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
)Group CoM Analysis 3
The used data here is mixed: the training blocks are cleaned of all trials that had a accuracy <0.8 and also the trials with xsens errors are deleted. The Test-Blocks (4 & 5) involve all trials except the ones with xsens errors.
#Root mean square
#Root mean square (RMS) of acceleration is an often-used value in gait analysis research to quantify the magnitude of body segment accelerations(Menz et al., 2003; Mizuike et al., 2009; Sekine et al., 2013; Senden et al., 2012). RMS can be easily computed with the raw accelerometer data and is seen as an uncomplicated approach to analyse the magnitude of accelerations in each axis(Mizuike et al., 2009; Sekine et al., 2013)). Although this study does not directly analyses gait performance, the movements performed in the ds-dsp task resemble walking movements and therefore it is seen as a suitable approach for the following analysis. In the present study, RMS of the center of mass acceleration is used to evaluate the movement characteristics across task phases and sequence lengths, providing insights into movement control and paired with its standard deviation movement variability.# -------- Step-Level Step Counts --------
step_counts <- tibble(
Block = c(1, 2, 3, 4, 5),
Steps = c(6, 12, 18, 18, 18)
)
# -------- Assign Steps Helper Function --------
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 Function (26 or 25 as end marker) --------
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))
}# Load Data
mixed_files <- list.files("/Users/can/Documents/Uni/Thesis/Data/Xsens/cleaned_csv/merged/Cleaned", pattern = "_mixed\\.csv$", full.names = TRUE)
all_data_mixed <- map_dfr(mixed_files, read_csv)
# Tag trial phases once
tagged_data <- tag_trial_phases(all_data_mixed) %>% mutate(DataType = "Mixed")
tagged_data2 <- tag_trial_phases(all_data_mixed) %>% mutate(DataType = "Mixed")# Compute RMS Function
compute_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"
) %>%
group_by(subject, Block, phase) %>%
arrange(trial) %>%
mutate(TrialInBlock = row_number()) %>%
ungroup()
}# Compute RMS per trial and phase (used throughout)
rms_data <- compute_rms(tagged_data) %>%
mutate(DataType = "Mixed")
group_rms_summary <- rms_data %>%
group_by(Block, TrialInBlock, phase) %>%
summarise(
mean_rms_x = mean(rms_x, na.rm = TRUE),
se_rms_x = sd(rms_x, na.rm = TRUE) / sqrt(n()),
mean_rms_y = mean(rms_y, na.rm = TRUE),
se_rms_y = sd(rms_y, na.rm = TRUE) / sqrt(n()),
mean_rms_z = mean(rms_z, na.rm = TRUE),
se_rms_z = sd(rms_z, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)1 Acceleration in Blocks and phases
#1.1.1 RMS Acceleration Box Plots - Execution
# ----- Execution Phase RMS Boxplots -----
exec_data <- rms_data %>% filter(phase == "Execution")
for (axis in c("x", "y", "z")) {
axis_col <- paste0("rms_", axis)
gg <- 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", color = "black") +
ylim(0, 2.5) +
labs(
title = paste("Execution Phase: CoM Acceleration RMS -", toupper(axis), "Axis"),
x = "Block",
y = "RMS Acceleration"
) +
theme_minimal() +
theme(text = element_text(size = 12), legend.position = "none")
print(gg)
}Warning: Removed 3 rows containing non-finite outside the scale range
(`stat_boxplot()`).Warning: Removed 3 rows containing missing values or values outside the scale range
(`geom_point()`).
Warning: Removed 16 rows containing non-finite outside the scale range
(`stat_boxplot()`).Warning: Removed 16 rows containing missing values or values outside the scale range
(`geom_point()`).
Warning: Removed 175 rows containing non-finite outside the scale range
(`stat_boxplot()`).Warning: Removed 175 rows containing missing values or values outside the scale range
(`geom_point()`).
#1.1.2 RMS Acceleration Box Plots - Preparation
# ----- Preparation Phase RMS Boxplots -----
# Extract 1500ms Preparation Window
prep_window_ms <- 1500
extract_preparation_phase <- function(df) {
df %>%
group_split(subject, Block, trial) %>%
map_dfr(function(trial_df) {
exec_start_row <- which(trial_df$Marker.Text == 27)[1]
if (!is.na(exec_start_row) && exec_start_row > 1) {
exec_start_ms <- trial_df$ms[exec_start_row]
trial_df %>%
filter(ms >= (exec_start_ms - prep_window_ms) & ms < exec_start_ms) %>%
mutate(phase = "Preparation")
} else {
NULL
}
})
}
prep_data <- extract_preparation_phase(tagged_data)
# Compute preparation phase RMS
prep_rms <- prep_data %>%
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"
)
# Plot preparation boxplots
for (axis in c("x", "y", "z")) {
axis_col <- paste0("rms_", axis)
fill_color <- switch(axis,
"x" = "skyblue",
"y" = "salmon",
"z" = "seagreen")
gg <- ggplot(prep_rms, aes(x = factor(Block), y = .data[[axis_col]])) +
geom_boxplot(fill = fill_color, alpha = 0.7, outlier.shape = NA) +
geom_jitter(width = 0.2, alpha = 0.4, size = 0.6) +
geom_vline(xintercept = 3.5, linetype = "dashed", color = "black") +
ylim(0, 0.5) +
labs(
title = paste("Preparation Phase: CoM RMS -", toupper(axis), "Axis"),
x = "Block",
y = "RMS Acceleration"
) +
theme_minimal()
print(gg)
}Warning: Removed 159 rows containing non-finite outside the scale range
(`stat_boxplot()`).Warning: Removed 159 rows containing missing values or values outside the scale range
(`geom_point()`).
Warning: Removed 159 rows containing non-finite outside the scale range
(`stat_boxplot()`).
Removed 159 rows containing missing values or values outside the scale range
(`geom_point()`).
Warning: Removed 217 rows containing non-finite outside the scale range
(`stat_boxplot()`).Warning: Removed 217 rows containing missing values or values outside the scale range
(`geom_point()`).
#1.2.1 LMM to assess whether block and phase significantly influence rms (per axis)
# --- Function: Run Random Intercept LMMs and Extract ANOVA P-Values ---
extract_rms_interceptonly_pvalues <- function(data, label) {
# Assume data is already tagged
rms_data <- compute_rms(data) %>%
mutate(Block = factor(Block), subject = factor(subject))
axes <- c("x", "y", "z")
results <- map_dfr(axes, function(axis) {
formula <- as.formula(paste0("rms_", axis, " ~ Block * phase + (1 | subject) + (1 | TrialInBlock)"))
model <- lmer(formula, data = rms_data, REML = FALSE)
anova_tbl <- anova(model)
tibble(
Dataset = label,
Axis = toupper(axis),
`Block p-value` = anova_tbl["Block", "Pr(>F)"],
`Phase p-value` = anova_tbl["phase", "Pr(>F)"],
`Interaction p-value` = anova_tbl["Block:phase", "Pr(>F)"]
)
})
return(results)
}
# --- Run Model on Mixed Data (Tagged Once) ---
interceptonly_pvals <- extract_rms_interceptonly_pvalues(tagged_data, "Mixed")boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')# --- Display Results ---
print(interceptonly_pvals)# A tibble: 3 × 5
Dataset Axis `Block p-value` `Phase p-value` `Interaction p-value`
<chr> <chr> <dbl> <dbl> <dbl>
1 Mixed X 2.09e-33 0 8.06e-76
2 Mixed Y 5.37e-32 0 6.63e-61
3 Mixed Z 2.30e-25 0 1.28e-43#results:
#block p-value <0.05 :This suggests learning or adaptation effects across blocks
#phase p-value 0 because it is either execution or preparation
#interaction <0.05 :this suggest the effect of block is different depending on phase
#clean vs unclean dataset: unclean p-values< clean p-values: probably because of more variability# --- Extended Function: Run Random Intercept LMMs and Extract All Outputs ---
extract_rms_intercept_model_diagnostics <- function(data, label) {
rms_data <- compute_rms(data) %>%
mutate(Block = factor(Block), subject = factor(subject))
axes <- c("x", "y", "z")
results <- list()
for (axis in axes) {
formula <- as.formula(paste0("rms_", axis, " ~ Block * phase + (1 | subject) + (1 | TrialInBlock)"))
model <- lmer(formula, data = rms_data, REML = FALSE)
# Store everything
key <- paste0(label, "_", toupper(axis))
results[[key]] <- list(
anova = anova(model),
emmeans = emmeans(model, ~ Block * phase),
fixed_effects = fixef(model),
random_effects = ranef(model),
scaled_residuals = resid(model, scaled = TRUE),
model = model # include model object in case you want to inspect further
)
}
return(results)
}
# --- Run and Store Full Diagnostics ---
intercept_diagnostics <- extract_rms_intercept_model_diagnostics(tagged_data, "Mixed")boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')cat("\n=== Axis X ===\n")
=== Axis X ===print(intercept_diagnostics$Mixed_X$anova)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 10.49 2.62 4 6521.7 40.303 < 2.2e-16 ***
phase 573.66 573.66 1 6521.0 8815.596 < 2.2e-16 ***
Block:phase 23.82 5.95 4 6521.0 91.511 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(intercept_diagnostics$Mixed_X$emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 0.8548 0.0338 22.8 0.78477 0.925
2 Execution 0.7951 0.0341 23.4 0.72470 0.866
3 Execution 0.6665 0.0345 24.7 0.59539 0.738
4 Execution 0.7384 0.0335 21.9 0.66885 0.808
5 Execution 0.5848 0.0335 21.9 0.51530 0.654
1 Preparation 0.0653 0.0338 22.6 -0.00462 0.135
2 Preparation 0.1178 0.0340 23.4 0.04746 0.188
3 Preparation 0.1670 0.0345 24.6 0.09596 0.238
4 Preparation 0.1246 0.0335 21.9 0.05505 0.194
5 Preparation 0.1266 0.0335 21.8 0.05716 0.196
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(intercept_diagnostics$Mixed_X$fixed_effects) (Intercept) Block2 Block3
0.85480165 -0.05969952 -0.18831819
Block4 Block5 phasePreparation
-0.11640071 -0.26998999 -0.78950868
Block2:phasePreparation Block3:phasePreparation Block4:phasePreparation
0.11223106 0.29001003 0.17566536
Block5:phasePreparation
0.33133074 print(intercept_diagnostics$Mixed_X$random_effects)$TrialInBlock
(Intercept)
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 0
36 0
37 0
38 0
39 0
40 0
41 0
42 0
43 0
44 0
45 0
46 0
47 0
$subject
(Intercept)
2 0.084959638
3 0.009364058
4 -0.080528746
5 -0.168694714
7 -0.048952044
8 0.054209259
10 0.363123135
11 0.234222092
13 -0.064380022
14 0.068621634
15 -0.055710537
16 -0.013565294
17 -0.107231929
18 -0.022325251
19 -0.152035277
20 -0.114690560
22 -0.094722921
23 0.108337479
with conditional variances for "TrialInBlock" "subject" print(head(intercept_diagnostics$Mixed_X$scaled_residuals)) 1 2 3 4 5 6
-0.6855627 -0.7206152 0.9381860 -0.8704046 -0.4171842 -0.7726668 cat("\n=== Axis Y ===\n")
=== Axis Y ===print(intercept_diagnostics$Mixed_Y$anova)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 14.13 3.53 4 6521.7 38.620 < 2.2e-16 ***
phase 640.85 640.85 1 6521.0 7007.730 < 2.2e-16 ***
Block:phase 26.84 6.71 4 6521.0 73.362 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(intercept_diagnostics$Mixed_Y$emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 0.902 0.0385 23.1 0.8225 0.982
2 Execution 0.844 0.0388 23.9 0.7636 0.924
3 Execution 0.696 0.0393 25.3 0.6154 0.777
4 Execution 0.769 0.0381 22.2 0.6902 0.848
5 Execution 0.608 0.0381 22.1 0.5293 0.687
1 Preparation 0.064 0.0384 22.9 -0.0155 0.144
2 Preparation 0.129 0.0388 23.8 0.0493 0.209
3 Preparation 0.173 0.0393 25.1 0.0924 0.254
4 Preparation 0.121 0.0381 22.1 0.0422 0.200
5 Preparation 0.121 0.0381 22.1 0.0417 0.200
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(intercept_diagnostics$Mixed_Y$fixed_effects) (Intercept) Block2 Block3
0.90214276 -0.05842377 -0.20575453
Block4 Block5 phasePreparation
-0.13285794 -0.29386277 -0.83811781
Block2:phasePreparation Block3:phasePreparation Block4:phasePreparation
0.12373927 0.31502104 0.19003310
Block5:phasePreparation
0.35047549 print(intercept_diagnostics$Mixed_Y$random_effects)$TrialInBlock
(Intercept)
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 0
36 0
37 0
38 0
39 0
40 0
41 0
42 0
43 0
44 0
45 0
46 0
47 0
$subject
(Intercept)
2 0.07607719
3 0.06014675
4 -0.12497919
5 -0.19130128
7 -0.05470047
8 0.12826081
10 0.38400762
11 0.24920485
13 -0.04855387
14 0.03922394
15 -0.07320697
16 -0.01721857
