.035056 0.0390 Inf 0.898 1.0000
StepF16 - StepF15 -0.044684 0.0390 Inf -1.145 1.0000
StepF17 - StepF16 -0.119025 0.0390 Inf -3.051 0.0365
StepF18 - StepF17 0.006650 0.0391 Inf 0.170 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.098301 0.0393 Inf 2.504 0.1721
StepF3 - StepF2 -0.029560 0.0390 Inf -0.758 1.0000
StepF4 - StepF3 -0.007110 0.0390 Inf -0.182 1.0000
StepF5 - StepF4 -0.061980 0.0390 Inf -1.588 1.0000
StepF6 - StepF5 0.115356 0.0390 Inf 2.957 0.0467
StepF7 - StepF6 -0.127699 0.0390 Inf -3.272 0.0182
StepF8 - StepF7 -0.011882 0.0390 Inf -0.305 1.0000
StepF9 - StepF8 0.068897 0.0390 Inf 1.765 0.8525
StepF10 - StepF9 -0.084497 0.0390 Inf -2.165 0.3649
StepF11 - StepF10 0.034017 0.0390 Inf 0.872 1.0000
StepF12 - StepF11 -0.094849 0.0390 Inf -2.431 0.1958
StepF13 - StepF12 0.056045 0.0390 Inf 1.435 1.0000
StepF14 - StepF13 0.000652 0.0390 Inf 0.017 1.0000
StepF15 - StepF14 0.035056 0.0390 Inf 0.898 1.0000
StepF16 - StepF15 -0.044684 0.0390 Inf -1.145 1.0000
StepF17 - StepF16 -0.119025 0.0390 Inf -3.051 0.0365
StepF18 - StepF17 0.006650 0.0391 Inf 0.170 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests
==============================
TEST (rt_ms) | Block 4 | 18 steps | Axis Y
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 177.5603 17 <2e-16 ***
Accuracy 0.4332 1 0.5104
rt_ms 1.0472 1 0.3062
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.777 0.0893 Inf 0.602 0.952
2 0.918 0.0892 Inf 0.743 1.093
3 0.885 0.0892 Inf 0.711 1.060
4 0.745 0.0892 Inf 0.570 0.919
5 0.779 0.0892 Inf 0.604 0.954
6 0.865 0.0892 Inf 0.690 1.040
7 0.769 0.0892 Inf 0.594 0.944
8 0.690 0.0892 Inf 0.515 0.865
9 0.740 0.0892 Inf 0.565 0.915
10 0.795 0.0892 Inf 0.620 0.969
11 0.696 0.0892 Inf 0.521 0.871
12 0.622 0.0892 Inf 0.447 0.797
13 0.686 0.0892 Inf 0.511 0.861
14 0.654 0.0892 Inf 0.479 0.829
15 0.700 0.0892 Inf 0.526 0.875
16 0.660 0.0892 Inf 0.486 0.835
17 0.627 0.0892 Inf 0.452 0.802
18 0.565 0.0892 Inf 0.390 0.740
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.803 0.0858 Inf 0.635 0.971
2 0.944 0.0857 Inf 0.776 1.112
3 0.911 0.0857 Inf 0.743 1.079
4 0.770 0.0857 Inf 0.602 0.938
5 0.805 0.0857 Inf 0.637 0.973
6 0.891 0.0857 Inf 0.723 1.059
7 0.795 0.0857 Inf 0.627 0.963
8 0.716 0.0857 Inf 0.548 0.884
9 0.766 0.0857 Inf 0.598 0.934
10 0.820 0.0857 Inf 0.652 0.988
11 0.722 0.0857 Inf 0.554 0.890
12 0.648 0.0857 Inf 0.480 0.816
13 0.712 0.0857 Inf 0.544 0.880
14 0.680 0.0857 Inf 0.512 0.848
15 0.726 0.0857 Inf 0.558 0.894
16 0.686 0.0857 Inf 0.518 0.854
17 0.653 0.0857 Inf 0.485 0.821
18 0.591 0.0857 Inf 0.423 0.759
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.14071 0.0418 Inf -3.363 0.0741
StepF1 - StepF3 -0.10814 0.0419 Inf -2.583 0.4663
StepF1 - StepF4 0.03267 0.0418 Inf 0.781 1.0000
StepF1 - StepF5 -0.00193 0.0418 Inf -0.046 1.0000
StepF1 - StepF6 -0.08754 0.0418 Inf -2.095 0.8195
StepF1 - StepF7 0.00827 0.0417 Inf 0.198 1.0000
StepF1 - StepF8 0.08732 0.0417 Inf 2.094 0.8203
StepF1 - StepF9 0.03688 0.0416 Inf 0.886 1.0000
StepF1 - StepF10 -0.01730 0.0417 Inf -0.415 1.0000
StepF1 - StepF11 0.08106 0.0417 Inf 1.942 0.8959
StepF1 - StepF12 0.15515 0.0418 Inf 3.713 0.0233
StepF1 - StepF13 0.09093 0.0417 Inf 2.183 0.7655
StepF1 - StepF14 0.12342 0.0417 Inf 2.960 0.2200
StepF1 - StepF15 0.07686 0.0418 Inf 1.839 0.9332
StepF1 - StepF16 0.11677 0.0418 Inf 2.795 0.3157
StepF1 - StepF17 0.15009 0.0418 Inf 3.592 0.0355
StepF1 - StepF18 0.21214 0.0416 Inf 5.099 0.0001
StepF2 - StepF3 0.03257 0.0416 Inf 0.783 1.0000
StepF2 - StepF4 0.17338 0.0416 Inf 4.170 0.0040
StepF2 - StepF5 0.13878 0.0416 Inf 3.337 0.0800
StepF2 - StepF6 0.05317 0.0416 Inf 1.279 0.9986
StepF2 - StepF7 0.14898 0.0416 Inf 3.580 0.0370
StepF2 - StepF8 0.22804 0.0416 Inf 5.481 <.0001
StepF2 - StepF9 0.17759 0.0416 Inf 4.264 0.0027
StepF2 - StepF10 0.12341 0.0416 Inf 2.967 0.2161
StepF2 - StepF11 0.22177 0.0416 Inf 5.333 <.0001
StepF2 - StepF12 0.29587 0.0416 Inf 7.115 <.0001
StepF2 - StepF13 0.23164 0.0416 Inf 5.564 <.0001
StepF2 - StepF14 0.26414 0.0416 Inf 6.349 <.0001
StepF2 - StepF15 0.21757 0.0416 Inf 5.233 <.0001
StepF2 - StepF16 0.25748 0.0416 Inf 6.192 <.0001
StepF2 - StepF17 0.29081 0.0416 Inf 6.994 <.0001
StepF2 - StepF18 0.35286 0.0417 Inf 8.463 <.0001
StepF3 - StepF4 0.14081 0.0416 Inf 3.387 0.0690
StepF3 - StepF5 0.10621 0.0416 Inf 2.554 0.4886
StepF3 - StepF6 0.02060 0.0416 Inf 0.495 1.0000
StepF3 - StepF7 0.11641 0.0416 Inf 2.797 0.3148
StepF3 - StepF8 0.19547 0.0416 Inf 4.698 0.0004
StepF3 - StepF9 0.14502 0.0417 Inf 3.481 0.0512
StepF3 - StepF10 0.09084 0.0416 Inf 2.184 0.7648
StepF3 - StepF11 0.18920 0.0416 Inf 4.549 0.0008
StepF3 - StepF12 0.26329 0.0416 Inf 6.332 <.0001
StepF3 - StepF13 0.19907 0.0416 Inf 4.781 0.0003
StepF3 - StepF14 0.23156 0.0416 Inf 5.565 <.0001
StepF3 - StepF15 0.18500 0.0416 Inf 4.449 0.0012
StepF3 - StepF16 0.22491 0.0416 Inf 5.408 <.0001
StepF3 - StepF17 0.25824 0.0416 Inf 6.210 <.0001
StepF3 - StepF18 0.32029 0.0417 Inf 7.679 <.0001
StepF4 - StepF5 -0.03460 0.0416 Inf -0.832 1.0000
StepF4 - StepF6 -0.12021 0.0416 Inf -2.891 0.2573
StepF4 - StepF7 -0.02440 0.0416 Inf -0.587 1.0000
StepF4 - StepF8 0.05465 0.0416 Inf 1.314 0.9980
StepF4 - StepF9 0.00421 0.0416 Inf 0.101 1.0000
StepF4 - StepF10 -0.04997 0.0416 Inf -1.202 0.9993
StepF4 - StepF11 0.04839 0.0416 Inf 1.164 0.9996
StepF4 - StepF12 0.12248 0.0416 Inf 2.946 0.2273
StepF4 - StepF13 0.05826 0.0416 Inf 1.400 0.9958
StepF4 - StepF14 0.09075 0.0416 Inf 2.182 0.7662
StepF4 - StepF15 0.04419 0.0416 Inf 1.063 0.9999
StepF4 - StepF16 0.08409 0.0416 Inf 2.022 0.8588
StepF4 - StepF17 0.11742 0.0416 Inf 2.824 0.2974
StepF4 - StepF18 0.17947 0.0417 Inf 4.306 0.0022
StepF5 - StepF6 -0.08561 0.0416 Inf -2.059 0.8397
StepF5 - StepF7 0.01020 0.0416 Inf 0.245 1.0000
StepF5 - StepF8 0.08925 0.0416 Inf 2.146 0.7888
StepF5 - StepF9 0.03881 0.0416 Inf 0.933 1.0000
StepF5 - StepF10 -0.01537 0.0416 Inf -0.370 1.0000
StepF5 - StepF11 0.08299 0.0416 Inf 1.996 0.8718
StepF5 - StepF12 0.15708 0.0416 Inf 3.778 0.0185
StepF5 - StepF13 0.09286 0.0416 Inf 2.232 0.7320
StepF5 - StepF14 0.12535 0.0416 Inf 3.014 0.1929
StepF5 - StepF15 0.07879 0.0416 Inf 1.895 0.9142
StepF5 - StepF16 0.11869 0.0416 Inf 2.855 0.2786
StepF5 - StepF17 0.15202 0.0416 Inf 3.656 0.0285
StepF5 - StepF18 0.21407 0.0416 Inf 5.141 <.0001
StepF6 - StepF7 0.09581 0.0416 Inf 2.303 0.6810
StepF6 - StepF8 0.17487 0.0416 Inf 4.205 0.0035
StepF6 - StepF9 0.12442 0.0416 Inf 2.990 0.2048
StepF6 - StepF10 0.07024 0.0416 Inf 1.689 0.9689
StepF6 - StepF11 0.16860 0.0416 Inf 4.055 0.0064
StepF6 - StepF12 0.24270 0.0416 Inf 5.837 <.0001
StepF6 - StepF13 0.17847 0.0416 Inf 4.290 0.0024
StepF6 - StepF14 0.21097 0.0416 Inf 5.073 0.0001
StepF6 - StepF15 0.16440 0.0416 Inf 3.954 0.0095
StepF6 - StepF16 0.20431 0.0416 Inf 4.914 0.0001
StepF6 - StepF17 0.23764 0.0416 Inf 5.715 <.0001
StepF6 - StepF18 0.29969 0.0417 Inf 7.195 <.0001
StepF7 - StepF8 0.07906 0.0416 Inf 1.901 0.9118
StepF7 - StepF9 0.02861 0.0416 Inf 0.688 1.0000
StepF7 - StepF10 -0.02557 0.0416 Inf -0.615 1.0000
StepF7 - StepF11 0.07279 0.0416 Inf 1.750 0.9567
StepF7 - StepF12 0.14689 0.0416 Inf 3.531 0.0435
StepF7 - StepF13 0.08266 0.0416 Inf 1.988 0.8754
StepF7 - StepF14 0.11516 0.0416 Inf 2.770 0.3325
StepF7 - StepF15 0.06859 0.0416 Inf 1.649 0.9754
StepF7 - StepF16 0.10850 0.0416 Inf 2.609 0.4469
StepF7 - StepF17 0.14183 0.0416 Inf 3.410 0.0642
StepF7 - StepF18 0.20388 0.0416 Inf 4.901 0.0001
StepF8 - StepF9 -0.05044 0.0416 Inf -1.213 0.9993
StepF8 - StepF10 -0.10462 0.0416 Inf -2.516 0.5176
StepF8 - StepF11 -0.00626 0.0416 Inf -0.151 1.0000
StepF8 - StepF12 0.06783 0.0416 Inf 1.631 0.9779
StepF8 - StepF13 0.00361 0.0416 Inf 0.087 1.0000
StepF8 - StepF14 0.03610 0.0416 Inf 0.868 1.0000
StepF8 - StepF15 -0.01046 0.0416 Inf -0.252 1.0000
StepF8 - StepF16 0.02944 0.0416 Inf 0.708 1.0000
StepF8 - StepF17 0.06277 0.0416 Inf 1.509 0.9901
StepF8 - StepF18 0.12482 0.0416 Inf 3.000 0.2000
StepF9 - StepF10 -0.05418 0.0416 Inf -1.303 0.9982
StepF9 - StepF11 0.04418 0.0416 Inf 1.062 0.9999
StepF9 - StepF12 0.11827 0.0416 Inf 2.842 0.2865
StepF9 - StepF13 0.05405 0.0416 Inf 1.300 0.9982
StepF9 - StepF14 0.08654 0.0416 Inf 2.081 0.8276
StepF9 - StepF15 0.03998 0.0416 Inf 0.960 1.0000
StepF9 - StepF16 0.07988 0.0416 Inf 1.920 0.9048
StepF9 - StepF17 0.11321 0.0416 Inf 2.720 0.3659
StepF9 - StepF18 0.17526 0.0416 Inf 4.215 0.0033
StepF10 - StepF11 0.09836 0.0416 Inf 2.366 0.6343
StepF10 - StepF12 0.17245 0.0416 Inf 4.147 0.0044
StepF10 - StepF13 0.10823 0.0416 Inf 2.602 0.4516
StepF10 - StepF14 0.14072 0.0416 Inf 3.384 0.0694
StepF10 - StepF15 0.09416 0.0416 Inf 2.264 0.7095
StepF10 - StepF16 0.13406 0.0416 Inf 3.224 0.1111
StepF10 - StepF17 0.16739 0.0416 Inf 4.026 0.0072
StepF10 - StepF18 0.22944 0.0416 Inf 5.512 <.0001
StepF11 - StepF12 0.07409 0.0416 Inf 1.782 0.9491
StepF11 - StepF13 0.00987 0.0416 Inf 0.237 1.0000
StepF11 - StepF14 0.04236 0.0416 Inf 1.019 0.9999
StepF11 - StepF15 -0.00420 0.0416 Inf -0.101 1.0000
StepF11 - StepF16 0.03571 0.0416 Inf 0.859 1.0000
StepF11 - StepF17 0.06903 0.0416 Inf 1.660 0.9737
StepF11 - StepF18 0.13109 0.0416 Inf 3.148 0.1368
StepF12 - StepF13 -0.06422 0.0416 Inf -1.544 0.9875
StepF12 - StepF14 -0.03173 0.0416 Inf -0.763 1.0000
StepF12 - StepF15 -0.07829 0.0416 Inf -1.883 0.9185
StepF12 - StepF16 -0.03839 0.0416 Inf -0.923 1.0000
StepF12 - StepF17 -0.00506 0.0416 Inf -0.122 1.0000
StepF12 - StepF18 0.05699 0.0417 Inf 1.368 0.9968
StepF13 - StepF14 0.03249 0.0416 Inf 0.781 1.0000
StepF13 - StepF15 -0.01407 0.0416 Inf -0.338 1.0000
StepF13 - StepF16 0.02583 0.0416 Inf 0.621 1.0000
StepF13 - StepF17 0.05916 0.0416 Inf 1.422 0.9949
StepF13 - StepF18 0.12121 0.0416 Inf 2.914 0.2442
StepF14 - StepF15 -0.04656 0.0416 Inf -1.120 0.9997
StepF14 - StepF16 -0.00666 0.0416 Inf -0.160 1.0000
StepF14 - StepF17 0.02667 0.0416 Inf 0.641 1.0000
StepF14 - StepF18 0.08872 0.0416 Inf 2.132 0.7975
StepF15 - StepF16 0.03991 0.0416 Inf 0.960 1.0000
StepF15 - StepF17 0.07323 0.0416 Inf 1.761 0.9541
StepF15 - StepF18 0.13529 0.0417 Inf 3.247 0.1043
StepF16 - StepF17 0.03333 0.0416 Inf 0.802 1.0000
StepF16 - StepF18 0.09538 0.0416 Inf 2.290 0.6909
StepF17 - StepF18 0.06205 0.0417 Inf 1.490 0.9915
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.14071 0.0418 Inf -3.363 0.0741
StepF1 - StepF3 -0.10814 0.0419 Inf -2.583 0.4663
StepF1 - StepF4 0.03267 0.0418 Inf 0.781 1.0000
StepF1 - StepF5 -0.00193 0.0418 Inf -0.046 1.0000
StepF1 - StepF6 -0.08754 0.0418 Inf -2.095 0.8195
StepF1 - StepF7 0.00827 0.0417 Inf 0.198 1.0000
StepF1 - StepF8 0.08732 0.0417 Inf 2.094 0.8203
StepF1 - StepF9 0.03688 0.0416 Inf 0.886 1.0000
StepF1 - StepF10 -0.01730 0.0417 Inf -0.415 1.0000
StepF1 - StepF11 0.08106 0.0417 Inf 1.942 0.8959
StepF1 - StepF12 0.15515 0.0418 Inf 3.713 0.0233
StepF1 - StepF13 0.09093 0.0417 Inf 2.183 0.7655
StepF1 - StepF14 0.12342 0.0417 Inf 2.960 0.2200
StepF1 - StepF15 0.07686 0.0418 Inf 1.839 0.9332
StepF1 - StepF16 0.11677 0.0418 Inf 2.795 0.3157
StepF1 - StepF17 0.15009 0.0418 Inf 3.592 0.0355
StepF1 - StepF18 0.21214 0.0416 Inf 5.099 0.0001
StepF2 - StepF3 0.03257 0.0416 Inf 0.783 1.0000
StepF2 - StepF4 0.17338 0.0416 Inf 4.170 0.0040
StepF2 - StepF5 0.13878 0.0416 Inf 3.337 0.0800
StepF2 - StepF6 0.05317 0.0416 Inf 1.279 0.9986
StepF2 - StepF7 0.14898 0.0416 Inf 3.580 0.0370
StepF2 - StepF8 0.22804 0.0416 Inf 5.481 <.0001
StepF2 - StepF9 0.17759 0.0416 Inf 4.264 0.0027
StepF2 - StepF10 0.12341 0.0416 Inf 2.967 0.2161
StepF2 - StepF11 0.22177 0.0416 Inf 5.333 <.0001
StepF2 - StepF12 0.29587 0.0416 Inf 7.115 <.0001
StepF2 - StepF13 0.23164 0.0416 Inf 5.564 <.0001
StepF2 - StepF14 0.26414 0.0416 Inf 6.349 <.0001
StepF2 - StepF15 0.21757 0.0416 Inf 5.233 <.0001
StepF2 - StepF16 0.25748 0.0416 Inf 6.192 <.0001
StepF2 - StepF17 0.29081 0.0416 Inf 6.994 <.0001
StepF2 - StepF18 0.35286 0.0417 Inf 8.463 <.0001
StepF3 - StepF4 0.14081 0.0416 Inf 3.387 0.0690
StepF3 - StepF5 0.10621 0.0416 Inf 2.554 0.4886
StepF3 - StepF6 0.02060 0.0416 Inf 0.495 1.0000
StepF3 - StepF7 0.11641 0.0416 Inf 2.797 0.3148
StepF3 - StepF8 0.19547 0.0416 Inf 4.698 0.0004
StepF3 - StepF9 0.14502 0.0417 Inf 3.481 0.0512
StepF3 - StepF10 0.09084 0.0416 Inf 2.184 0.7648
StepF3 - StepF11 0.18920 0.0416 Inf 4.549 0.0008
StepF3 - StepF12 0.26329 0.0416 Inf 6.332 <.0001
StepF3 - StepF13 0.19907 0.0416 Inf 4.781 0.0003
StepF3 - StepF14 0.23156 0.0416 Inf 5.565 <.0001
StepF3 - StepF15 0.18500 0.0416 Inf 4.449 0.0012
StepF3 - StepF16 0.22491 0.0416 Inf 5.408 <.0001
StepF3 - StepF17 0.25824 0.0416 Inf 6.210 <.0001
StepF3 - StepF18 0.32029 0.0417 Inf 7.679 <.0001
StepF4 - StepF5 -0.03460 0.0416 Inf -0.832 1.0000
StepF4 - StepF6 -0.12021 0.0416 Inf -2.891 0.2573
StepF4 - StepF7 -0.02440 0.0416 Inf -0.587 1.0000
StepF4 - StepF8 0.05465 0.0416 Inf 1.314 0.9980
StepF4 - StepF9 0.00421 0.0416 Inf 0.101 1.0000
StepF4 - StepF10 -0.04997 0.0416 Inf -1.202 0.9993
StepF4 - StepF11 0.04839 0.0416 Inf 1.164 0.9996
StepF4 - StepF12 0.12248 0.0416 Inf 2.946 0.2273
StepF4 - StepF13 0.05826 0.0416 Inf 1.400 0.9958
StepF4 - StepF14 0.09075 0.0416 Inf 2.182 0.7662
StepF4 - StepF15 0.04419 0.0416 Inf 1.063 0.9999
StepF4 - StepF16 0.08409 0.0416 Inf 2.022 0.8588
StepF4 - StepF17 0.11742 0.0416 Inf 2.824 0.2974
StepF4 - StepF18 0.17947 0.0417 Inf 4.306 0.0022
StepF5 - StepF6 -0.08561 0.0416 Inf -2.059 0.8397
StepF5 - StepF7 0.01020 0.0416 Inf 0.245 1.0000
StepF5 - StepF8 0.08925 0.0416 Inf 2.146 0.7888
StepF5 - StepF9 0.03881 0.0416 Inf 0.933 1.0000
StepF5 - StepF10 -0.01537 0.0416 Inf -0.370 1.0000
StepF5 - StepF11 0.08299 0.0416 Inf 1.996 0.8718
StepF5 - StepF12 0.15708 0.0416 Inf 3.778 0.0185
StepF5 - StepF13 0.09286 0.0416 Inf 2.232 0.7320
StepF5 - StepF14 0.12535 0.0416 Inf 3.014 0.1929
StepF5 - StepF15 0.07879 0.0416 Inf 1.895 0.9142
StepF5 - StepF16 0.11869 0.0416 Inf 2.855 0.2786
StepF5 - StepF17 0.15202 0.0416 Inf 3.656 0.0285
StepF5 - StepF18 0.21407 0.0416 Inf 5.141 <.0001
StepF6 - StepF7 0.09581 0.0416 Inf 2.303 0.6810
StepF6 - StepF8 0.17487 0.0416 Inf 4.205 0.0035
StepF6 - StepF9 0.12442 0.0416 Inf 2.990 0.2048
StepF6 - StepF10 0.07024 0.0416 Inf 1.689 0.9689
StepF6 - StepF11 0.16860 0.0416 Inf 4.055 0.0064
StepF6 - StepF12 0.24270 0.0416 Inf 5.837 <.0001
StepF6 - StepF13 0.17847 0.0416 Inf 4.290 0.0024
StepF6 - StepF14 0.21097 0.0416 Inf 5.073 0.0001
StepF6 - StepF15 0.16440 0.0416 Inf 3.954 0.0095
StepF6 - StepF16 0.20431 0.0416 Inf 4.914 0.0001
StepF6 - StepF17 0.23764 0.0416 Inf 5.715 <.0001
StepF6 - StepF18 0.29969 0.0417 Inf 7.195 <.0001
StepF7 - StepF8 0.07906 0.0416 Inf 1.901 0.9118
StepF7 - StepF9 0.02861 0.0416 Inf 0.688 1.0000
StepF7 - StepF10 -0.02557 0.0416 Inf -0.615 1.0000
StepF7 - StepF11 0.07279 0.0416 Inf 1.750 0.9567
StepF7 - StepF12 0.14689 0.0416 Inf 3.531 0.0435
StepF7 - StepF13 0.08266 0.0416 Inf 1.988 0.8754
StepF7 - StepF14 0.11516 0.0416 Inf 2.770 0.3325
StepF7 - StepF15 0.06859 0.0416 Inf 1.649 0.9754
StepF7 - StepF16 0.10850 0.0416 Inf 2.609 0.4469
StepF7 - StepF17 0.14183 0.0416 Inf 3.410 0.0642
StepF7 - StepF18 0.20388 0.0416 Inf 4.901 0.0001
StepF8 - StepF9 -0.05044 0.0416 Inf -1.213 0.9993
StepF8 - StepF10 -0.10462 0.0416 Inf -2.516 0.5176
StepF8 - StepF11 -0.00626 0.0416 Inf -0.151 1.0000
StepF8 - StepF12 0.06783 0.0416 Inf 1.631 0.9779
StepF8 - StepF13 0.00361 0.0416 Inf 0.087 1.0000
StepF8 - StepF14 0.03610 0.0416 Inf 0.868 1.0000
StepF8 - StepF15 -0.01046 0.0416 Inf -0.252 1.0000
StepF8 - StepF16 0.02944 0.0416 Inf 0.708 1.0000
StepF8 - StepF17 0.06277 0.0416 Inf 1.509 0.9901
StepF8 - StepF18 0.12482 0.0416 Inf 3.000 0.2000
StepF9 - StepF10 -0.05418 0.0416 Inf -1.303 0.9982
StepF9 - StepF11 0.04418 0.0416 Inf 1.062 0.9999
StepF9 - StepF12 0.11827 0.0416 Inf 2.842 0.2865
StepF9 - StepF13 0.05405 0.0416 Inf 1.300 0.9982
StepF9 - StepF14 0.08654 0.0416 Inf 2.081 0.8276
StepF9 - StepF15 0.03998 0.0416 Inf 0.960 1.0000
StepF9 - StepF16 0.07988 0.0416 Inf 1.920 0.9048
StepF9 - StepF17 0.11321 0.0416 Inf 2.720 0.3659
StepF9 - StepF18 0.17526 0.0416 Inf 4.215 0.0033
StepF10 - StepF11 0.09836 0.0416 Inf 2.366 0.6343
StepF10 - StepF12 0.17245 0.0416 Inf 4.147 0.0044
StepF10 - StepF13 0.10823 0.0416 Inf 2.602 0.4516
StepF10 - StepF14 0.14072 0.0416 Inf 3.384 0.0694
StepF10 - StepF15 0.09416 0.0416 Inf 2.264 0.7095
StepF10 - StepF16 0.13406 0.0416 Inf 3.224 0.1111
StepF10 - StepF17 0.16739 0.0416 Inf 4.026 0.0072
StepF10 - StepF18 0.22944 0.0416 Inf 5.512 <.0001
StepF11 - StepF12 0.07409 0.0416 Inf 1.782 0.9491
StepF11 - StepF13 0.00987 0.0416 Inf 0.237 1.0000
StepF11 - StepF14 0.04236 0.0416 Inf 1.019 0.9999
StepF11 - StepF15 -0.00420 0.0416 Inf -0.101 1.0000
StepF11 - StepF16 0.03571 0.0416 Inf 0.859 1.0000
StepF11 - StepF17 0.06903 0.0416 Inf 1.660 0.9737
StepF11 - StepF18 0.13109 0.0416 Inf 3.148 0.1368
StepF12 - StepF13 -0.06422 0.0416 Inf -1.544 0.9875
StepF12 - StepF14 -0.03173 0.0416 Inf -0.763 1.0000
StepF12 - StepF15 -0.07829 0.0416 Inf -1.883 0.9185
StepF12 - StepF16 -0.03839 0.0416 Inf -0.923 1.0000
StepF12 - StepF17 -0.00506 0.0416 Inf -0.122 1.0000
StepF12 - StepF18 0.05699 0.0417 Inf 1.368 0.9968
StepF13 - StepF14 0.03249 0.0416 Inf 0.781 1.0000
StepF13 - StepF15 -0.01407 0.0416 Inf -0.338 1.0000
StepF13 - StepF16 0.02583 0.0416 Inf 0.621 1.0000
StepF13 - StepF17 0.05916 0.0416 Inf 1.422 0.9949
StepF13 - StepF18 0.12121 0.0416 Inf 2.914 0.2442
StepF14 - StepF15 -0.04656 0.0416 Inf -1.120 0.9997
StepF14 - StepF16 -0.00666 0.0416 Inf -0.160 1.0000
StepF14 - StepF17 0.02667 0.0416 Inf 0.641 1.0000
StepF14 - StepF18 0.08872 0.0416 Inf 2.132 0.7975
StepF15 - StepF16 0.03991 0.0416 Inf 0.960 1.0000
StepF15 - StepF17 0.07323 0.0416 Inf 1.761 0.9541
StepF15 - StepF18 0.13529 0.0417 Inf 3.247 0.1043
StepF16 - StepF17 0.03333 0.0416 Inf 0.802 1.0000
StepF16 - StepF18 0.09538 0.0416 Inf 2.290 0.6909
StepF17 - StepF18 0.06205 0.0417 Inf 1.490 0.9915
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1407 0.0418 Inf 3.363 0.0123
StepF3 - StepF2 -0.0326 0.0416 Inf -0.783 1.0000
StepF4 - StepF3 -0.1408 0.0416 Inf -3.387 0.0120
StepF5 - StepF4 0.0346 0.0416 Inf 0.832 1.0000
StepF6 - StepF5 0.0856 0.0416 Inf 2.059 0.5133
StepF7 - StepF6 -0.0958 0.0416 Inf -2.303 0.2976
StepF8 - StepF7 -0.0791 0.0416 Inf -1.901 0.6871
StepF9 - StepF8 0.0504 0.0416 Inf 1.213 1.0000
StepF10 - StepF9 0.0542 0.0416 Inf 1.303 1.0000
StepF11 - StepF10 -0.0984 0.0416 Inf -2.366 0.2700
StepF12 - StepF11 -0.0741 0.0416 Inf -1.782 0.8223
StepF13 - StepF12 0.0642 0.0416 Inf 1.544 1.0000
StepF14 - StepF13 -0.0325 0.0416 Inf -0.781 1.0000
StepF15 - StepF14 0.0466 0.0416 Inf 1.120 1.0000
StepF16 - StepF15 -0.0399 0.0416 Inf -0.960 1.0000
StepF17 - StepF16 -0.0333 0.0416 Inf -0.802 1.0000
StepF18 - StepF17 -0.0621 0.0417 Inf -1.490 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1407 0.0418 Inf 3.363 0.0123
StepF3 - StepF2 -0.0326 0.0416 Inf -0.783 1.0000
StepF4 - StepF3 -0.1408 0.0416 Inf -3.387 0.0120
StepF5 - StepF4 0.0346 0.0416 Inf 0.832 1.0000
StepF6 - StepF5 0.0856 0.0416 Inf 2.059 0.5133
StepF7 - StepF6 -0.0958 0.0416 Inf -2.303 0.2976
StepF8 - StepF7 -0.0791 0.0416 Inf -1.901 0.6871
StepF9 - StepF8 0.0504 0.0416 Inf 1.213 1.0000
StepF10 - StepF9 0.0542 0.0416 Inf 1.303 1.0000
StepF11 - StepF10 -0.0984 0.0416 Inf -2.366 0.2700
StepF12 - StepF11 -0.0741 0.0416 Inf -1.782 0.8223
StepF13 - StepF12 0.0642 0.0416 Inf 1.544 1.0000
StepF14 - StepF13 -0.0325 0.0416 Inf -0.781 1.0000
StepF15 - StepF14 0.0466 0.0416 Inf 1.120 1.0000
StepF16 - StepF15 -0.0399 0.0416 Inf -0.960 1.0000
StepF17 - StepF16 -0.0333 0.0416 Inf -0.802 1.0000
StepF18 - StepF17 -0.0621 0.0417 Inf -1.490 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests
==============================
TEST (rt_ms) | Block 4 | 18 steps | Axis Z
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 199.0504 17 <2e-16 ***
Accuracy 0.6371 1 0.4248
rt_ms 0.1227 1 0.7261
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.576 0.164 Inf 1.255 1.90
2 1.743 0.163 Inf 1.423 2.06
3 1.674 0.163 Inf 1.353 1.99
4 1.560 0.163 Inf 1.239 1.88
5 1.455 0.163 Inf 1.135 1.78
6 1.535 0.163 Inf 1.215 1.86
7 1.460 0.163 Inf 1.139 1.78
8 1.487 0.163 Inf 1.167 1.81
9 1.437 0.163 Inf 1.117 1.76
10 1.482 0.163 Inf 1.162 1.80
11 1.496 0.163 Inf 1.175 1.82
12 1.293 0.163 Inf 0.973 1.61
13 1.342 0.163 Inf 1.022 1.66
14 1.435 0.163 Inf 1.114 1.76
15 1.430 0.163 Inf 1.110 1.75
16 1.204 0.163 Inf 0.884 1.52
17 1.196 0.163 Inf 0.875 1.52
18 0.975 0.164 Inf 0.654 1.30
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.637 0.156 Inf 1.330 1.94
2 1.804 0.156 Inf 1.498 2.11
3 1.734 0.156 Inf 1.428 2.04
4 1.620 0.156 Inf 1.314 1.93
5 1.516 0.156 Inf 1.210 1.82
6 1.596 0.156 Inf 1.290 1.90
7 1.520 0.156 Inf 1.214 1.83
8 1.548 0.156 Inf 1.242 1.85
9 1.498 0.156 Inf 1.192 1.80
10 1.543 0.156 Inf 1.237 1.85
11 1.556 0.156 Inf 1.250 1.86
12 1.354 0.156 Inf 1.048 1.66
13 1.403 0.156 Inf 1.097 1.71
14 1.496 0.156 Inf 1.190 1.80
15 1.491 0.156 Inf 1.185 1.80
16 1.265 0.156 Inf 0.959 1.57
17 1.257 0.156 Inf 0.950 1.56
18 1.036 0.156 Inf 0.730 1.34
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.16757 0.0753 Inf -2.227 0.7360
StepF1 - StepF3 -0.09783 0.0753 Inf -1.299 0.9983
StepF1 - StepF4 0.01616 0.0752 Inf 0.215 1.0000
StepF1 - StepF5 0.12063 0.0751 Inf 1.606 0.9811
StepF1 - StepF6 0.04075 0.0752 Inf 0.542 1.0000
StepF1 - StepF7 0.11618 0.0750 Inf 1.550 0.9869
StepF1 - StepF8 0.08853 0.0750 Inf 1.180 0.9995
StepF1 - StepF9 0.13879 0.0749 Inf 1.853 0.9286
StepF1 - StepF10 0.09323 0.0751 Inf 1.242 0.9990
StepF1 - StepF11 0.08016 0.0751 Inf 1.068 0.9999
StepF1 - StepF12 0.28281 0.0752 Inf 3.763 0.0195
StepF1 - StepF13 0.23356 0.0749 Inf 3.117 0.1488
StepF1 - StepF14 0.14091 0.0750 Inf 1.879 0.9201
StepF1 - StepF15 0.14547 0.0752 Inf 1.935 0.8988
StepF1 - StepF16 0.37120 0.0751 Inf 4.940 0.0001
StepF1 - StepF17 0.37995 0.0752 Inf 5.056 0.0001
StepF1 - StepF18 0.60090 0.0748 Inf 8.029 <.0001
StepF2 - StepF3 0.06974 0.0748 Inf 0.933 1.0000
StepF2 - StepF4 0.18373 0.0748 Inf 2.457 0.5639
StepF2 - StepF5 0.28820 0.0748 Inf 3.853 0.0140
StepF2 - StepF6 0.20832 0.0748 Inf 2.785 0.3221
StepF2 - StepF7 0.28375 0.0749 Inf 3.791 0.0176
StepF2 - StepF8 0.25610 0.0748 Inf 3.423 0.0617
StepF2 - StepF9 0.30637 0.0749 Inf 4.090 0.0056
StepF2 - StepF10 0.26080 0.0748 Inf 3.486 0.0504
StepF2 - StepF11 0.24773 0.0748 Inf 3.312 0.0864
StepF2 - StepF12 0.45038 0.0748 Inf 6.022 <.0001
StepF2 - StepF13 0.40113 0.0749 Inf 5.357 <.0001
StepF2 - StepF14 0.30848 0.0748 Inf 4.122 0.0049
StepF2 - StepF15 0.31304 0.0748 Inf 4.186 0.0037
StepF2 - StepF16 0.53877 0.0748 Inf 7.203 <.0001
StepF2 - StepF17 0.54752 0.0748 Inf 7.321 <.0001
StepF2 - StepF18 0.76847 0.0750 Inf 10.247 <.0001
StepF3 - StepF4 0.11399 0.0748 Inf 1.524 0.9891
StepF3 - StepF5 0.21846 0.0748 Inf 2.920 0.2410
StepF3 - StepF6 0.13857 0.0748 Inf 1.853 0.9288
StepF3 - StepF7 0.21401 0.0749 Inf 2.858 0.2765
StepF3 - StepF8 0.18636 0.0748 Inf 2.490 0.5380
StepF3 - StepF9 0.23662 0.0749 Inf 3.158 0.1334
StepF3 - StepF10 0.19106 0.0748 Inf 2.553 0.4888
StepF3 - StepF11 0.17799 0.0748 Inf 2.379 0.6241
StepF3 - StepF12 0.38063 0.0748 Inf 5.089 0.0001
StepF3 - StepF13 0.33139 0.0749 Inf 4.424 0.0013
StepF3 - StepF14 0.23873 0.0748 Inf 3.190 0.1224
StepF3 - StepF15 0.24330 0.0748 Inf 3.253 0.1025
StepF3 - StepF16 0.46903 0.0748 Inf 6.270 <.0001
StepF3 - StepF17 0.47778 0.0748 Inf 6.388 <.0001
StepF3 - StepF18 0.69873 0.0750 Inf 9.313 <.0001
StepF4 - StepF5 0.10447 0.0748 Inf 1.397 0.9959
StepF4 - StepF6 0.02459 0.0748 Inf 0.329 1.0000
StepF4 - StepF7 0.10002 0.0748 Inf 1.337 0.9975
StepF4 - StepF8 0.07237 0.0748 Inf 0.967 1.0000
StepF4 - StepF9 0.12263 0.0749 Inf 1.638 0.9770
StepF4 - StepF10 0.07707 0.0748 Inf 1.030 0.9999
StepF4 - StepF11 0.06400 0.0748 Inf 0.856 1.0000
StepF4 - StepF12 0.26665 0.0748 Inf 3.565 0.0389
StepF4 - StepF13 0.21740 0.0749 Inf 2.904 0.2499
StepF4 - StepF14 0.12475 0.0748 Inf 1.667 0.9726
StepF4 - StepF15 0.12931 0.0748 Inf 1.729 0.9613
StepF4 - StepF16 0.35504 0.0748 Inf 4.747 0.0003
StepF4 - StepF17 0.36379 0.0748 Inf 4.864 0.0002
StepF4 - StepF18 0.58474 0.0750 Inf 7.800 <.0001
StepF5 - StepF6 -0.07988 0.0748 Inf -1.068 0.9999
StepF5 - StepF7 -0.00445 0.0748 Inf -0.059 1.0000
StepF5 - StepF8 -0.03210 0.0748 Inf -0.429 1.0000
StepF5 - StepF9 0.01816 0.0748 Inf 0.243 1.0000
StepF5 - StepF10 -0.02740 0.0748 Inf -0.366 1.0000
StepF5 - StepF11 -0.04047 0.0748 Inf -0.541 1.0000
StepF5 - StepF12 0.16218 0.0748 Inf 2.169 0.7747
StepF5 - StepF13 0.11293 0.0748 Inf 1.509 0.9901
StepF5 - StepF14 0.02027 0.0748 Inf 0.271 1.0000
StepF5 - StepF15 0.02484 0.0748 Inf 0.332 1.0000
StepF5 - StepF16 0.25057 0.0748 Inf 3.351 0.0769
StepF5 - StepF17 0.25932 0.0748 Inf 3.467 0.0535
StepF5 - StepF18 0.48027 0.0749 Inf 6.412 <.0001
StepF6 - StepF7 0.07544 0.0748 Inf 1.008 0.9999
StepF6 - StepF8 0.04778 0.0748 Inf 0.639 1.0000
StepF6 - StepF9 0.09805 0.0749 Inf 1.310 0.9981
StepF6 - StepF10 0.05249 0.0748 Inf 0.702 1.0000
StepF6 - StepF11 0.03941 0.0748 Inf 0.527 1.0000
StepF6 - StepF12 0.24206 0.0748 Inf 3.237 0.1073
StepF6 - StepF13 0.19282 0.0748 Inf 2.577 0.4711
StepF6 - StepF14 0.10016 0.0748 Inf 1.339 0.9975
StepF6 - StepF15 0.10473 0.0748 Inf 1.400 0.9957
StepF6 - StepF16 0.33046 0.0748 Inf 4.419 0.0014
StepF6 - StepF17 0.33920 0.0748 Inf 4.536 0.0008
StepF6 - StepF18 0.56015 0.0749 Inf 7.476 <.0001
StepF7 - StepF8 -0.02765 0.0748 Inf -0.370 1.0000
StepF7 - StepF9 0.02261 0.0748 Inf 0.302 1.0000
StepF7 - StepF10 -0.02295 0.0748 Inf -0.307 1.0000
StepF7 - StepF11 -0.03602 0.0748 Inf -0.482 1.0000
StepF7 - StepF12 0.16663 0.0748 Inf 2.227 0.7356
StepF7 - StepF13 0.11738 0.0748 Inf 1.570 0.9851
StepF7 - StepF14 0.02472 0.0748 Inf 0.331 1.0000
StepF7 - StepF15 0.02929 0.0748 Inf 0.391 1.0000
StepF7 - StepF16 0.25502 0.0748 Inf 3.409 0.0644
StepF7 - StepF17 0.26377 0.0748 Inf 3.526 0.0443
StepF7 - StepF18 0.48472 0.0748 Inf 6.478 <.0001
StepF8 - StepF9 0.05026 0.0748 Inf 0.672 1.0000
StepF8 - StepF10 0.00470 0.0748 Inf 0.063 1.0000
StepF8 - StepF11 -0.00837 0.0748 Inf -0.112 1.0000
StepF8 - StepF12 0.19428 0.0748 Inf 2.597 0.4555
StepF8 - StepF13 0.14503 0.0748 Inf 1.939 0.8970
StepF8 - StepF14 0.05238 0.0748 Inf 0.700 1.0000
StepF8 - StepF15 0.05694 0.0748 Inf 0.761 1.0000
StepF8 - StepF16 0.28267 0.0748 Inf 3.779 0.0184
StepF8 - StepF17 0.29142 0.0748 Inf 3.896 0.0119
StepF8 - StepF18 0.51237 0.0748 Inf 6.846 <.0001
StepF9 - StepF10 -0.04556 0.0748 Inf -0.609 1.0000
StepF9 - StepF11 -0.05863 0.0748 Inf -0.784 1.0000
StepF9 - StepF12 0.14401 0.0749 Inf 1.924 0.9032
StepF9 - StepF13 0.09477 0.0748 Inf 1.267 0.9987
StepF9 - StepF14 0.00211 0.0748 Inf 0.028 1.0000
StepF9 - StepF15 0.00668 0.0749 Inf 0.089 1.0000
StepF9 - StepF16 0.23241 0.0748 Inf 3.105 0.1535
StepF9 - StepF17 0.24116 0.0749 Inf 3.222 0.1120
StepF9 - StepF18 0.46211 0.0748 Inf 6.178 <.0001
StepF10 - StepF11 -0.01307 0.0748 Inf -0.175 1.0000
StepF10 - StepF12 0.18957 0.0748 Inf 2.535 0.5033
StepF10 - StepF13 0.14033 0.0748 Inf 1.876 0.9210
StepF10 - StepF14 0.04767 0.0748 Inf 0.637 1.0000
StepF10 - StepF15 0.05224 0.0748 Inf 0.698 1.0000
StepF10 - StepF16 0.27797 0.0748 Inf 3.717 0.0231
StepF10 - StepF17 0.28672 0.0748 Inf 3.834 0.0151
StepF10 - StepF18 0.50767 0.0749 Inf 6.781 <.0001
StepF11 - StepF12 0.20265 0.0748 Inf 2.710 0.3734
StepF11 - StepF13 0.15340 0.0748 Inf 2.050 0.8443
StepF11 - StepF14 0.06075 0.0748 Inf 0.812 1.0000
StepF11 - StepF15 0.06531 0.0748 Inf 0.873 1.0000
StepF11 - StepF16 0.29104 0.0748 Inf 3.892 0.0121
StepF11 - StepF17 0.29979 0.0748 Inf 4.009 0.0077
StepF11 - StepF18 0.52074 0.0749 Inf 6.954 <.0001
StepF12 - StepF13 -0.04924 0.0748 Inf -0.658 1.0000
StepF12 - StepF14 -0.14190 0.0748 Inf -1.897 0.9135
StepF12 - StepF15 -0.13733 0.0748 Inf -1.836 0.9339
StepF12 - StepF16 0.08839 0.0748 Inf 1.182 0.9995
StepF12 - StepF17 0.09714 0.0748 Inf 1.299 0.9983
StepF12 - StepF18 0.31809 0.0749 Inf 4.245 0.0029
StepF13 - StepF14 -0.09266 0.0748 Inf -1.239 0.9990
StepF13 - StepF15 -0.08809 0.0748 Inf -1.177 0.9995
StepF13 - StepF16 0.13764 0.0748 Inf 1.839 0.9330
StepF13 - StepF17 0.14639 0.0748 Inf 1.956 0.8898
StepF13 - StepF18 0.36734 0.0748 Inf 4.910 0.0001
StepF14 - StepF15 0.00457 0.0748 Inf 0.061 1.0000
StepF14 - StepF16 0.23030 0.0748 Inf 3.079 0.1642
StepF14 - StepF17 0.23904 0.0748 Inf 3.196 0.1204
StepF14 - StepF18 0.45999 0.0748 Inf 6.146 <.0001
StepF15 - StepF16 0.22573 0.0748 Inf 3.018 0.1911
StepF15 - StepF17 0.23448 0.0748 Inf 3.135 0.1417
StepF15 - StepF18 0.45543 0.0749 Inf 6.077 <.0001
StepF16 - StepF17 0.00875 0.0748 Inf 0.117 1.0000
StepF16 - StepF18 0.22970 0.0749 Inf 3.066 0.1696
StepF17 - StepF18 0.22095 0.0749 Inf 2.949 0.2256
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.16757 0.0753 Inf -2.227 0.7360
StepF1 - StepF3 -0.09783 0.0753 Inf -1.299 0.9983
StepF1 - StepF4 0.01616 0.0752 Inf 0.215 1.0000
StepF1 - StepF5 0.12063 0.0751 Inf 1.606 0.9811
StepF1 - StepF6 0.04075 0.0752 Inf 0.542 1.0000
StepF1 - StepF7 0.11618 0.0750 Inf 1.550 0.9869
StepF1 - StepF8 0.08853 0.0750 Inf 1.180 0.9995
StepF1 - StepF9 0.13879 0.0749 Inf 1.853 0.9286
StepF1 - StepF10 0.09323 0.0751 Inf 1.242 0.9990
StepF1 - StepF11 0.08016 0.0751 Inf 1.068 0.9999
StepF1 - StepF12 0.28281 0.0752 Inf 3.763 0.0195
StepF1 - StepF13 0.23356 0.0749 Inf 3.117 0.1488
StepF1 - StepF14 0.14091 0.0750 Inf 1.879 0.9201
StepF1 - StepF15 0.14547 0.0752 Inf 1.935 0.8988
StepF1 - StepF16 0.37120 0.0751 Inf 4.940 0.0001
StepF1 - StepF17 0.37995 0.0752 Inf 5.056 0.0001
StepF1 - StepF18 0.60090 0.0748 Inf 8.029 <.0001
StepF2 - StepF3 0.06974 0.0748 Inf 0.933 1.0000
StepF2 - StepF4 0.18373 0.0748 Inf 2.457 0.5639
StepF2 - StepF5 0.28820 0.0748 Inf 3.853 0.0140
StepF2 - StepF6 0.20832 0.0748 Inf 2.785 0.3221
StepF2 - StepF7 0.28375 0.0749 Inf 3.791 0.0176
StepF2 - StepF8 0.25610 0.0748 Inf 3.423 0.0617
StepF2 - StepF9 0.30637 0.0749 Inf 4.090 0.0056
StepF2 - StepF10 0.26080 0.0748 Inf 3.486 0.0504
StepF2 - StepF11 0.24773 0.0748 Inf 3.312 0.0864
StepF2 - StepF12 0.45038 0.0748 Inf 6.022 <.0001
StepF2 - StepF13 0.40113 0.0749 Inf 5.357 <.0001
StepF2 - StepF14 0.30848 0.0748 Inf 4.122 0.0049
StepF2 - StepF15 0.31304 0.0748 Inf 4.186 0.0037
StepF2 - StepF16 0.53877 0.0748 Inf 7.203 <.0001
StepF2 - StepF17 0.54752 0.0748 Inf 7.321 <.0001
StepF2 - StepF18 0.76847 0.0750 Inf 10.247 <.0001
StepF3 - StepF4 0.11399 0.0748 Inf 1.524 0.9891
StepF3 - StepF5 0.21846 0.0748 Inf 2.920 0.2410
StepF3 - StepF6 0.13857 0.0748 Inf 1.853 0.9288
StepF3 - StepF7 0.21401 0.0749 Inf 2.858 0.2765
StepF3 - StepF8 0.18636 0.0748 Inf 2.490 0.5380
StepF3 - StepF9 0.23662 0.0749 Inf 3.158 0.1334
StepF3 - StepF10 0.19106 0.0748 Inf 2.553 0.4888
StepF3 - StepF11 0.17799 0.0748 Inf 2.379 0.6241
StepF3 - StepF12 0.38063 0.0748 Inf 5.089 0.0001
StepF3 - StepF13 0.33139 0.0749 Inf 4.424 0.0013
StepF3 - StepF14 0.23873 0.0748 Inf 3.190 0.1224
StepF3 - StepF15 0.24330 0.0748 Inf 3.253 0.1025
StepF3 - StepF16 0.46903 0.0748 Inf 6.270 <.0001
StepF3 - StepF17 0.47778 0.0748 Inf 6.388 <.0001
StepF3 - StepF18 0.69873 0.0750 Inf 9.313 <.0001
StepF4 - StepF5 0.10447 0.0748 Inf 1.397 0.9959
StepF4 - StepF6 0.02459 0.0748 Inf 0.329 1.0000
StepF4 - StepF7 0.10002 0.0748 Inf 1.337 0.9975
StepF4 - StepF8 0.07237 0.0748 Inf 0.967 1.0000
StepF4 - StepF9 0.12263 0.0749 Inf 1.638 0.9770
StepF4 - StepF10 0.07707 0.0748 Inf 1.030 0.9999
StepF4 - StepF11 0.06400 0.0748 Inf 0.856 1.0000
StepF4 - StepF12 0.26665 0.0748 Inf 3.565 0.0389
StepF4 - StepF13 0.21740 0.0749 Inf 2.904 0.2499
StepF4 - StepF14 0.12475 0.0748 Inf 1.667 0.9726
StepF4 - StepF15 0.12931 0.0748 Inf 1.729 0.9613
StepF4 - StepF16 0.35504 0.0748 Inf 4.747 0.0003
StepF4 - StepF17 0.36379 0.0748 Inf 4.864 0.0002
StepF4 - StepF18 0.58474 0.0750 Inf 7.800 <.0001
StepF5 - StepF6 -0.07988 0.0748 Inf -1.068 0.9999
StepF5 - StepF7 -0.00445 0.0748 Inf -0.059 1.0000
StepF5 - StepF8 -0.03210 0.0748 Inf -0.429 1.0000
StepF5 - StepF9 0.01816 0.0748 Inf 0.243 1.0000
StepF5 - StepF10 -0.02740 0.0748 Inf -0.366 1.0000
StepF5 - StepF11 -0.04047 0.0748 Inf -0.541 1.0000
StepF5 - StepF12 0.16218 0.0748 Inf 2.169 0.7747
StepF5 - StepF13 0.11293 0.0748 Inf 1.509 0.9901
StepF5 - StepF14 0.02027 0.0748 Inf 0.271 1.0000
StepF5 - StepF15 0.02484 0.0748 Inf 0.332 1.0000
StepF5 - StepF16 0.25057 0.0748 Inf 3.351 0.0769
StepF5 - StepF17 0.25932 0.0748 Inf 3.467 0.0535
StepF5 - StepF18 0.48027 0.0749 Inf 6.412 <.0001
StepF6 - StepF7 0.07544 0.0748 Inf 1.008 0.9999
StepF6 - StepF8 0.04778 0.0748 Inf 0.639 1.0000
StepF6 - StepF9 0.09805 0.0749 Inf 1.310 0.9981
StepF6 - StepF10 0.05249 0.0748 Inf 0.702 1.0000
StepF6 - StepF11 0.03941 0.0748 Inf 0.527 1.0000
StepF6 - StepF12 0.24206 0.0748 Inf 3.237 0.1073
StepF6 - StepF13 0.19282 0.0748 Inf 2.577 0.4711
StepF6 - StepF14 0.10016 0.0748 Inf 1.339 0.9975
StepF6 - StepF15 0.10473 0.0748 Inf 1.400 0.9957
StepF6 - StepF16 0.33046 0.0748 Inf 4.419 0.0014
StepF6 - StepF17 0.33920 0.0748 Inf 4.536 0.0008
StepF6 - StepF18 0.56015 0.0749 Inf 7.476 <.0001
StepF7 - StepF8 -0.02765 0.0748 Inf -0.370 1.0000
StepF7 - StepF9 0.02261 0.0748 Inf 0.302 1.0000
StepF7 - StepF10 -0.02295 0.0748 Inf -0.307 1.0000
StepF7 - StepF11 -0.03602 0.0748 Inf -0.482 1.0000
StepF7 - StepF12 0.16663 0.0748 Inf 2.227 0.7356
StepF7 - StepF13 0.11738 0.0748 Inf 1.570 0.9851
StepF7 - StepF14 0.02472 0.0748 Inf 0.331 1.0000
StepF7 - StepF15 0.02929 0.0748 Inf 0.391 1.0000
StepF7 - StepF16 0.25502 0.0748 Inf 3.409 0.0644
StepF7 - StepF17 0.26377 0.0748 Inf 3.526 0.0443
StepF7 - StepF18 0.48472 0.0748 Inf 6.478 <.0001
StepF8 - StepF9 0.05026 0.0748 Inf 0.672 1.0000
StepF8 - StepF10 0.00470 0.0748 Inf 0.063 1.0000
StepF8 - StepF11 -0.00837 0.0748 Inf -0.112 1.0000
StepF8 - StepF12 0.19428 0.0748 Inf 2.597 0.4555
StepF8 - StepF13 0.14503 0.0748 Inf 1.939 0.8970
StepF8 - StepF14 0.05238 0.0748 Inf 0.700 1.0000
StepF8 - StepF15 0.05694 0.0748 Inf 0.761 1.0000
StepF8 - StepF16 0.28267 0.0748 Inf 3.779 0.0184
StepF8 - StepF17 0.29142 0.0748 Inf 3.896 0.0119
StepF8 - StepF18 0.51237 0.0748 Inf 6.846 <.0001
StepF9 - StepF10 -0.04556 0.0748 Inf -0.609 1.0000
StepF9 - StepF11 -0.05863 0.0748 Inf -0.784 1.0000
StepF9 - StepF12 0.14401 0.0749 Inf 1.924 0.9032
StepF9 - StepF13 0.09477 0.0748 Inf 1.267 0.9987
StepF9 - StepF14 0.00211 0.0748 Inf 0.028 1.0000
StepF9 - StepF15 0.00668 0.0749 Inf 0.089 1.0000
StepF9 - StepF16 0.23241 0.0748 Inf 3.105 0.1535
StepF9 - StepF17 0.24116 0.0749 Inf 3.222 0.1120
StepF9 - StepF18 0.46211 0.0748 Inf 6.178 <.0001
StepF10 - StepF11 -0.01307 0.0748 Inf -0.175 1.0000
StepF10 - StepF12 0.18957 0.0748 Inf 2.535 0.5033
StepF10 - StepF13 0.14033 0.0748 Inf 1.876 0.9210
StepF10 - StepF14 0.04767 0.0748 Inf 0.637 1.0000
StepF10 - StepF15 0.05224 0.0748 Inf 0.698 1.0000
StepF10 - StepF16 0.27797 0.0748 Inf 3.717 0.0231
StepF10 - StepF17 0.28672 0.0748 Inf 3.834 0.0151
StepF10 - StepF18 0.50767 0.0749 Inf 6.781 <.0001
StepF11 - StepF12 0.20265 0.0748 Inf 2.710 0.3734
StepF11 - StepF13 0.15340 0.0748 Inf 2.050 0.8443
StepF11 - StepF14 0.06075 0.0748 Inf 0.812 1.0000
StepF11 - StepF15 0.06531 0.0748 Inf 0.873 1.0000
StepF11 - StepF16 0.29104 0.0748 Inf 3.892 0.0121
StepF11 - StepF17 0.29979 0.0748 Inf 4.009 0.0077
StepF11 - StepF18 0.52074 0.0749 Inf 6.954 <.0001
StepF12 - StepF13 -0.04924 0.0748 Inf -0.658 1.0000
StepF12 - StepF14 -0.14190 0.0748 Inf -1.897 0.9135
StepF12 - StepF15 -0.13733 0.0748 Inf -1.836 0.9339
StepF12 - StepF16 0.08839 0.0748 Inf 1.182 0.9995
StepF12 - StepF17 0.09714 0.0748 Inf 1.299 0.9983
StepF12 - StepF18 0.31809 0.0749 Inf 4.245 0.0029
StepF13 - StepF14 -0.09266 0.0748 Inf -1.239 0.9990
StepF13 - StepF15 -0.08809 0.0748 Inf -1.177 0.9995
StepF13 - StepF16 0.13764 0.0748 Inf 1.839 0.9330
StepF13 - StepF17 0.14639 0.0748 Inf 1.956 0.8898
StepF13 - StepF18 0.36734 0.0748 Inf 4.910 0.0001