17 -0.09868179
18 -0.03594357
19 -0.18125383
20 -0.13068631
22 -0.13748388
23 0.15708857
with conditional variances for "TrialInBlock" "subject" print(head(intercept_diagnostics$Mixed_Y$scaled_residuals)) 1 2 3 4 5 6
-0.5318674 -0.5359392 0.3359262 -0.3116383 -0.4810701 -0.5891674 cat("\n=== Axis Z ===\n")
=== Axis Z ===print(intercept_diagnostics$Mixed_Z$anova)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 29.25 7.31 4 6521.7 30.709 < 2.2e-16 ***
phase 1874.32 1874.32 1 6521.0 7871.441 < 2.2e-16 ***
Block:phase 50.03 12.51 4 6521.0 52.528 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(intercept_diagnostics$Mixed_Z$emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 1.4005 0.0637 22.9 1.2686 1.532
2 Execution 1.3897 0.0642 23.6 1.2572 1.522
3 Execution 1.1908 0.0650 24.9 1.0569 1.325
4 Execution 1.2873 0.0631 22.0 1.1564 1.418
5 Execution 1.0224 0.0631 22.0 0.8916 1.153
1 Preparation 0.0654 0.0636 22.7 -0.0662 0.197
2 Preparation 0.1707 0.0641 23.5 0.0382 0.303
3 Preparation 0.2442 0.0649 24.8 0.1104 0.378
4 Preparation 0.1603 0.0631 21.9 0.0295 0.291
5 Preparation 0.1581 0.0630 21.9 0.0274 0.289
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(intercept_diagnostics$Mixed_Z$fixed_effects) (Intercept) Block2 Block3
1.40045721 -0.01071856 -0.20969227
Block4 Block5 phasePreparation
-0.11316313 -0.37806730 -1.33509017
Block2:phasePreparation Block3:phasePreparation Block4:phasePreparation
0.11606228 0.38849177 0.20809330
Block5:phasePreparation
0.47083282 print(intercept_diagnostics$Mixed_Z$random_effects)$TrialInBlock
(Intercept)
1 -4.672739e-18
2 -3.785541e-18
3 -2.639675e-18
4 -3.196648e-18
5 -2.018497e-18
6 -1.254622e-18
7 -1.531263e-18
8 -1.534912e-18
9 -9.670340e-19
10 -8.655370e-19
11 -1.800131e-18
12 -1.742469e-18
13 -1.414453e-18
14 7.297968e-19
15 -1.288192e-18
16 1.223828e-19
17 -8.045113e-20
18 1.364653e-18
19 8.512410e-19
20 7.695664e-19
21 7.388552e-19
22 6.277646e-19
23 2.888206e-18
24 1.126255e-18
25 8.512645e-19
26 1.405709e-18
27 4.969577e-19
28 4.371513e-19
29 1.793159e-18
30 7.012849e-19
31 2.616306e-18
32 1.360328e-18
33 5.462224e-19
34 2.275377e-18
35 1.018464e-18
36 -3.964937e-19
37 6.194290e-19
38 1.006329e-19
39 3.669487e-19
40 -1.097710e-18
41 2.495015e-19
42 7.556181e-19
43 4.742588e-20
44 1.341924e-18
45 1.992059e-18
46 2.020313e-18
47 7.156944e-20
$subject
(Intercept)
2 -0.06278903
3 0.11146166
4 -0.16036249
5 -0.33042021
7 0.03383043
8 0.25270668
10 0.57795953
11 0.40688787
13 -0.01408191
14 -0.01304987
15 -0.09268749
16 0.10293051
17 -0.25400560
18 0.05092086
19 -0.30976159
20 -0.22418418
22 -0.32333051
23 0.24797532
with conditional variances for "TrialInBlock" "subject" print(head(intercept_diagnostics$Mixed_Z$scaled_residuals)) 1 2 3 4 5 6
-0.009895962 -0.166656384 0.182396834 -0.682980337 -0.517337694 -0.905689179 #1.2.2 Random slope model to assess whether block and phase significantly influence rms (per axis)
# --- Function to Run Random Slope LMMs and Extract ANOVA P-Values ---
extract_rms_randomslope_pvalues <- function(tagged_df, label) {
# Compute RMS and prepare data
rms_data <- compute_rms(tagged_df) %>%
mutate(Block = factor(Block), subject = factor(subject))
# Axes to iterate over
axes <- c("x", "y", "z")
# Fit models and extract p-values in loop
map_dfr(axes, function(axis) {
formula <- as.formula(paste0("rms_", axis, " ~ Block * phase + (1 + Block | subject) + (1 | TrialInBlock)"))
model <- lmer(formula, data = rms_data, REML = FALSE)
aov_tbl <- anova(model)
tibble(
Dataset = label,
Axis = toupper(axis),
`Block p-value` = aov_tbl["Block", "Pr(>F)"],
`Phase p-value` = aov_tbl["phase", "Pr(>F)"],
`Interaction p-value` = aov_tbl["Block:phase", "Pr(>F)"]
)
})
}
# Run Random Slope LMMs for tagged and cleaned "Mixed" dataset
randomslope_pvals_mixed <- extract_rms_randomslope_pvalues(tagged_data, "Mixed")boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')# View results
print(randomslope_pvals_mixed)# A tibble: 3 × 5
Dataset Axis `Block p-value` `Phase p-value` `Interaction p-value`
<chr> <chr> <dbl> <dbl> <dbl>
1 Mixed X 0.0291 0 1.72e-81
2 Mixed Y 0.0497 0 1.54e-66
3 Mixed Z 0.0799 0 5.21e-47#compared to the first model this also includes a random slope for Block within subjects
#results:
#block p-value >0.05 (z - axis) :This suggests no learning or adaptation effects across blocks after accounting for between subject
#block p-value <0.05 (x & y axis) :This suggests learning or adaptation effects across blocks after accounting for between subject variation
#phase p-value 0 :because it is either execution or preparation
#interaction <0.05 :this suggest the effect of block is different depending on phase
#clean vs unclean dataset: unclean p-values< clean p-values: probably because of more variability# --- Extended: Run Random Slope LMMs + Extract Diagnostics per Axis ---
extract_rms_randomslope_model_diagnostics <- function(tagged_df, label) {
# Compute RMS and prepare data
rms_data <- compute_rms(tagged_df) %>%
mutate(Block = factor(Block), subject = factor(subject))
axes <- c("x", "y", "z")
results <- list()
for (axis in axes) {
formula <- as.formula(paste0("rms_", axis, " ~ Block * phase + (1 + Block | subject) + (1 | TrialInBlock)"))
model <- lmer(formula, data = rms_data, REML = FALSE)
key <- paste0(label, "_", toupper(axis))
results[[key]] <- list(
anova = anova(model),
emmeans = emmeans(model, ~ Block * phase),
fixed_effects = fixef(model),
random_effects = ranef(model),
scaled_residuals = resid(model, scaled = TRUE),
model = model
)
}
return(results)
}
# --- Run Full Diagnostic Extraction ---
randomslope_diagnostics <- extract_rms_randomslope_model_diagnostics(tagged_data, "Mixed")boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')Warning: Model failed to converge with 1 negative eigenvalue: -2.8e+02boundary (singular) fit: see help('isSingular')
boundary (singular) fit: see help('isSingular')# --- Example: Output for Axis X ---
cat("\n=== RANDOM SLOPE MODEL: Axis X ===\n")
=== RANDOM SLOPE MODEL: Axis X ===print(randomslope_diagnostics$Mixed_X$anova)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 0.84 0.21 4 18.2 3.4446 0.0291 *
phase 573.74 573.74 1 6449.7 9439.2430 <2e-16 ***
Block:phase 23.95 5.99 4 6449.6 98.4879 <2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(randomslope_diagnostics$Mixed_X$emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 0.8581 0.0462 20.1 0.7617 0.954
2 Execution 0.7961 0.0458 20.2 0.7006 0.892
3 Execution 0.6647 0.0337 21.7 0.5948 0.735
4 Execution 0.7388 0.0372 20.3 0.6612 0.816
5 Execution 0.5846 0.0233 22.2 0.5363 0.633
1 Preparation 0.0679 0.0462 20.0 -0.0284 0.164
2 Preparation 0.1189 0.0458 20.2 0.0235 0.214
3 Preparation 0.1654 0.0336 21.6 0.0956 0.235
4 Preparation 0.1246 0.0372 20.2 0.0471 0.202
5 Preparation 0.1268 0.0233 22.1 0.0785 0.175
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(randomslope_diagnostics$Mixed_X$fixed_effects) (Intercept) Block2 Block3
0.85807572 -0.06199209 -0.19338294
Block4 Block5 phasePreparation
-0.11932482 -0.27343346 -0.79017599
Block2:phasePreparation Block3:phasePreparation Block4:phasePreparation
0.11300841 0.29089739 0.17599842
Block5:phasePreparation
0.33233393 print(randomslope_diagnostics$Mixed_X$random_effects)$TrialInBlock
(Intercept)
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 0
36 0
37 0
38 0
39 0
40 0
41 0
42 0
43 0
44 0
45 0
46 0
47 0
$subject
(Intercept) Block2 Block3 Block4 Block5
2 -0.002130586 0.14017493 0.148701112 0.08993780 0.060847144
3 0.210340137 -0.19493023 -0.262292535 -0.27672991 -0.248451306
4 -0.085446616 -0.05020446 -0.016821648 -0.01101010 0.060940174
5 -0.114092690 -0.06979523 -0.052808530 -0.09701340 -0.035909232
7 -0.082545142 0.03209474 0.069011823 0.06584268 0.008289092
8 0.024054267 0.03420203 0.004816552 0.05241983 0.044426694
10 0.320462541 0.17465061 0.053780616 0.09681161 -0.085178938
11 0.419658078 -0.04906292 -0.228674348 -0.17200762 -0.340715848
13 -0.104908274 -0.01293570 0.032276596 0.08150349 0.073389640
14 0.121877414 -0.04187897 -0.085279110 -0.08689654 -0.062858733
15 -0.119542720 0.01146563 0.051490105 0.01877142 0.183808910
16 -0.102998188 0.06985160 0.128249919 0.16846852 0.071799365
17 -0.191048839 0.04562005 0.120572790 0.08978650 0.150921427
18 -0.098169526 0.06185725 0.127720268 0.13486697 0.066073017
19 -0.198929688 0.01751672 0.078972777 0.05765067 0.087451508
20 -0.125914157 -0.01192369 0.014789375 -0.02058285 0.060390331
22 -0.155593412 0.02648825 0.076921180 0.05137631 0.128263021
23 0.284927402 -0.18319063 -0.261426942 -0.24319536 -0.223486265
with conditional variances for "TrialInBlock" "subject" print(head(randomslope_diagnostics$Mixed_X$scaled_residuals)) 1 2 3 4 5 6
-0.7209621 -0.6398466 0.1449765 -0.5947548 -0.1244542 -0.6062213 cat("\n=== RANDOM SLOPE MODEL: Axis Y ===\n")
=== RANDOM SLOPE MODEL: Axis Y ===print(randomslope_diagnostics$Mixed_Y$anova)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 0.99 0.25 4 17.9 2.9365 0.04966 *
phase 641.04 641.04 1 6451.0 7581.0440 < 2e-16 ***
Block:phase 27.13 6.78 4 6451.0 80.2193 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(randomslope_diagnostics$Mixed_Y$emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 0.9082 0.0583 19.9 0.7866 1.030
2 Execution 0.8494 0.0544 20.2 0.7359 0.963
3 Execution 0.6935 0.0349 22.0 0.6211 0.766
4 Execution 0.7690 0.0421 20.2 0.6813 0.857
5 Execution 0.6079 0.0242 23.3 0.5579 0.658
1 Preparation 0.0681 0.0582 19.8 -0.0534 0.190
2 Preparation 0.1356 0.0544 20.2 0.0221 0.249
3 Preparation 0.1709 0.0349 21.8 0.0985 0.243
4 Preparation 0.1209 0.0421 20.1 0.0332 0.209
5 Preparation 0.1207 0.0242 23.1 0.0707 0.171
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(randomslope_diagnostics$Mixed_Y$fixed_effects) (Intercept) Block2 Block3
0.90817438 -0.05879782 -0.21466453
Block4 Block5 phasePreparation
-0.13912680 -0.30022914 -0.84004818
Block2:phasePreparation Block3:phasePreparation Block4:phasePreparation
0.12622211 0.31739871 0.19188449
Block5:phasePreparation
0.35280793 print(randomslope_diagnostics$Mixed_Y$random_effects)$TrialInBlock
(Intercept)
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 0
36 0
37 0
38 0
39 0
40 0
41 0
42 0
43 0
44 0
45 0
46 0
47 0
$subject
(Intercept) Block2 Block3 Block4 Block5
2 -0.08078052 0.181935400 0.21041561 0.18173095 0.17466746
3 0.04556358 0.030170428 0.03304694 0.02365688 -0.01363942
4 -0.18623694 -0.016290519 0.08655674 0.04621311 0.15558167
5 -0.22381098 -0.006486568 0.06116852 0.01923111 0.08840549
7 -0.10910846 0.025773837 0.06166384 0.03566723 0.11834885
8 0.14429595 0.083351323 -0.04796118 0.01425454 -0.09529695
10 0.44806386 0.019942566 -0.08193563 -0.01763671 -0.22373269
11 0.49539636 0.049356876 -0.36602737 -0.19090847 -0.52891486
13 0.08060563 -0.171227191 -0.19367681 -0.17094475 -0.11370305
14 0.13504171 -0.098395040 -0.14268455 -0.12321746 -0.13346534
15 -0.12719732 -0.045048083 0.07748791 0.03302485 0.16035012
16 -0.12430409 0.075884559 0.14903445 0.12545229 0.16456399
17 -0.24725183 0.127249131 0.20977745 0.16258130 0.22582123
18 -0.08917553 0.020109577 0.12522703 0.07813967 0.06961063
19 -0.21076853 0.022316268 0.04029086 0.01720718 0.06758009
20 -0.15632996 0.044182478 0.04304839 0.02083403 0.03819070
22 -0.20436428 0.004752615 0.08691285 0.04679237 0.15696899
23 0.41036134 -0.347577658 -0.35234508 -0.30207813 -0.31133692
with conditional variances for "TrialInBlock" "subject" print(head(randomslope_diagnostics$Mixed_Y$scaled_residuals)) 1 2 3 4 5 6
-0.6375648 -0.6188274 0.3853927 -0.3544758 -0.5304047 -0.5144974 cat("\n=== RANDOM SLOPE MODEL: Axis Z ===\n")
=== RANDOM SLOPE MODEL: Axis Z ===print(randomslope_diagnostics$Mixed_Z$anova)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 2.22 0.55 4 18.1 2.4874 0.07987 .