StepF14 - StepF15 0.00457 0.0748 Inf 0.061 1.0000
StepF14 - StepF16 0.23030 0.0748 Inf 3.079 0.1642
StepF14 - StepF17 0.23904 0.0748 Inf 3.196 0.1204
StepF14 - StepF18 0.45999 0.0748 Inf 6.146 <.0001
StepF15 - StepF16 0.22573 0.0748 Inf 3.018 0.1911
StepF15 - StepF17 0.23448 0.0748 Inf 3.135 0.1417
StepF15 - StepF18 0.45543 0.0749 Inf 6.077 <.0001
StepF16 - StepF17 0.00875 0.0748 Inf 0.117 1.0000
StepF16 - StepF18 0.22970 0.0749 Inf 3.066 0.1696
StepF17 - StepF18 0.22095 0.0749 Inf 2.949 0.2256
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.16757 0.0753 Inf 2.227 0.3637
StepF3 - StepF2 -0.06974 0.0748 Inf -0.933 1.0000
StepF4 - StepF3 -0.11399 0.0748 Inf -1.524 1.0000
StepF5 - StepF4 -0.10447 0.0748 Inf -1.397 1.0000
StepF6 - StepF5 0.07988 0.0748 Inf 1.068 1.0000
StepF7 - StepF6 -0.07544 0.0748 Inf -1.008 1.0000
StepF8 - StepF7 0.02765 0.0748 Inf 0.370 1.0000
StepF9 - StepF8 -0.05026 0.0748 Inf -0.672 1.0000
StepF10 - StepF9 0.04556 0.0748 Inf 0.609 1.0000
StepF11 - StepF10 0.01307 0.0748 Inf 0.175 1.0000
StepF12 - StepF11 -0.20265 0.0748 Inf -2.710 0.1010
StepF13 - StepF12 0.04925 0.0748 Inf 0.658 1.0000
StepF14 - StepF13 0.09266 0.0748 Inf 1.239 1.0000
StepF15 - StepF14 -0.00457 0.0748 Inf -0.061 1.0000
StepF16 - StepF15 -0.22573 0.0748 Inf -3.018 0.0432
StepF17 - StepF16 -0.00875 0.0748 Inf -0.117 1.0000
StepF18 - StepF17 -0.22095 0.0749 Inf -2.949 0.0510
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.16757 0.0753 Inf 2.227 0.3637
StepF3 - StepF2 -0.06974 0.0748 Inf -0.933 1.0000
StepF4 - StepF3 -0.11399 0.0748 Inf -1.524 1.0000
StepF5 - StepF4 -0.10447 0.0748 Inf -1.397 1.0000
StepF6 - StepF5 0.07988 0.0748 Inf 1.068 1.0000
StepF7 - StepF6 -0.07544 0.0748 Inf -1.008 1.0000
StepF8 - StepF7 0.02765 0.0748 Inf 0.370 1.0000
StepF9 - StepF8 -0.05026 0.0748 Inf -0.672 1.0000
StepF10 - StepF9 0.04556 0.0748 Inf 0.609 1.0000
StepF11 - StepF10 0.01307 0.0748 Inf 0.175 1.0000
StepF12 - StepF11 -0.20265 0.0748 Inf -2.710 0.1010
StepF13 - StepF12 0.04925 0.0748 Inf 0.658 1.0000
StepF14 - StepF13 0.09266 0.0748 Inf 1.239 1.0000
StepF15 - StepF14 -0.00457 0.0748 Inf -0.061 1.0000
StepF16 - StepF15 -0.22573 0.0748 Inf -3.018 0.0432
StepF17 - StepF16 -0.00875 0.0748 Inf -0.117 1.0000
StepF18 - StepF17 -0.22095 0.0749 Inf -2.949 0.0510
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests # Block 5
.report_step_test_rtms(sw_b5_6_rt, "5", "6 steps")
==============================
TEST (rt_ms) | Block 5 | 6 steps | Axis X
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 12.8240 5 0.02509 *
Accuracy 1.4901 1 0.22220
rt_ms 1.6018 1 0.20565
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.654 0.0491 Inf 0.557 0.750
2 0.604 0.0486 Inf 0.509 0.700
3 0.637 0.0486 Inf 0.542 0.733
4 0.616 0.0486 Inf 0.521 0.711
5 0.574 0.0486 Inf 0.479 0.669
6 0.563 0.0486 Inf 0.467 0.658
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.616 0.0487 Inf 0.521 0.712
2 0.567 0.0486 Inf 0.472 0.662
3 0.600 0.0486 Inf 0.505 0.695
4 0.579 0.0486 Inf 0.484 0.674
5 0.537 0.0485 Inf 0.442 0.632
6 0.525 0.0485 Inf 0.430 0.620
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.0493 0.0319 Inf 1.546 0.6345
StepF1 - StepF3 0.0163 0.0318 Inf 0.512 0.9957
StepF1 - StepF4 0.0376 0.0319 Inf 1.179 0.8473
StepF1 - StepF5 0.0797 0.0315 Inf 2.532 0.1147
StepF1 - StepF6 0.0910 0.0316 Inf 2.881 0.0457
StepF2 - StepF3 -0.0330 0.0309 Inf -1.068 0.8941
StepF2 - StepF4 -0.0117 0.0309 Inf -0.379 0.9990
StepF2 - StepF5 0.0304 0.0310 Inf 0.982 0.9239
StepF2 - StepF6 0.0417 0.0309 Inf 1.349 0.7572
StepF3 - StepF4 0.0213 0.0309 Inf 0.689 0.9832
StepF3 - StepF5 0.0634 0.0309 Inf 2.050 0.3141
StepF3 - StepF6 0.0747 0.0309 Inf 2.418 0.1499
StepF4 - StepF5 0.0421 0.0310 Inf 1.360 0.7511
StepF4 - StepF6 0.0534 0.0309 Inf 1.728 0.5132
StepF5 - StepF6 0.0113 0.0309 Inf 0.367 0.9991
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.0493 0.0319 Inf 1.546 0.6345
StepF1 - StepF3 0.0163 0.0318 Inf 0.512 0.9957
StepF1 - StepF4 0.0376 0.0319 Inf 1.179 0.8473
StepF1 - StepF5 0.0797 0.0315 Inf 2.532 0.1147
StepF1 - StepF6 0.0910 0.0316 Inf 2.881 0.0457
StepF2 - StepF3 -0.0330 0.0309 Inf -1.068 0.8941
StepF2 - StepF4 -0.0117 0.0309 Inf -0.379 0.9990
StepF2 - StepF5 0.0304 0.0310 Inf 0.982 0.9239
StepF2 - StepF6 0.0417 0.0309 Inf 1.349 0.7572
StepF3 - StepF4 0.0213 0.0309 Inf 0.689 0.9832
StepF3 - StepF5 0.0634 0.0309 Inf 2.050 0.3141
StepF3 - StepF6 0.0747 0.0309 Inf 2.418 0.1499
StepF4 - StepF5 0.0421 0.0310 Inf 1.360 0.7511
StepF4 - StepF6 0.0534 0.0309 Inf 1.728 0.5132
StepF5 - StepF6 0.0113 0.0309 Inf 0.367 0.9991
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.0493 0.0319 Inf -1.546 0.6109
StepF3 - StepF2 0.0330 0.0309 Inf 1.068 0.8566
StepF4 - StepF3 -0.0213 0.0309 Inf -0.689 0.9813
StepF5 - StepF4 -0.0421 0.0310 Inf -1.360 0.6955
StepF6 - StepF5 -0.0113 0.0309 Inf -0.367 0.9813
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.0493 0.0319 Inf -1.546 0.6109
StepF3 - StepF2 0.0330 0.0309 Inf 1.068 0.8566
StepF4 - StepF3 -0.0213 0.0309 Inf -0.689 0.9813
StepF5 - StepF4 -0.0421 0.0310 Inf -1.360 0.6955
StepF6 - StepF5 -0.0113 0.0309 Inf -0.367 0.9813
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests
==============================
TEST (rt_ms) | Block 5 | 6 steps | Axis Y
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 30.1689 5 1.366e-05 ***
Accuracy 3.1395 1 0.07642 .
rt_ms 3.6615 1 0.05568 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.655 0.0587 Inf 0.540 0.770
2 0.756 0.0582 Inf 0.642 0.870
3 0.671 0.0582 Inf 0.557 0.785
4 0.685 0.0582 Inf 0.571 0.800
5 0.613 0.0582 Inf 0.499 0.727
6 0.592 0.0582 Inf 0.478 0.706
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.600 0.0583 Inf 0.485 0.714
2 0.700 0.0582 Inf 0.586 0.814
3 0.616 0.0581 Inf 0.502 0.730
4 0.630 0.0582 Inf 0.516 0.744
5 0.557 0.0581 Inf 0.443 0.671
6 0.537 0.0581 Inf 0.423 0.650
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.1007 0.0343 Inf -2.933 0.0394
StepF1 - StepF3 -0.0161 0.0342 Inf -0.471 0.9971
StepF1 - StepF4 -0.0302 0.0343 Inf -0.879 0.9515
StepF1 - StepF5 0.0425 0.0339 Inf 1.254 0.8100
StepF1 - StepF6 0.0631 0.0340 Inf 1.856 0.4299
StepF2 - StepF3 0.0846 0.0333 Inf 2.541 0.1124
StepF2 - StepF4 0.0705 0.0333 Inf 2.118 0.2777
StepF2 - StepF5 0.1431 0.0333 Inf 4.294 0.0003
StepF2 - StepF6 0.1638 0.0333 Inf 4.918 <.0001
StepF3 - StepF4 -0.0141 0.0333 Inf -0.423 0.9983
StepF3 - StepF5 0.0586 0.0333 Inf 1.759 0.4926
StepF3 - StepF6 0.0792 0.0333 Inf 2.380 0.1631
StepF4 - StepF5 0.0727 0.0333 Inf 2.180 0.2472
StepF4 - StepF6 0.0933 0.0333 Inf 2.801 0.0572
StepF5 - StepF6 0.0206 0.0333 Inf 0.620 0.9896
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.1007 0.0343 Inf -2.933 0.0394
StepF1 - StepF3 -0.0161 0.0342 Inf -0.471 0.9971
StepF1 - StepF4 -0.0302 0.0343 Inf -0.879 0.9515
StepF1 - StepF5 0.0425 0.0339 Inf 1.254 0.8100
StepF1 - StepF6 0.0631 0.0340 Inf 1.856 0.4299
StepF2 - StepF3 0.0846 0.0333 Inf 2.541 0.1124
StepF2 - StepF4 0.0705 0.0333 Inf 2.118 0.2777
StepF2 - StepF5 0.1431 0.0333 Inf 4.294 0.0003
StepF2 - StepF6 0.1638 0.0333 Inf 4.918 <.0001
StepF3 - StepF4 -0.0141 0.0333 Inf -0.423 0.9983
StepF3 - StepF5 0.0586 0.0333 Inf 1.759 0.4926
StepF3 - StepF6 0.0792 0.0333 Inf 2.380 0.1631
StepF4 - StepF5 0.0727 0.0333 Inf 2.180 0.2472
StepF4 - StepF6 0.0933 0.0333 Inf 2.801 0.0572
StepF5 - StepF6 0.0206 0.0333 Inf 0.620 0.9896
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1007 0.0343 Inf 2.933 0.0168
StepF3 - StepF2 -0.0846 0.0333 Inf -2.541 0.0442
StepF4 - StepF3 0.0141 0.0333 Inf 0.423 1.0000
StepF5 - StepF4 -0.0727 0.0333 Inf -2.180 0.0879
StepF6 - StepF5 -0.0206 0.0333 Inf -0.620 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1007 0.0343 Inf 2.933 0.0168
StepF3 - StepF2 -0.0846 0.0333 Inf -2.541 0.0442
StepF4 - StepF3 0.0141 0.0333 Inf 0.423 1.0000
StepF5 - StepF4 -0.0727 0.0333 Inf -2.180 0.0879
StepF6 - StepF5 -0.0206 0.0333 Inf -0.620 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests
==============================
TEST (rt_ms) | Block 5 | 6 steps | Axis Z
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 24.0992 5 0.0002078 ***
Accuracy 3.0682 1 0.0798396 .
rt_ms 0.0013 1 0.9707594
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.33 0.130 Inf 1.074 1.58
2 1.51 0.129 Inf 1.258 1.76
3 1.45 0.129 Inf 1.196 1.70
4 1.38 0.129 Inf 1.130 1.63
5 1.29 0.129 Inf 1.034 1.54
6 1.23 0.129 Inf 0.979 1.48
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.19 0.129 Inf 0.941 1.45
2 1.37 0.128 Inf 1.123 1.63
3 1.31 0.128 Inf 1.061 1.56
4 1.25 0.128 Inf 0.995 1.50
5 1.15 0.128 Inf 0.900 1.40
6 1.10 0.128 Inf 0.845 1.35
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.1816 0.0684 Inf -2.654 0.0847
StepF1 - StepF3 -0.1197 0.0682 Inf -1.756 0.4948
StepF1 - StepF4 -0.0540 0.0684 Inf -0.789 0.9694
StepF1 - StepF5 0.0418 0.0675 Inf 0.620 0.9896
StepF1 - StepF6 0.0968 0.0678 Inf 1.428 0.7097
StepF2 - StepF3 0.0620 0.0663 Inf 0.935 0.9374
StepF2 - StepF4 0.1276 0.0663 Inf 1.926 0.3859
StepF2 - StepF5 0.2235 0.0664 Inf 3.366 0.0099
StepF2 - StepF6 0.2785 0.0663 Inf 4.199 0.0004
StepF3 - StepF4 0.0657 0.0663 Inf 0.991 0.9211
StepF3 - StepF5 0.1615 0.0663 Inf 2.434 0.1444
StepF3 - StepF6 0.2165 0.0663 Inf 3.266 0.0139
StepF4 - StepF5 0.0958 0.0664 Inf 1.443 0.7004
StepF4 - StepF6 0.1508 0.0663 Inf 2.274 0.2046
StepF5 - StepF6 0.0550 0.0663 Inf 0.830 0.9621
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.1816 0.0684 Inf -2.654 0.0847
StepF1 - StepF3 -0.1197 0.0682 Inf -1.756 0.4948
StepF1 - StepF4 -0.0540 0.0684 Inf -0.789 0.9694
StepF1 - StepF5 0.0418 0.0675 Inf 0.620 0.9896
StepF1 - StepF6 0.0968 0.0678 Inf 1.428 0.7097
StepF2 - StepF3 0.0620 0.0663 Inf 0.935 0.9374
StepF2 - StepF4 0.1276 0.0663 Inf 1.926 0.3859
StepF2 - StepF5 0.2235 0.0664 Inf 3.366 0.0099
StepF2 - StepF6 0.2785 0.0663 Inf 4.199 0.0004
StepF3 - StepF4 0.0657 0.0663 Inf 0.991 0.9211
StepF3 - StepF5 0.1615 0.0663 Inf 2.434 0.1444
StepF3 - StepF6 0.2165 0.0663 Inf 3.266 0.0139
StepF4 - StepF5 0.0958 0.0664 Inf 1.443 0.7004
StepF4 - StepF6 0.1508 0.0663 Inf 2.274 0.2046
StepF5 - StepF6 0.0550 0.0663 Inf 0.830 0.9621
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 6 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1816 0.0684 Inf 2.654 0.0397
StepF3 - StepF2 -0.0620 0.0663 Inf -0.935 0.9656
StepF4 - StepF3 -0.0657 0.0663 Inf -0.991 0.9656
StepF5 - StepF4 -0.0958 0.0664 Inf -1.443 0.5957
StepF6 - StepF5 -0.0550 0.0663 Inf -0.830 0.9656
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.1816 0.0684 Inf 2.654 0.0397
StepF3 - StepF2 -0.0620 0.0663 Inf -0.935 0.9656
StepF4 - StepF3 -0.0657 0.0663 Inf -0.991 0.9656
StepF5 - StepF4 -0.0958 0.0664 Inf -1.443 0.5957
StepF6 - StepF5 -0.0550 0.0663 Inf -0.830 0.9656
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 5 tests .report_step_test_rtms(sw_b5_12_rt, "5", "12 steps")
==============================
TEST (rt_ms) | Block 5 | 12 steps | Axis X
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 53.6589 11 1.362e-07 ***
Accuracy 1.2839 1 0.2572
rt_ms 1.4777 1 0.2241
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.662 0.0536 Inf 0.556 0.767
2 0.641 0.0533 Inf 0.537 0.746
3 0.693 0.0533 Inf 0.588 0.797
4 0.601 0.0533 Inf 0.496 0.705
5 0.582 0.0533 Inf 0.478 0.687
6 0.612 0.0533 Inf 0.507 0.716
7 0.604 0.0533 Inf 0.500 0.709
8 0.587 0.0533 Inf 0.482 0.691
9 0.520 0.0533 Inf 0.415 0.625
10 0.576 0.0533 Inf 0.472 0.681
11 0.620 0.0533 Inf 0.515 0.724
12 0.535 0.0533 Inf 0.431 0.640
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.633 0.0539 Inf 0.528 0.739
2 0.613 0.0537 Inf 0.508 0.718
3 0.664 0.0537 Inf 0.559 0.770
4 0.572 0.0537 Inf 0.467 0.678
5 0.554 0.0537 Inf 0.449 0.659
6 0.583 0.0537 Inf 0.478 0.689
7 0.576 0.0537 Inf 0.471 0.681
8 0.558 0.0537 Inf 0.453 0.664
9 0.492 0.0537 Inf 0.387 0.597
10 0.548 0.0537 Inf 0.443 0.653
11 0.591 0.0537 Inf 0.486 0.697
12 0.507 0.0537 Inf 0.402 0.613
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.02035 0.0319 Inf 0.639 1.0000
StepF1 - StepF3 -0.03098 0.0319 Inf -0.970 0.9983
StepF1 - StepF4 0.06087 0.0318 Inf 1.915 0.7498
StepF1 - StepF5 0.07942 0.0317 Inf 2.503 0.3378
StepF1 - StepF6 0.04985 0.0317 Inf 1.570 0.9199
StepF1 - StepF7 0.05744 0.0314 Inf 1.830 0.8018
StepF1 - StepF8 0.07490 0.0316 Inf 2.367 0.4284
StepF1 - StepF9 0.14153 0.0314 Inf 4.505 0.0004
StepF1 - StepF10 0.08530 0.0317 Inf 2.688 0.2321
StepF1 - StepF11 0.04185 0.0318 Inf 1.316 0.9772
StepF1 - StepF12 0.12605 0.0317 Inf 3.971 0.0041
StepF2 - StepF3 -0.05132 0.0312 Inf -1.645 0.8925
StepF2 - StepF4 0.04053 0.0312 Inf 1.299 0.9795
StepF2 - StepF5 0.05908 0.0312 Inf 1.893 0.7640
StepF2 - StepF6 0.02951 0.0312 Inf 0.946 0.9986
StepF2 - StepF7 0.03709 0.0313 Inf 1.183 0.9903
StepF2 - StepF8 0.05456 0.0312 Inf 1.747 0.8461
StepF2 - StepF9 0.12118 0.0313 Inf 3.868 0.0062
StepF2 - StepF10 0.06495 0.0312 Inf 2.081 0.6363
StepF2 - StepF11 0.02150 0.0312 Inf 0.689 0.9999
StepF2 - StepF12 0.10570 0.0312 Inf 3.387 0.0343
StepF3 - StepF4 0.09185 0.0312 Inf 2.943 0.1260
StepF3 - StepF5 0.11040 0.0312 Inf 3.536 0.0208
StepF3 - StepF6 0.08083 0.0312 Inf 2.589 0.2855
StepF3 - StepF7 0.08842 0.0314 Inf 2.818 0.1724
StepF3 - StepF8 0.10588 0.0312 Inf 3.389 0.0341
StepF3 - StepF9 0.17251 0.0314 Inf 5.500 <.0001
StepF3 - StepF10 0.11628 0.0312 Inf 3.725 0.0106
StepF3 - StepF11 0.07283 0.0312 Inf 2.333 0.4525
StepF3 - StepF12 0.15702 0.0312 Inf 5.030 <.0001
StepF4 - StepF5 0.01855 0.0312 Inf 0.595 1.0000
StepF4 - StepF6 -0.01102 0.0312 Inf -0.353 1.0000
StepF4 - StepF7 -0.00343 0.0313 Inf -0.110 1.0000
StepF4 - StepF8 0.01403 0.0312 Inf 0.450 1.0000
StepF4 - StepF9 0.08066 0.0313 Inf 2.577 0.2925
StepF4 - StepF10 0.02443 0.0312 Inf 0.783 0.9998
StepF4 - StepF11 -0.01902 0.0312 Inf -0.610 1.0000
StepF4 - StepF12 0.06517 0.0312 Inf 2.089 0.6309
StepF5 - StepF6 -0.02957 0.0312 Inf -0.948 0.9986
StepF5 - StepF7 -0.02198 0.0313 Inf -0.703 0.9999
StepF5 - StepF8 -0.00452 0.0312 Inf -0.145 1.0000
StepF5 - StepF9 0.06210 0.0313 Inf 1.986 0.7034
StepF5 - StepF10 0.00588 0.0312 Inf 0.188 1.0000
StepF5 - StepF11 -0.03758 0.0312 Inf -1.204 0.9888
StepF5 - StepF12 0.04662 0.0312 Inf 1.494 0.9426
StepF6 - StepF7 0.00759 0.0313 Inf 0.242 1.0000
StepF6 - StepF8 0.02505 0.0312 Inf 0.803 0.9997
StepF6 - StepF9 0.09167 0.0313 Inf 2.931 0.1301
StepF6 - StepF10 0.03545 0.0312 Inf 1.136 0.9931
StepF6 - StepF11 -0.00801 0.0312 Inf -0.257 1.0000
StepF6 - StepF12 0.07619 0.0312 Inf 2.442 0.3775
StepF7 - StepF8 0.01746 0.0313 Inf 0.559 1.0000
StepF7 - StepF9 0.08409 0.0312 Inf 2.695 0.2285
StepF7 - StepF10 0.02786 0.0313 Inf 0.890 0.9992
StepF7 - StepF11 -0.01559 0.0313 Inf -0.498 1.0000
StepF7 - StepF12 0.06861 0.0313 Inf 2.192 0.5551
StepF8 - StepF9 0.06663 0.0312 Inf 2.132 0.5992
StepF8 - StepF10 0.01040 0.0312 Inf 0.333 1.0000
StepF8 - StepF11 -0.03305 0.0312 Inf -1.059 0.9962
StepF8 - StepF12 0.05114 0.0312 Inf 1.639 0.8949
StepF9 - StepF10 -0.05623 0.0313 Inf -1.798 0.8197
StepF9 - StepF11 -0.09968 0.0313 Inf -3.185 0.0643
StepF9 - StepF12 -0.01548 0.0313 Inf -0.495 1.0000
StepF10 - StepF11 -0.04345 0.0312 Inf -1.393 0.9652
StepF10 - StepF12 0.04075 0.0312 Inf 1.306 0.9786
StepF11 - StepF12 0.08420 0.0312 Inf 2.698 0.2268
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.02035 0.0319 Inf 0.639 1.0000
StepF1 - StepF3 -0.03098 0.0319 Inf -0.970 0.9983
StepF1 - StepF4 0.06087 0.0318 Inf 1.915 0.7498
StepF1 - StepF5 0.07942 0.0317 Inf 2.503 0.3378
StepF1 - StepF6 0.04985 0.0317 Inf 1.570 0.9199
StepF1 - StepF7 0.05744 0.0314 Inf 1.830 0.8018
StepF1 - StepF8 0.07490 0.0316 Inf 2.367 0.4284
StepF1 - StepF9 0.14153 0.0314 Inf 4.505 0.0004
StepF1 - StepF10 0.08530 0.0317 Inf 2.688 0.2321
StepF1 - StepF11 0.04185 0.0318 Inf 1.316 0.9772
StepF1 - StepF12 0.12605 0.0317 Inf 3.971 0.0041
StepF2 - StepF3 -0.05132 0.0312 Inf -1.645 0.8925
StepF2 - StepF4 0.04053 0.0312 Inf 1.299 0.9795
StepF2 - StepF5 0.05908 0.0312 Inf 1.893 0.7640
StepF2 - StepF6 0.02951 0.0312 Inf 0.946 0.9986
StepF2 - StepF7 0.03709 0.0313 Inf 1.183 0.9903
StepF2 - StepF8 0.05456 0.0312 Inf 1.747 0.8461
StepF2 - StepF9 0.12118 0.0313 Inf 3.868 0.0062
StepF2 - StepF10 0.06495 0.0312 Inf 2.081 0.6363
StepF2 - StepF11 0.02150 0.0312 Inf 0.689 0.9999
StepF2 - StepF12 0.10570 0.0312 Inf 3.387 0.0343
StepF3 - StepF4 0.09185 0.0312 Inf 2.943 0.1260
StepF3 - StepF5 0.11040 0.0312 Inf 3.536 0.0208
StepF3 - StepF6 0.08083 0.0312 Inf 2.589 0.2855
StepF3 - StepF7 0.08842 0.0314 Inf 2.818 0.1724
StepF3 - StepF8 0.10588 0.0312 Inf 3.389 0.0341
StepF3 - StepF9 0.17251 0.0314 Inf 5.500 <.0001
StepF3 - StepF10 0.11628 0.0312 Inf 3.725 0.0106
StepF3 - StepF11 0.07283 0.0312 Inf 2.333 0.4525
StepF3 - StepF12 0.15702 0.0312 Inf 5.030 <.0001
StepF4 - StepF5 0.01855 0.0312 Inf 0.595 1.0000
StepF4 - StepF6 -0.01102 0.0312 Inf -0.353 1.0000
StepF4 - StepF7 -0.00343 0.0313 Inf -0.110 1.0000
StepF4 - StepF8 0.01403 0.0312 Inf 0.450 1.0000
StepF4 - StepF9 0.08066 0.0313 Inf 2.577 0.2925
StepF4 - StepF10 0.02443 0.0312 Inf 0.783 0.9998
StepF4 - StepF11 -0.01902 0.0312 Inf -0.610 1.0000
StepF4 - StepF12 0.06517 0.0312 Inf 2.089 0.6309
StepF5 - StepF6 -0.02957 0.0312 Inf -0.948 0.9986
StepF5 - StepF7 -0.02198 0.0313 Inf -0.703 0.9999
StepF5 - StepF8 -0.00452 0.0312 Inf -0.145 1.0000
StepF5 - StepF9 0.06210 0.0313 Inf 1.986 0.7034
StepF5 - StepF10 0.00588 0.0312 Inf 0.188 1.0000
StepF5 - StepF11 -0.03758 0.0312 Inf -1.204 0.9888
StepF5 - StepF12 0.04662 0.0312 Inf 1.494 0.9426
StepF6 - StepF7 0.00759 0.0313 Inf 0.242 1.0000
StepF6 - StepF8 0.02505 0.0312 Inf 0.803 0.9997
StepF6 - StepF9 0.09167 0.0313 Inf 2.931 0.1301
StepF6 - StepF10 0.03545 0.0312 Inf 1.136 0.9931
StepF6 - StepF11 -0.00801 0.0312 Inf -0.257 1.0000
StepF6 - StepF12 0.07619 0.0312 Inf 2.442 0.3775
StepF7 - StepF8 0.01746 0.0313 Inf 0.559 1.0000
StepF7 - StepF9 0.08409 0.0312 Inf 2.695 0.2285
StepF7 - StepF10 0.02786 0.0313 Inf 0.890 0.9992
StepF7 - StepF11 -0.01559 0.0313 Inf -0.498 1.0000
StepF7 - StepF12 0.06861 0.0313 Inf 2.192 0.5551
StepF8 - StepF9 0.06663 0.0312 Inf 2.132 0.5992
StepF8 - StepF10 0.01040 0.0312 Inf 0.333 1.0000
StepF8 - StepF11 -0.03305 0.0312 Inf -1.059 0.9962
StepF8 - StepF12 0.05114 0.0312 Inf 1.639 0.8949
StepF9 - StepF10 -0.05623 0.0313 Inf -1.798 0.8197
StepF9 - StepF11 -0.09968 0.0313 Inf -3.185 0.0643
StepF9 - StepF12 -0.01548 0.0313 Inf -0.495 1.0000
StepF10 - StepF11 -0.04345 0.0312 Inf -1.393 0.9652
StepF10 - StepF12 0.04075 0.0312 Inf 1.306 0.9786
StepF11 - StepF12 0.08420 0.0312 Inf 2.698 0.2268
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.02035 0.0319 Inf -0.639 1.0000
StepF3 - StepF2 0.05132 0.0312 Inf 1.645 0.7001
StepF4 - StepF3 -0.09185 0.0312 Inf -2.943 0.0358
StepF5 - StepF4 -0.01855 0.0312 Inf -0.595 1.0000
StepF6 - StepF5 0.02957 0.0312 Inf 0.948 1.0000
StepF7 - StepF6 -0.00759 0.0313 Inf -0.242 1.0000
StepF8 - StepF7 -0.01746 0.0313 Inf -0.559 1.0000
StepF9 - StepF8 -0.06663 0.0312 Inf -2.132 0.2967
StepF10 - StepF9 0.05623 0.0313 Inf 1.798 0.5777
StepF11 - StepF10 0.04345 0.0312 Inf 1.393 0.9825
StepF12 - StepF11 -0.08420 0.0312 Inf -2.698 0.0697
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.02035 0.0319 Inf -0.639 1.0000
StepF3 - StepF2 0.05132 0.0312 Inf 1.645 0.7001
StepF4 - StepF3 -0.09185 0.0312 Inf -2.943 0.0358
StepF5 - StepF4 -0.01855 0.0312 Inf -0.595 1.0000
StepF6 - StepF5 0.02957 0.0312 Inf 0.948 1.0000
StepF7 - StepF6 -0.00759 0.0313 Inf -0.242 1.0000
StepF8 - StepF7 -0.01746 0.0313 Inf -0.559 1.0000
StepF9 - StepF8 -0.06663 0.0312 Inf -2.132 0.2967
StepF10 - StepF9 0.05623 0.0313 Inf 1.798 0.5777
StepF11 - StepF10 0.04345 0.0312 Inf 1.393 0.9825
StepF12 - StepF11 -0.08420 0.0312 Inf -2.698 0.0697
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests
==============================
TEST (rt_ms) | Block 5 | 12 steps | Axis Y
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 114.6655 11 < 2e-16 ***
Accuracy 3.9200 1 0.04771 *
rt_ms 1.3543 1 0.24454
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.631 0.0614 Inf 0.511 0.752
2 0.812 0.0611 Inf 0.692 0.931
3 0.691 0.0611 Inf 0.571 0.811
4 0.691 0.0611 Inf 0.571 0.811
5 0.624 0.0611 Inf 0.504 0.744
6 0.542 0.0611 Inf 0.422 0.662
7 0.607 0.0612 Inf 0.488 0.727
8 0.635 0.0611 Inf 0.515 0.755
9 0.557 0.0612 Inf 0.437 0.676
10 0.660 0.0611 Inf 0.540 0.779
11 0.615 0.0611 Inf 0.496 0.735
12 0.549 0.0611 Inf 0.429 0.669
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.584 0.0616 Inf 0.464 0.705
2 0.765 0.0615 Inf 0.644 0.885
3 0.644 0.0615 Inf 0.523 0.764
4 0.644 0.0614 Inf 0.524 0.765
5 0.577 0.0614 Inf 0.457 0.697
6 0.495 0.0614 Inf 0.374 0.615
7 0.561 0.0614 Inf 0.440 0.681
8 0.588 0.0614 Inf 0.468 0.709
9 0.510 0.0614 Inf 0.389 0.630
10 0.613 0.0614 Inf 0.492 0.733
11 0.569 0.0614 Inf 0.448 0.689
12 0.502 0.0614 Inf 0.381 0.622
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.180423 0.0334 Inf -5.405 <.0001
StepF1 - StepF3 -0.059538 0.0335 Inf -1.779 0.8297
StepF1 - StepF4 -0.060122 0.0333 Inf -1.806 0.8154
StepF1 - StepF5 0.007261 0.0333 Inf 0.218 1.0000
StepF1 - StepF6 0.089408 0.0333 Inf 2.688 0.2322
StepF1 - StepF7 0.023710 0.0329 Inf 0.721 0.9999
StepF1 - StepF8 -0.004092 0.0332 Inf -0.123 1.0000
StepF1 - StepF9 0.074656 0.0329 Inf 2.268 0.4998
StepF1 - StepF10 -0.028443 0.0333 Inf -0.855 0.9995
StepF1 - StepF11 0.015755 0.0333 Inf 0.473 1.0000
StepF1 - StepF12 0.082420 0.0333 Inf 2.478 0.3539
StepF2 - StepF3 0.120885 0.0327 Inf 3.697 0.0118
StepF2 - StepF4 0.120301 0.0327 Inf 3.679 0.0126
StepF2 - StepF5 0.187684 0.0327 Inf 5.738 <.0001
StepF2 - StepF6 0.269831 0.0327 Inf 8.250 <.0001
StepF2 - StepF7 0.204132 0.0328 Inf 6.215 <.0001
StepF2 - StepF8 0.176330 0.0327 Inf 5.389 <.0001
StepF2 - StepF9 0.255079 0.0328 Inf 7.770 <.0001
StepF2 - StepF10 0.151980 0.0327 Inf 4.647 0.0002
StepF2 - StepF11 0.196178 0.0327 Inf 5.999 <.0001
StepF2 - StepF12 0.262843 0.0327 Inf 8.037 <.0001
StepF3 - StepF4 -0.000584 0.0327 Inf -0.018 1.0000
StepF3 - StepF5 0.066799 0.0327 Inf 2.042 0.6646
StepF3 - StepF6 0.148946 0.0327 Inf 4.553 0.0003
StepF3 - StepF7 0.083247 0.0329 Inf 2.531 0.3201
StepF3 - StepF8 0.055445 0.0327 Inf 1.693 0.8717
StepF3 - StepF9 0.134194 0.0329 Inf 4.083 0.0026
StepF3 - StepF10 0.031095 0.0327 Inf 0.950 0.9986
StepF3 - StepF11 0.075293 0.0327 Inf 2.302 0.4751
StepF3 - StepF12 0.141958 0.0327 Inf 4.339 0.0009
StepF4 - StepF5 0.067383 0.0327 Inf 2.061 0.6512
StepF4 - StepF6 0.149530 0.0327 Inf 4.573 0.0003
StepF4 - StepF7 0.083831 0.0328 Inf 2.555 0.3057
StepF4 - StepF8 0.056029 0.0327 Inf 1.713 0.8628
StepF4 - StepF9 0.134778 0.0328 Inf 4.110 0.0023
StepF4 - StepF10 0.031679 0.0327 Inf 0.969 0.9983
StepF4 - StepF11 0.075877 0.0327 Inf 2.320 0.4618
StepF4 - StepF12 0.142542 0.0327 Inf 4.359 0.0008
StepF5 - StepF6 0.082147 0.0327 Inf 2.512 0.3320
StepF5 - StepF7 0.016449 0.0328 Inf 0.502 1.0000
StepF5 - StepF8 -0.011354 0.0327 Inf -0.347 1.0000
StepF5 - StepF9 0.067395 0.0328 Inf 2.056 0.6543
StepF5 - StepF10 -0.035704 0.0327 Inf -1.092 0.9951
StepF5 - StepF11 0.008494 0.0327 Inf 0.260 1.0000
StepF5 - StepF12 0.075159 0.0327 Inf 2.298 0.4776
StepF6 - StepF7 -0.065698 0.0328 Inf -2.003 0.6914
StepF6 - StepF8 -0.093500 0.0327 Inf -2.859 0.1559
StepF6 - StepF9 -0.014752 0.0328 Inf -0.450 1.0000
StepF6 - StepF10 -0.117851 0.0327 Inf -3.604 0.0164
StepF6 - StepF11 -0.073653 0.0327 Inf -2.252 0.5111
StepF6 - StepF12 -0.006988 0.0327 Inf -0.214 1.0000
StepF7 - StepF8 -0.027802 0.0328 Inf -0.849 0.9995
StepF7 - StepF9 0.050946 0.0327 Inf 1.558 0.9239
StepF7 - StepF10 -0.052152 0.0328 Inf -1.591 0.9130
StepF7 - StepF11 -0.007954 0.0328 Inf -0.242 1.0000
StepF7 - StepF12 0.058711 0.0328 Inf 1.790 0.8237
StepF8 - StepF9 0.078748 0.0327 Inf 2.405 0.4023
StepF8 - StepF10 -0.024350 0.0327 Inf -0.745 0.9999
StepF8 - StepF11 0.019848 0.0327 Inf 0.607 1.0000
StepF8 - StepF12 0.086513 0.0327 Inf 2.645 0.2544
StepF9 - StepF10 -0.103099 0.0328 Inf -3.145 0.0722
StepF9 - StepF11 -0.058901 0.0328 Inf -1.796 0.8208
StepF9 - StepF12 0.007764 0.0328 Inf 0.237 1.0000
StepF10 - StepF11 0.044198 0.0327 Inf 1.352 0.9722
StepF10 - StepF12 0.110863 0.0327 Inf 3.390 0.0339
StepF11 - StepF12 0.066665 0.0327 Inf 2.039 0.6667
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.180423 0.0334 Inf -5.405 <.0001
StepF1 - StepF3 -0.059538 0.0335 Inf -1.779 0.8297
StepF1 - StepF4 -0.060122 0.0333 Inf -1.806 0.8154
StepF1 - StepF5 0.007261 0.0333 Inf 0.218 1.0000
StepF1 - StepF6 0.089408 0.0333 Inf 2.688 0.2322
StepF1 - StepF7 0.023710 0.0329 Inf 0.721 0.9999
StepF1 - StepF8 -0.004092 0.0332 Inf -0.123 1.0000
StepF1 - StepF9 0.074656 0.0329 Inf 2.268 0.4998
StepF1 - StepF10 -0.028443 0.0333 Inf -0.855 0.9995
StepF1 - StepF11 0.015755 0.0333 Inf 0.473 1.0000
StepF1 - StepF12 0.082420 0.0333 Inf 2.478 0.3539
StepF2 - StepF3 0.120885 0.0327 Inf 3.697 0.0118
StepF2 - StepF4 0.120301 0.0327 Inf 3.679 0.0126
StepF2 - StepF5 0.187684 0.0327 Inf 5.738 <.0001
StepF2 - StepF6 0.269831 0.0327 Inf 8.250 <.0001
StepF2 - StepF7 0.204132 0.0328 Inf 6.215 <.0001
StepF2 - StepF8 0.176330 0.0327 Inf 5.389 <.0001
StepF2 - StepF9 0.255079 0.0328 Inf 7.770 <.0001
StepF2 - StepF10 0.151980 0.0327 Inf 4.647 0.0002
StepF2 - StepF11 0.196178 0.0327 Inf 5.999 <.0001
StepF2 - StepF12 0.262843 0.0327 Inf 8.037 <.0001
StepF3 - StepF4 -0.000584 0.0327 Inf -0.018 1.0000
StepF3 - StepF5 0.066799 0.0327 Inf 2.042 0.6646
StepF3 - StepF6 0.148946 0.0327 Inf 4.553 0.0003
StepF3 - StepF7 0.083247 0.0329 Inf 2.531 0.3201
StepF3 - StepF8 0.055445 0.0327 Inf 1.693 0.8717
StepF3 - StepF9 0.134194 0.0329 Inf 4.083 0.0026
StepF3 - StepF10 0.031095 0.0327 Inf 0.950 0.9986
StepF3 - StepF11 0.075293 0.0327 Inf 2.302 0.4751
StepF3 - StepF12 0.141958 0.0327 Inf 4.339 0.0009
StepF4 - StepF5 0.067383 0.0327 Inf 2.061 0.6512
StepF4 - StepF6 0.149530 0.0327 Inf 4.573 0.0003
StepF4 - StepF7 0.083831 0.0328 Inf 2.555 0.3057
StepF4 - StepF8 0.056029 0.0327 Inf 1.713 0.8628
StepF4 - StepF9 0.134778 0.0328 Inf 4.110 0.0023
StepF4 - StepF10 0.031679 0.0327 Inf 0.969 0.9983
StepF4 - StepF11 0.075877 0.0327 Inf 2.320 0.4618
StepF4 - StepF12 0.142542 0.0327 Inf 4.359 0.0008
StepF5 - StepF6 0.082147 0.0327 Inf 2.512 0.3320
StepF5 - StepF7 0.016449 0.0328 Inf 0.502 1.0000
StepF5 - StepF8 -0.011354 0.0327 Inf -0.347 1.0000
StepF5 - StepF9 0.067395 0.0328 Inf 2.056 0.6543
StepF5 - StepF10 -0.035704 0.0327 Inf -1.092 0.9951
StepF5 - StepF11 0.008494 0.0327 Inf 0.260 1.0000
StepF5 - StepF12 0.075159 0.0327 Inf 2.298 0.4776
StepF6 - StepF7 -0.065698 0.0328 Inf -2.003 0.6914
StepF6 - StepF8 -0.093500 0.0327 Inf -2.859 0.1559
StepF6 - StepF9 -0.014752 0.0328 Inf -0.450 1.0000
StepF6 - StepF10 -0.117851 0.0327 Inf -3.604 0.0164
StepF6 - StepF11 -0.073653 0.0327 Inf -2.252 0.5111
StepF6 - StepF12 -0.006988 0.0327 Inf -0.214 1.0000
StepF7 - StepF8 -0.027802 0.0328 Inf -0.849 0.9995
StepF7 - StepF9 0.050946 0.0327 Inf 1.558 0.9239
StepF7 - StepF10 -0.052152 0.0328 Inf -1.591 0.9130
StepF7 - StepF11 -0.007954 0.0328 Inf -0.242 1.0000
StepF7 - StepF12 0.058711 0.0328 Inf 1.790 0.8237
StepF8 - StepF9 0.078748 0.0327 Inf 2.405 0.4023
StepF8 - StepF10 -0.024350 0.0327 Inf -0.745 0.9999
StepF8 - StepF11 0.019848 0.0327 Inf 0.607 1.0000
StepF8 - StepF12 0.086513 0.0327 Inf 2.645 0.2544
StepF9 - StepF10 -0.103099 0.0328 Inf -3.145 0.0722
StepF9 - StepF11 -0.058901 0.0328 Inf -1.796 0.8208
StepF9 - StepF12 0.007764 0.0328 Inf 0.237 1.0000
StepF10 - StepF11 0.044198 0.0327 Inf 1.352 0.9722
StepF10 - StepF12 0.110863 0.0327 Inf 3.390 0.0339
StepF11 - StepF12 0.066665 0.0327 Inf 2.039 0.6667
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.180423 0.0334 Inf 5.405 <.0001
StepF3 - StepF2 -0.120885 0.0327 Inf -3.697 0.0022
StepF4 - StepF3 0.000584 0.0327 Inf 0.018 0.9858
StepF5 - StepF4 -0.067383 0.0327 Inf -2.061 0.2360
StepF6 - StepF5 -0.082147 0.0327 Inf -2.512 0.0960
StepF7 - StepF6 0.065698 0.0328 Inf 2.003 0.2360
StepF8 - StepF7 0.027802 0.0328 Inf 0.849 0.7919
StepF9 - StepF8 -0.078748 0.0327 Inf -2.405 0.1132
StepF10 - StepF9 0.103099 0.0328 Inf 3.145 0.0149
StepF11 - StepF10 -0.044198 0.0327 Inf -1.352 0.5295
StepF12 - StepF11 -0.066665 0.0327 Inf -2.039 0.2360
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.180423 0.0334 Inf 5.405 <.0001
StepF3 - StepF2 -0.120885 0.0327 Inf -3.697 0.0022
StepF4 - StepF3 0.000584 0.0327 Inf 0.018 0.9858
StepF5 - StepF4 -0.067383 0.0327 Inf -2.061 0.2360
StepF6 - StepF5 -0.082147 0.0327 Inf -2.512 0.0960
StepF7 - StepF6 0.065698 0.0328 Inf 2.003 0.2360
StepF8 - StepF7 0.027802 0.0328 Inf 0.849 0.7919
StepF9 - StepF8 -0.078748 0.0327 Inf -2.405 0.1132
StepF10 - StepF9 0.103099 0.0328 Inf 3.145 0.0149
StepF11 - StepF10 -0.044198 0.0327 Inf -1.352 0.5295
StepF12 - StepF11 -0.066665 0.0327 Inf -2.039 0.2360
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests
==============================
TEST (rt_ms) | Block 5 | 12 steps | Axis Z
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 90.1815 11 1.536e-14 ***
Accuracy 3.1320 1 0.07677 .
rt_ms 0.8732 1 0.35008
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.37 0.141 Inf 1.090 1.64
2 1.60 0.140 Inf 1.321 1.87
3 1.44 0.140 Inf 1.168 1.72
4 1.31 0.140 Inf 1.032 1.58
5 1.31 0.140 Inf 1.040 1.59
6 1.30 0.140 Inf 1.025 1.57
7 1.32 0.140 Inf 1.042 1.59
8 1.25 0.140 Inf 0.972 1.52
9 1.21 0.140 Inf 0.936 1.49
10 1.28 0.140 Inf 1.003 1.55
11 1.26 0.140 Inf 0.981 1.53
12 1.15 0.140 Inf 0.871 1.42
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.25 0.141 Inf 0.972 1.53
2 1.48 0.141 Inf 1.202 1.76
3 1.33 0.141 Inf 1.049 1.60
4 1.19 0.141 Inf 0.913 1.47
5 1.20 0.141 Inf 0.921 1.47
6 1.18 0.141 Inf 0.906 1.46
7 1.20 0.141 Inf 0.924 1.48
8 1.13 0.141 Inf 0.853 1.41
9 1.09 0.141 Inf 0.818 1.37
10 1.16 0.141 Inf 0.884 1.44
11 1.14 0.141 Inf 0.862 1.42
12 1.03 0.141 Inf 0.753 1.31
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.22995 0.0583 Inf -3.945 0.0046
StepF1 - StepF3 -0.07660 0.0584 Inf -1.311 0.9780
StepF1 - StepF4 0.05927 0.0581 Inf 1.019 0.9973
StepF1 - StepF5 0.05145 0.0581 Inf 0.886 0.9993
StepF1 - StepF6 0.06629 0.0581 Inf 1.141 0.9928
StepF1 - StepF7 0.04863 0.0574 Inf 0.847 0.9995
StepF1 - StepF8 0.11899 0.0579 Inf 2.056 0.6546
StepF1 - StepF9 0.15492 0.0575 Inf 2.696 0.2278
StepF1 - StepF10 0.08832 0.0581 Inf 1.521 0.9351
StepF1 - StepF11 0.11011 0.0582 Inf 1.893 0.7639
StepF1 - StepF12 0.21980 0.0581 Inf 3.785 0.0085
StepF2 - StepF3 0.15335 0.0571 Inf 2.687 0.2323
StepF2 - StepF4 0.28922 0.0571 Inf 5.068 <.0001
StepF2 - StepF5 0.28140 0.0571 Inf 4.931 0.0001
StepF2 - StepF6 0.29624 0.0571 Inf 5.191 <.0001
StepF2 - StepF7 0.27858 0.0573 Inf 4.860 0.0001
StepF2 - StepF8 0.34894 0.0571 Inf 6.111 <.0001
StepF2 - StepF9 0.38487 0.0573 Inf 6.717 <.0001
StepF2 - StepF10 0.31827 0.0571 Inf 5.577 <.0001
StepF2 - StepF11 0.34006 0.0571 Inf 5.959 <.0001
StepF2 - StepF12 0.44975 0.0571 Inf 7.881 <.0001
StepF3 - StepF4 0.13587 0.0571 Inf 2.380 0.4194
StepF3 - StepF5 0.12805 0.0571 Inf 2.243 0.5181
StepF3 - StepF6 0.14289 0.0571 Inf 2.503 0.3378
StepF3 - StepF7 0.12523 0.0574 Inf 2.182 0.5629
StepF3 - StepF8 0.19558 0.0571 Inf 3.423 0.0305
StepF3 - StepF9 0.23152 0.0574 Inf 4.036 0.0032
StepF3 - StepF10 0.16492 0.0571 Inf 2.889 0.1447
StepF3 - StepF11 0.18671 0.0571 Inf 3.271 0.0495
StepF3 - StepF12 0.29640 0.0571 Inf 5.192 <.0001
StepF4 - StepF5 -0.00782 0.0571 Inf -0.137 1.0000
StepF4 - StepF6 0.00702 0.0571 Inf 0.123 1.0000
StepF4 - StepF7 -0.01064 0.0573 Inf -0.186 1.0000
StepF4 - StepF8 0.05971 0.0571 Inf 1.046 0.9966
StepF4 - StepF9 0.09565 0.0572 Inf 1.671 0.8814
StepF4 - StepF10 0.02905 0.0571 Inf 0.509 1.0000
StepF4 - StepF11 0.05084 0.0571 Inf 0.891 0.9992
StepF4 - StepF12 0.16053 0.0571 Inf 2.813 0.1742
StepF5 - StepF6 0.01483 0.0571 Inf 0.260 1.0000
StepF5 - StepF7 -0.00282 0.0572 Inf -0.049 1.0000
StepF5 - StepF8 0.06753 0.0571 Inf 1.183 0.9903
StepF5 - StepF9 0.10347 0.0572 Inf 1.809 0.8135
StepF5 - StepF10 0.03687 0.0571 Inf 0.646 1.0000
StepF5 - StepF11 0.05865 0.0571 Inf 1.028 0.9971
StepF5 - StepF12 0.16834 0.0571 Inf 2.950 0.1236
StepF6 - StepF7 -0.01765 0.0572 Inf -0.308 1.0000
StepF6 - StepF8 0.05270 0.0571 Inf 0.923 0.9989
StepF6 - StepF9 0.08863 0.0572 Inf 1.549 0.9267
StepF6 - StepF10 0.02203 0.0571 Inf 0.386 1.0000
StepF6 - StepF11 0.04382 0.0571 Inf 0.768 0.9998
StepF6 - StepF12 0.15351 0.0571 Inf 2.690 0.2308
StepF7 - StepF8 0.07035 0.0572 Inf 1.231 0.9866
StepF7 - StepF9 0.10629 0.0571 Inf 1.863 0.7825
StepF7 - StepF10 0.03969 0.0572 Inf 0.694 0.9999
StepF7 - StepF11 0.06148 0.0573 Inf 1.073 0.9958
StepF7 - StepF12 0.17116 0.0572 Inf 2.991 0.1111
StepF8 - StepF9 0.03594 0.0571 Inf 0.629 1.0000
StepF8 - StepF10 -0.03066 0.0571 Inf -0.537 1.0000
StepF8 - StepF11 -0.00888 0.0571 Inf -0.156 1.0000
StepF8 - StepF12 0.10081 0.0571 Inf 1.766 0.8364
StepF9 - StepF10 -0.06660 0.0572 Inf -1.164 0.9915
StepF9 - StepF11 -0.04481 0.0572 Inf -0.783 0.9998
StepF9 - StepF12 0.06488 0.0572 Inf 1.134 0.9932
StepF10 - StepF11 0.02179 0.0571 Inf 0.382 1.0000
StepF10 - StepF12 0.13148 0.0571 Inf 2.304 0.4735
StepF11 - StepF12 0.10969 0.0571 Inf 1.922 0.7454
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.22995 0.0583 Inf -3.945 0.0046
StepF1 - StepF3 -0.07660 0.0584 Inf -1.311 0.9780
StepF1 - StepF4 0.05927 0.0581 Inf 1.019 0.9973
StepF1 - StepF5 0.05145 0.0581 Inf 0.886 0.9993
StepF1 - StepF6 0.06629 0.0581 Inf 1.141 0.9928
StepF1 - StepF7 0.04863 0.0574 Inf 0.847 0.9995
StepF1 - StepF8 0.11899 0.0579 Inf 2.056 0.6546
StepF1 - StepF9 0.15492 0.0575 Inf 2.696 0.2278
StepF1 - StepF10 0.08832 0.0581 Inf 1.521 0.9351
StepF1 - StepF11 0.11011 0.0582 Inf 1.893 0.7639
StepF1 - StepF12 0.21980 0.0581 Inf 3.785 0.0085
StepF2 - StepF3 0.15335 0.0571 Inf 2.687 0.2323
StepF2 - StepF4 0.28922 0.0571 Inf 5.068 <.0001
StepF2 - StepF5 0.28140 0.0571 Inf 4.931 0.0001
StepF2 - StepF6 0.29624 0.0571 Inf 5.191 <.0001
StepF2 - StepF7 0.27858 0.0573 Inf 4.860 0.0001
StepF2 - StepF8 0.34894 0.0571 Inf 6.111 <.0001
StepF2 - StepF9 0.38487 0.0573 Inf 6.717 <.0001
StepF2 - StepF10 0.31827 0.0571 Inf 5.577 <.0001
StepF2 - StepF11 0.34006 0.0571 Inf 5.959 <.0001
StepF2 - StepF12 0.44975 0.0571 Inf 7.881 <.0001
StepF3 - StepF4 0.13587 0.0571 Inf 2.380 0.4194
StepF3 - StepF5 0.12805 0.0571 Inf 2.243 0.5181
StepF3 - StepF6 0.14289 0.0571 Inf 2.503 0.3378
StepF3 - StepF7 0.12523 0.0574 Inf 2.182 0.5629
StepF3 - StepF8 0.19558 0.0571 Inf 3.423 0.0305
StepF3 - StepF9 0.23152 0.0574 Inf 4.036 0.0032
StepF3 - StepF10 0.16492 0.0571 Inf 2.889 0.1447
StepF3 - StepF11 0.18671 0.0571 Inf 3.271 0.0495
StepF3 - StepF12 0.29640 0.0571 Inf 5.192 <.0001
StepF4 - StepF5 -0.00782 0.0571 Inf -0.137 1.0000
StepF4 - StepF6 0.00702 0.0571 Inf 0.123 1.0000
StepF4 - StepF7 -0.01064 0.0573 Inf -0.186 1.0000
StepF4 - StepF8 0.05971 0.0571 Inf 1.046 0.9966
StepF4 - StepF9 0.09565 0.0572 Inf 1.671 0.8814
StepF4 - StepF10 0.02905 0.0571 Inf 0.509 1.0000
StepF4 - StepF11 0.05084 0.0571 Inf 0.891 0.9992
StepF4 - StepF12 0.16053 0.0571 Inf 2.813 0.1742
StepF5 - StepF6 0.01483 0.0571 Inf 0.260 1.0000
StepF5 - StepF7 -0.00282 0.0572 Inf -0.049 1.0000
StepF5 - StepF8 0.06753 0.0571 Inf 1.183 0.9903
StepF5 - StepF9 0.10347 0.0572 Inf 1.809 0.8135
StepF5 - StepF10 0.03687 0.0571 Inf 0.646 1.0000
StepF5 - StepF11 0.05865 0.0571 Inf 1.028 0.9971
StepF5 - StepF12 0.16834 0.0571 Inf 2.950 0.1236
StepF6 - StepF7 -0.01765 0.0572 Inf -0.308 1.0000
StepF6 - StepF8 0.05270 0.0571 Inf 0.923 0.9989
StepF6 - StepF9 0.08863 0.0572 Inf 1.549 0.9267
StepF6 - StepF10 0.02203 0.0571 Inf 0.386 1.0000
StepF6 - StepF11 0.04382 0.0571 Inf 0.768 0.9998
StepF6 - StepF12 0.15351 0.0571 Inf 2.690 0.2308
StepF7 - StepF8 0.07035 0.0572 Inf 1.231 0.9866
StepF7 - StepF9 0.10629 0.0571 Inf 1.863 0.7825
StepF7 - StepF10 0.03969 0.0572 Inf 0.694 0.9999
StepF7 - StepF11 0.06148 0.0573 Inf 1.073 0.9958