phase 1874.02 1874.02 1 6450.9 8416.9276 < 2e-16 ***
Block:phase 50.41 12.60 4 6450.9 56.6080 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(randomslope_diagnostics$Mixed_Z$emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 1.4106 0.0868 20.0 1.22941 1.592
2 Execution 1.3960 0.0880 20.2 1.21265 1.579
3 Execution 1.1816 0.0663 21.6 1.04381 1.319
4 Execution 1.2868 0.0665 20.4 1.14826 1.425
5 Execution 1.0219 0.0506 21.4 0.91673 1.127
1 Preparation 0.0737 0.0867 19.9 -0.10723 0.255
2 Preparation 0.1777 0.0879 20.1 -0.00565 0.361
3 Preparation 0.2358 0.0663 21.5 0.09815 0.373
4 Preparation 0.1597 0.0665 20.4 0.02121 0.298
5 Preparation 0.1582 0.0506 21.4 0.05313 0.263
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(randomslope_diagnostics$Mixed_Z$fixed_effects) (Intercept) Block2 Block3
1.41055951 -0.01451272 -0.22900373
Block4 Block5 phasePreparation
-0.12374563 -0.38869059 -1.33681079
Block2:phasePreparation Block3:phasePreparation Block4:phasePreparation
0.11844394 0.39103417 0.20968663
Block5:phasePreparation
0.47312389 print(randomslope_diagnostics$Mixed_Z$random_effects)$TrialInBlock
(Intercept)
1 0
2 0
3 0
4 0
5 0
6 0
7 0
8 0
9 0
10 0
11 0
12 0
13 0
14 0
15 0
16 0
17 0
18 0
19 0
20 0
21 0
22 0
23 0
24 0
25 0
26 0
27 0
28 0
29 0
30 0
31 0
32 0
33 0
34 0
35 0
36 0
37 0
38 0
39 0
40 0
41 0
42 0
43 0
44 0
45 0
46 0
47 0
$subject
(Intercept) Block2 Block3 Block4 Block5
2 -0.304860092 0.232026688 0.34789249 0.26744037 0.32438026
3 0.335394305 -0.215269615 -0.27735356 -0.26123632 -0.33676956
4 -0.163414581 -0.112151047 -0.04214627 -0.03622428 0.12262390
5 -0.318903056 -0.028105370 0.00237320 -0.01340532 -0.01312176
7 -0.008331758 0.024204490 0.10292948 0.09537122 -0.01174119
8 0.236457901 0.161103937 -0.03235719 0.04204253 -0.04791900
10 0.480200692 0.202724692 0.16116345 0.10935513 0.03266552
11 0.843843879 0.008190312 -0.60477913 -0.43901462 -0.82331915
13 0.116667046 -0.237355164 -0.27024477 -0.21975774 0.01838803
14 -0.027545810 -0.007698254 0.07932719 0.04880952 -0.03417139
15 -0.139398643 -0.100411511 -0.04265057 -0.05971883 0.31589393
16 -0.080901993 0.246104730 0.25249949 0.22399302 0.18511139
17 -0.445123398 0.102517498 0.29627519 0.23720892 0.30187712
18 -0.076939097 0.093506386 0.33161479 0.24947682 0.04223143
19 -0.310712178 -0.034224562 -0.01156170 -0.01691626 0.03859358
20 -0.253668742 -0.009628290 0.02563412 0.01478410 0.08087746
22 -0.391165577 -0.021001007 0.05325682 0.03750079 0.18759053
23 0.508401103 -0.304533911 -0.37187302 -0.27970905 -0.38319112
with conditional variances for "TrialInBlock" "subject" print(head(randomslope_diagnostics$Mixed_Z$scaled_residuals)) 1 2 3 4 5 6
-0.06271144 -0.34689028 -0.30371480 -0.62623216 -0.45466235 -0.69638425 #1.2.3 Model: CoM RMS Acceleration changes over time
# --- Optimized: Extract p-values from RMS learning LMMs (TrialInBlock * Block * Phase) ---
extract_learning_pvalues <- function(df, label) {
rms_df <- compute_rms(df) %>%
mutate(
Block = factor(Block),
subject = factor(subject),
phase = factor(phase)
)
fit_model_and_anova <- function(axis) {
model <- lmer(as.formula(paste0("rms_", axis, " ~ TrialInBlock * Block * phase + (1 + TrialInBlock | subject)")),
data = rms_df)
anova(model)
}
an_x <- fit_model_and_anova("x")
an_y <- fit_model_and_anova("y")
an_z <- fit_model_and_anova("z")
tibble(
Dataset = label,
Axis = c("X", "Y", "Z"),
`TrialInBlock p-value` = c(an_x["TrialInBlock", "Pr(>F)"], an_y["TrialInBlock", "Pr(>F)"], an_z["TrialInBlock", "Pr(>F)"]),
`Block p-value` = c(an_x["Block", "Pr(>F)"], an_y["Block", "Pr(>F)"], an_z["Block", "Pr(>F)"]),
`Phase p-value` = c(an_x["phase", "Pr(>F)"], an_y["phase", "Pr(>F)"], an_z["phase", "Pr(>F)"]),
`TrialInBlock:Block p` = c(an_x["TrialInBlock:Block", "Pr(>F)"], an_y["TrialInBlock:Block", "Pr(>F)"], an_z["TrialInBlock:Block", "Pr(>F)"]),
`TrialInBlock:Phase p` = c(an_x["TrialInBlock:phase", "Pr(>F)"], an_y["TrialInBlock:phase", "Pr(>F)"], an_z["TrialInBlock:phase", "Pr(>F)"]),
`Block:Phase p` = c(an_x["Block:phase", "Pr(>F)"], an_y["Block:phase", "Pr(>F)"], an_z["Block:phase", "Pr(>F)"]),
`3-way p-value` = c(an_x["TrialInBlock:Block:phase", "Pr(>F)"],
an_y["TrialInBlock:Block:phase", "Pr(>F)"],
an_z["TrialInBlock:Block:phase", "Pr(>F)"])
)
}
# Use pre-tagged data (tagged_data) instead of tagging again
learning_pvals_mixed <- extract_learning_pvalues(tagged_data, "Mixed")Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
unable to evaluate scaled gradientWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge: degenerate Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -3.1e+00Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.886971 (tol = 0.002, component 1)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.345604 (tol = 0.002, component 1)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?# Show result
print(learning_pvals_mixed)# A tibble: 3 × 9
Dataset Axis `TrialInBlock p-value` `Block p-value` `Phase p-value`
<chr> <chr> <dbl> <dbl> <dbl>
1 Mixed X 0.537 2.48e-25 0
2 Mixed Y 0.504 2.30e-15 0
3 Mixed Z 0.0791 1.87e-14 0
# ℹ 4 more variables: `TrialInBlock:Block p` <dbl>,
# `TrialInBlock:Phase p` <dbl>, `Block:Phase p` <dbl>, `3-way p-value` <dbl>#results:
#trial in block p-value >0.05 : Participants do not change rms within block
#block p-value <0.05 : RMS differs significantly between blocks
#phase p-value : 0 because it is either execution or preparation
#Trial in block x Block <0.05 : significant changes across blocks (except y )
#Trial in block x phase <0.05 : as before different phases differ
#Block x phase <0.05 : changes in blocks differ across phases
#trial in block x block x phase <0.05 : trial in block changes across both blocks and phases
#clean vs unclean dataset: unclean p-values< clean p-values: probably because of more variability# --- Global Options to Suppress Emmeans Warnings ---
emmeans::emm_options(
lmerTest.limit = Inf,
pbkrtest.limit = Inf
)
# --- Extended: Extract Full Diagnostics from Learning LMM (TrialInBlock * Block * Phase) ---
extract_learning_model_diagnostics <- function(df, label) {
rms_df <- compute_rms(df) %>%
mutate(
Block = factor(Block),
subject = factor(subject),
phase = factor(phase)
)
axes <- c("x", "y", "z")
results <- list()
for (axis in axes) {
formula <- as.formula(paste0("rms_", axis, " ~ TrialInBlock * Block * phase + (1 + TrialInBlock | subject)"))
model <- lmer(formula, data = rms_df)
key <- paste0(label, "_", toupper(axis))
results[[key]] <- list(
anova = anova(model),
emmeans = emmeans(model, ~ Block * phase),
fixed_effects = fixef(model),
random_effects = ranef(model),
scaled_residuals = resid(model, scaled = TRUE),
model = model
)
}
return(results)
}
# --- Run Diagnostics on Mixed Data ---
learning_diagnostics <- extract_learning_model_diagnostics(tagged_data, "Mixed")Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
unable to evaluate scaled gradientWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge: degenerate Hessian with 1 negative eigenvaluesWarning: Model failed to converge with 1 negative eigenvalue: -3.1e+00NOTE: Results may be misleading due to involvement in interactionsWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.886971 (tol = 0.002, component 1)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?NOTE: Results may be misleading due to involvement in interactionsWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model failed to converge with max|grad| = 0.345604 (tol = 0.002, component 1)
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, : Model is nearly unidentifiable: very large eigenvalue
- Rescale variables?NOTE: Results may be misleading due to involvement in interactions# --- Display Example for Axis X ---
cat("\n=== LEARNING MODEL: Axis X ===\n")
=== LEARNING MODEL: Axis X ===print(learning_diagnostics$Mixed_X$anova)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
TrialInBlock 0.024 0.024 1 14.9 0.4001 0.5366
Block 7.478 1.869 4 6510.9 30.6708 < 2.2e-16 ***
phase 247.417 247.417 1 6502.0 4059.3063 < 2.2e-16 ***
TrialInBlock:Block 1.579 0.395 4 6486.8 6.4766 3.379e-05 ***
TrialInBlock:phase 17.990 17.990 1 6502.5 295.1528 < 2.2e-16 ***
Block:phase 6.830 1.707 4 6502.1 28.0133 < 2.2e-16 ***
TrialInBlock:Block:phase 7.787 1.947 4 6503.0 31.9385 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(learning_diagnostics$Mixed_X$emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 0.8534 0.0604 17.9 0.7264 0.980
2 Execution 0.7689 0.0606 18.1 0.6416 0.896
3 Execution 0.6016 0.0612 18.9 0.4734 0.730
4 Execution 0.7505 0.0602 17.7 0.6238 0.877
5 Execution 0.5829 0.0602 17.7 0.4562 0.710
1 Preparation 0.0652 0.0603 17.8 -0.0616 0.192
2 Preparation 0.1409 0.0606 18.1 0.0137 0.268
3 Preparation 0.2242 0.0611 18.8 0.0962 0.352
4 Preparation 0.1080 0.0602 17.7 -0.0187 0.235
5 Preparation 0.1090 0.0602 17.7 -0.0177 0.236
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(learning_diagnostics$Mixed_X$fixed_effects) (Intercept) TrialInBlock
0.869554190 -0.000815170
Block2 Block3
0.056231080 -0.046320814
Block4 Block5
-0.030141574 -0.298745743
phasePreparation TrialInBlock:Block2
-0.802993291 -0.007086100
TrialInBlock:Block3 TrialInBlock:Block4
-0.010345664 -0.003661642
TrialInBlock:Block5 TrialInBlock:phasePreparation
0.001422747 0.000747590
Block2:phasePreparation Block3:phasePreparation
-0.131199560 -0.005336387
Block4:phasePreparation Block5:phasePreparation
-0.033760839 0.241061209
TrialInBlock:Block2:phasePreparation TrialInBlock:Block3:phasePreparation
0.014672477 0.020955325
TrialInBlock:Block4:phasePreparation TrialInBlock:Block5:phasePreparation
0.009034563 0.003688619 print(learning_diagnostics$Mixed_X$random_effects)$subject
(Intercept) TrialInBlock
2 0.07302469 5.308416e-04
3 0.01624823 -3.243428e-04
4 -0.08363521 1.630923e-04
5 -0.18109175 6.672144e-04
7 -0.04714231 -9.913308e-05
8 0.03653227 8.672690e-04
10 0.47350198 -5.353684e-03
11 0.24204458 -4.588085e-04
13 -0.06004387 -1.949274e-04
14 0.10671382 -1.819133e-03
15 -0.08623060 1.476287e-03
16 -0.02883266 7.445906e-04
17 -0.11511557 4.420390e-04
18 -0.03941152 8.196021e-04
19 -0.16774728 6.936795e-04
20 -0.13467107 9.835804e-04
22 -0.12154706 1.325688e-03
23 0.11740332 -4.638557e-04
with conditional variances for "subject" print(head(learning_diagnostics$Mixed_X$scaled_residuals)) 1 2 3 4 5 6
-0.18990813 -0.24184350 0.93796009 -1.31694436 0.01463411 -0.77067947 # --- Display Example for Axis Y ---
cat("\n=== LEARNING MODEL: Axis Y ===\n")
=== LEARNING MODEL: Axis Y ===print(learning_diagnostics$Mixed_Y$anova)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
TrialInBlock 0.041 0.041 1 13.1 0.4724 0.5038
Block 6.540 1.635 4 6496.5 18.7812 2.300e-15 ***
phase 277.980 277.980 1 6486.7 3193.3384 < 2.2e-16 ***
TrialInBlock:Block 0.674 0.168 4 6450.8 1.9345 0.1018
TrialInBlock:phase 20.516 20.516 1 6487.4 235.6777 < 2.2e-16 ***
Block:phase 5.954 1.488 4 6486.9 17.0991 5.858e-14 ***
TrialInBlock:Block:phase 8.840 2.210 4 6488.0 25.3869 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(learning_diagnostics$Mixed_Y$emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 0.9023 0.0466 19.2 0.80476 1.000
2 Execution 0.8161 0.0470 19.8 0.71804 0.914
3 Execution 0.6231 0.0482 21.9 0.52317 0.723
4 Execution 0.7800 0.0463 18.7 0.68293 0.877
5 Execution 0.6116 0.0463 18.7 0.51455 0.709
1 Preparation 0.0639 0.0465 19.0 -0.03346 0.161
2 Preparation 0.1544 0.0469 19.7 0.05643 0.252
3 Preparation 0.2321 0.0480 21.5 0.13244 0.332
4 Preparation 0.1034 0.0463 18.7 0.00637 0.200
5 Preparation 0.1030 0.0463 18.7 0.00598 0.200
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(learning_diagnostics$Mixed_Y$fixed_effects) (Intercept) TrialInBlock
0.8965891748 0.0002860064
Block2 Block3
0.0834540742 -0.0231160443
Block4 Block5
-0.0366819627 -0.2625843564
phasePreparation TrialInBlock:Block2
-0.8353361241 -0.0085408622
TrialInBlock:Block3 TrialInBlock:Block4
-0.0128963730 -0.0043103349
TrialInBlock:Block5 TrialInBlock:phasePreparation
-0.0014139899 -0.0001539001
Block2:phasePreparation Block3:phasePreparation
-0.1539901171 -0.0211594939
Block4:phasePreparation Block5:phasePreparation
-0.0335017769 0.2046221909
TrialInBlock:Block2:phasePreparation TrialInBlock:Block3:phasePreparation
0.0166549728 0.0235975593
TrialInBlock:Block4:phasePreparation TrialInBlock:Block5:phasePreparation
0.0098371767 0.0063052323 print(learning_diagnostics$Mixed_Y$random_effects)$subject
(Intercept) TrialInBlock
2 0.06495950 4.992502e-04
3 0.06287877 -1.311103e-04
4 -0.15039502 1.250827e-03
5 -0.21147593 1.057579e-03
7 -0.05367103 -3.655988e-05
8 0.12560239 1.045282e-04
10 0.48398030 -4.861964e-03
11 0.25602323 -4.358573e-04
13 -0.03175058 -7.612633e-04
14 0.05977602 -9.739708e-04
15 -0.09637827 1.127104e-03
16 -0.03957080 1.076302e-03
17 -0.11636312 8.997991e-04
18 -0.05116744 7.111814e-04
19 -0.19782374 7.779060e-04
20 -0.14111958 5.308198e-04
22 -0.16429801 1.335118e-03
23 0.20079332 -2.169688e-03
with conditional variances for "subject" print(head(learning_diagnostics$Mixed_Y$scaled_residuals)) 1 2 3 4 5 6
-0.08727244 -0.13262099 0.34444084 -0.62174085 -0.08001239 -0.69605215 # --- Display Example for Axis Z ---
cat("\n=== LEARNING MODEL: Axis Z ===\n")
=== LEARNING MODEL: Axis Z ===print(learning_diagnostics$Mixed_Z$anova)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
TrialInBlock 0.77 0.77 1 21.2 3.4021 0.07910 .