StepF7 - StepF12 0.17116 0.0572 Inf 2.991 0.1111
StepF8 - StepF9 0.03594 0.0571 Inf 0.629 1.0000
StepF8 - StepF10 -0.03066 0.0571 Inf -0.537 1.0000
StepF8 - StepF11 -0.00888 0.0571 Inf -0.156 1.0000
StepF8 - StepF12 0.10081 0.0571 Inf 1.766 0.8364
StepF9 - StepF10 -0.06660 0.0572 Inf -1.164 0.9915
StepF9 - StepF11 -0.04481 0.0572 Inf -0.783 0.9998
StepF9 - StepF12 0.06488 0.0572 Inf 1.134 0.9932
StepF10 - StepF11 0.02179 0.0571 Inf 0.382 1.0000
StepF10 - StepF12 0.13148 0.0571 Inf 2.304 0.4735
StepF11 - StepF12 0.10969 0.0571 Inf 1.922 0.7454
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 12 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.22995 0.0583 Inf 3.945 0.0009
StepF3 - StepF2 -0.15335 0.0571 Inf -2.687 0.0720
StepF4 - StepF3 -0.13587 0.0571 Inf -2.380 0.1557
StepF5 - StepF4 0.00782 0.0571 Inf 0.137 1.0000
StepF6 - StepF5 -0.01483 0.0571 Inf -0.260 1.0000
StepF7 - StepF6 0.01765 0.0572 Inf 0.308 1.0000
StepF8 - StepF7 -0.07035 0.0572 Inf -1.231 1.0000
StepF9 - StepF8 -0.03594 0.0571 Inf -0.629 1.0000
StepF10 - StepF9 0.06660 0.0572 Inf 1.164 1.0000
StepF11 - StepF10 -0.02179 0.0571 Inf -0.382 1.0000
StepF12 - StepF11 -0.10969 0.0571 Inf -1.922 0.4366
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.22995 0.0583 Inf 3.945 0.0009
StepF3 - StepF2 -0.15335 0.0571 Inf -2.687 0.0720
StepF4 - StepF3 -0.13587 0.0571 Inf -2.380 0.1557
StepF5 - StepF4 0.00782 0.0571 Inf 0.137 1.0000
StepF6 - StepF5 -0.01483 0.0571 Inf -0.260 1.0000
StepF7 - StepF6 0.01765 0.0572 Inf 0.308 1.0000
StepF8 - StepF7 -0.07035 0.0572 Inf -1.231 1.0000
StepF9 - StepF8 -0.03594 0.0571 Inf -0.629 1.0000
StepF10 - StepF9 0.06660 0.0572 Inf 1.164 1.0000
StepF11 - StepF10 -0.02179 0.0571 Inf -0.382 1.0000
StepF12 - StepF11 -0.10969 0.0571 Inf -1.922 0.4366
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 11 tests .report_step_test_rtms(sw_b5_18_rt, "5", "18 steps")
==============================
TEST (rt_ms) | Block 5 | 18 steps | Axis X
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 72.3183 17 8.571e-09 ***
Accuracy 0.4428 1 0.5058
rt_ms 0.8153 1 0.3666
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.675 0.0554 Inf 0.566 0.783
2 0.625 0.0553 Inf 0.516 0.733
3 0.690 0.0553 Inf 0.582 0.799
4 0.613 0.0553 Inf 0.505 0.722
5 0.573 0.0553 Inf 0.464 0.681
6 0.609 0.0553 Inf 0.501 0.717
7 0.636 0.0553 Inf 0.527 0.744
8 0.571 0.0553 Inf 0.463 0.680
9 0.576 0.0553 Inf 0.468 0.685
10 0.618 0.0553 Inf 0.509 0.726
11 0.648 0.0553 Inf 0.540 0.757
12 0.532 0.0553 Inf 0.424 0.640
13 0.574 0.0553 Inf 0.466 0.682
14 0.609 0.0553 Inf 0.501 0.718
15 0.598 0.0553 Inf 0.489 0.706
16 0.544 0.0553 Inf 0.435 0.652
17 0.529 0.0553 Inf 0.421 0.637
18 0.523 0.0553 Inf 0.415 0.632
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.652 0.0562 Inf 0.542 0.762
2 0.602 0.0562 Inf 0.492 0.712
3 0.668 0.0562 Inf 0.558 0.778
4 0.591 0.0562 Inf 0.481 0.701
5 0.550 0.0561 Inf 0.440 0.660
6 0.587 0.0561 Inf 0.477 0.697
7 0.613 0.0561 Inf 0.503 0.723
8 0.549 0.0561 Inf 0.439 0.659
9 0.554 0.0561 Inf 0.444 0.664
10 0.595 0.0561 Inf 0.485 0.705
11 0.626 0.0562 Inf 0.516 0.736
12 0.510 0.0561 Inf 0.400 0.620
13 0.552 0.0561 Inf 0.442 0.662
14 0.587 0.0561 Inf 0.477 0.697
15 0.575 0.0562 Inf 0.465 0.685
16 0.521 0.0562 Inf 0.411 0.631
17 0.506 0.0562 Inf 0.396 0.617
18 0.501 0.0561 Inf 0.391 0.611
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.049822 0.0335 Inf 1.487 0.9916
StepF1 - StepF3 -0.015630 0.0335 Inf -0.466 1.0000
StepF1 - StepF4 0.061234 0.0335 Inf 1.830 0.9358
StepF1 - StepF5 0.101836 0.0334 Inf 3.050 0.1768
StepF1 - StepF6 0.065505 0.0334 Inf 1.963 0.8868
StepF1 - StepF7 0.038961 0.0333 Inf 1.169 0.9995
StepF1 - StepF8 0.103341 0.0334 Inf 3.090 0.1594
StepF1 - StepF9 0.098451 0.0332 Inf 2.962 0.2190
StepF1 - StepF10 0.056819 0.0333 Inf 1.705 0.9660
StepF1 - StepF11 0.026170 0.0335 Inf 0.782 1.0000
StepF1 - StepF12 0.142616 0.0334 Inf 4.267 0.0026
StepF1 - StepF13 0.100626 0.0332 Inf 3.027 0.1873
StepF1 - StepF14 0.065300 0.0334 Inf 1.957 0.8895
StepF1 - StepF15 0.077028 0.0335 Inf 2.300 0.6835
StepF1 - StepF16 0.130821 0.0335 Inf 3.904 0.0115
StepF1 - StepF17 0.145720 0.0335 Inf 4.356 0.0018
StepF1 - StepF18 0.151198 0.0334 Inf 4.524 0.0008
StepF2 - StepF3 -0.065452 0.0332 Inf -1.971 0.8833
StepF2 - StepF4 0.011412 0.0332 Inf 0.344 1.0000
StepF2 - StepF5 0.052014 0.0332 Inf 1.566 0.9855
StepF2 - StepF6 0.015683 0.0332 Inf 0.472 1.0000
StepF2 - StepF7 -0.010861 0.0332 Inf -0.327 1.0000
StepF2 - StepF8 0.053519 0.0332 Inf 1.611 0.9805
StepF2 - StepF9 0.048628 0.0333 Inf 1.459 0.9932
StepF2 - StepF10 0.006997 0.0333 Inf 0.210 1.0000
StepF2 - StepF11 -0.023652 0.0332 Inf -0.712 1.0000
StepF2 - StepF12 0.092793 0.0332 Inf 2.794 0.3168
StepF2 - StepF13 0.050803 0.0333 Inf 1.524 0.9890
StepF2 - StepF14 0.015478 0.0332 Inf 0.466 1.0000
StepF2 - StepF15 0.027206 0.0332 Inf 0.819 1.0000
StepF2 - StepF16 0.080998 0.0332 Inf 2.439 0.5778
StepF2 - StepF17 0.095897 0.0332 Inf 2.887 0.2595
StepF2 - StepF18 0.101376 0.0332 Inf 3.052 0.1758
StepF3 - StepF4 0.076864 0.0332 Inf 2.314 0.6730
StepF3 - StepF5 0.117466 0.0332 Inf 3.535 0.0429
StepF3 - StepF6 0.081135 0.0332 Inf 2.441 0.5759
StepF3 - StepF7 0.054591 0.0333 Inf 1.642 0.9764
StepF3 - StepF8 0.118971 0.0332 Inf 3.582 0.0368
StepF3 - StepF9 0.114081 0.0333 Inf 3.421 0.0620
StepF3 - StepF10 0.072449 0.0333 Inf 2.178 0.7684
StepF3 - StepF11 0.041800 0.0332 Inf 1.259 0.9988
StepF3 - StepF12 0.158246 0.0332 Inf 4.764 0.0003
StepF3 - StepF13 0.116256 0.0333 Inf 3.487 0.0503
StepF3 - StepF14 0.080931 0.0332 Inf 2.435 0.5807
StepF3 - StepF15 0.092658 0.0332 Inf 2.790 0.3191
StepF3 - StepF16 0.146451 0.0332 Inf 4.410 0.0014
StepF3 - StepF17 0.161350 0.0332 Inf 4.858 0.0002
StepF3 - StepF18 0.166828 0.0332 Inf 5.022 0.0001
StepF4 - StepF5 0.040602 0.0332 Inf 1.222 0.9992
StepF4 - StepF6 0.004271 0.0332 Inf 0.129 1.0000
StepF4 - StepF7 -0.022273 0.0332 Inf -0.670 1.0000
StepF4 - StepF8 0.042107 0.0332 Inf 1.268 0.9987
StepF4 - StepF9 0.037216 0.0333 Inf 1.117 0.9997
StepF4 - StepF10 -0.004415 0.0332 Inf -0.133 1.0000
StepF4 - StepF11 -0.035064 0.0332 Inf -1.056 0.9999
StepF4 - StepF12 0.081381 0.0332 Inf 2.450 0.5689
StepF4 - StepF13 0.039391 0.0333 Inf 1.183 0.9995
StepF4 - StepF14 0.004066 0.0332 Inf 0.122 1.0000
StepF4 - StepF15 0.015794 0.0332 Inf 0.476 1.0000
StepF4 - StepF16 0.069587 0.0332 Inf 2.095 0.8196
StepF4 - StepF17 0.084485 0.0332 Inf 2.544 0.4962
StepF4 - StepF18 0.089964 0.0332 Inf 2.709 0.3740
StepF5 - StepF6 -0.036331 0.0332 Inf -1.094 0.9998
StepF5 - StepF7 -0.062875 0.0332 Inf -1.893 0.9150
StepF5 - StepF8 0.001505 0.0332 Inf 0.045 1.0000
StepF5 - StepF9 -0.003385 0.0333 Inf -0.102 1.0000
StepF5 - StepF10 -0.045017 0.0332 Inf -1.355 0.9971
StepF5 - StepF11 -0.075666 0.0332 Inf -2.278 0.6997
StepF5 - StepF12 0.040780 0.0332 Inf 1.228 0.9991
StepF5 - StepF13 -0.001210 0.0333 Inf -0.036 1.0000
StepF5 - StepF14 -0.036535 0.0332 Inf -1.100 0.9998
StepF5 - StepF15 -0.024807 0.0332 Inf -0.747 1.0000
StepF5 - StepF16 0.028985 0.0332 Inf 0.872 1.0000
StepF5 - StepF17 0.043884 0.0332 Inf 1.321 0.9979
StepF5 - StepF18 0.049362 0.0332 Inf 1.486 0.9917
StepF6 - StepF7 -0.026544 0.0332 Inf -0.799 1.0000
StepF6 - StepF8 0.037836 0.0332 Inf 1.139 0.9997
StepF6 - StepF9 0.032945 0.0333 Inf 0.991 1.0000
StepF6 - StepF10 -0.008686 0.0332 Inf -0.262 1.0000
StepF6 - StepF11 -0.039335 0.0332 Inf -1.184 0.9995
StepF6 - StepF12 0.077110 0.0332 Inf 2.322 0.6675
StepF6 - StepF13 0.035121 0.0333 Inf 1.056 0.9999
StepF6 - StepF14 -0.000205 0.0332 Inf -0.006 1.0000
StepF6 - StepF15 0.011523 0.0332 Inf 0.347 1.0000
StepF6 - StepF16 0.065316 0.0332 Inf 1.965 0.8858
StepF6 - StepF17 0.080214 0.0332 Inf 2.415 0.5966
StepF6 - StepF18 0.085693 0.0332 Inf 2.580 0.4685
StepF7 - StepF8 0.064380 0.0332 Inf 1.938 0.8976
StepF7 - StepF9 0.059489 0.0332 Inf 1.790 0.9471
StepF7 - StepF10 0.017858 0.0332 Inf 0.538 1.0000
StepF7 - StepF11 -0.012791 0.0332 Inf -0.385 1.0000
StepF7 - StepF12 0.103654 0.0332 Inf 3.120 0.1476
StepF7 - StepF13 0.061664 0.0332 Inf 1.855 0.9279
StepF7 - StepF14 0.026339 0.0332 Inf 0.793 1.0000
StepF7 - StepF15 0.038067 0.0332 Inf 1.145 0.9997
StepF7 - StepF16 0.091859 0.0333 Inf 2.763 0.3371
StepF7 - StepF17 0.106758 0.0332 Inf 3.212 0.1149
StepF7 - StepF18 0.112237 0.0332 Inf 3.378 0.0707
StepF8 - StepF9 -0.004890 0.0333 Inf -0.147 1.0000
StepF8 - StepF10 -0.046522 0.0332 Inf -1.400 0.9958
StepF8 - StepF11 -0.077171 0.0332 Inf -2.324 0.6661
StepF8 - StepF12 0.039275 0.0332 Inf 1.183 0.9995
StepF8 - StepF13 -0.002715 0.0333 Inf -0.082 1.0000
StepF8 - StepF14 -0.038040 0.0332 Inf -1.145 0.9997
StepF8 - StepF15 -0.026313 0.0332 Inf -0.792 1.0000
StepF8 - StepF16 0.027480 0.0332 Inf 0.827 1.0000
StepF8 - StepF17 0.042379 0.0332 Inf 1.276 0.9986
StepF8 - StepF18 0.047857 0.0332 Inf 1.441 0.9941
StepF9 - StepF10 -0.041632 0.0332 Inf -1.253 0.9989
StepF9 - StepF11 -0.072281 0.0333 Inf -2.169 0.7745
StepF9 - StepF12 0.044165 0.0333 Inf 1.327 0.9978
StepF9 - StepF13 0.002175 0.0332 Inf 0.065 1.0000
StepF9 - StepF14 -0.033150 0.0333 Inf -0.997 0.9999
StepF9 - StepF15 -0.021422 0.0333 Inf -0.643 1.0000
StepF9 - StepF16 0.032370 0.0333 Inf 0.971 1.0000
StepF9 - StepF17 0.047269 0.0333 Inf 1.419 0.9950
StepF9 - StepF18 0.052747 0.0333 Inf 1.585 0.9835
StepF10 - StepF11 -0.030649 0.0332 Inf -0.922 1.0000
StepF10 - StepF12 0.085797 0.0332 Inf 2.582 0.4669
StepF10 - StepF13 0.043807 0.0332 Inf 1.318 0.9979
StepF10 - StepF14 0.008482 0.0332 Inf 0.255 1.0000
StepF10 - StepF15 0.020209 0.0333 Inf 0.608 1.0000
StepF10 - StepF16 0.074002 0.0333 Inf 2.225 0.7370
StepF10 - StepF17 0.088901 0.0332 Inf 2.675 0.3983
StepF10 - StepF18 0.094379 0.0332 Inf 2.840 0.2873
StepF11 - StepF12 0.116446 0.0332 Inf 3.506 0.0473
StepF11 - StepF13 0.074456 0.0333 Inf 2.235 0.7302
StepF11 - StepF14 0.039131 0.0332 Inf 1.178 0.9995
StepF11 - StepF15 0.050859 0.0332 Inf 1.531 0.9885
StepF11 - StepF16 0.104651 0.0332 Inf 3.151 0.1359
StepF11 - StepF17 0.119550 0.0332 Inf 3.600 0.0346
StepF11 - StepF18 0.125028 0.0332 Inf 3.764 0.0194
StepF12 - StepF13 -0.041990 0.0333 Inf -1.262 0.9988
StepF12 - StepF14 -0.077315 0.0332 Inf -2.328 0.6629
StepF12 - StepF15 -0.065587 0.0332 Inf -1.975 0.8816
StepF12 - StepF16 -0.011795 0.0332 Inf -0.355 1.0000
StepF12 - StepF17 0.003104 0.0332 Inf 0.093 1.0000
StepF12 - StepF18 0.008582 0.0332 Inf 0.258 1.0000
StepF13 - StepF14 -0.035325 0.0333 Inf -1.062 0.9999
StepF13 - StepF15 -0.023597 0.0333 Inf -0.708 1.0000
StepF13 - StepF16 0.030195 0.0333 Inf 0.906 1.0000
StepF13 - StepF17 0.045094 0.0333 Inf 1.354 0.9971
StepF13 - StepF18 0.050572 0.0333 Inf 1.520 0.9894
StepF14 - StepF15 0.011728 0.0332 Inf 0.353 1.0000
StepF14 - StepF16 0.065520 0.0332 Inf 1.972 0.8830
StepF14 - StepF17 0.080419 0.0332 Inf 2.421 0.5919
StepF14 - StepF18 0.085897 0.0332 Inf 2.586 0.4639
StepF15 - StepF16 0.053792 0.0332 Inf 1.620 0.9794
StepF15 - StepF17 0.068691 0.0332 Inf 2.068 0.8347
StepF15 - StepF18 0.074170 0.0332 Inf 2.233 0.7316
StepF16 - StepF17 0.014899 0.0332 Inf 0.449 1.0000
StepF16 - StepF18 0.020377 0.0332 Inf 0.613 1.0000
StepF17 - StepF18 0.005478 0.0332 Inf 0.165 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 0.049822 0.0335 Inf 1.487 0.9916
StepF1 - StepF3 -0.015630 0.0335 Inf -0.466 1.0000
StepF1 - StepF4 0.061234 0.0335 Inf 1.830 0.9358
StepF1 - StepF5 0.101836 0.0334 Inf 3.050 0.1768
StepF1 - StepF6 0.065505 0.0334 Inf 1.963 0.8868
StepF1 - StepF7 0.038961 0.0333 Inf 1.169 0.9995
StepF1 - StepF8 0.103341 0.0334 Inf 3.090 0.1594
StepF1 - StepF9 0.098451 0.0332 Inf 2.962 0.2190
StepF1 - StepF10 0.056819 0.0333 Inf 1.705 0.9660
StepF1 - StepF11 0.026170 0.0335 Inf 0.782 1.0000
StepF1 - StepF12 0.142616 0.0334 Inf 4.267 0.0026
StepF1 - StepF13 0.100626 0.0332 Inf 3.027 0.1873
StepF1 - StepF14 0.065300 0.0334 Inf 1.957 0.8895
StepF1 - StepF15 0.077028 0.0335 Inf 2.300 0.6835
StepF1 - StepF16 0.130821 0.0335 Inf 3.904 0.0115
StepF1 - StepF17 0.145720 0.0335 Inf 4.356 0.0018
StepF1 - StepF18 0.151198 0.0334 Inf 4.524 0.0008
StepF2 - StepF3 -0.065452 0.0332 Inf -1.971 0.8833
StepF2 - StepF4 0.011412 0.0332 Inf 0.344 1.0000
StepF2 - StepF5 0.052014 0.0332 Inf 1.566 0.9855
StepF2 - StepF6 0.015683 0.0332 Inf 0.472 1.0000
StepF2 - StepF7 -0.010861 0.0332 Inf -0.327 1.0000
StepF2 - StepF8 0.053519 0.0332 Inf 1.611 0.9805
StepF2 - StepF9 0.048628 0.0333 Inf 1.459 0.9932
StepF2 - StepF10 0.006997 0.0333 Inf 0.210 1.0000
StepF2 - StepF11 -0.023652 0.0332 Inf -0.712 1.0000
StepF2 - StepF12 0.092793 0.0332 Inf 2.794 0.3168
StepF2 - StepF13 0.050803 0.0333 Inf 1.524 0.9890
StepF2 - StepF14 0.015478 0.0332 Inf 0.466 1.0000
StepF2 - StepF15 0.027206 0.0332 Inf 0.819 1.0000
StepF2 - StepF16 0.080998 0.0332 Inf 2.439 0.5778
StepF2 - StepF17 0.095897 0.0332 Inf 2.887 0.2595
StepF2 - StepF18 0.101376 0.0332 Inf 3.052 0.1758
StepF3 - StepF4 0.076864 0.0332 Inf 2.314 0.6730
StepF3 - StepF5 0.117466 0.0332 Inf 3.535 0.0429
StepF3 - StepF6 0.081135 0.0332 Inf 2.441 0.5759
StepF3 - StepF7 0.054591 0.0333 Inf 1.642 0.9764
StepF3 - StepF8 0.118971 0.0332 Inf 3.582 0.0368
StepF3 - StepF9 0.114081 0.0333 Inf 3.421 0.0620
StepF3 - StepF10 0.072449 0.0333 Inf 2.178 0.7684
StepF3 - StepF11 0.041800 0.0332 Inf 1.259 0.9988
StepF3 - StepF12 0.158246 0.0332 Inf 4.764 0.0003
StepF3 - StepF13 0.116256 0.0333 Inf 3.487 0.0503
StepF3 - StepF14 0.080931 0.0332 Inf 2.435 0.5807
StepF3 - StepF15 0.092658 0.0332 Inf 2.790 0.3191
StepF3 - StepF16 0.146451 0.0332 Inf 4.410 0.0014
StepF3 - StepF17 0.161350 0.0332 Inf 4.858 0.0002
StepF3 - StepF18 0.166828 0.0332 Inf 5.022 0.0001
StepF4 - StepF5 0.040602 0.0332 Inf 1.222 0.9992
StepF4 - StepF6 0.004271 0.0332 Inf 0.129 1.0000
StepF4 - StepF7 -0.022273 0.0332 Inf -0.670 1.0000
StepF4 - StepF8 0.042107 0.0332 Inf 1.268 0.9987
StepF4 - StepF9 0.037216 0.0333 Inf 1.117 0.9997
StepF4 - StepF10 -0.004415 0.0332 Inf -0.133 1.0000
StepF4 - StepF11 -0.035064 0.0332 Inf -1.056 0.9999
StepF4 - StepF12 0.081381 0.0332 Inf 2.450 0.5689
StepF4 - StepF13 0.039391 0.0333 Inf 1.183 0.9995
StepF4 - StepF14 0.004066 0.0332 Inf 0.122 1.0000
StepF4 - StepF15 0.015794 0.0332 Inf 0.476 1.0000
StepF4 - StepF16 0.069587 0.0332 Inf 2.095 0.8196
StepF4 - StepF17 0.084485 0.0332 Inf 2.544 0.4962
StepF4 - StepF18 0.089964 0.0332 Inf 2.709 0.3740
StepF5 - StepF6 -0.036331 0.0332 Inf -1.094 0.9998
StepF5 - StepF7 -0.062875 0.0332 Inf -1.893 0.9150
StepF5 - StepF8 0.001505 0.0332 Inf 0.045 1.0000
StepF5 - StepF9 -0.003385 0.0333 Inf -0.102 1.0000
StepF5 - StepF10 -0.045017 0.0332 Inf -1.355 0.9971
StepF5 - StepF11 -0.075666 0.0332 Inf -2.278 0.6997
StepF5 - StepF12 0.040780 0.0332 Inf 1.228 0.9991
StepF5 - StepF13 -0.001210 0.0333 Inf -0.036 1.0000
StepF5 - StepF14 -0.036535 0.0332 Inf -1.100 0.9998
StepF5 - StepF15 -0.024807 0.0332 Inf -0.747 1.0000
StepF5 - StepF16 0.028985 0.0332 Inf 0.872 1.0000
StepF5 - StepF17 0.043884 0.0332 Inf 1.321 0.9979
StepF5 - StepF18 0.049362 0.0332 Inf 1.486 0.9917
StepF6 - StepF7 -0.026544 0.0332 Inf -0.799 1.0000
StepF6 - StepF8 0.037836 0.0332 Inf 1.139 0.9997
StepF6 - StepF9 0.032945 0.0333 Inf 0.991 1.0000
StepF6 - StepF10 -0.008686 0.0332 Inf -0.262 1.0000
StepF6 - StepF11 -0.039335 0.0332 Inf -1.184 0.9995
StepF6 - StepF12 0.077110 0.0332 Inf 2.322 0.6675
StepF6 - StepF13 0.035121 0.0333 Inf 1.056 0.9999
StepF6 - StepF14 -0.000205 0.0332 Inf -0.006 1.0000
StepF6 - StepF15 0.011523 0.0332 Inf 0.347 1.0000
StepF6 - StepF16 0.065316 0.0332 Inf 1.965 0.8858
StepF6 - StepF17 0.080214 0.0332 Inf 2.415 0.5966
StepF6 - StepF18 0.085693 0.0332 Inf 2.580 0.4685
StepF7 - StepF8 0.064380 0.0332 Inf 1.938 0.8976
StepF7 - StepF9 0.059489 0.0332 Inf 1.790 0.9471
StepF7 - StepF10 0.017858 0.0332 Inf 0.538 1.0000
StepF7 - StepF11 -0.012791 0.0332 Inf -0.385 1.0000
StepF7 - StepF12 0.103654 0.0332 Inf 3.120 0.1476
StepF7 - StepF13 0.061664 0.0332 Inf 1.855 0.9279
StepF7 - StepF14 0.026339 0.0332 Inf 0.793 1.0000
StepF7 - StepF15 0.038067 0.0332 Inf 1.145 0.9997
StepF7 - StepF16 0.091859 0.0333 Inf 2.763 0.3371
StepF7 - StepF17 0.106758 0.0332 Inf 3.212 0.1149
StepF7 - StepF18 0.112237 0.0332 Inf 3.378 0.0707
StepF8 - StepF9 -0.004890 0.0333 Inf -0.147 1.0000
StepF8 - StepF10 -0.046522 0.0332 Inf -1.400 0.9958
StepF8 - StepF11 -0.077171 0.0332 Inf -2.324 0.6661
StepF8 - StepF12 0.039275 0.0332 Inf 1.183 0.9995
StepF8 - StepF13 -0.002715 0.0333 Inf -0.082 1.0000
StepF8 - StepF14 -0.038040 0.0332 Inf -1.145 0.9997
StepF8 - StepF15 -0.026313 0.0332 Inf -0.792 1.0000
StepF8 - StepF16 0.027480 0.0332 Inf 0.827 1.0000
StepF8 - StepF17 0.042379 0.0332 Inf 1.276 0.9986
StepF8 - StepF18 0.047857 0.0332 Inf 1.441 0.9941
StepF9 - StepF10 -0.041632 0.0332 Inf -1.253 0.9989
StepF9 - StepF11 -0.072281 0.0333 Inf -2.169 0.7745
StepF9 - StepF12 0.044165 0.0333 Inf 1.327 0.9978
StepF9 - StepF13 0.002175 0.0332 Inf 0.065 1.0000
StepF9 - StepF14 -0.033150 0.0333 Inf -0.997 0.9999
StepF9 - StepF15 -0.021422 0.0333 Inf -0.643 1.0000
StepF9 - StepF16 0.032370 0.0333 Inf 0.971 1.0000
StepF9 - StepF17 0.047269 0.0333 Inf 1.419 0.9950
StepF9 - StepF18 0.052747 0.0333 Inf 1.585 0.9835
StepF10 - StepF11 -0.030649 0.0332 Inf -0.922 1.0000
StepF10 - StepF12 0.085797 0.0332 Inf 2.582 0.4669
StepF10 - StepF13 0.043807 0.0332 Inf 1.318 0.9979
StepF10 - StepF14 0.008482 0.0332 Inf 0.255 1.0000
StepF10 - StepF15 0.020209 0.0333 Inf 0.608 1.0000
StepF10 - StepF16 0.074002 0.0333 Inf 2.225 0.7370
StepF10 - StepF17 0.088901 0.0332 Inf 2.675 0.3983
StepF10 - StepF18 0.094379 0.0332 Inf 2.840 0.2873
StepF11 - StepF12 0.116446 0.0332 Inf 3.506 0.0473
StepF11 - StepF13 0.074456 0.0333 Inf 2.235 0.7302
StepF11 - StepF14 0.039131 0.0332 Inf 1.178 0.9995
StepF11 - StepF15 0.050859 0.0332 Inf 1.531 0.9885
StepF11 - StepF16 0.104651 0.0332 Inf 3.151 0.1359
StepF11 - StepF17 0.119550 0.0332 Inf 3.600 0.0346
StepF11 - StepF18 0.125028 0.0332 Inf 3.764 0.0194
StepF12 - StepF13 -0.041990 0.0333 Inf -1.262 0.9988
StepF12 - StepF14 -0.077315 0.0332 Inf -2.328 0.6629
StepF12 - StepF15 -0.065587 0.0332 Inf -1.975 0.8816
StepF12 - StepF16 -0.011795 0.0332 Inf -0.355 1.0000
StepF12 - StepF17 0.003104 0.0332 Inf 0.093 1.0000
StepF12 - StepF18 0.008582 0.0332 Inf 0.258 1.0000
StepF13 - StepF14 -0.035325 0.0333 Inf -1.062 0.9999
StepF13 - StepF15 -0.023597 0.0333 Inf -0.708 1.0000
StepF13 - StepF16 0.030195 0.0333 Inf 0.906 1.0000
StepF13 - StepF17 0.045094 0.0333 Inf 1.354 0.9971
StepF13 - StepF18 0.050572 0.0333 Inf 1.520 0.9894
StepF14 - StepF15 0.011728 0.0332 Inf 0.353 1.0000
StepF14 - StepF16 0.065520 0.0332 Inf 1.972 0.8830
StepF14 - StepF17 0.080419 0.0332 Inf 2.421 0.5919
StepF14 - StepF18 0.085897 0.0332 Inf 2.586 0.4639
StepF15 - StepF16 0.053792 0.0332 Inf 1.620 0.9794
StepF15 - StepF17 0.068691 0.0332 Inf 2.068 0.8347
StepF15 - StepF18 0.074170 0.0332 Inf 2.233 0.7316
StepF16 - StepF17 0.014899 0.0332 Inf 0.449 1.0000
StepF16 - StepF18 0.020377 0.0332 Inf 0.613 1.0000
StepF17 - StepF18 0.005478 0.0332 Inf 0.165 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.04982 0.0335 Inf -1.487 1.0000
StepF3 - StepF2 0.06545 0.0332 Inf 1.971 0.7312
StepF4 - StepF3 -0.07686 0.0332 Inf -2.314 0.3304
StepF5 - StepF4 -0.04060 0.0332 Inf -1.222 1.0000
StepF6 - StepF5 0.03633 0.0332 Inf 1.094 1.0000
StepF7 - StepF6 0.02654 0.0332 Inf 0.799 1.0000
StepF8 - StepF7 -0.06438 0.0332 Inf -1.938 0.7375
StepF9 - StepF8 0.00489 0.0333 Inf 0.147 1.0000
StepF10 - StepF9 0.04163 0.0332 Inf 1.253 1.0000
StepF11 - StepF10 0.03065 0.0332 Inf 0.922 1.0000
StepF12 - StepF11 -0.11645 0.0332 Inf -3.506 0.0077
StepF13 - StepF12 0.04199 0.0333 Inf 1.262 1.0000
StepF14 - StepF13 0.03533 0.0333 Inf 1.062 1.0000
StepF15 - StepF14 -0.01173 0.0332 Inf -0.353 1.0000
StepF16 - StepF15 -0.05379 0.0332 Inf -1.620 1.0000
StepF17 - StepF16 -0.01490 0.0332 Inf -0.449 1.0000
StepF18 - StepF17 -0.00548 0.0332 Inf -0.165 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 -0.04982 0.0335 Inf -1.487 1.0000
StepF3 - StepF2 0.06545 0.0332 Inf 1.971 0.7312
StepF4 - StepF3 -0.07686 0.0332 Inf -2.314 0.3304
StepF5 - StepF4 -0.04060 0.0332 Inf -1.222 1.0000
StepF6 - StepF5 0.03633 0.0332 Inf 1.094 1.0000
StepF7 - StepF6 0.02654 0.0332 Inf 0.799 1.0000
StepF8 - StepF7 -0.06438 0.0332 Inf -1.938 0.7375
StepF9 - StepF8 0.00489 0.0333 Inf 0.147 1.0000
StepF10 - StepF9 0.04163 0.0332 Inf 1.253 1.0000
StepF11 - StepF10 0.03065 0.0332 Inf 0.922 1.0000
StepF12 - StepF11 -0.11645 0.0332 Inf -3.506 0.0077
StepF13 - StepF12 0.04199 0.0333 Inf 1.262 1.0000
StepF14 - StepF13 0.03533 0.0333 Inf 1.062 1.0000
StepF15 - StepF14 -0.01173 0.0332 Inf -0.353 1.0000
StepF16 - StepF15 -0.05379 0.0332 Inf -1.620 1.0000
StepF17 - StepF16 -0.01490 0.0332 Inf -0.449 1.0000
StepF18 - StepF17 -0.00548 0.0332 Inf -0.165 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests
==============================
TEST (rt_ms) | Block 5 | 18 steps | Axis Y
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 57.2234 17 2.996e-06 ***
Accuracy 0.7343 1 0.39148
rt_ms 3.1504 1 0.07591 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.670 0.0825 Inf 0.508 0.832
2 0.835 0.0821 Inf 0.674 0.996
3 0.778 0.0821 Inf 0.617 0.939
4 0.669 0.0821 Inf 0.508 0.830
5 0.581 0.0822 Inf 0.420 0.742
6 0.653 0.0822 Inf 0.492 0.814
7 0.650 0.0822 Inf 0.489 0.811
8 0.687 0.0821 Inf 0.526 0.848
9 0.753 0.0823 Inf 0.591 0.914
10 0.767 0.0822 Inf 0.606 0.928
11 0.711 0.0821 Inf 0.550 0.872
12 0.602 0.0821 Inf 0.441 0.763
13 0.571 0.0823 Inf 0.410 0.732
14 0.654 0.0822 Inf 0.493 0.815
15 0.655 0.0821 Inf 0.494 0.816
16 0.600 0.0821 Inf 0.439 0.761
17 0.551 0.0821 Inf 0.390 0.712
18 0.545 0.0821 Inf 0.384 0.706
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 0.606 0.0851 Inf 0.439 0.773
2 0.771 0.0850 Inf 0.604 0.938
3 0.714 0.0850 Inf 0.548 0.881
4 0.605 0.0850 Inf 0.439 0.772
5 0.517 0.0850 Inf 0.351 0.684
6 0.589 0.0849 Inf 0.422 0.755
7 0.587 0.0849 Inf 0.420 0.753
8 0.623 0.0850 Inf 0.457 0.790
9 0.689 0.0850 Inf 0.523 0.856
10 0.703 0.0849 Inf 0.536 0.869
11 0.647 0.0850 Inf 0.481 0.814
12 0.539 0.0850 Inf 0.372 0.705
13 0.507 0.0849 Inf 0.341 0.674
14 0.590 0.0849 Inf 0.424 0.757
15 0.591 0.0850 Inf 0.424 0.758
16 0.536 0.0850 Inf 0.370 0.703
17 0.487 0.0850 Inf 0.321 0.654
18 0.481 0.0850 Inf 0.314 0.647
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.164782 0.0638 Inf -2.584 0.4654
StepF1 - StepF3 -0.108208 0.0638 Inf -1.696 0.9677
StepF1 - StepF4 0.000930 0.0637 Inf 0.015 1.0000
StepF1 - StepF5 0.088997 0.0636 Inf 1.400 0.9958
StepF1 - StepF6 0.017264 0.0635 Inf 0.272 1.0000
StepF1 - StepF7 0.019625 0.0634 Inf 0.309 1.0000
StepF1 - StepF8 -0.016914 0.0637 Inf -0.266 1.0000
StepF1 - StepF9 -0.082915 0.0633 Inf -1.310 0.9981
StepF1 - StepF10 -0.096744 0.0634 Inf -1.525 0.9890
StepF1 - StepF11 -0.041108 0.0637 Inf -0.645 1.0000
StepF1 - StepF12 0.067538 0.0636 Inf 1.062 0.9999
StepF1 - StepF13 0.099042 0.0633 Inf 1.565 0.9855
StepF1 - StepF14 0.015847 0.0635 Inf 0.249 1.0000
StepF1 - StepF15 0.015075 0.0637 Inf 0.236 1.0000
StepF1 - StepF16 0.069992 0.0638 Inf 1.097 0.9998
StepF1 - StepF17 0.118925 0.0637 Inf 1.867 0.9239
StepF1 - StepF18 0.125225 0.0636 Inf 1.969 0.8843
StepF2 - StepF3 0.056574 0.0632 Inf 0.895 1.0000
StepF2 - StepF4 0.165712 0.0632 Inf 2.621 0.4375
StepF2 - StepF5 0.253779 0.0632 Inf 4.013 0.0076
StepF2 - StepF6 0.182046 0.0633 Inf 2.878 0.2648
StepF2 - StepF7 0.184407 0.0633 Inf 2.914 0.2445
StepF2 - StepF8 0.147868 0.0632 Inf 2.339 0.6546
StepF2 - StepF9 0.081867 0.0635 Inf 1.290 0.9984
StepF2 - StepF10 0.068038 0.0633 Inf 1.075 0.9999
StepF2 - StepF11 0.123674 0.0632 Inf 1.956 0.8897
StepF2 - StepF12 0.232320 0.0632 Inf 3.674 0.0268
StepF2 - StepF13 0.263824 0.0634 Inf 4.158 0.0042
StepF2 - StepF14 0.180629 0.0633 Inf 2.856 0.2781
StepF2 - StepF15 0.179857 0.0632 Inf 2.845 0.2844
StepF2 - StepF16 0.234774 0.0632 Inf 3.714 0.0233
StepF2 - StepF17 0.283708 0.0632 Inf 4.488 0.0010
StepF2 - StepF18 0.290007 0.0632 Inf 4.587 0.0006
StepF3 - StepF4 0.109137 0.0632 Inf 1.726 0.9619
StepF3 - StepF5 0.197204 0.0632 Inf 3.118 0.1483
StepF3 - StepF6 0.125472 0.0633 Inf 1.983 0.8776
StepF3 - StepF7 0.127832 0.0633 Inf 2.020 0.8602
StepF3 - StepF8 0.091294 0.0632 Inf 1.444 0.9940
StepF3 - StepF9 0.025293 0.0635 Inf 0.398 1.0000
StepF3 - StepF10 0.011463 0.0633 Inf 0.181 1.0000
StepF3 - StepF11 0.067099 0.0632 Inf 1.061 0.9999
StepF3 - StepF12 0.175746 0.0632 Inf 2.779 0.3260
StepF3 - StepF13 0.207250 0.0635 Inf 3.266 0.0987
StepF3 - StepF14 0.124055 0.0633 Inf 1.961 0.8877
StepF3 - StepF15 0.123283 0.0632 Inf 1.950 0.8924
StepF3 - StepF16 0.178199 0.0632 Inf 2.819 0.3006
StepF3 - StepF17 0.227133 0.0632 Inf 3.593 0.0355
StepF3 - StepF18 0.233433 0.0632 Inf 3.692 0.0252
StepF4 - StepF5 0.088067 0.0632 Inf 1.393 0.9960
StepF4 - StepF6 0.016334 0.0632 Inf 0.258 1.0000
StepF4 - StepF7 0.018695 0.0633 Inf 0.296 1.0000
StepF4 - StepF8 -0.017844 0.0632 Inf -0.282 1.0000
StepF4 - StepF9 -0.083845 0.0634 Inf -1.322 0.9978
StepF4 - StepF10 -0.097674 0.0633 Inf -1.544 0.9875
StepF4 - StepF11 -0.042038 0.0632 Inf -0.665 1.0000
StepF4 - StepF12 0.066609 0.0632 Inf 1.054 0.9999
StepF4 - StepF13 0.098112 0.0634 Inf 1.548 0.9871
StepF4 - StepF14 0.014917 0.0632 Inf 0.236 1.0000
StepF4 - StepF15 0.014145 0.0632 Inf 0.224 1.0000
StepF4 - StepF16 0.069062 0.0632 Inf 1.092 0.9998
StepF4 - StepF17 0.117996 0.0632 Inf 1.867 0.9242
StepF4 - StepF18 0.124295 0.0632 Inf 1.966 0.8854
StepF5 - StepF6 -0.071733 0.0632 Inf -1.135 0.9997
StepF5 - StepF7 -0.069372 0.0632 Inf -1.097 0.9998
StepF5 - StepF8 -0.105911 0.0632 Inf -1.675 0.9713
StepF5 - StepF9 -0.171912 0.0633 Inf -2.714 0.3701
StepF5 - StepF10 -0.185741 0.0632 Inf -2.937 0.2317
StepF5 - StepF11 -0.130105 0.0632 Inf -2.058 0.8405
StepF5 - StepF12 -0.021458 0.0632 Inf -0.339 1.0000
StepF5 - StepF13 0.010045 0.0633 Inf 0.159 1.0000
StepF5 - StepF14 -0.073150 0.0632 Inf -1.157 0.9996
StepF5 - StepF15 -0.073922 0.0632 Inf -1.169 0.9995
StepF5 - StepF16 -0.019005 0.0632 Inf -0.301 1.0000
StepF5 - StepF17 0.029929 0.0632 Inf 0.473 1.0000
StepF5 - StepF18 0.036228 0.0632 Inf 0.573 1.0000
StepF6 - StepF7 0.002361 0.0632 Inf 0.037 1.0000
StepF6 - StepF8 -0.034178 0.0632 Inf -0.541 1.0000
StepF6 - StepF9 -0.100179 0.0633 Inf -1.582 0.9838
StepF6 - StepF10 -0.114008 0.0632 Inf -1.803 0.9435
StepF6 - StepF11 -0.058372 0.0632 Inf -0.923 1.0000
StepF6 - StepF12 0.050274 0.0632 Inf 0.795 1.0000
StepF6 - StepF13 0.081778 0.0633 Inf 1.292 0.9984
StepF6 - StepF14 -0.001417 0.0632 Inf -0.022 1.0000
StepF6 - StepF15 -0.002189 0.0632 Inf -0.035 1.0000
StepF6 - StepF16 0.052728 0.0633 Inf 0.834 1.0000
StepF6 - StepF17 0.101661 0.0632 Inf 1.608 0.9809
StepF6 - StepF18 0.107961 0.0632 Inf 1.708 0.9656
StepF7 - StepF8 -0.036539 0.0632 Inf -0.578 1.0000
StepF7 - StepF9 -0.102540 0.0633 Inf -1.621 0.9793
StepF7 - StepF10 -0.116369 0.0632 Inf -1.841 0.9325
StepF7 - StepF11 -0.060733 0.0633 Inf -0.960 1.0000
StepF7 - StepF12 0.047914 0.0632 Inf 0.758 1.0000
StepF7 - StepF13 0.079417 0.0633 Inf 1.255 0.9989
StepF7 - StepF14 -0.003778 0.0632 Inf -0.060 1.0000
StepF7 - StepF15 -0.004550 0.0633 Inf -0.072 1.0000
StepF7 - StepF16 0.050367 0.0633 Inf 0.796 1.0000
StepF7 - StepF17 0.099301 0.0633 Inf 1.570 0.9851
StepF7 - StepF18 0.105600 0.0632 Inf 1.670 0.9722
StepF8 - StepF9 -0.066001 0.0634 Inf -1.041 0.9999
StepF8 - StepF10 -0.079830 0.0633 Inf -1.262 0.9988
StepF8 - StepF11 -0.024194 0.0632 Inf -0.383 1.0000
StepF8 - StepF12 0.084452 0.0632 Inf 1.336 0.9976
StepF8 - StepF13 0.115956 0.0634 Inf 1.830 0.9359
StepF8 - StepF14 0.032761 0.0632 Inf 0.518 1.0000
StepF8 - StepF15 0.031989 0.0632 Inf 0.506 1.0000
StepF8 - StepF16 0.086906 0.0632 Inf 1.375 0.9966
StepF8 - StepF17 0.135840 0.0632 Inf 2.149 0.7872
StepF8 - StepF18 0.142139 0.0632 Inf 2.248 0.7207
StepF9 - StepF10 -0.013829 0.0633 Inf -0.219 1.0000
StepF9 - StepF11 0.041807 0.0634 Inf 0.659 1.0000
StepF9 - StepF12 0.150453 0.0634 Inf 2.374 0.6277
StepF9 - StepF13 0.181957 0.0632 Inf 2.878 0.2647
StepF9 - StepF14 0.098762 0.0633 Inf 1.560 0.9860
StepF9 - StepF15 0.097990 0.0635 Inf 1.544 0.9874
StepF9 - StepF16 0.152907 0.0635 Inf 2.409 0.6011
StepF9 - StepF17 0.201840 0.0634 Inf 3.183 0.1246
StepF9 - StepF18 0.208140 0.0634 Inf 3.285 0.0935
StepF10 - StepF11 0.055636 0.0633 Inf 0.879 1.0000
StepF10 - StepF12 0.164282 0.0632 Inf 2.597 0.4553
StepF10 - StepF13 0.195786 0.0633 Inf 3.095 0.1574
StepF10 - StepF14 0.112591 0.0632 Inf 1.781 0.9494
StepF10 - StepF15 0.111819 0.0633 Inf 1.767 0.9529
StepF10 - StepF16 0.166736 0.0633 Inf 2.634 0.4281
StepF10 - StepF17 0.215670 0.0633 Inf 3.409 0.0644
StepF10 - StepF18 0.221969 0.0632 Inf 3.510 0.0467
StepF11 - StepF12 0.108647 0.0632 Inf 1.718 0.9634
StepF11 - StepF13 0.140150 0.0634 Inf 2.210 0.7473
StepF11 - StepF14 0.056955 0.0632 Inf 0.901 1.0000
StepF11 - StepF15 0.056183 0.0632 Inf 0.889 1.0000
StepF11 - StepF16 0.111100 0.0632 Inf 1.757 0.9551
StepF11 - StepF17 0.160034 0.0632 Inf 2.532 0.5058
StepF11 - StepF18 0.166333 0.0632 Inf 2.631 0.4303
StepF12 - StepF13 0.031504 0.0634 Inf 0.497 1.0000
StepF12 - StepF14 -0.051691 0.0632 Inf -0.818 1.0000
StepF12 - StepF15 -0.052463 0.0632 Inf -0.830 1.0000
StepF12 - StepF16 0.002453 0.0632 Inf 0.039 1.0000
StepF12 - StepF17 0.051387 0.0632 Inf 0.813 1.0000
StepF12 - StepF18 0.057687 0.0632 Inf 0.913 1.0000
StepF13 - StepF14 -0.083195 0.0633 Inf -1.314 0.9980
StepF13 - StepF15 -0.083967 0.0634 Inf -1.324 0.9978
StepF13 - StepF16 -0.029050 0.0635 Inf -0.458 1.0000
StepF13 - StepF17 0.019883 0.0634 Inf 0.314 1.0000
StepF13 - StepF18 0.026183 0.0633 Inf 0.413 1.0000
StepF14 - StepF15 -0.000772 0.0632 Inf -0.012 1.0000
StepF14 - StepF16 0.054145 0.0633 Inf 0.856 1.0000
StepF14 - StepF17 0.103078 0.0632 Inf 1.630 0.9781
StepF14 - StepF18 0.109378 0.0632 Inf 1.730 0.9611
StepF15 - StepF16 0.054917 0.0632 Inf 0.869 1.0000
StepF15 - StepF17 0.103850 0.0632 Inf 1.643 0.9763
StepF15 - StepF18 0.110150 0.0632 Inf 1.742 0.9585
StepF16 - StepF17 0.048934 0.0632 Inf 0.774 1.0000
StepF16 - StepF18 0.055233 0.0632 Inf 0.874 1.0000
StepF17 - StepF18 0.006300 0.0632 Inf 0.100 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.164782 0.0638 Inf -2.584 0.4654
StepF1 - StepF3 -0.108208 0.0638 Inf -1.696 0.9677
StepF1 - StepF4 0.000930 0.0637 Inf 0.015 1.0000
StepF1 - StepF5 0.088997 0.0636 Inf 1.400 0.9958
StepF1 - StepF6 0.017264 0.0635 Inf 0.272 1.0000
StepF1 - StepF7 0.019625 0.0634 Inf 0.309 1.0000
StepF1 - StepF8 -0.016914 0.0637 Inf -0.266 1.0000
StepF1 - StepF9 -0.082915 0.0633 Inf -1.310 0.9981
StepF1 - StepF10 -0.096744 0.0634 Inf -1.525 0.9890
StepF1 - StepF11 -0.041108 0.0637 Inf -0.645 1.0000
StepF1 - StepF12 0.067538 0.0636 Inf 1.062 0.9999
StepF1 - StepF13 0.099042 0.0633 Inf 1.565 0.9855
StepF1 - StepF14 0.015847 0.0635 Inf 0.249 1.0000
StepF1 - StepF15 0.015075 0.0637 Inf 0.236 1.0000
StepF1 - StepF16 0.069992 0.0638 Inf 1.097 0.9998
StepF1 - StepF17 0.118925 0.0637 Inf 1.867 0.9239
StepF1 - StepF18 0.125225 0.0636 Inf 1.969 0.8843
StepF2 - StepF3 0.056574 0.0632 Inf 0.895 1.0000
StepF2 - StepF4 0.165712 0.0632 Inf 2.621 0.4375
StepF2 - StepF5 0.253779 0.0632 Inf 4.013 0.0076
StepF2 - StepF6 0.182046 0.0633 Inf 2.878 0.2648
StepF2 - StepF7 0.184407 0.0633 Inf 2.914 0.2445
StepF2 - StepF8 0.147868 0.0632 Inf 2.339 0.6546
StepF2 - StepF9 0.081867 0.0635 Inf 1.290 0.9984
StepF2 - StepF10 0.068038 0.0633 Inf 1.075 0.9999
StepF2 - StepF11 0.123674 0.0632 Inf 1.956 0.8897
StepF2 - StepF12 0.232320 0.0632 Inf 3.674 0.0268
StepF2 - StepF13 0.263824 0.0634 Inf 4.158 0.0042
StepF2 - StepF14 0.180629 0.0633 Inf 2.856 0.2781
StepF2 - StepF15 0.179857 0.0632 Inf 2.845 0.2844
StepF2 - StepF16 0.234774 0.0632 Inf 3.714 0.0233
StepF2 - StepF17 0.283708 0.0632 Inf 4.488 0.0010
StepF2 - StepF18 0.290007 0.0632 Inf 4.587 0.0006
StepF3 - StepF4 0.109137 0.0632 Inf 1.726 0.9619
StepF3 - StepF5 0.197204 0.0632 Inf 3.118 0.1483
StepF3 - StepF6 0.125472 0.0633 Inf 1.983 0.8776
StepF3 - StepF7 0.127832 0.0633 Inf 2.020 0.8602
StepF3 - StepF8 0.091294 0.0632 Inf 1.444 0.9940
StepF3 - StepF9 0.025293 0.0635 Inf 0.398 1.0000
StepF3 - StepF10 0.011463 0.0633 Inf 0.181 1.0000
StepF3 - StepF11 0.067099 0.0632 Inf 1.061 0.9999
StepF3 - StepF12 0.175746 0.0632 Inf 2.779 0.3260
StepF3 - StepF13 0.207250 0.0635 Inf 3.266 0.0987
StepF3 - StepF14 0.124055 0.0633 Inf 1.961 0.8877
StepF3 - StepF15 0.123283 0.0632 Inf 1.950 0.8924
StepF3 - StepF16 0.178199 0.0632 Inf 2.819 0.3006
StepF3 - StepF17 0.227133 0.0632 Inf 3.593 0.0355
StepF3 - StepF18 0.233433 0.0632 Inf 3.692 0.0252
StepF4 - StepF5 0.088067 0.0632 Inf 1.393 0.9960
StepF4 - StepF6 0.016334 0.0632 Inf 0.258 1.0000
StepF4 - StepF7 0.018695 0.0633 Inf 0.296 1.0000
StepF4 - StepF8 -0.017844 0.0632 Inf -0.282 1.0000
StepF4 - StepF9 -0.083845 0.0634 Inf -1.322 0.9978
StepF4 - StepF10 -0.097674 0.0633 Inf -1.544 0.9875
StepF4 - StepF11 -0.042038 0.0632 Inf -0.665 1.0000
StepF4 - StepF12 0.066609 0.0632 Inf 1.054 0.9999
StepF4 - StepF13 0.098112 0.0634 Inf 1.548 0.9871
StepF4 - StepF14 0.014917 0.0632 Inf 0.236 1.0000
StepF4 - StepF15 0.014145 0.0632 Inf 0.224 1.0000
StepF4 - StepF16 0.069062 0.0632 Inf 1.092 0.9998
StepF4 - StepF17 0.117996 0.0632 Inf 1.867 0.9242
StepF4 - StepF18 0.124295 0.0632 Inf 1.966 0.8854
StepF5 - StepF6 -0.071733 0.0632 Inf -1.135 0.9997
StepF5 - StepF7 -0.069372 0.0632 Inf -1.097 0.9998
StepF5 - StepF8 -0.105911 0.0632 Inf -1.675 0.9713
StepF5 - StepF9 -0.171912 0.0633 Inf -2.714 0.3701
StepF5 - StepF10 -0.185741 0.0632 Inf -2.937 0.2317
StepF5 - StepF11 -0.130105 0.0632 Inf -2.058 0.8405
StepF5 - StepF12 -0.021458 0.0632 Inf -0.339 1.0000
StepF5 - StepF13 0.010045 0.0633 Inf 0.159 1.0000
StepF5 - StepF14 -0.073150 0.0632 Inf -1.157 0.9996
StepF5 - StepF15 -0.073922 0.0632 Inf -1.169 0.9995
StepF5 - StepF16 -0.019005 0.0632 Inf -0.301 1.0000
StepF5 - StepF17 0.029929 0.0632 Inf 0.473 1.0000
StepF5 - StepF18 0.036228 0.0632 Inf 0.573 1.0000
StepF6 - StepF7 0.002361 0.0632 Inf 0.037 1.0000
StepF6 - StepF8 -0.034178 0.0632 Inf -0.541 1.0000
StepF6 - StepF9 -0.100179 0.0633 Inf -1.582 0.9838
StepF6 - StepF10 -0.114008 0.0632 Inf -1.803 0.9435
StepF6 - StepF11 -0.058372 0.0632 Inf -0.923 1.0000
StepF6 - StepF12 0.050274 0.0632 Inf 0.795 1.0000
StepF6 - StepF13 0.081778 0.0633 Inf 1.292 0.9984
StepF6 - StepF14 -0.001417 0.0632 Inf -0.022 1.0000
StepF6 - StepF15 -0.002189 0.0632 Inf -0.035 1.0000
StepF6 - StepF16 0.052728 0.0633 Inf 0.834 1.0000
StepF6 - StepF17 0.101661 0.0632 Inf 1.608 0.9809
StepF6 - StepF18 0.107961 0.0632 Inf 1.708 0.9656
StepF7 - StepF8 -0.036539 0.0632 Inf -0.578 1.0000
StepF7 - StepF9 -0.102540 0.0633 Inf -1.621 0.9793
StepF7 - StepF10 -0.116369 0.0632 Inf -1.841 0.9325
StepF7 - StepF11 -0.060733 0.0633 Inf -0.960 1.0000
StepF7 - StepF12 0.047914 0.0632 Inf 0.758 1.0000
StepF7 - StepF13 0.079417 0.0633 Inf 1.255 0.9989
StepF7 - StepF14 -0.003778 0.0632 Inf -0.060 1.0000
StepF7 - StepF15 -0.004550 0.0633 Inf -0.072 1.0000
StepF7 - StepF16 0.050367 0.0633 Inf 0.796 1.0000
StepF7 - StepF17 0.099301 0.0633 Inf 1.570 0.9851
StepF7 - StepF18 0.105600 0.0632 Inf 1.670 0.9722
StepF8 - StepF9 -0.066001 0.0634 Inf -1.041 0.9999
StepF8 - StepF10 -0.079830 0.0633 Inf -1.262 0.9988
StepF8 - StepF11 -0.024194 0.0632 Inf -0.383 1.0000
StepF8 - StepF12 0.084452 0.0632 Inf 1.336 0.9976
StepF8 - StepF13 0.115956 0.0634 Inf 1.830 0.9359
StepF8 - StepF14 0.032761 0.0632 Inf 0.518 1.0000
StepF8 - StepF15 0.031989 0.0632 Inf 0.506 1.0000
StepF8 - StepF16 0.086906 0.0632 Inf 1.375 0.9966
StepF8 - StepF17 0.135840 0.0632 Inf 2.149 0.7872
StepF8 - StepF18 0.142139 0.0632 Inf 2.248 0.7207
StepF9 - StepF10 -0.013829 0.0633 Inf -0.219 1.0000
StepF9 - StepF11 0.041807 0.0634 Inf 0.659 1.0000
StepF9 - StepF12 0.150453 0.0634 Inf 2.374 0.6277
StepF9 - StepF13 0.181957 0.0632 Inf 2.878 0.2647