Block 15.99 4.00 4 6494.4 17.6929 1.869e-14 ***
phase 780.96 780.96 1 6485.6 3456.6273 < 2.2e-16 ***
TrialInBlock:Block 2.44 0.61 4 6473.9 2.7037 0.02881 *
TrialInBlock:phase 50.18 50.18 1 6486.1 222.1007 < 2.2e-16 ***
Block:phase 16.82 4.21 4 6485.7 18.6161 3.162e-15 ***
TrialInBlock:Block:phase 25.85 6.46 4 6486.6 28.6040 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(learning_diagnostics$Mixed_Z$emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 1.4038 0.0644 20.1 1.26954 1.538
2 Execution 1.3592 0.0651 21.0 1.22387 1.495
3 Execution 1.0714 0.0673 23.9 0.93260 1.210
4 Execution 1.3077 0.0638 19.4 1.17430 1.441
5 Execution 1.0219 0.0638 19.4 0.88852 1.155
1 Preparation 0.0652 0.0642 19.8 -0.06868 0.199
2 Preparation 0.2134 0.0650 20.9 0.07819 0.349
3 Preparation 0.3485 0.0669 23.5 0.21023 0.487
4 Preparation 0.1298 0.0638 19.4 -0.00360 0.263
5 Preparation 0.1270 0.0638 19.4 -0.00635 0.260
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(learning_diagnostics$Mixed_Z$fixed_effects) (Intercept) TrialInBlock
1.353577419 0.002528096
Block2 Block3
0.187019467 0.126105269
Block4 Block5
0.105883854 -0.334139230
phasePreparation TrialInBlock:Block2
-1.299114412 -0.011662011
TrialInBlock:Block3 TrialInBlock:Block4
-0.023089554 -0.010171168
TrialInBlock:Block5 TrialInBlock:phasePreparation
-0.002404067 -0.001985429
Block2:phasePreparation Block3:phasePreparation
-0.305102557 -0.213476645
Block4:phasePreparation Block5:phasePreparation
-0.223802256 0.230539774
TrialInBlock:Block2:phasePreparation TrialInBlock:Block3:phasePreparation
0.025071416 0.041757496
TrialInBlock:Block4:phasePreparation TrialInBlock:Block5:phasePreparation
0.019360686 0.010732750 print(learning_diagnostics$Mixed_Z$random_effects)$subject
(Intercept) TrialInBlock
2 -0.12659865 0.0029943843
3 0.07188886 0.0019215453
4 -0.18003179 0.0010373833
5 -0.34805899 0.0010072593
7 0.05044426 -0.0008655676
8 0.24469200 0.0004400951
10 0.73778702 -0.0079000916
11 0.47466205 -0.0034692325
13 0.03063876 -0.0021687529
14 0.04276772 -0.0026600999
15 -0.12972566 0.0018099850
16 0.04714354 0.0025942608
17 -0.27112242 0.0008305766
18 0.04492925 0.0001822943
19 -0.33312577 0.0011472055
20 -0.25977048 0.0019207945
22 -0.37044512 0.0025131660
23 0.27392543 -0.0013352055
with conditional variances for "subject" print(head(learning_diagnostics$Mixed_Z$scaled_residuals)) 1 2 3 4 5 6
0.56805124 0.37425066 0.28826247 -0.96807903 -0.00162998 -0.84463387 2 Step-Analysis
#2.1 Plots for RMS ± SD: Separate Plot per Block
plot_stepwise_rms_by_block_split <- function(tagged_data) {
step_markers <- c(14, 15, 16, 17)
buffer <- 3
# Assign step numbers and buffer rows
step_data <- tagged_data %>%
filter(phase == "Execution", Marker.Text %in% step_markers) %>%
assign_steps_by_block() %>%
arrange(subject, Block, trial, ms) %>%
group_by(subject, Block, trial) %>%
mutate(row_id = row_number()) %>%
ungroup()
step_indices <- step_data %>%
select(subject, Block, trial, row_id, Step)
window_data <- purrr::map_dfr(1:nrow(step_indices), function(i) {
step <- step_indices[i, ]
rows <- (step$row_id - buffer):(step$row_id + buffer)
step_data %>%
filter(subject == step$subject,
Block == step$Block,
trial == step$trial,
row_id %in% rows) %>%
mutate(Step = step$Step)
})
# Compute RMS
step_summary <- window_data %>%
group_by(subject, Block, Step) %>%
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"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = toupper(gsub("rms_", "", Axis)),
Step = as.numeric(Step),
Block = factor(Block)
)
# Summary for plotting
plot_data <- step_summary %>%
group_by(Block, Step, Axis) %>%
summarise(
mean_rms = mean(RMS, na.rm = TRUE),
sd_rms = sd(RMS, na.rm = TRUE),
.groups = "drop"
)
# === ORIGINAL: Separate plots per block ===
blocks <- unique(plot_data$Block)
block_plots <- purrr::map(blocks, function(b) {
block_data <- filter(plot_data, Block == b)
ggplot(block_data, aes(x = Step, y = mean_rms)) +
geom_point(color = "steelblue", size = 2) +
geom_errorbar(aes(ymin = mean_rms - sd_rms, ymax = mean_rms + sd_rms), width = 0.3) +
facet_wrap(~ Axis, scales = "free_y") +
ylim(0, 3.25) +
labs(
title = paste("Block", b, "- Step-Wise CoM RMS Acceleration ± SD"),
x = "Step Number",
y = "RMS Acceleration (m/s²)"
) +
theme_minimal() +
theme(
text = element_text(size = 12),
strip.text = element_text(face = "bold")
)
})
names(block_plots) <- paste0("Block_", blocks)
# === ADDITIONAL: Bar plots comparing blocks per axis ===
axis_labels <- unique(plot_data$Axis)
comparative_plots <- purrr::map(axis_labels, function(ax) {
axis_data <- filter(plot_data, Axis == ax)
ggplot(axis_data, aes(x = Step, y = mean_rms, fill = Block)) +
geom_col(position = position_dodge(width = 0.8), width = 0.7) +
geom_errorbar(
aes(ymin = mean_rms - sd_rms, ymax = mean_rms + sd_rms),
position = position_dodge(width = 0.8),
width = 0.3
) +
ylim(0, 3.25) +
labs(
title = paste("Step-wise CoM RMS Acceleration ± SD — Axis", ax),
x = "Step Number",
y = "RMS Acceleration (m/s²)",
fill = "Block"
) +
theme_minimal() +
theme(
text = element_text(size = 12),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 0, vjust = 0.5)
)
})
names(comparative_plots) <- paste0("Axis_", axis_labels)
# Return both sets of plots
return(list(
per_block = block_plots,
comparative = comparative_plots
))
}
# ---- Generate and Show Plots ----
stepwise_plots <- plot_stepwise_rms_by_block_split(tagged_data)
# Show per-block plots
for (plot_name in names(stepwise_plots$per_block)) {
cat("\n\n=====", plot_name, "=====\n\n")
print(stepwise_plots$per_block[[plot_name]])
}
===== Block_1 =====
===== Block_2 =====
===== Block_3 =====
===== Block_4 =====
===== Block_5 =====
# Show comparative axis-based plots
for (plot_name in names(stepwise_plots$comparative)) {
cat("\n\n=====", plot_name, "=====\n\n")
print(stepwise_plots$comparative[[plot_name]])
}
===== Axis_X =====
===== Axis_Y =====
===== Axis_Z =====
#2.2 Step Pairwise Model RMS: model <- lmer(RMS ~ Step + (1 | subject) + (1 | trial), data = data_sub)
# -------- Global Settings to Suppress Emmeans Warnings --------
emmeans::emm_options(
lmerTest.limit = Inf,
pbkrtest.limit = Inf
)
# -------- Step-Wise LMM + Full Diagnostics --------
run_stepwise_lmm_full_diagnostics <- function(tagged_data, dataset_name = "Mixed") {
step_markers <- c(14, 15, 16, 17)
buffer <- 3
step_data <- tagged_data %>%
filter(phase == "Execution", Marker.Text %in% step_markers) %>%
assign_steps_by_block() %>%
arrange(subject, Block, trial, ms) %>%
group_by(subject, Block, trial) %>%
mutate(row_id = row_number()) %>%
ungroup()
step_indices <- step_data %>%
select(subject, Block, trial, row_id, Step)
window_data <- map_dfr(1:nrow(step_indices), function(i) {
step <- step_indices[i, ]
rows <- (step$row_id - buffer):(step$row_id + buffer)
step_data %>%
filter(subject == step$subject,
Block == step$Block,
trial == step$trial,
row_id %in% rows) %>%
mutate(Step = step$Step)
})
step_summary <- window_data %>%
group_by(subject, Block, trial, Step) %>%
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"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = toupper(gsub("rms_", "", Axis)),
Step = factor(Step),
Block = factor(Block),
subject = factor(subject)
) %>%
mutate(
trial_id = interaction(subject, trial, drop = TRUE)
)
axis_labels <- c("X", "Y", "Z")
blocks <- unique(step_summary$Block)
results <- list()
for (blk in blocks) {
for (axis in axis_labels) {
data_sub <- step_summary %>%
filter(Block == blk, Axis == axis)
model <- lmer(RMS ~ Step + (1 | subject) + (1 | trial_id), data = data_sub)
aov_tbl <- anova(model)
emmeans_out <- emmeans(model, pairwise ~ Step)
key <- glue::glue("{dataset_name} - Block {blk} - Axis {axis}")
results[[key]] <- list(
ANOVA = aov_tbl,
Pairwise = summary(emmeans_out$contrasts),
Emmeans = summary(emmeans_out$emmeans),
FixedEffects = fixef(model),
RandomEffects = ranef(model),
ScaledResiduals = resid(model, scaled = TRUE),
Model = model
)
}
}
return(results)
}
# -------- Run Full Diagnostic LMMs --------
stepwise_lmm_diag_results <- run_stepwise_lmm_full_diagnostics(tagged_data)Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.00381883 (tol = 0.002, component 1)# -------- Print Function --------
print_stepwise_lmm_diagnostics <- function(results_list, dataset_name = "Mixed") {
cat(glue::glue("\n=========== STEPWISE LMM DIAGNOSTICS: {dataset_name} ===========\n"))
for (key in names(results_list)) {
cat("\n---", key, "---\n")
cat("ANOVA:\n")
print(results_list[[key]]$ANOVA)
cat("\nPairwise Comparisons:\n")
print(results_list[[key]]$Pairwise)
cat("\nFixed Effects:\n")
print(results_list[[key]]$FixedEffects)
cat("\nRandom Effects:\n")
print(results_list[[key]]$RandomEffects)
cat("\nSample Scaled Residuals:\n")
print(head(results_list[[key]]$ScaledResiduals))
cat("\n=============================================================\n")
}
}
# -------- Output Diagnostics --------
print_stepwise_lmm_diagnostics(stepwise_lmm_diag_results)=========== STEPWISE LMM DIAGNOSTICS: Mixed ===========
--- Mixed - Block 1 - Axis X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 0.1645 0.0329 5 3151 2.2702 0.04511 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.000397 0.00677 3151 -0.059 1.0000