StepF9 - StepF14 0.098762 0.0633 Inf 1.560 0.9860
StepF9 - StepF15 0.097990 0.0635 Inf 1.544 0.9874
StepF9 - StepF16 0.152907 0.0635 Inf 2.409 0.6011
StepF9 - StepF17 0.201840 0.0634 Inf 3.183 0.1246
StepF9 - StepF18 0.208140 0.0634 Inf 3.285 0.0935
StepF10 - StepF11 0.055636 0.0633 Inf 0.879 1.0000
StepF10 - StepF12 0.164282 0.0632 Inf 2.597 0.4553
StepF10 - StepF13 0.195786 0.0633 Inf 3.095 0.1574
StepF10 - StepF14 0.112591 0.0632 Inf 1.781 0.9494
StepF10 - StepF15 0.111819 0.0633 Inf 1.767 0.9529
StepF10 - StepF16 0.166736 0.0633 Inf 2.634 0.4281
StepF10 - StepF17 0.215670 0.0633 Inf 3.409 0.0644
StepF10 - StepF18 0.221969 0.0632 Inf 3.510 0.0467
StepF11 - StepF12 0.108647 0.0632 Inf 1.718 0.9634
StepF11 - StepF13 0.140150 0.0634 Inf 2.210 0.7473
StepF11 - StepF14 0.056955 0.0632 Inf 0.901 1.0000
StepF11 - StepF15 0.056183 0.0632 Inf 0.889 1.0000
StepF11 - StepF16 0.111100 0.0632 Inf 1.757 0.9551
StepF11 - StepF17 0.160034 0.0632 Inf 2.532 0.5058
StepF11 - StepF18 0.166333 0.0632 Inf 2.631 0.4303
StepF12 - StepF13 0.031504 0.0634 Inf 0.497 1.0000
StepF12 - StepF14 -0.051691 0.0632 Inf -0.818 1.0000
StepF12 - StepF15 -0.052463 0.0632 Inf -0.830 1.0000
StepF12 - StepF16 0.002453 0.0632 Inf 0.039 1.0000
StepF12 - StepF17 0.051387 0.0632 Inf 0.813 1.0000
StepF12 - StepF18 0.057687 0.0632 Inf 0.913 1.0000
StepF13 - StepF14 -0.083195 0.0633 Inf -1.314 0.9980
StepF13 - StepF15 -0.083967 0.0634 Inf -1.324 0.9978
StepF13 - StepF16 -0.029050 0.0635 Inf -0.458 1.0000
StepF13 - StepF17 0.019883 0.0634 Inf 0.314 1.0000
StepF13 - StepF18 0.026183 0.0633 Inf 0.413 1.0000
StepF14 - StepF15 -0.000772 0.0632 Inf -0.012 1.0000
StepF14 - StepF16 0.054145 0.0633 Inf 0.856 1.0000
StepF14 - StepF17 0.103078 0.0632 Inf 1.630 0.9781
StepF14 - StepF18 0.109378 0.0632 Inf 1.730 0.9611
StepF15 - StepF16 0.054917 0.0632 Inf 0.869 1.0000
StepF15 - StepF17 0.103850 0.0632 Inf 1.643 0.9763
StepF15 - StepF18 0.110150 0.0632 Inf 1.742 0.9585
StepF16 - StepF17 0.048934 0.0632 Inf 0.774 1.0000
StepF16 - StepF18 0.055233 0.0632 Inf 0.874 1.0000
StepF17 - StepF18 0.006300 0.0632 Inf 0.100 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.164782 0.0638 Inf 2.584 0.1659
StepF3 - StepF2 -0.056574 0.0632 Inf -0.895 1.0000
StepF4 - StepF3 -0.109137 0.0632 Inf -1.726 1.0000
StepF5 - StepF4 -0.088067 0.0632 Inf -1.393 1.0000
StepF6 - StepF5 0.071733 0.0632 Inf 1.135 1.0000
StepF7 - StepF6 -0.002361 0.0632 Inf -0.037 1.0000
StepF8 - StepF7 0.036539 0.0632 Inf 0.578 1.0000
StepF9 - StepF8 0.066001 0.0634 Inf 1.041 1.0000
StepF10 - StepF9 0.013829 0.0633 Inf 0.219 1.0000
StepF11 - StepF10 -0.055636 0.0633 Inf -0.879 1.0000
StepF12 - StepF11 -0.108647 0.0632 Inf -1.718 1.0000
StepF13 - StepF12 -0.031504 0.0634 Inf -0.497 1.0000
StepF14 - StepF13 0.083195 0.0633 Inf 1.314 1.0000
StepF15 - StepF14 0.000772 0.0632 Inf 0.012 1.0000
StepF16 - StepF15 -0.054917 0.0632 Inf -0.869 1.0000
StepF17 - StepF16 -0.048934 0.0632 Inf -0.774 1.0000
StepF18 - StepF17 -0.006300 0.0632 Inf -0.100 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.164782 0.0638 Inf 2.584 0.1659
StepF3 - StepF2 -0.056574 0.0632 Inf -0.895 1.0000
StepF4 - StepF3 -0.109137 0.0632 Inf -1.726 1.0000
StepF5 - StepF4 -0.088067 0.0632 Inf -1.393 1.0000
StepF6 - StepF5 0.071733 0.0632 Inf 1.135 1.0000
StepF7 - StepF6 -0.002361 0.0632 Inf -0.037 1.0000
StepF8 - StepF7 0.036539 0.0632 Inf 0.578 1.0000
StepF9 - StepF8 0.066001 0.0634 Inf 1.041 1.0000
StepF10 - StepF9 0.013829 0.0633 Inf 0.219 1.0000
StepF11 - StepF10 -0.055636 0.0633 Inf -0.879 1.0000
StepF12 - StepF11 -0.108647 0.0632 Inf -1.718 1.0000
StepF13 - StepF12 -0.031504 0.0634 Inf -0.497 1.0000
StepF14 - StepF13 0.083195 0.0633 Inf 1.314 1.0000
StepF15 - StepF14 0.000772 0.0632 Inf 0.012 1.0000
StepF16 - StepF15 -0.054917 0.0632 Inf -0.869 1.0000
StepF17 - StepF16 -0.048934 0.0632 Inf -0.774 1.0000
StepF18 - StepF17 -0.006300 0.0632 Inf -0.100 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests
==============================
TEST (rt_ms) | Block 5 | 18 steps | Axis Z
==============================
Type II Wald χ² (StepF, Accuracy, rt_ms):
Analysis of Deviance Table (Type II Wald chisquare tests)
Response: RMS
Chisq Df Pr(>Chisq)
StepF 89.9297 17 6.287e-12 ***
Accuracy 0.7889 1 0.3744
rt_ms 0.1217 1 0.7272
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
EMMs per step | Accuracy (adjusted at mean rt_ms):
Accuracy = 0:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.46 0.177 Inf 1.114 1.81
2 1.67 0.177 Inf 1.322 2.02
3 1.53 0.177 Inf 1.180 1.87
4 1.44 0.177 Inf 1.089 1.78
5 1.38 0.177 Inf 1.037 1.73
6 1.37 0.177 Inf 1.027 1.72
7 1.48 0.177 Inf 1.137 1.83
8 1.44 0.177 Inf 1.089 1.78
9 1.40 0.177 Inf 1.056 1.75
10 1.46 0.177 Inf 1.110 1.80
11 1.33 0.177 Inf 0.987 1.68
12 1.23 0.177 Inf 0.886 1.58
13 1.27 0.177 Inf 0.922 1.62
14 1.40 0.177 Inf 1.051 1.74
15 1.32 0.177 Inf 0.972 1.67
16 1.22 0.177 Inf 0.877 1.57
17 1.17 0.177 Inf 0.821 1.51
18 1.18 0.177 Inf 0.831 1.52
Accuracy = 1:
StepF emmean SE df asymp.LCL asymp.UCL
1 1.38 0.179 Inf 1.025 1.73
2 1.58 0.179 Inf 1.233 1.94
3 1.44 0.179 Inf 1.090 1.79
4 1.35 0.179 Inf 1.000 1.70
5 1.30 0.179 Inf 0.948 1.65
6 1.29 0.179 Inf 0.938 1.64
7 1.40 0.179 Inf 1.048 1.75
8 1.35 0.179 Inf 1.000 1.70
9 1.32 0.179 Inf 0.967 1.67
10 1.37 0.179 Inf 1.021 1.72
11 1.25 0.179 Inf 0.898 1.60
12 1.15 0.179 Inf 0.797 1.50
13 1.18 0.179 Inf 0.833 1.53
14 1.31 0.179 Inf 0.962 1.66
15 1.23 0.179 Inf 0.883 1.59
16 1.14 0.179 Inf 0.788 1.49
17 1.08 0.179 Inf 0.732 1.43
18 1.09 0.179 Inf 0.742 1.44
Degrees-of-freedom method: asymptotic
Confidence level used: 0.95
All-pairs (Tukey) among steps | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.207675 0.0804 Inf -2.584 0.4653
StepF1 - StepF3 -0.064939 0.0804 Inf -0.808 1.0000
StepF1 - StepF4 0.025362 0.0803 Inf 0.316 1.0000
StepF1 - StepF5 0.077231 0.0801 Inf 0.964 1.0000
StepF1 - StepF6 0.087151 0.0800 Inf 1.089 0.9998
StepF1 - StepF7 -0.022747 0.0800 Inf -0.284 1.0000
StepF1 - StepF8 0.025175 0.0802 Inf 0.314 1.0000
StepF1 - StepF9 0.058634 0.0797 Inf 0.735 1.0000
StepF1 - StepF10 0.004265 0.0799 Inf 0.053 1.0000
StepF1 - StepF11 0.127279 0.0803 Inf 1.585 0.9835
StepF1 - StepF12 0.228514 0.0802 Inf 2.850 0.2814
StepF1 - StepF13 0.192354 0.0798 Inf 2.412 0.5988
StepF1 - StepF14 0.063752 0.0800 Inf 0.796 1.0000
StepF1 - StepF15 0.142354 0.0803 Inf 1.772 0.9516
StepF1 - StepF16 0.237832 0.0804 Inf 2.959 0.2206
StepF1 - StepF17 0.293933 0.0803 Inf 3.662 0.0279
StepF1 - StepF18 0.283660 0.0802 Inf 3.538 0.0425
StepF2 - StepF3 0.142735 0.0797 Inf 1.792 0.9466
StepF2 - StepF4 0.233037 0.0797 Inf 2.925 0.2384
StepF2 - StepF5 0.284906 0.0797 Inf 3.575 0.0376
StepF2 - StepF6 0.294826 0.0797 Inf 3.699 0.0246
StepF2 - StepF7 0.184928 0.0798 Inf 2.319 0.6697
StepF2 - StepF8 0.232850 0.0797 Inf 2.922 0.2398
StepF2 - StepF9 0.266309 0.0800 Inf 3.330 0.0819
StepF2 - StepF10 0.211940 0.0798 Inf 2.657 0.4112
StepF2 - StepF11 0.334954 0.0797 Inf 4.204 0.0035
StepF2 - StepF12 0.436189 0.0797 Inf 5.474 <.0001
StepF2 - StepF13 0.400029 0.0800 Inf 5.003 0.0001
StepF2 - StepF14 0.271427 0.0797 Inf 3.405 0.0652
StepF2 - StepF15 0.350029 0.0797 Inf 4.394 0.0015
StepF2 - StepF16 0.445507 0.0797 Inf 5.592 <.0001
StepF2 - StepF17 0.501608 0.0797 Inf 6.296 <.0001
StepF2 - StepF18 0.491335 0.0797 Inf 6.166 <.0001
StepF3 - StepF4 0.090302 0.0797 Inf 1.133 0.9997
StepF3 - StepF5 0.142170 0.0797 Inf 1.784 0.9487
StepF3 - StepF6 0.152090 0.0797 Inf 1.908 0.9094
StepF3 - StepF7 0.042193 0.0798 Inf 0.529 1.0000
StepF3 - StepF8 0.090114 0.0797 Inf 1.131 0.9997
StepF3 - StepF9 0.123573 0.0800 Inf 1.545 0.9874
StepF3 - StepF10 0.069205 0.0798 Inf 0.867 1.0000
StepF3 - StepF11 0.192218 0.0797 Inf 2.413 0.5981
StepF3 - StepF12 0.293454 0.0797 Inf 3.683 0.0260
StepF3 - StepF13 0.257294 0.0800 Inf 3.217 0.1134
StepF3 - StepF14 0.128691 0.0797 Inf 1.614 0.9801
StepF3 - StepF15 0.207293 0.0797 Inf 2.602 0.4519
StepF3 - StepF16 0.302771 0.0797 Inf 3.800 0.0170
StepF3 - StepF17 0.358872 0.0797 Inf 4.504 0.0009
StepF3 - StepF18 0.348600 0.0797 Inf 4.375 0.0017
StepF4 - StepF5 0.051868 0.0797 Inf 0.651 1.0000
StepF4 - StepF6 0.061788 0.0797 Inf 0.775 1.0000
StepF4 - StepF7 -0.048109 0.0797 Inf -0.603 1.0000
StepF4 - StepF8 -0.000188 0.0797 Inf -0.002 1.0000
StepF4 - StepF9 0.033272 0.0799 Inf 0.416 1.0000
StepF4 - StepF10 -0.021097 0.0797 Inf -0.265 1.0000
StepF4 - StepF11 0.101917 0.0797 Inf 1.279 0.9986
StepF4 - StepF12 0.203152 0.0797 Inf 2.550 0.4916
StepF4 - StepF13 0.166992 0.0799 Inf 2.090 0.8224
StepF4 - StepF14 0.038389 0.0797 Inf 0.482 1.0000
StepF4 - StepF15 0.116992 0.0797 Inf 1.468 0.9927
StepF4 - StepF16 0.212469 0.0797 Inf 2.667 0.4040
StepF4 - StepF17 0.268570 0.0797 Inf 3.371 0.0723
StepF4 - StepF18 0.258298 0.0797 Inf 3.242 0.1057
StepF5 - StepF6 0.009920 0.0797 Inf 0.125 1.0000
StepF5 - StepF7 -0.099978 0.0797 Inf -1.255 0.9989
StepF5 - StepF8 -0.052056 0.0797 Inf -0.653 1.0000
StepF5 - StepF9 -0.018597 0.0798 Inf -0.233 1.0000
StepF5 - StepF10 -0.072966 0.0797 Inf -0.916 1.0000
StepF5 - StepF11 0.050048 0.0797 Inf 0.628 1.0000
StepF5 - StepF12 0.151284 0.0797 Inf 1.899 0.9127
StepF5 - StepF13 0.115124 0.0798 Inf 1.443 0.9940
StepF5 - StepF14 -0.013479 0.0797 Inf -0.169 1.0000
StepF5 - StepF15 0.065123 0.0797 Inf 0.817 1.0000
StepF5 - StepF16 0.160601 0.0797 Inf 2.015 0.8625
StepF5 - StepF17 0.216702 0.0797 Inf 2.720 0.3663
StepF5 - StepF18 0.206429 0.0797 Inf 2.591 0.4601
StepF6 - StepF7 -0.109898 0.0797 Inf -1.379 0.9964
StepF6 - StepF8 -0.061976 0.0797 Inf -0.778 1.0000
StepF6 - StepF9 -0.028517 0.0798 Inf -0.357 1.0000
StepF6 - StepF10 -0.082886 0.0797 Inf -1.040 0.9999
StepF6 - StepF11 0.040128 0.0797 Inf 0.503 1.0000
StepF6 - StepF12 0.141364 0.0797 Inf 1.774 0.9510
StepF6 - StepF13 0.105204 0.0798 Inf 1.319 0.9979
StepF6 - StepF14 -0.023399 0.0797 Inf -0.294 1.0000
StepF6 - StepF15 0.055203 0.0797 Inf 0.693 1.0000
StepF6 - StepF16 0.150681 0.0797 Inf 1.890 0.9160
StepF6 - StepF17 0.206782 0.0797 Inf 2.595 0.4573
StepF6 - StepF18 0.196509 0.0797 Inf 2.466 0.5564
StepF7 - StepF8 0.047922 0.0797 Inf 0.601 1.0000
StepF7 - StepF9 0.081381 0.0797 Inf 1.021 0.9999
StepF7 - StepF10 0.027012 0.0797 Inf 0.339 1.0000
StepF7 - StepF11 0.150026 0.0797 Inf 1.882 0.9190
StepF7 - StepF12 0.251261 0.0797 Inf 3.153 0.1353
StepF7 - StepF13 0.215101 0.0797 Inf 2.698 0.3815
StepF7 - StepF14 0.086499 0.0797 Inf 1.086 0.9998
StepF7 - StepF15 0.165101 0.0797 Inf 2.070 0.8336
StepF7 - StepF16 0.260579 0.0798 Inf 3.267 0.0985
StepF7 - StepF17 0.316680 0.0797 Inf 3.972 0.0089
StepF7 - StepF18 0.306407 0.0797 Inf 3.845 0.0144
StepF8 - StepF9 0.033459 0.0799 Inf 0.419 1.0000
StepF8 - StepF10 -0.020910 0.0797 Inf -0.262 1.0000
StepF8 - StepF11 0.102104 0.0797 Inf 1.282 0.9985
StepF8 - StepF12 0.203340 0.0797 Inf 2.552 0.4897
StepF8 - StepF13 0.167180 0.0799 Inf 2.093 0.8206
StepF8 - StepF14 0.038577 0.0797 Inf 0.484 1.0000
StepF8 - StepF15 0.117179 0.0797 Inf 1.471 0.9926
StepF8 - StepF16 0.212657 0.0797 Inf 2.669 0.4024
StepF8 - StepF17 0.268758 0.0797 Inf 3.373 0.0718
StepF8 - StepF18 0.258485 0.0797 Inf 3.245 0.1050
StepF9 - StepF10 -0.054369 0.0797 Inf -0.682 1.0000
StepF9 - StepF11 0.068645 0.0799 Inf 0.859 1.0000
StepF9 - StepF12 0.169880 0.0799 Inf 2.127 0.8005
StepF9 - StepF13 0.133720 0.0797 Inf 1.678 0.9708
StepF9 - StepF14 0.005118 0.0798 Inf 0.064 1.0000
StepF9 - StepF15 0.083720 0.0800 Inf 1.047 0.9999
StepF9 - StepF16 0.179198 0.0800 Inf 2.240 0.7266
StepF9 - StepF17 0.235299 0.0799 Inf 2.945 0.2279
StepF9 - StepF18 0.225026 0.0799 Inf 2.818 0.3012
StepF10 - StepF11 0.123014 0.0798 Inf 1.542 0.9876
StepF10 - StepF12 0.224249 0.0797 Inf 2.813 0.3041
StepF10 - StepF13 0.188089 0.0797 Inf 2.360 0.6389
StepF10 - StepF14 0.059487 0.0797 Inf 0.747 1.0000
StepF10 - StepF15 0.138089 0.0798 Inf 1.731 0.9608
StepF10 - StepF16 0.233567 0.0798 Inf 2.928 0.2370
StepF10 - StepF17 0.289668 0.0797 Inf 3.633 0.0309
StepF10 - StepF18 0.279395 0.0797 Inf 3.505 0.0474
StepF11 - StepF12 0.101235 0.0797 Inf 1.271 0.9987
StepF11 - StepF13 0.065075 0.0799 Inf 0.814 1.0000
StepF11 - StepF14 -0.063527 0.0797 Inf -0.797 1.0000
StepF11 - StepF15 0.015075 0.0797 Inf 0.189 1.0000
StepF11 - StepF16 0.110553 0.0797 Inf 1.388 0.9962
StepF11 - StepF17 0.166654 0.0797 Inf 2.092 0.8215
StepF11 - StepF18 0.156381 0.0797 Inf 1.963 0.8869
StepF12 - StepF13 -0.036160 0.0798 Inf -0.453 1.0000
StepF12 - StepF14 -0.164763 0.0797 Inf -2.068 0.8349
StepF12 - StepF15 -0.086160 0.0797 Inf -1.081 0.9998
StepF12 - StepF16 0.009317 0.0797 Inf 0.117 1.0000
StepF12 - StepF17 0.065419 0.0797 Inf 0.821 1.0000
StepF12 - StepF18 0.055146 0.0797 Inf 0.692 1.0000
StepF13 - StepF14 -0.128603 0.0798 Inf -1.612 0.9804
StepF13 - StepF15 -0.050001 0.0799 Inf -0.625 1.0000
StepF13 - StepF16 0.045477 0.0800 Inf 0.569 1.0000
StepF13 - StepF17 0.101579 0.0799 Inf 1.271 0.9987
StepF13 - StepF18 0.091306 0.0798 Inf 1.144 0.9997
StepF14 - StepF15 0.078602 0.0797 Inf 0.986 1.0000
StepF14 - StepF16 0.174080 0.0797 Inf 2.184 0.7649
StepF14 - StepF17 0.230181 0.0797 Inf 2.888 0.2589
StepF14 - StepF18 0.219909 0.0797 Inf 2.760 0.3388
StepF15 - StepF16 0.095478 0.0797 Inf 1.198 0.9994
StepF15 - StepF17 0.151579 0.0797 Inf 1.903 0.9114
StepF15 - StepF18 0.141306 0.0797 Inf 1.773 0.9512
StepF16 - StepF17 0.056101 0.0797 Inf 0.704 1.0000
StepF16 - StepF18 0.045829 0.0797 Inf 0.575 1.0000
StepF17 - StepF18 -0.010273 0.0797 Inf -0.129 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF1 - StepF2 -0.207675 0.0804 Inf -2.584 0.4653
StepF1 - StepF3 -0.064939 0.0804 Inf -0.808 1.0000
StepF1 - StepF4 0.025362 0.0803 Inf 0.316 1.0000
StepF1 - StepF5 0.077231 0.0801 Inf 0.964 1.0000
StepF1 - StepF6 0.087151 0.0800 Inf 1.089 0.9998
StepF1 - StepF7 -0.022747 0.0800 Inf -0.284 1.0000
StepF1 - StepF8 0.025175 0.0802 Inf 0.314 1.0000
StepF1 - StepF9 0.058634 0.0797 Inf 0.735 1.0000
StepF1 - StepF10 0.004265 0.0799 Inf 0.053 1.0000
StepF1 - StepF11 0.127279 0.0803 Inf 1.585 0.9835
StepF1 - StepF12 0.228514 0.0802 Inf 2.850 0.2814
StepF1 - StepF13 0.192354 0.0798 Inf 2.412 0.5988
StepF1 - StepF14 0.063752 0.0800 Inf 0.796 1.0000
StepF1 - StepF15 0.142354 0.0803 Inf 1.772 0.9516
StepF1 - StepF16 0.237832 0.0804 Inf 2.959 0.2206
StepF1 - StepF17 0.293933 0.0803 Inf 3.662 0.0279
StepF1 - StepF18 0.283660 0.0802 Inf 3.538 0.0425
StepF2 - StepF3 0.142735 0.0797 Inf 1.792 0.9466
StepF2 - StepF4 0.233037 0.0797 Inf 2.925 0.2384
StepF2 - StepF5 0.284906 0.0797 Inf 3.575 0.0376
StepF2 - StepF6 0.294826 0.0797 Inf 3.699 0.0246
StepF2 - StepF7 0.184928 0.0798 Inf 2.319 0.6697
StepF2 - StepF8 0.232850 0.0797 Inf 2.922 0.2398
StepF2 - StepF9 0.266309 0.0800 Inf 3.330 0.0819
StepF2 - StepF10 0.211940 0.0798 Inf 2.657 0.4112
StepF2 - StepF11 0.334954 0.0797 Inf 4.204 0.0035
StepF2 - StepF12 0.436189 0.0797 Inf 5.474 <.0001
StepF2 - StepF13 0.400029 0.0800 Inf 5.003 0.0001
StepF2 - StepF14 0.271427 0.0797 Inf 3.405 0.0652
StepF2 - StepF15 0.350029 0.0797 Inf 4.394 0.0015
StepF2 - StepF16 0.445507 0.0797 Inf 5.592 <.0001
StepF2 - StepF17 0.501608 0.0797 Inf 6.296 <.0001
StepF2 - StepF18 0.491335 0.0797 Inf 6.166 <.0001
StepF3 - StepF4 0.090302 0.0797 Inf 1.133 0.9997
StepF3 - StepF5 0.142170 0.0797 Inf 1.784 0.9487
StepF3 - StepF6 0.152090 0.0797 Inf 1.908 0.9094
StepF3 - StepF7 0.042193 0.0798 Inf 0.529 1.0000
StepF3 - StepF8 0.090114 0.0797 Inf 1.131 0.9997
StepF3 - StepF9 0.123573 0.0800 Inf 1.545 0.9874
StepF3 - StepF10 0.069205 0.0798 Inf 0.867 1.0000
StepF3 - StepF11 0.192218 0.0797 Inf 2.413 0.5981
StepF3 - StepF12 0.293454 0.0797 Inf 3.683 0.0260
StepF3 - StepF13 0.257294 0.0800 Inf 3.217 0.1134
StepF3 - StepF14 0.128691 0.0797 Inf 1.614 0.9801
StepF3 - StepF15 0.207293 0.0797 Inf 2.602 0.4519
StepF3 - StepF16 0.302771 0.0797 Inf 3.800 0.0170
StepF3 - StepF17 0.358872 0.0797 Inf 4.504 0.0009
StepF3 - StepF18 0.348600 0.0797 Inf 4.375 0.0017
StepF4 - StepF5 0.051868 0.0797 Inf 0.651 1.0000
StepF4 - StepF6 0.061788 0.0797 Inf 0.775 1.0000
StepF4 - StepF7 -0.048109 0.0797 Inf -0.603 1.0000
StepF4 - StepF8 -0.000188 0.0797 Inf -0.002 1.0000
StepF4 - StepF9 0.033272 0.0799 Inf 0.416 1.0000
StepF4 - StepF10 -0.021097 0.0797 Inf -0.265 1.0000
StepF4 - StepF11 0.101917 0.0797 Inf 1.279 0.9986
StepF4 - StepF12 0.203152 0.0797 Inf 2.550 0.4916
StepF4 - StepF13 0.166992 0.0799 Inf 2.090 0.8224
StepF4 - StepF14 0.038389 0.0797 Inf 0.482 1.0000
StepF4 - StepF15 0.116992 0.0797 Inf 1.468 0.9927
StepF4 - StepF16 0.212469 0.0797 Inf 2.667 0.4040
StepF4 - StepF17 0.268570 0.0797 Inf 3.371 0.0723
StepF4 - StepF18 0.258298 0.0797 Inf 3.242 0.1057
StepF5 - StepF6 0.009920 0.0797 Inf 0.125 1.0000
StepF5 - StepF7 -0.099978 0.0797 Inf -1.255 0.9989
StepF5 - StepF8 -0.052056 0.0797 Inf -0.653 1.0000
StepF5 - StepF9 -0.018597 0.0798 Inf -0.233 1.0000
StepF5 - StepF10 -0.072966 0.0797 Inf -0.916 1.0000
StepF5 - StepF11 0.050048 0.0797 Inf 0.628 1.0000
StepF5 - StepF12 0.151284 0.0797 Inf 1.899 0.9127
StepF5 - StepF13 0.115124 0.0798 Inf 1.443 0.9940
StepF5 - StepF14 -0.013479 0.0797 Inf -0.169 1.0000
StepF5 - StepF15 0.065123 0.0797 Inf 0.817 1.0000
StepF5 - StepF16 0.160601 0.0797 Inf 2.015 0.8625
StepF5 - StepF17 0.216702 0.0797 Inf 2.720 0.3663
StepF5 - StepF18 0.206429 0.0797 Inf 2.591 0.4601
StepF6 - StepF7 -0.109898 0.0797 Inf -1.379 0.9964
StepF6 - StepF8 -0.061976 0.0797 Inf -0.778 1.0000
StepF6 - StepF9 -0.028517 0.0798 Inf -0.357 1.0000
StepF6 - StepF10 -0.082886 0.0797 Inf -1.040 0.9999
StepF6 - StepF11 0.040128 0.0797 Inf 0.503 1.0000
StepF6 - StepF12 0.141364 0.0797 Inf 1.774 0.9510
StepF6 - StepF13 0.105204 0.0798 Inf 1.319 0.9979
StepF6 - StepF14 -0.023399 0.0797 Inf -0.294 1.0000
StepF6 - StepF15 0.055203 0.0797 Inf 0.693 1.0000
StepF6 - StepF16 0.150681 0.0797 Inf 1.890 0.9160
StepF6 - StepF17 0.206782 0.0797 Inf 2.595 0.4573
StepF6 - StepF18 0.196509 0.0797 Inf 2.466 0.5564
StepF7 - StepF8 0.047922 0.0797 Inf 0.601 1.0000
StepF7 - StepF9 0.081381 0.0797 Inf 1.021 0.9999
StepF7 - StepF10 0.027012 0.0797 Inf 0.339 1.0000
StepF7 - StepF11 0.150026 0.0797 Inf 1.882 0.9190
StepF7 - StepF12 0.251261 0.0797 Inf 3.153 0.1353
StepF7 - StepF13 0.215101 0.0797 Inf 2.698 0.3815
StepF7 - StepF14 0.086499 0.0797 Inf 1.086 0.9998
StepF7 - StepF15 0.165101 0.0797 Inf 2.070 0.8336
StepF7 - StepF16 0.260579 0.0798 Inf 3.267 0.0985
StepF7 - StepF17 0.316680 0.0797 Inf 3.972 0.0089
StepF7 - StepF18 0.306407 0.0797 Inf 3.845 0.0144
StepF8 - StepF9 0.033459 0.0799 Inf 0.419 1.0000
StepF8 - StepF10 -0.020910 0.0797 Inf -0.262 1.0000
StepF8 - StepF11 0.102104 0.0797 Inf 1.282 0.9985
StepF8 - StepF12 0.203340 0.0797 Inf 2.552 0.4897
StepF8 - StepF13 0.167180 0.0799 Inf 2.093 0.8206
StepF8 - StepF14 0.038577 0.0797 Inf 0.484 1.0000
StepF8 - StepF15 0.117179 0.0797 Inf 1.471 0.9926
StepF8 - StepF16 0.212657 0.0797 Inf 2.669 0.4024
StepF8 - StepF17 0.268758 0.0797 Inf 3.373 0.0718
StepF8 - StepF18 0.258485 0.0797 Inf 3.245 0.1050
StepF9 - StepF10 -0.054369 0.0797 Inf -0.682 1.0000
StepF9 - StepF11 0.068645 0.0799 Inf 0.859 1.0000
StepF9 - StepF12 0.169880 0.0799 Inf 2.127 0.8005
StepF9 - StepF13 0.133720 0.0797 Inf 1.678 0.9708
StepF9 - StepF14 0.005118 0.0798 Inf 0.064 1.0000
StepF9 - StepF15 0.083720 0.0800 Inf 1.047 0.9999
StepF9 - StepF16 0.179198 0.0800 Inf 2.240 0.7266
StepF9 - StepF17 0.235299 0.0799 Inf 2.945 0.2279
StepF9 - StepF18 0.225026 0.0799 Inf 2.818 0.3012
StepF10 - StepF11 0.123014 0.0798 Inf 1.542 0.9876
StepF10 - StepF12 0.224249 0.0797 Inf 2.813 0.3041
StepF10 - StepF13 0.188089 0.0797 Inf 2.360 0.6389
StepF10 - StepF14 0.059487 0.0797 Inf 0.747 1.0000
StepF10 - StepF15 0.138089 0.0798 Inf 1.731 0.9608
StepF10 - StepF16 0.233567 0.0798 Inf 2.928 0.2370
StepF10 - StepF17 0.289668 0.0797 Inf 3.633 0.0309
StepF10 - StepF18 0.279395 0.0797 Inf 3.505 0.0474
StepF11 - StepF12 0.101235 0.0797 Inf 1.271 0.9987
StepF11 - StepF13 0.065075 0.0799 Inf 0.814 1.0000
StepF11 - StepF14 -0.063527 0.0797 Inf -0.797 1.0000
StepF11 - StepF15 0.015075 0.0797 Inf 0.189 1.0000
StepF11 - StepF16 0.110553 0.0797 Inf 1.388 0.9962
StepF11 - StepF17 0.166654 0.0797 Inf 2.092 0.8215
StepF11 - StepF18 0.156381 0.0797 Inf 1.963 0.8869
StepF12 - StepF13 -0.036160 0.0798 Inf -0.453 1.0000
StepF12 - StepF14 -0.164763 0.0797 Inf -2.068 0.8349
StepF12 - StepF15 -0.086160 0.0797 Inf -1.081 0.9998
StepF12 - StepF16 0.009317 0.0797 Inf 0.117 1.0000
StepF12 - StepF17 0.065419 0.0797 Inf 0.821 1.0000
StepF12 - StepF18 0.055146 0.0797 Inf 0.692 1.0000
StepF13 - StepF14 -0.128603 0.0798 Inf -1.612 0.9804
StepF13 - StepF15 -0.050001 0.0799 Inf -0.625 1.0000
StepF13 - StepF16 0.045477 0.0800 Inf 0.569 1.0000
StepF13 - StepF17 0.101579 0.0799 Inf 1.271 0.9987
StepF13 - StepF18 0.091306 0.0798 Inf 1.144 0.9997
StepF14 - StepF15 0.078602 0.0797 Inf 0.986 1.0000
StepF14 - StepF16 0.174080 0.0797 Inf 2.184 0.7649
StepF14 - StepF17 0.230181 0.0797 Inf 2.888 0.2589
StepF14 - StepF18 0.219909 0.0797 Inf 2.760 0.3388
StepF15 - StepF16 0.095478 0.0797 Inf 1.198 0.9994
StepF15 - StepF17 0.151579 0.0797 Inf 1.903 0.9114
StepF15 - StepF18 0.141306 0.0797 Inf 1.773 0.9512
StepF16 - StepF17 0.056101 0.0797 Inf 0.704 1.0000
StepF16 - StepF18 0.045829 0.0797 Inf 0.575 1.0000
StepF17 - StepF18 -0.010273 0.0797 Inf -0.129 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: tukey method for comparing a family of 18 estimates
Adjacent steps (consec; Holm) | Accuracy:
Accuracy = 0:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.20767 0.0804 Inf 2.584 0.1659
StepF3 - StepF2 -0.14274 0.0797 Inf -1.792 1.0000
StepF4 - StepF3 -0.09030 0.0797 Inf -1.133 1.0000
StepF5 - StepF4 -0.05187 0.0797 Inf -0.651 1.0000
StepF6 - StepF5 -0.00992 0.0797 Inf -0.125 1.0000
StepF7 - StepF6 0.10990 0.0797 Inf 1.379 1.0000
StepF8 - StepF7 -0.04792 0.0797 Inf -0.601 1.0000
StepF9 - StepF8 -0.03346 0.0799 Inf -0.419 1.0000
StepF10 - StepF9 0.05437 0.0797 Inf 0.682 1.0000
StepF11 - StepF10 -0.12301 0.0798 Inf -1.542 1.0000
StepF12 - StepF11 -0.10124 0.0797 Inf -1.271 1.0000
StepF13 - StepF12 0.03616 0.0798 Inf 0.453 1.0000
StepF14 - StepF13 0.12860 0.0798 Inf 1.612 1.0000
StepF15 - StepF14 -0.07860 0.0797 Inf -0.986 1.0000
StepF16 - StepF15 -0.09548 0.0797 Inf -1.198 1.0000
StepF17 - StepF16 -0.05610 0.0797 Inf -0.704 1.0000
StepF18 - StepF17 0.01027 0.0797 Inf 0.129 1.0000
Accuracy = 1:
contrast estimate SE df z.ratio p.value
StepF2 - StepF1 0.20767 0.0804 Inf 2.584 0.1659
StepF3 - StepF2 -0.14274 0.0797 Inf -1.792 1.0000
StepF4 - StepF3 -0.09030 0.0797 Inf -1.133 1.0000
StepF5 - StepF4 -0.05187 0.0797 Inf -0.651 1.0000
StepF6 - StepF5 -0.00992 0.0797 Inf -0.125 1.0000
StepF7 - StepF6 0.10990 0.0797 Inf 1.379 1.0000
StepF8 - StepF7 -0.04792 0.0797 Inf -0.601 1.0000
StepF9 - StepF8 -0.03346 0.0799 Inf -0.419 1.0000
StepF10 - StepF9 0.05437 0.0797 Inf 0.682 1.0000
StepF11 - StepF10 -0.12301 0.0798 Inf -1.542 1.0000
StepF12 - StepF11 -0.10124 0.0797 Inf -1.271 1.0000
StepF13 - StepF12 0.03616 0.0798 Inf 0.453 1.0000
StepF14 - StepF13 0.12860 0.0798 Inf 1.612 1.0000
StepF15 - StepF14 -0.07860 0.0797 Inf -0.986 1.0000
StepF16 - StepF15 -0.09548 0.0797 Inf -1.198 1.0000
StepF17 - StepF16 -0.05610 0.0797 Inf -0.704 1.0000
StepF18 - StepF17 0.01027 0.0797 Inf 0.129 1.0000
Degrees-of-freedom method: asymptotic
P value adjustment: holm method for 17 tests # ---- 3.2b PLOTS (rt_ms): unified y-scale (based on Z) + adjacent-step significance markers ----
suppressPackageStartupMessages({
library(dplyr); library(ggplot2); library(lme4); library(emmeans); library(patchwork); library(stringr)
})
emm_options(lmer.df = "asymptotic")
# Robust RHS-step parser: works for "2 - 1", "S2 - S1", etc., without look-behind
.get_rhs_step <- function(x) {
nums <- stringr::str_extract_all(x, "\\d+")
vapply(nums, function(v) {
if (length(v) >= 2) as.integer(v[2]) else NA_integer_
}, integer(1))
}
# Compute EMMs (adjusted at mean rt_ms), SD ribbons, and adjacent-step markers for ONE axis
.compute_emm_sd_and_markers_rtms <- function(df_axis, alpha = 0.05) {
if (nrow(df_axis) == 0) return(NULL)
dd <- df_axis %>%
dplyr::filter(as.character(Accuracy) == "1") %>% # correct-only for plots
dplyr::mutate(
StepF = factor(Step, levels = sort(unique(Step))),
subject = factor(subject),
trial_id = factor(trial_id)
)
if (nrow(dd) == 0 || nlevels(dd$StepF) < 2) return(NULL)
# RT-adjusted (actual RT) model for plotting
m <- suppressWarnings(lme4::lmer(RMS ~ StepF + rt_ms + (1|subject) + (1|trial_id), data = dd, REML = TRUE))
# EMMs per Step (rt_ms held at its mean by default)
em_df <- as.data.frame(emmeans::emmeans(m, ~ StepF)) %>%
dplyr::transmute(Step = as.numeric(as.character(StepF)),
emmean = emmean)
# SD per Step from raw (correct-only)
sd_df <- dd %>%
dplyr::group_by(StepF) %>%
dplyr::summarise(sd = stats::sd(RMS, na.rm = TRUE), .groups = "drop") %>%
dplyr::transmute(Step = as.numeric(as.character(StepF)), sd = sd)
emms_tbl <- dplyr::left_join(em_df, sd_df, by = "Step") %>%
dplyr::mutate(ymin = pmax(0, emmean - sd),
ymax = emmean + sd)
# Adjacent-step contrasts (Holm), based on EMMs
cons_df <- as.data.frame(
emmeans::contrast(emmeans::emmeans(m, ~ StepF), method = "consec", adjust = "holm")
)
# Place marker at the LOWER step (RHS of "k+1 - k"), direction by sign of estimate
markers <- cons_df %>%
dplyr::mutate(
Step = .get_rhs_step(contrast),
dir = ifelse(estimate > 0, "up", "down"),
sig = !is.na(p.value) & (p.value < alpha)
) %>%
dplyr::filter(sig) %>%
dplyr::select(Step, dir, p.value)
list(emms = emms_tbl, markers = markers)
}
# Build plots for a block (X/Y/Z) with unified y-scale based on Z-axis maxima
.plot_block_stepwise_sd_correct_rtms_unified <- function(df_block, block_title) {
axes_map <- c("x"="X","y"="Y","z"="Z")
# Collect EMMs & markers for each axis
out <- lapply(names(axes_map), function(ax) {
comp <- .compute_emm_sd_and_markers_rtms(df_block %>% dplyr::filter(Axis == ax))
if (is.null(comp)) return(NULL)
comp$emms <- comp$emms %>% dplyr::mutate(Axis = axes_map[[ax]])
comp$markers <- comp$markers %>% dplyr::mutate(Axis = axes_map[[ax]])
comp
})
out <- out[!vapply(out, is.null, logical(1))]
if (length(out) == 0) return(invisible(NULL))
emms_tbl <- dplyr::bind_rows(lapply(out, `[[`, "emms"))
mark_tbl <- dplyr::bind_rows(lapply(out, `[[`, "markers"))
# Unified y-limit driven by Z-axis ymax; fallback to overall if Z missing
if ("Z" %in% unique(emms_tbl$Axis)) {
z_max <- max(emms_tbl$ymax[emms_tbl$Axis == "Z"], na.rm = TRUE)
} else {
z_max <- max(emms_tbl$ymax, na.rm = TRUE)
}
y_upper <- if (is.finite(z_max)) z_max * 1.08 else 1
y_lower <- 0
# Marker y-positions near the top; stagger in overlay to avoid overlap
if (nrow(mark_tbl) > 0) {
mark_facets <- mark_tbl %>% dplyr::mutate(star_y = y_upper * 0.97)
axis_offset <- c("X"=0.97, "Y"=0.94, "Z"=0.91)
mark_overlay <- mark_tbl %>% dplyr::mutate(star_y = y_upper * axis_offset[Axis])
} else {
mark_facets <- mark_overlay <- mark_tbl
}
# Faceted plot (fixed unified y-scale)
p_facets <- ggplot(emms_tbl, aes(x = Step, y = emmean)) +
geom_ribbon(aes(ymin = pmin(ymin, y_upper), ymax = pmin(ymax, y_upper)), alpha = 0.18) +
geom_line(size = 0.9) +
geom_point(size = 1.2) +
facet_wrap(~ Axis, nrow = 1, scales = "fixed") +
coord_cartesian(ylim = c(y_lower, y_upper), expand = FALSE) +
{
if (nrow(mark_facets) > 0)
geom_point(data = mark_facets,
aes(x = Step, y = star_y, color = dir),
shape = 8, size = 3.2, inherit.aes = FALSE)
} +
scale_color_manual(values = c(up = "red3", down = "dodgerblue3"),
name = "Adjacent change",
labels = c(up = "↑ next higher", down = "↓ next lower")) +
labs(title = paste0(block_title, " — Step-wise EMMs (±SD) — Correct trials (RT-adjusted @ mean rt_ms)"),
x = "Step", y = "EMM RMS") +
theme_classic() +
theme(legend.position = "bottom")
# Overlay plot (single panel; unified y-scale)
p_overlay <- ggplot(emms_tbl, aes(x = Step, y = emmean, color = Axis, fill = Axis, group = Axis)) +
geom_ribbon(aes(ymin = pmin(ymin, y_upper), ymax = pmin(ymax, y_upper)), alpha = 0.15, color = NA) +
geom_line(size = 0.9) +
geom_point(size = 1.2) +
{
if (nrow(mark_overlay) > 0)
geom_point(data = mark_overlay,
aes(x = Step, y = star_y, shape = dir),
size = 3.0, inherit.aes = FALSE, show.legend = TRUE)
} +
scale_shape_manual(values = c(up = 24, down = 25), name = "Adjacent change",
labels = c(up = "↑ next higher", down = "↓ next lower")) +
coord_cartesian(ylim = c(y_lower, y_upper), expand = FALSE) +
labs(title = paste0(block_title, " — Step-wise EMMs (±SD) — Correct trials (X/Y/Z overlaid, RT-adjusted @ mean rt_ms)"),
x = "Step", y = "EMM RMS") +
theme_classic() +
theme(legend.position = "bottom")
list(facets = p_facets, overlay = p_overlay)
}
# Length-specific wrapper for TEST blocks
.plot_block_len_sd_correct_rtms_unified <- function(df_block, title_prefix) {
.plot_block_stepwise_sd_correct_rtms_unified(df_block, title_prefix)
}
# ---- RENDER: TRAINING (Blocks 1–3) ----
res_b1_rtms <- .plot_block_stepwise_sd_correct_rtms_unified(stepwise_6_rt, "Block 1 (6 steps)")
res_b2_rtms <- .plot_block_stepwise_sd_correct_rtms_unified(stepwise_12_rt, "Block 2 (12 steps)")
res_b3_rtms <- .plot_block_stepwise_sd_correct_rtms_unified(stepwise_18_rt, "Block 3 (18 steps)")
if (!is.null(res_b1_rtms)) { print(res_b1_rtms$facets); print(res_b1_rtms$overlay) }
if (!is.null(res_b2_rtms)) { print(res_b2_rtms$facets); print(res_b2_rtms$overlay) }
if (!is.null(res_b3_rtms)) { print(res_b3_rtms$facets); print(res_b3_rtms$overlay) }
# ---- RENDER: TEST (Blocks 4–5) ----
# Block 4
res_b4_6_rtms <- .plot_block_len_sd_correct_rtms_unified(sw_b4_6_rt, "Block 4 — 6 steps")
res_b4_12_rtms <- .plot_block_len_sd_correct_rtms_unified(sw_b4_12_rt, "Block 4 — 12 steps")
res_b4_18_rtms <- .plot_block_len_sd_correct_rtms_unified(sw_b4_18_rt, "Block 4 — 18 steps")
if (!is.null(res_b4_6_rtms)) { print(res_b4_6_rtms$facets); print(res_b4_6_rtms$overlay) }
if (!is.null(res_b4_12_rtms)) { print(res_b4_12_rtms$facets); print(res_b4_12_rtms$overlay) }
if (!is.null(res_b4_18_rtms)) { print(res_b4_18_rtms$facets); print(res_b4_18_rtms$overlay) }
# Block 5
res_b5_6_rtms <- .plot_block_len_sd_correct_rtms_unified(sw_b5_6_rt, "Block 5 — 6 steps")
res_b5_12_rtms <- .plot_block_len_sd_correct_rtms_unified(sw_b5_12_rt, "Block 5 — 12 steps")
res_b5_18_rtms <- .plot_block_len_sd_correct_rtms_unified(sw_b5_18_rt, "Block 5 — 18 steps")
if (!is.null(res_b5_6_rtms)) { print(res_b5_6_rtms$facets); print(res_b5_6_rtms$overlay) }
if (!is.null(res_b5_12_rtms)) { print(res_b5_12_rtms$facets); print(res_b5_12_rtms$overlay) }
if (!is.null(res_b5_18_rtms)) { print(res_b5_18_rtms$facets); print(res_b5_18_rtms$overlay) }
# ---- 3.2d PLOTS (rt_ms): Overlay Block 4 vs Block 5, unified y-scale, adjacent-step markers ----
suppressPackageStartupMessages({
library(dplyr); library(ggplot2); library(lme4); library(emmeans); library(stringr)
})
emm_options(lmer.df = "asymptotic")
# Robust RHS-step parser (no look-behind)
.get_rhs_step <- function(x) {
nums <- stringr::str_extract_all(x, "\\d+")
vapply(nums, function(v) if (length(v) >= 2) as.integer(v[2]) else NA_integer_, integer(1))
}
# Compute EMMs (adjusted @ mean rt_ms), SD ribbons, and adjacent-step markers for ONE axis & ONE block
.compute_emm_sd_markers_rtms_cond <- function(df_axis, cond_label, alpha = 0.05) {
if (nrow(df_axis) == 0) return(NULL)
dd <- df_axis %>%
dplyr::filter(as.character(Accuracy) == "1") %>% # correct-only
dplyr::mutate(
StepF = factor(Step, levels = sort(unique(Step))),
subject = factor(subject),
trial_id = factor(trial_id)
)
if (nrow(dd) == 0 || nlevels(dd$StepF) < 2) return(NULL)
# Model for plotting: RMS ~ StepF + rt_ms + (1|subject) + (1|trial_id)
m <- suppressWarnings(lme4::lmer(RMS ~ StepF + rt_ms + (1|subject) + (1|trial_id), data = dd, REML = TRUE))
# EMMs per Step (rt_ms held at its mean)
em_df <- as.data.frame(emmeans::emmeans(m, ~ StepF)) %>%
dplyr::transmute(Step = as.numeric(as.character(StepF)), emmean = emmean, Condition = cond_label)
# SD per Step from raw (correct-only)
sd_df <- dd %>%
dplyr::group_by(StepF) %>%
dplyr::summarise(sd = stats::sd(RMS, na.rm = TRUE), .groups = "drop") %>%
dplyr::transmute(Step = as.numeric(as.character(StepF)), sd = sd, Condition = cond_label)
emms_tbl <- dplyr::left_join(em_df, sd_df, by = c("Step","Condition")) %>%
dplyr::mutate(ymin = pmax(0, emmean - sd),
ymax = emmean + sd)
# Adjacent-step contrasts (Holm)
cons_df <- as.data.frame(
emmeans::contrast(emmeans::emmeans(m, ~ StepF), method = "consec", adjust = "holm")
)
markers <- cons_df %>%
dplyr::mutate(
Step = .get_rhs_step(contrast),
dir = ifelse(estimate > 0, "up", "down"),
sig = !is.na(p.value) & p.value < alpha
) %>%
dplyr::filter(sig) %>%
dplyr::transmute(Step, dir, Condition = cond_label)
list(emms = emms_tbl, markers = markers)
}
# Build overlay (Block 4 vs 5) for a given sequence length subset pair
.plot_len_overlay_b4b5_rtms_unified <- function(df_b4, df_b5, title_prefix) {
axes_map <- c("x"="X","y"="Y","z"="Z")
out <- lapply(names(axes_map), function(ax) {
comp4 <- .compute_emm_sd_markers_rtms_cond(
df_b4 %>% dplyr::filter(Axis == ax),
cond_label = "Block 4 (Familiar)"
)
comp5 <- .compute_emm_sd_markers_rtms_cond(
df_b5 %>% dplyr::filter(Axis == ax),
cond_label = "Block 5 (Unfamiliar)"
)
lst <- list(comp4, comp5)
lst <- lst[!vapply(lst, is.null, logical(1))]
if (!length(lst)) return(NULL)
emms <- dplyr::bind_rows(lapply(lst, `[[`, "emms")) %>% dplyr::mutate(Axis = axes_map[[ax]])
marks <- dplyr::bind_rows(lapply(lst, `[[`, "markers")) %>% dplyr::mutate(Axis = axes_map[[ax]])
list(emms = emms, markers = marks)
})
out <- out[!vapply(out, is.null, logical(1))]
if (!length(out)) return(invisible(NULL))
emms_tbl <- dplyr::bind_rows(lapply(out, `[[`, "emms"))
mark_tbl <- dplyr::bind_rows(lapply(out, `[[`, "markers"))
# Unified y-limit driven by Z-axis max across BOTH blocks
if ("Z" %in% unique(emms_tbl$Axis)) {
z_max <- max(emms_tbl$ymax[emms_tbl$Axis == "Z"], na.rm = TRUE)
} else {
z_max <- max(emms_tbl$ymax, na.rm = TRUE)
}
y_upper <- if (is.finite(z_max)) z_max * 1.08 else 1
y_lower <- 0
# Marker positions: stagger by Condition to avoid overlap
if (nrow(mark_tbl) > 0) {
cond_off <- c("Block 4 (Familiar)" = 0.97, "Block 5 (Unfamiliar)" = 0.94)
mark_facets <- mark_tbl %>% dplyr::mutate(star_y = y_upper * cond_off[Condition])
} else {
mark_facets <- mark_tbl
}
# Faceted by Axis; overlay both blocks; unified y-scale
p_facets <- ggplot(emms_tbl, aes(x = Step, y = emmean, color = Condition, fill = Condition, group = Condition)) +
geom_ribbon(aes(ymin = pmin(ymin, y_upper), ymax = pmin(ymax, y_upper)), alpha = 0.15, color = NA) +
geom_line(size = 0.9) +
geom_point(size = 1.2) +
facet_wrap(~ Axis, nrow = 1, scales = "fixed") +
coord_cartesian(ylim = c(y_lower, y_upper), expand = FALSE) +
{
if (nrow(mark_facets) > 0)
geom_point(data = mark_facets,
aes(x = Step, y = star_y, shape = dir, color = Condition),
size = 3.0, inherit.aes = FALSE, show.legend = TRUE)
} +
scale_shape_manual(values = c(up = 24, down = 25),
name = "Adjacent change",
labels = c(up = "↑ next higher", down = "↓ next lower")) +
labs(title = paste0(title_prefix, " — Step-wise EMMs (±SD) — Correct trials, RT-adjusted @ mean rt_ms"),
x = "Step", y = "EMM RMS", color = NULL, fill = NULL) +
theme_classic() +
theme(legend.position = "bottom")
p_facets
}
# ---- RENDER: Block 4 vs Block 5 overlays ----
# 6 steps
p_b45_6 <- .plot_len_overlay_b4b5_rtms_unified(sw_b4_6_rt, sw_b5_6_rt, "6 steps (B4 vs B5)")
# 12 steps
p_b45_12 <- .plot_len_overlay_b4b5_rtms_unified(sw_b4_12_rt, sw_b5_12_rt, "12 steps (B4 vs B5)")
# 18 steps
p_b45_18 <- .plot_len_overlay_b4b5_rtms_unified(sw_b4_18_rt, sw_b5_18_rt, "18 steps (B4 vs B5)")
if (!is.null(p_b45_6)) print(p_b45_6)
if (!is.null(p_b45_12)) print(p_b45_12)
if (!is.null(p_b45_18)) print(p_b45_18)