Step1 - Step3 0.007712 0.00677 3151 1.139 0.8653
Step1 - Step4 0.007607 0.00678 3151 1.121 0.8726
Step1 - Step5 -0.011848 0.00677 3151 -1.749 0.4991
Step1 - Step6 0.003061 0.00677 3151 0.452 0.9976
Step2 - Step3 0.008109 0.00677 3151 1.197 0.8384
Step2 - Step4 0.008004 0.00678 3151 1.180 0.8465
Step2 - Step5 -0.011451 0.00677 3151 -1.691 0.5381
Step2 - Step6 0.003458 0.00677 3151 0.511 0.9958
Step3 - Step4 -0.000106 0.00679 3151 -0.016 1.0000
Step3 - Step5 -0.019561 0.00678 3151 -2.886 0.0453
Step3 - Step6 -0.004651 0.00677 3151 -0.687 0.9835
Step4 - Step5 -0.019455 0.00679 3151 -2.865 0.0481
Step4 - Step6 -0.004546 0.00678 3151 -0.670 0.9852
Step5 - Step6 0.014909 0.00677 3151 2.201 0.2374
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 6 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5
0.8681882108 0.0003971126 -0.0077122210 -0.0076066700 0.0118484333
Step6
-0.0030608525
Random Effects:
$trial_id
(Intercept)
14.1 2.685725e-01
7.2 2.181518e-01
18.2 -2.249061e-01
20.2 -1.986507e-01
23.2 -2.008950e-01
4.3 1.660555e-01
5.3 -2.601874e-02
10.3 2.143537e-01
15.3 3.988128e-01
16.3 2.455727e-01
17.3 -5.572928e-02
2.4 -3.593609e-02
7.4 -2.631619e-01
14.4 4.736172e-01
15.4 5.693907e-01
17.4 1.686108e-02
23.4 -1.655601e-01
3.5 2.996368e-01
4.5 -1.431230e-01
7.5 -1.986744e-01
10.5 -9.089346e-01
14.5 3.509545e-01
18.5 -1.441938e-01
19.5 -2.564294e-02
20.5 -2.031300e-01
22.5 -8.499156e-02
3.6 -8.015004e-02
5.6 -6.793401e-02
14.6 4.314668e-01
15.6 3.964350e-01
16.6 2.350752e-01
17.6 -1.350342e-01
18.6 -1.686790e-01
19.6 1.055164e-02
20.6 -1.013320e-01
22.6 2.037766e-01
2.7 -3.612899e-02
3.7 2.852472e-02
4.7 8.354007e-02
5.7 -2.562498e-01
7.7 3.086760e-01
8.7 -2.550900e-01
10.7 -6.602823e-01
11.7 -3.357470e-01
15.7 4.216950e-02
16.7 1.888427e-01
17.7 5.705658e-02
18.7 -2.480981e-01
22.7 -8.480812e-02
23.7 3.711666e-01
2.8 -8.881431e-02
3.8 -1.643909e-03
4.8 4.202974e-01
5.8 -2.405759e-01
7.8 5.510160e-02
8.8 -8.904536e-01
10.8 7.583179e-01
11.8 6.684579e-02
14.8 -1.053177e-01
15.8 1.181018e-01
16.8 2.976483e-01
17.8 -1.195488e-01
18.8 -4.984203e-03
19.8 -6.833946e-02
20.8 9.018688e-02
22.8 -7.270372e-02
2.9 -1.342283e-01
3.9 5.860631e-01
4.9 -5.162709e-02
7.9 -3.382774e-01
10.9 -2.404793e-01
11.9 -1.037613e-01
13.9 7.897322e-02
14.9 6.314206e-02
15.9 4.169523e-02
16.9 -1.234155e-01
17.9 -1.812074e-02
18.9 -1.306546e-01
19.9 8.369020e-02
20.9 -2.323599e-02
22.9 1.449358e-01
23.9 -8.988055e-01
2.10 -3.894624e-02
3.10 5.093475e-01
4.10 1.570298e-01
5.10 -9.748009e-02
7.10 -2.332931e-01
8.10 -4.107488e-01
10.10 5.084932e-01
11.10 4.010019e-01
14.10 2.492416e-01
15.10 -4.005323e-01
17.10 -4.125528e-02
18.10 1.677404e-01
3.11 5.023870e-02
5.11 -1.600795e-01
7.11 1.907342e-01
8.11 -7.010762e-01
10.11 5.014631e-01
14.11 2.320214e-01
15.11 -1.991632e-02
17.11 2.598475e-02
18.11 2.557835e-01
19.11 8.665962e-02
22.11 -1.953512e-01
23.11 -1.756302e-01
2.12 -1.007107e-02
3.12 -1.547449e-01
4.12 1.806337e-01
7.12 -1.896180e-01
8.12 -5.534997e-01
10.12 -6.824374e-01
13.12 5.477371e-05
14.12 2.599194e-02
15.12 -3.593499e-01
16.12 -6.743347e-02
17.12 3.612282e-02
18.12 1.019149e-01
19.12 -4.846811e-02
23.12 4.468331e-01
3.13 1.054606e-01
4.13 -7.605871e-03
5.13 -2.078577e-01
7.13 3.101531e-02
8.13 -3.034046e-01
10.13 -5.781796e-01
13.13 8.278510e-02
14.13 2.505911e-01
15.13 -2.099092e-01
16.13 5.694023e-01
17.13 -5.505216e-02
18.13 -5.815379e-02
19.13 -7.925013e-02
22.13 -8.271172e-02
23.13 6.272013e-01
2.14 -2.151026e-01
3.14 2.050655e-01
5.14 -1.562726e-01
7.14 1.158310e-01
8.14 -5.370485e-01
10.14 1.230332e-01
11.14 -2.317414e-01
14.14 -1.106743e-01
15.14 -2.199225e-01
16.14 1.141437e-01
17.14 2.600838e-01
18.14 1.280968e-01
22.14 -8.464644e-02
23.14 1.436821e+00
2.15 1.288155e-01
4.15 7.131352e-02
5.15 -1.350658e-01
8.15 -2.574010e-01
10.15 3.287337e-01
11.15 -7.539125e-01
13.15 1.867509e-01
15.15 -1.411929e-01
16.15 -7.651658e-02
17.15 -2.195343e-03
18.15 1.914732e-01
19.15 2.107960e-02
20.15 -1.369375e-01
23.15 -8.955239e-02
3.16 -3.083465e-02
4.16 2.421793e-01
5.16 4.730376e-02
7.16 -1.842321e-01
8.16 -3.935254e-01
10.16 -6.041487e-01
11.16 -6.776095e-01
13.16 -1.025992e-01
14.16 5.716159e-02
15.16 -3.419713e-01
16.16 2.648562e-02
19.16 1.274303e-02
20.16 1.862621e-01
22.16 9.107076e-02
23.16 1.670889e-01
3.17 -3.692022e-02
4.17 -1.061579e-01
5.17 1.102904e-01
8.17 -4.806593e-01
10.17 -1.218403e+00
13.17 -1.559250e-01
14.17 -2.101237e-01
15.17 -1.088755e-01
16.17 -2.745365e-02
17.17 -7.552261e-02
18.17 -2.345732e-01
19.17 -2.393874e-02
20.17 -1.892715e-02
22.17 -8.733994e-02
23.17 2.571898e+00
2.18 -2.875053e-02
3.18 -1.423067e-01
4.18 -5.527905e-02
5.18 -4.162256e-02
7.18 3.252677e-01
10.18 -7.702617e-01
13.18 -1.725158e-01
16.18 -8.503824e-02
18.18 -1.321428e-01
20.18 -5.786070e-02
22.18 -2.155336e-01
23.18 2.678386e-01
3.19 1.106849e-01
4.19 1.082460e-01
5.19 -1.749158e-01
7.19 7.471249e-02
8.19 -1.081167e-01
10.19 -1.086252e+00
11.19 4.890615e-01
13.19 -1.350100e-01
14.19 3.248716e-01
15.19 1.270724e-01
16.19 -1.892129e-01
17.19 -4.723252e-02
18.19 9.449079e-03
19.19 -1.301005e-01
20.19 -1.976741e-01
22.19 -4.884937e-02
23.19 -3.086096e-01
3.20 -5.899570e-03
4.20 -1.010684e-01
5.20 -1.353373e-01
7.20 -2.308199e-01
8.20 -1.767363e-01
13.20 -1.961909e-01
14.20 -2.617731e-01
16.20 -4.859857e-02
17.20 3.080025e-02
18.20 -2.523899e-02
19.20 -9.378427e-02
20.20 -1.726464e-02
22.20 -1.009806e-01
23.20 1.241227e+00
2.21 -3.489891e-02
3.21 -6.600649e-01
4.21 1.342702e-01
5.21 -2.539780e-01
7.21 -1.666229e-01
8.21 2.486274e-01
10.21 -2.019720e-01
11.21 -9.705355e-01
14.21 2.114700e-02
15.21 -1.237186e-01
16.21 -2.299161e-02
17.21 -9.687592e-03
18.21 1.156902e-01
19.21 -8.027464e-02
20.21 -3.211510e-01
22.21 9.824140e-02
23.21 -7.167874e-02
2.22 -1.437409e-01
3.22 -3.134492e-01
4.22 1.092324e-01
7.22 1.615214e-01
8.22 1.714724e-01
10.22 2.350652e-01
11.22 -4.509906e-01
13.22 4.923534e-01
14.22 1.852885e-01
15.22 2.795959e-02
16.22 -2.660942e-01
17.22 -8.478092e-02
18.22 -1.032404e-01
19.22 1.056856e-01
20.22 -1.451047e-01
22.22 -5.780709e-02
23.22 1.094174e+00
2.23 1.630387e-01
4.23 2.711805e-01
5.23 -6.710984e-02
7.23 1.078855e-01
8.23 2.942230e-01
10.23 4.702579e-01
11.23 1.555298e-01
13.23 1.012130e-01
15.23 8.264836e-02
17.23 1.108723e-01
18.23 1.070316e-01
19.23 1.128826e-01
20.23 2.792300e-01
22.23 -9.790403e-02
2.24 -1.133855e-01
4.24 4.336839e-02
5.24 -1.068182e-01
7.24 1.376968e-01
8.24 2.637795e-01
10.24 6.842806e-01
13.24 5.878387e-02
14.24 -2.757947e-01
15.24 5.265077e-02
16.24 1.859226e-01
17.24 6.441907e-02
19.24 1.069688e-01
20.24 1.751188e-01
22.24 -5.555568e-02
23.24 -3.659642e-01
2.25 1.601031e-02
3.25 -1.784220e-01
4.25 4.114271e-02
5.25 -1.121506e-01
7.25 -9.387643e-02
10.25 -8.295314e-01
13.25 4.758808e-02
14.25 5.008545e-02
15.25 8.347037e-02
17.25 -8.726725e-02
18.25 -3.304464e-02
19.25 3.638821e-02
20.25 4.349722e-02
22.25 5.747227e-02
2.26 8.151129e-03
3.26 -1.446678e-01
4.26 1.926887e-01
5.26 6.249508e-02
7.26 1.956466e-02
8.26 1.326760e-01
10.26 -2.617424e-01
14.26 3.279892e-01
15.26 1.458522e-01
16.26 -2.068557e-01
18.26 2.200125e-01
19.26 -9.210064e-02
20.26 -8.561738e-02
2.27 -3.039721e-01
3.27 -9.677634e-02
4.27 5.724536e-02
5.27 1.241236e-01
8.27 1.366288e+00
10.27 4.366075e-01
14.27 -2.180079e-01
15.27 2.971468e-01
16.27 -2.500964e-01
17.27 -1.377912e-01
18.27 3.694701e-02
19.27 -1.598070e-01
22.27 -5.733499e-03
23.27 -6.630202e-01
2.28 -9.287797e-02
3.28 -4.839125e-01
4.28 -1.216817e-01
7.28 5.093070e-03
10.28 3.064554e-01
11.28 1.855245e-01
15.28 4.244366e-01
16.28 -2.952009e-02
17.28 -1.517977e-01
19.28 1.555645e-01
20.28 -1.010535e-01
22.28 2.205467e-01
23.28 1.786833e-01
2.29 -2.424109e-01
3.29 -2.451738e-01
4.29 -2.400663e-01
5.29 -1.085197e-01
7.29 3.398765e-03
8.29 -3.036769e-01
13.29 8.347110e-03
14.29 -3.742990e-02
16.29 7.363958e-02
17.29 1.038590e-01
18.29 3.222792e-01
20.29 -2.126068e-01
22.29 -3.243939e-02
23.29 -6.538212e-01
2.30 -2.480734e-01
3.30 4.457236e-01
5.30 2.273017e-02
7.30 -8.249598e-03
10.30 1.102854e+00
11.30 9.076654e-01
13.30 -9.258343e-02
14.30 1.331701e-01
15.30 -6.414701e-02
18.30 4.383158e-01
19.30 -1.575905e-01
20.30 -2.468640e-01
22.30 -2.239311e-01
23.30 -7.733883e-01
2.31 -1.744407e-02
3.31 -1.409643e-01
4.31 -2.224939e-01
7.31 -2.594405e-01
8.31 4.855451e-02
10.31 5.497895e-01
11.31 1.964615e-01
13.31 1.049497e-01
14.31 6.453158e-02
16.31 5.533085e-03
17.31 -9.317107e-02
18.31 1.966623e-01
19.31 -8.389995e-02
20.31 -1.255802e-01
22.31 5.997394e-02
23.31 -5.487091e-01
2.32 -2.373464e-01
4.32 -9.609216e-02
5.32 2.160180e-02
7.32 1.222133e-01
8.32 5.808713e-01
13.32 -1.368530e-01
14.32 -3.498555e-02
15.32 2.152468e-01
17.32 -8.273620e-02
18.32 1.055714e-01
19.32 -3.822352e-02
20.32 -1.152383e-01
22.32 3.065356e-02
23.32 1.015824e+00
2.33 -2.015264e-01
4.33 -2.194942e-01
7.33 -8.742828e-02
8.33 2.364160e-01
13.33 6.634835e-02
15.33 1.341434e-01
16.33 -9.599237e-02
17.33 6.145768e-02
18.33 -7.402172e-02
19.33 -6.116264e-02
20.33 2.203658e-01
22.33 -2.626157e-02
2.34 -7.057808e-02
3.34 1.135730e-01
5.34 7.389653e-01
8.34 1.195921e-01
10.34 1.218231e+00
11.34 -7.074377e-01
13.34 2.323908e-02
14.34 -1.617373e-01
16.34 -7.138652e-02
17.34 1.360346e-01
18.34 -2.247795e-01
19.34 -4.777659e-02
22.34 -5.175498e-02
23.34 -3.048357e-01
2.35 -2.257382e-01
3.35 -2.463031e-01
4.35 -1.939092e-01
5.35 -7.960561e-02
7.35 5.826613e-02
8.35 3.385079e-01
10.35 1.111933e-01
13.35 3.629080e-02
14.35 4.457607e-02
15.35 -2.455640e-01
16.35 2.128481e-01
17.35 -4.117490e-02
18.35 7.517433e-02
19.35 4.674353e-02
20.35 -1.469796e-01
22.35 1.518017e-01
23.35 -1.162149e+00
2.36 4.730054e-02
3.36 -4.009397e-01
5.36 -1.120917e-01
7.36 -3.490213e-02
8.36 1.545074e-01
10.36 -1.623859e-01
11.36 6.646953e-01
13.36 -1.725332e-01
17.36 1.111652e-01
18.36 -2.609522e-02
20.36 6.243375e-02
22.36 4.166740e-01
23.36 5.585861e-01
2.37 3.540214e-01
3.37 -4.367580e-01
4.37 -4.031327e-03
7.37 1.405898e-01
8.37 3.240504e-01
13.37 -2.152056e-01
14.37 5.053446e-03
16.37 1.419418e-01
17.37 -4.975568e-02
18.37 -3.548100e-02
19.37 7.811817e-02
20.37 -5.307813e-03
22.37 -1.480168e-01
23.37 -3.003428e-01
2.38 5.408895e-01
4.38 -2.419795e-02
7.38 -5.469323e-02
8.38 -2.402543e-01
11.38 2.782650e-01
14.38 4.724150e-02
15.38 -1.378584e-01
16.38 -2.064164e-01
17.38 1.412517e-01
18.38 -5.159429e-02
20.38 9.627381e-02
22.38 -3.266479e-02
3.39 -1.495644e-02
4.39 -1.854346e-01
5.39 7.675490e-02
7.39 -2.730809e-01
10.39 1.164811e-01
11.39 1.107867e+00
13.39 -3.850346e-02
14.39 9.514426e-02
15.39 -1.769909e-01
16.39 7.219977e-02
17.39 1.524035e-01
18.39 -2.516368e-02
19.39 6.135026e-02
20.39 3.442931e-01
22.39 1.442567e-02
23.39 -9.102488e-01
2.40 2.151102e-01
3.40 5.853586e-02
4.40 -1.688027e-01
5.40 2.009926e-01
8.40 4.110111e-01
10.40 -7.351627e-01
11.40 -8.894360e-02
13.40 -2.591243e-01
14.40 -7.448025e-02
15.40 -8.075356e-02
16.40 -3.501617e-01
17.40 1.497524e-02
18.40 -1.550667e-02
19.40 2.632445e-03
20.40 -5.795474e-02
22.40 -1.949058e-02
23.40 -7.913716e-01
2.41 -1.295410e-01
3.41 -2.758162e-01
4.41 -2.660740e-01
5.41 8.902017e-02
7.41 7.367503e-02
8.41 2.715906e-01
10.41 2.017385e-01
13.41 -1.164160e-02
14.41 -3.508111e-01
16.41 -1.873779e-01
17.41 -8.161985e-03
19.41 -5.011155e-02
20.41 3.480269e-01
23.41 -4.520999e-01
2.42 5.891618e-01
3.42 5.937360e-01
5.42 2.289540e-01
7.42 1.058530e-01
8.42 7.712217e-01
10.42 9.537387e-01
13.42 -2.101866e-01
14.42 -2.771048e-01
15.42 -7.782139e-02
16.42 4.179126e-02
17.42 7.533950e-02
18.42 -1.679376e-01
19.42 1.247291e-01
20.42 1.841457e-01
22.42 2.367635e-02
23.42 -2.606028e-01
2.43 6.649494e-02
4.43 -3.012023e-01
5.43 1.424907e-01
7.43 1.113866e-01
8.43 1.648847e-01
10.43 4.526749e-01
11.43 -7.019784e-01
14.43 -1.132764e-01
16.43 1.817257e-01
17.43 2.515822e-02
18.43 -1.003457e-01
19.43 4.784532e-02
20.43 2.022571e-01
22.43 -5.428157e-02
23.43 8.085397e-02
2.44 7.531330e-02
3.44 -1.518418e-01
4.44 -2.560914e-02
5.44 8.902938e-02
7.44 -2.543992e-01
10.44 -1.264061e-01
11.44 1.191556e+00
13.44 1.187449e-01
14.44 -5.398540e-01
15.44 -1.064277e-01
16.44 1.629965e-01
17.44 -3.740511e-02
18.44 -3.224241e-01
19.44 -7.545664e-02
20.44 5.572802e-02
22.44 1.452884e-01
23.44 1.805334e-01
2.45 3.277246e-01
3.45 1.888337e-01
4.45 2.927485e-02
5.45 1.910595e-01
7.45 2.003727e-01
11.45 9.494963e-01
13.45 -1.477909e-02
14.45 -1.203135e-01
15.45 -2.239874e-01
16.45 -2.317522e-01
17.45 -2.174553e-01
18.45 -2.020216e-02
19.45 6.307338e-02
20.45 1.199719e-01
23.45 5.889845e-01
2.46 -7.661599e-03
4.46 1.900191e-01
5.46 7.699739e-03
7.46 2.281548e-01
10.46 1.068484e-01
11.46 -7.232590e-01
13.46 5.196113e-01
14.46 -3.708090e-01
15.46 1.334899e-01
17.46 -7.665556e-02
18.46 8.182840e-02
20.46 -2.638618e-02
22.46 8.297910e-02
23.46 1.523788e-01
3.47 1.113021e+00
4.47 -1.064822e-01
5.47 1.089587e-01
7.47 -1.844373e-02
8.47 -1.273028e-01
11.47 -3.543872e-01
13.47 -1.189041e-01
14.47 -3.307427e-01
15.47 -2.690307e-01
16.47 -3.436262e-01
18.47 -1.197347e-01
19.47 -6.563307e-02
22.47 -1.517937e-02
23.47 -9.292019e-01
5.48 1.317653e-01
23.48 -5.995544e-01
$subject
(Intercept)
2 -0.31358771
3 0.41571928
4 -0.35762562
5 -0.36447351
7 -0.23568086
8 0.39913442
10 0.78691914
11 1.23705880
13 -0.26692918
14 0.27219549
15 -0.04321952
16 -0.31115635
17 -0.51040921
18 -0.34386805
19 -0.56345539
20 -0.34346882
22 -0.34435662
23 0.88720371
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5
-0.1320247745 0.0927011090 0.0018054628 0.0009286751 0.1874497471
6
-0.1883382772
=============================================================
--- Mixed - Block 1 - Axis Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 0.04349 0.008698 5 3151.1 0.3736 0.8671
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -5.48e-03 0.00858 3151 -0.639 0.9881
Step1 - Step3 -3.55e-03 0.00859 3151 -0.413 0.9985
Step1 - Step4 -3.50e-03 0.00860 3151 -0.407 0.9986
Step1 - Step5 -2.64e-04 0.00859 3151 -0.031 1.0000
Step1 - Step6 4.92e-03 0.00858 3151 0.573 0.9927
Step2 - Step3 1.93e-03 0.00859 3151 0.224 0.9999
Step2 - Step4 1.98e-03 0.00860 3151 0.230 0.9999
Step2 - Step5 5.21e-03 0.00859 3151 0.607 0.9906
Step2 - Step6 1.04e-02 0.00858 3151 1.212 0.8312
Step3 - Step4 5.01e-05 0.00861 3151 0.006 1.0000
Step3 - Step5 3.29e-03 0.00859 3151 0.383 0.9989
Step3 - Step6 8.47e-03 0.00859 3151 0.986 0.9224
Step4 - Step5 3.24e-03 0.00861 3151 0.376 0.9990
Step4 - Step6 8.42e-03 0.00860 3151 0.979 0.9247
Step5 - Step6 5.18e-03 0.00859 3151 0.604 0.9908
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 6 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5
0.8619831734 0.0054766894 0.0035499106 0.0034998587 0.0002640287
Step6
-0.0049178222
Random Effects:
$trial_id
(Intercept)
14.1 -0.116611580
7.2 -0.284162652
18.2 -0.119880104
20.2 -0.140499393
23.2 0.270395308
4.3 0.130605340
5.3 0.091486439
10.3 -0.986016389
15.3 0.018088181
16.3 0.071141107
17.3 -0.064382296
2.4 -0.291945503
7.4 0.011910607
14.4 0.051907552
15.4 0.301609883
17.4 0.045148825
23.4 0.087777146
3.5 -0.484176198
4.5 -0.011757256
7.5 -0.202466135
10.5 -1.060876702
14.5 0.406979760
18.5 -0.115192020
19.5 -0.022173524
20.5 0.050351108
22.5 0.159427469
3.6 -0.147334676
5.6 -0.013262600
14.6 0.180365713
15.6 0.014636145
16.6 0.035453104
17.6 -0.085211377
18.6 -0.165761832
19.6 -0.011978882
20.6 -0.109686604
22.6 -0.013534831
2.7 -0.044020918
3.7 -0.292593514
4.7 -0.043261272
5.7 -0.101537919
7.7 0.536633966
8.7 -0.086705288
10.7 -0.725740962
11.7 -0.431678515
15.7 -0.094773925
16.7 0.059432750
17.7 -0.131320166
18.7 -0.338718187
22.7 0.174749072
23.7 1.211688216
2.8 -0.119763459
3.8 -0.321275379
4.8 -0.068471531
5.8 -0.075890799
7.8 0.187525180
8.8 -0.486632770
10.8 0.542459797
11.8 -0.618757124
14.8 -0.010520602
15.8 -0.183914471
16.8 0.281659465
17.8 -0.145404198
18.8 -0.074033038
19.8 -0.105938606
20.8 -0.090646869
22.8 0.139994363
2.9 0.011368227
3.9 -0.385536442
4.9 0.023204999
7.9 0.004609366
10.9 -0.769798276
11.9 -0.811948076
13.9 0.144366357
14.9 0.463476220
15.9 0.048121272
16.9 0.343111917
17.9 -0.150891096
18.9 -0.139347747
19.9 -0.123324974
20.9 -0.121794631
22.9 -0.012334481
23.9 0.099907029
2.10 -0.104794709
3.10 0.223391725
4.10 -0.094555995
5.10 -0.109544184
7.10 -0.226517541
8.10 0.236512307
10.10 -0.236838524
11.10 0.150728731
14.10 0.261752104
15.10 0.252931116
17.10 -0.031404687
18.10 0.095617583
3.11 -0.091905844
5.11 -0.197314452
7.11 0.529457797
8.11 0.637621596
10.11 0.559192304
14.11 0.250865477
15.11 0.056448868
17.11 -0.045176297
18.11 -0.091201272
19.11 -0.053843643
22.11 0.029182116
23.11 -0.015589986
2.12 -0.074763994
3.12 0.342819274
4.12 -0.032821434
7.12 0.067076890
8.12 -0.334460849
10.12 -0.703588075
13.12 0.083327431
14.12 -0.056160996
15.12 -0.105516681
16.12 0.123561337
17.12 0.063531071
18.12 -0.236317030
19.12 0.062040253
23.12 0.660564672
3.13 0.060825135
4.13 -0.046077322
5.13 -0.015764718
7.13 -0.034356594
8.13 0.254011677
10.13 -1.312509914
13.13 -0.476159075
14.13 0.365526925
15.13 -0.125954429
16.13 0.176877677
17.13 -0.169826524
18.13 0.015576972
19.13 0.015412751
22.13 -0.182303954
23.13 0.465473513
2.14 -0.233849061
3.14 -0.138696408
5.14 -0.072495225
7.14 -0.009261849
8.14 0.456982868
10.14 -0.462817154
11.14 -0.931911186
14.14 -0.087984646
15.14 -0.027231054
16.14 -0.143731028
17.14 0.043586019
18.14 0.028068096
22.14 -0.180396827
23.14 0.073746586
2.15 -0.191740640
4.15 0.110224673
5.15 -0.228872316
8.15 -0.161038182
10.15 -0.592538599
11.15 -0.597843310
13.15 -0.049629207
15.15 -0.068061771
16.15 0.161572318
17.15 0.100658617
18.15 0.078523363
19.15 -0.032048998
20.15 -0.098804337
23.15 0.056586110
3.16 -0.136822702
4.16 -0.163838559
5.16 0.013510367
7.16 -0.144579548
8.16 -0.404942846
10.16 -0.801224519
11.16 -0.641839987
13.16 -0.094699841
14.16 -0.067801756
15.16 -0.126235611
16.16 -0.084290355
19.16 0.067336596
20.16 -0.176558313
22.16 -0.134834897
23.16 0.676634207
3.17 0.321782349
4.17 -0.111123303
5.17 -0.111124699
8.17 -0.347961019
10.17 -0.856599548
13.17 -0.191808905
14.17 0.004936509
15.17 -0.026578797
16.17 -0.202941975
17.17 0.066517310
18.17 -0.177098725
19.17 -0.068600151
20.17 -0.126878761
22.17 -0.022649736
23.17 -0.301017111
2.18 -0.081450304
3.18 -0.362941289
4.18 0.051378472
5.18 -0.104029109
7.18 -0.042610062
10.18 -1.052527597
13.18 -0.210956065
16.18 -0.058878516
18.18 -0.268883698
20.18 -0.008592179
22.18 -0.011679460
23.18 0.742306243
3.19 -0.011297915
4.19 -0.121697605
5.19 -0.138599179
7.19 -0.100272987
8.19 -0.051181130
10.19 -1.177362073
11.19 0.028452530
13.19 -0.261925649
14.19 0.059904548
15.19 -0.188412597
16.19 -0.120648655
17.19 0.216199726
18.19 0.011864235
19.19 0.094703190
20.19 -0.064102198
22.19 -0.084323025
23.19 -0.447687297
3.20 0.604343809
4.20 0.032054859
5.20 0.022160146
7.20 -0.156918331
8.20 0.095579840
13.20 -0.065063240
14.20 -0.241600656
16.20 -0.289227218
17.20 -0.100563557
18.20 -0.004209508
19.20 -0.090447971
20.20 -0.030062864
22.20 0.122251137
23.20 -0.184803045
2.21 -0.110948673
3.21 0.031617764
4.21 0.011852604
5.21 -0.029295253
7.21 -0.125054585
8.21 -0.205360170
10.21 0.459116652
11.21 -1.029113760
14.21 -0.306495992
15.21 -0.098916210
16.21 -0.188275509
17.21 -0.080761491
18.21 -0.100439880
19.21 0.123631053
20.21 0.082415166
22.21 0.099551337
23.21 1.013794414
2.22 -0.038705950
3.22 0.153002119
4.22 0.130542704
7.22 -0.092752607
8.22 -0.309096557
10.22 0.214078903
11.22 -0.784932727
13.22 0.151130525
14.22 -0.105097638
15.22 -0.064399877
16.22 -0.016096730
17.22 0.091384144
18.22 -0.019968479
19.22 -0.014255839
20.22 -0.260039489
22.22 0.017401509
23.22 0.347401917
2.23 -0.092203002
4.23 -0.061013228
5.23 0.213294731
7.23 -0.121307381
8.23 0.367197824
10.23 -0.785679326
11.23 0.213596832
13.23 -0.213139963
15.23 -0.032847561
17.23 -0.122666825
18.23 0.018550393
19.23 0.104610046
20.23 0.046625274
22.23 0.030526593
2.24 -0.035372115
4.24 -0.129297977
5.24 0.056993921
7.24 0.091271031
8.24 0.219609869
10.24 -0.210111370
13.24 0.161517966
14.24 -0.130808921
15.24 -0.174283406
16.24 -0.233971436
17.24 0.029379474
19.24 0.034443819
20.24 -0.097247892
22.24 -0.068090869
23.24 -0.220809994
2.25 -0.137738744
3.25 0.115010480
4.25 0.088280386
5.25 0.027617028
7.25 0.107603278
10.25 -0.127421682
13.25 -0.032000884
14.25 -0.308735086
15.25 -0.076154132
17.25 -0.158317137
18.25 0.091095407
19.25 -0.073386115
20.25 -0.023655991
22.25 -0.130639662
2.26 0.113968927
3.26 -0.090486589
4.26 -0.085672906
5.26 0.016372446
7.26 -0.223942684
8.26 -0.821909240
10.26 0.072323651
14.26 -0.072808610
15.26 0.348385028
16.26 -0.021706043
18.26 -0.151045472
19.26 0.001480580
20.26 0.119803277
2.27 -0.139641565
3.27 -0.042090834
4.27 0.001737437
5.27 0.067629591
8.27 -0.676585262
10.27 0.239935836
14.27 -0.177429304
15.27 -0.183648241
16.27 0.179845400
17.27 -0.118740716
18.27 0.079233366
19.27 -0.039041306
22.27 -0.153703757
23.27 -0.179416625
2.28 -0.087205117
3.28 0.212336152
4.28 -0.094993765
7.28 0.152802853
10.28 0.358101464
11.28 -0.905361933
15.28 -0.038951925
16.28 -0.199006210
17.28 -0.143297437
19.28 0.002844455
20.28 -0.006670544
22.28 0.080797409
23.28 -0.248343516
2.29 -0.153668404
3.29 0.005913395
4.29 0.105680918
5.29 -0.121298612
7.29 -0.281116859
8.29 -0.374020430
13.29 0.143788496
14.29 -0.044919375
16.29 0.012482373
17.29 -0.061109915
18.29 0.269569111
20.29 -0.142777750
22.29 -0.210843227
23.29 -0.829810011
2.30 -0.258820972
3.30 0.358779048
5.30 -0.036918773
7.30 -0.039548377
10.30 1.556776360
11.30 0.487349496
13.30 -0.180595542
14.30 -0.075047231
15.30 0.092943122
18.30 -0.069910420
19.30 0.022139913
20.30 -0.106185536
22.30 -0.042117458
23.30 -0.294986733
2.31 0.255883023
3.31 0.483267020
4.31 0.028381115
7.31 -0.267796230
8.31 -0.396134645
10.31 0.095948833
11.31 2.738895509
13.31 -0.328444475
14.31 0.018702919
16.31 0.068452734
17.31 -0.002178906
18.31 -0.086190561
19.31 0.029928796
20.31 0.075047101
22.31 -0.008731144
23.31 0.159714835
2.32 -0.136676752
4.32 -0.050029982
5.32 0.326607595
7.32 0.137949709
8.32 0.139305026
13.32 -0.338798223
14.32 -0.078823775
15.32 -0.101254738
17.32 0.012865597
18.32 -0.098682311
19.32 -0.126905945
20.32 -0.205861477
22.32 0.157251812
23.32 -0.240106683
2.33 -0.147286077
4.33 -0.139725811
7.33 0.108371286
8.33 1.313633491
13.33 -0.173667941
15.33 -0.109342138
16.33 -0.250758256
17.33 -0.114279862
18.33 0.132949833
19.33 0.085445697
20.33 -0.130452097
22.33 0.007852470
2.34 -0.004370372
3.34 0.057214006
5.34 0.113689642
8.34 0.488262921
10.34 1.766539632
11.34 -0.533406413
13.34 -0.092185162
14.34 -0.148061787
16.34 -0.035807040
17.34 0.230641365
18.34 0.358968729
19.34 0.013774127
22.34 0.006750783
23.34 -0.029507220
2.35 -0.212424035
3.35 -0.324584979
4.35 -0.016936558
5.35 -0.043556180
7.35 -0.214428721
8.35 -0.399589187
10.35 2.174339705
13.35 0.197822536
14.35 -0.102226682
15.35 0.027295497
16.35 0.010696836
17.35 -0.050331396
18.35 0.098421067
19.35 -0.084651925
20.35 0.012777631
22.35 -0.041987655
23.35 -1.147192154
2.36 -0.305346248
3.36 -0.376794639
5.36 0.028348319
7.36 -0.198816084
8.36 -0.463085262
10.36 0.218576090
11.36 -0.271800183
13.36 0.049185785
17.36 -0.129250732
18.36 0.310214493
20.36 -0.167720143
22.36 0.144488955
23.36 0.253499416
2.37 0.445565577
3.37 -0.410119132
4.37 -0.027323947
7.37 0.050376365
8.37 1.456762505
13.37 -0.328112550
14.37 -0.157736826
16.37 0.068054641
17.37 -0.106921771
18.37 0.253854136
19.37 0.186745288
20.37 -0.003015787
22.37 0.037031883
23.37 0.246072639
2.38 0.631470965
4.38 -0.061823937
7.38 -0.026876760
8.38 -0.116215818
11.38 1.081726270
14.38 -0.329677300
15.38 -0.018465761
16.38 -0.001731865
17.38 -0.012354527
18.38 0.320169737
20.38 0.127643133
22.38 0.059441314
3.39 -0.459374984
4.39 0.072340383
5.39 0.070764945
7.39 0.314891918
10.39 0.969547146
11.39 1.006795285
13.39 -0.052172166
14.39 0.142174381
15.39 0.020129110
16.39 -0.067036469
17.39 0.117361832
18.39 -0.135248018
19.39 -0.222262509
20.39 0.348776922
22.39 0.012198849
23.39 -0.410175732
2.40 -0.087495602
3.40 -0.098647083
4.40 -0.132457569
5.40 0.049476645
8.40 0.585596024
10.40 0.456790077
11.40 1.155737255
13.40 -0.429785957
14.40 0.111221005
15.40 -0.007339134
16.40 0.146872576
17.40 0.195953326
18.40 0.229682999
19.40 -0.041389610
20.40 0.116392975
22.40 -0.092338099
23.40 -0.780135712
2.41 0.137149895
3.41 0.529065096
4.41 -0.118206040
5.41 -0.130242772
7.41 -0.169803609
8.41 -0.128909293
10.41 0.578293779
13.41 -0.347046182
14.41 0.041428164
16.41 -0.122781330
17.41 0.251428772
19.41 -0.102802845
20.41 0.025998449
23.41 -0.521167773
2.42 0.374081450
3.42 0.423162690
5.42 -0.176780025
7.42 0.041677290
8.42 -0.032905698
10.42 -0.259002436
13.42 -0.281203993
14.42 -0.049057382
15.42 -0.060894845
16.42 -0.015722147
17.42 0.049263558
18.42 0.163404213
19.42 0.072708033
20.42 0.025013061
22.42 -0.021550817
23.42 1.764759939
2.43 0.248831909
4.43 0.156088203
5.43 0.030342911
7.43 0.146404084
8.43 0.058889296
10.43 -0.244887618
11.43 0.065975057
14.43 0.285954885
16.43 0.080573384
17.43 0.080008653
18.43 0.142943892
19.43 -0.096696348
20.43 0.485220520
22.43 -0.058199907
23.43 -0.552300578
2.44 0.167512143
3.44 -0.240950757
4.44 0.007918817
5.44 0.045492732
7.44 0.036071328
10.44 0.997436840
11.44 0.516728883
13.44 0.273620259
14.44 0.083410704
15.44 -0.004035759
16.44 -0.077163388
17.44 0.106558643
18.44 0.087877179
19.44 0.124827992
20.44 0.220720574
22.44 0.137540604
23.44 -0.030920094
2.45 0.357434280
3.45 0.086709739
4.45 0.316149855
5.45 0.225634204
7.45 -0.148090811
11.45 -0.834249191
13.45 0.726218791
14.45 -0.076558143
15.45 0.308781227
16.45 0.082326314
17.45 0.165083334
18.45 0.061990666
19.45 -0.008541938
20.45 0.103554568
23.45 -0.670360692
2.46 0.227698248
4.46 0.246662910
5.46 0.049735712
7.46 0.437046407
10.46 1.632750814
11.46 -1.091252069
13.46 0.421955538
14.46 0.038060410
15.46 0.182986603
17.46 -0.034736973
18.46 -0.287575413
20.46 0.153073269
22.46 -0.097035002
23.46 -0.525776200
3.47 0.400225498
4.47 -0.086595495
5.47 0.061244855
7.47 0.019112267
8.47 -0.365258106
11.47 2.591503194
13.47 1.764557202
14.47 0.141058294
15.47 0.118756395
16.47 0.072666166
18.47 -0.279282957
19.47 0.103383229
22.47 -0.038857018
23.47 -0.142609307
5.48 0.032113870
23.48 -0.049936301
$subject
(Intercept)
2 -0.31203347
3 -0.01612672
4 -0.48290124
5 -0.42909261
7 -0.33982119
8 0.38713526
10 1.37789148
11 1.44781710
13 -0.07823661
14 0.42791678
15 -0.32991041
16 -0.40550283
17 -0.50639462
18 -0.28886302
19 -0.45217993
20 -0.30829814
22 -0.49633916
23 0.80493934
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.36732185 -0.16127923 0.01344194 0.01376996 0.06401542 0.14803012
=============================================================
--- Mixed - Block 1 - Axis Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 1.942 0.3884 5 3151.2 4.3 0.0006697 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.07159 0.0169 3151 -4.238 0.0003
Step1 - Step3 -0.03566 0.0169 3151 -2.109 0.2830
Step1 - Step4 -0.03589 0.0169 3151 -2.119 0.2775
Step1 - Step5 -0.04665 0.0169 3151 -2.759 0.0646
Step1 - Step6 -0.01574 0.0169 3151 -0.932 0.9384
Step2 - Step3 0.03594 0.0169 3151 2.125 0.2744
Step2 - Step4 0.03571 0.0169 3151 2.109 0.2830
Step2 - Step5 0.02494 0.0169 3151 1.475 0.6803
Step2 - Step6 0.05585 0.0169 3151 3.306 0.0123
Step3 - Step4 -0.00023 0.0170 3151 -0.014 1.0000
Step3 - Step5 -0.01099 0.0169 3151 -0.650 0.9871
Step3 - Step6 0.01992 0.0169 3151 1.178 0.8475
Step4 - Step5 -0.01076 0.0170 3151 -0.635 0.9884
Step4 - Step6 0.02015 0.0169 3151 1.190 0.8419
Step5 - Step6 0.03091 0.0169 3151 1.828 0.4477
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 6 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5 Step6
1.77809141 0.07159200 0.03565592 0.03588628 0.04664855 0.01573742
Random Effects:
$trial_id
(Intercept)
14.1 0.0310326430
7.2 0.7923597172
18.2 -1.5449978965
20.2 0.0258649701
23.2 -0.6689585029
4.3 0.1646379234
5.3 -0.1257642483
10.3 1.1975465344
15.3 0.3135354818
16.3 -0.6365720278
17.3 0.0154424284
2.4 -0.2040571887
7.4 0.4762449512
14.4 0.1555567695
15.4 0.7291088812
17.4 -0.0253246193
23.4 -0.7940679056
3.5 -0.8276012987
4.5 -0.3658089327
7.5 0.6560742510
10.5 0.6835668024
14.5 -0.0125879458
18.5 -0.9840668764
19.5 -0.4874462410
20.5 -0.1406533893
22.5 -0.2363683487
3.6 -0.4750263444
5.6 -0.0188829390
14.6 -0.3539695746
15.6 -0.3193124382
16.6 -0.6986765202
17.6 -0.2118993646
18.6 -0.6923351523
19.6 -0.2463116089
20.6 -0.1371500577
22.6 -0.0435709510
2.7 -0.0196696231
3.7 -0.6152370450
4.7 0.1453727957
5.7 -0.0983662715
7.7 -0.8448461179
8.7 -0.6549890771
10.7 -0.1444406611
11.7 -2.3636814769
15.7 -0.2417766933
16.7 -0.2635296958
17.7 -0.1377788213
18.7 -1.0815048425
22.7 0.3283787154
23.7 0.3199953144
2.8 0.1998675237
3.8 -0.0532793346
4.8 -0.1198763073
5.8 -0.2476495236
7.8 0.3189255221
8.8 -1.3109104847
10.8 2.4088174829
11.8 1.9542527292
14.8 0.2758929233
15.8 0.0826823003
16.8 0.0041318004
17.8 -0.0232317239
18.8 -0.8595693545
19.8 -0.3768344622
20.8 -0.1225114762
22.8 -0.0307349137
2.9 0.4000967519
3.9 0.0933680866
4.9 0.1902489663
7.9 0.5310651575
10.9 -0.5998971145
11.9 -1.2712407772
13.9 0.7581701668
14.9 -0.2920874312
15.9 0.4993383810
16.9 0.0633687675
17.9 0.0471584463
18.9 -0.9032584652
19.9 -0.1808716546
20.9 -0.4085057469
22.9 -0.2630288608
23.9 -1.0107559391
2.10 -0.4565343957
3.10 0.1384784389
4.10 0.2448795950
5.10 -0.4787499005
7.10 0.3203456017
8.10 -0.4429817547
10.10 4.5906218970
11.10 -1.9836164328
14.10 0.2441157322
15.10 0.6815883466
17.10 -0.0893822503
18.10 -0.3015186088
3.11 -0.2490439716
5.11 -0.2511583114
7.11 0.8345879487
8.11 0.4555071187
10.11 3.2719285413
14.11 -0.2156649053
15.11 0.8109721598
17.11 0.0924524736
18.11 -0.0085789590
19.11 -0.0228723561
22.11 -0.1621344290
23.11 -1.5608888177
2.12 -0.1505553359
3.12 -0.4650033968
4.12 0.1106040659
7.12 -0.1117626696
8.12 -1.0852863020
10.12 -1.2059606587
13.12 0.7196119528
14.12 -0.3850398440
15.12 0.1108987334
16.12 0.4190397095
17.12 0.1135565070
18.12 -0.0288995066
19.12 0.0389338881
23.12 0.2295814374
3.13 -0.3771064793
4.13 0.0699456023
5.13 -0.5974787467
7.13 0.3096585849
8.13 -0.6538307440
10.13 -0.2100206083
13.13 -0.0571936243
14.13 0.0765105345
15.13 -0.1486660807
16.13 -0.5816597146
17.13 -0.0691443520
18.13 -0.0792238007
19.13 -0.0730433448
22.13 -0.2088136695
23.13 0.7393807139
2.14 -0.4799041342
3.14 0.0429662177
5.14 -0.5358328034
7.14 0.4014564907
8.14 -1.5775988155
10.14 -0.0479511402
11.14 -1.9403520535
14.14 -0.2530772185
15.14 -0.2862241153
16.14 0.0787551071
17.14 0.0265391024
18.14 -0.5628291265
22.14 -0.5402438870
23.14 -0.1750153066
2.15 -0.4841753839
4.15 -0.0497227773
5.15 -0.4261586494
8.15 -1.1183774994
10.15 0.0236986820
11.15 0.0288746897
13.15 0.4195270874
15.15 -0.2038761194
16.15 0.0967921740
17.15 0.0485205745
18.15 -0.3875406735
19.15 -0.1245816535
20.15 -0.4948657276
23.15 0.5086647586
3.16 -0.3302975311
4.16 0.1767873074
5.16 -0.1853504170
7.16 -0.0365448478
8.16 -0.2578912865
10.16 -0.4115380333
11.16 0.1996665121
13.16 0.3188729604
14.16 -0.2503187194
15.16 -0.3321234423
16.16 0.5058177595
19.16 -0.3889623408
20.16 -0.2627288194
22.16 0.0442784503
23.16 -0.0692493745
3.17 -0.7236498574
4.17 0.4688075806
5.17 -0.1206421864
8.17 -0.4084936265
10.17 0.5436250493
13.17 0.0059979902
14.17 0.0701759101
15.17 0.2758167115
16.17 -0.3496498983
17.17 -0.0878553346
18.17 -0.3155344102
19.17 -0.1918277541
20.17 0.0248886785
22.17 0.0185697337
23.17 0.5356832319
2.18 0.5142914577
3.18 0.0125777671
4.18 -0.0759059101
5.18 -0.4823325960
7.18 0.0647820201
10.18 0.0465254280
13.18 -0.1696524058
16.18 -0.1610706207
18.18 -0.6882878202
20.18 0.0379247110
22.18 -0.1518661380
23.18 -0.0810386220
3.19 -0.5385990198
4.19 -0.1352045538
5.19 -0.3464164396
7.19 0.3765839937
8.19 -0.3624901866
10.19 -0.9502627909
11.19 0.8844839964
13.19 0.2195772678
14.19 -0.3682919374
15.19 0.2044035770
16.19 0.3724705047
17.19 0.1619943427
18.19 -0.3897538573
19.19 0.2541758102
20.19 -0.0222397167
22.19 0.0017746477
23.19 0.6473871237
3.20 -1.1396690519
4.20 -0.1666577931
5.20 -0.5995050909
7.20 0.0143843210
8.20 0.7335103804
13.20 0.9644892272
14.20 0.1239780208
16.20 -0.0214393693
17.20 -0.1516851603
18.20 -0.5410911559
19.20 -0.0751905575
20.20 -0.3598506406
22.20 -0.2419198070
23.20 2.7303834859
2.21 -0.2219105323
3.21 -0.9085175774
4.21 -0.1696232315
5.21 -0.0571372583
7.21 -0.5282029310
8.21 -0.2584264369
10.21 0.5957270845
11.21 -0.6363837109
14.21 -0.0882838557
15.21 0.1092676681
16.21 -0.0126356749
17.21 -0.0841637519
18.21 -0.5548192749
19.21 0.3244225333
20.21 -0.1002243936
22.21 0.1472172752
23.21 2.5580440955
2.22 -0.0145598381
3.22 -0.3222140830
4.22 -0.0306658277
7.22 -0.2157042884
8.22 -0.5942377543
10.22 -1.6407591514
11.22 -0.9261756893
13.22 0.6517301374
14.22 0.4500582645
15.22 0.2178153795
16.22 -0.2216736716
17.22 -0.1134276382
18.22 -0.3351951489
19.22 0.0083859541
20.22 -0.2666001652
22.22 -0.0521621908
23.22 0.7232963905
2.23 -0.1284683564
4.23 0.3442556864
5.23 0.2915079890
7.23 0.0148436462
8.23 -0.2163542274
10.23 -1.2030277717
11.23 -0.7437645516
13.23 0.0979295770
15.23 0.0145048403
17.23 -0.0105355610
18.23 0.1627697776
19.23 0.2634102107
20.23 -0.3050350635
22.23 -0.2482912208
2.24 -0.0608145217
4.24 0.1468070981
5.24 -0.2807539020
7.24 0.2739366834
8.24 -0.4323949241
10.24 -0.9708674140
13.24 -0.0948884529
14.24 0.7207530774
15.24 -0.4614797950
16.24 0.0497954521
17.24 0.0086816481
19.24 -0.2954560002
20.24 0.0087182051
22.24 -0.2757293678
23.24 0.2306331889
2.25 -0.6250430850
3.25 -1.3152517460
4.25 -0.0578255286
5.25 -0.0389116370
7.25 0.3812886312
10.25 -0.4905658205
13.25 0.9427460517
14.25 -0.1026051474
15.25 0.1027336800
17.25 0.0719358861
18.25 -0.1023203125
19.25 -0.1063828351
20.25 -0.1850315360
22.25 -0.0768759361
2.26 0.1520407241
3.26 -0.1238441520
4.26 -0.1571532795
5.26 0.1441510559
7.26 -0.0618937397
8.26 0.8077194272
10.26 -0.1853478969
14.26 0.0003245317
15.26 0.4397425972
16.26 -0.0020027955
18.26 -0.1210737555
19.26 0.0195486249
20.26 -0.2134425568
2.27 0.2712828127
3.27 1.1831094267
4.27 0.2373657291
5.27 0.6045174904
8.27 0.5045495916
10.27 -0.6002517681
14.27 -0.2440969131
15.27 -0.3238599620
16.27 -0.1958495860
17.27 -0.1872673617
18.27 -0.2274508790
19.27 -0.4366045828
22.27 -0.1502931103
23.27 0.4313875717
2.28 -0.1605267788
3.28 -0.1855684248
4.28 0.2049731090
7.28 -0.2654513953
10.28 -0.8245419764
11.28 -1.2736790315
15.28 -0.0139648565
16.28 -0.2801289749
17.28 -0.2207943806
19.28 -0.1021687866
20.28 -0.0150391837
22.28 -0.2177467189
23.28 -1.2597971698
2.29 0.6339642893
3.29 0.8284787787
4.29 -0.0519198079
5.29 0.2599848188
7.29 -0.0341455333
8.29 -0.3158943122
13.29 0.4938120446
14.29 0.3012398032
16.29 0.0207787339
17.29 0.0488214189
18.29 -0.2709453772
20.29 -0.3024379391
22.29 -0.0531260824
23.29 -0.4823593763
2.30 -0.2873217324
3.30 0.4870818452
5.30 0.4993756785
7.30 -0.0678075226
10.30 -0.7694882191
11.30 3.6245397134
13.30 -0.0556396068
14.30 0.2903040680
15.30 -0.1617183085
18.30 0.4063359657
19.30 0.0605862108
20.30 0.1145732414
22.30 -0.1081076606
23.30 -0.7269831220
2.31 -0.2202983930
3.31 -0.0149382199
4.31 -0.1983941836
7.31 -0.8994338309
8.31 -0.4501195324
10.31 0.3140021244
11.31 2.7130972897
13.31 -0.2187327198
14.31 -0.2926378072
16.31 0.6254916199
17.31 -0.0800349868
18.31 0.9611897382
19.31 -0.0597982627
20.31 -0.4233646435
22.31 -0.0526532217
23.31 1.4543145890
2.32 -0.1788044890
4.32 -0.1157616380
5.32 -0.0877354852
7.32 -0.4133613530
8.32 1.2391856011
13.32 -0.4164905431
14.32 0.5400920878
15.32 -0.1338498503
17.32 0.0376345811
18.32 0.2207239653
19.32 0.4706682023
20.32 0.6160531036
22.32 -0.1406187941
23.32 3.5343533680
2.33 -0.4951737461
4.33 0.2241725897
7.33 -0.5493134577
8.33 2.6697337975
13.33 -0.0883678028
15.33 -0.6673938100
16.33 -0.2269893820
17.33 0.0807424999
18.33 0.1500991684
19.33 0.0092641598
20.33 0.1698479843
22.33 0.0051818910
2.34 -0.1491076071
3.34 0.0813862118
5.34 0.3363186795
8.34 0.4964220360
10.34 0.3094857943
11.34 -0.3491689171
13.34 0.3974953604
14.34 -0.0532125209
16.34 -0.4423421236
17.34 -0.0505242475
18.34 0.0800886291
19.34 0.2215984562
22.34 0.4390279816
23.34 0.2802824078
2.35 -0.1572118726
3.35 0.8249635172
4.35 -0.2606250891
5.35 0.0996937392
7.35 -0.3128443786
8.35 0.7154017074
10.35 -0.1303918151
13.35 -0.1069123430
14.35 0.1163357785
15.35 -0.4086032638
16.35 -0.3564510589
17.35 -0.0193365105
18.35 0.6170640690
19.35 0.2709648452
20.35 0.3913201464
22.35 0.0394341418
23.35 -0.1116393693
2.36 0.0440250060
3.36 0.4246299331
5.36 0.1380882838
7.36 0.0201880863
8.36 0.0817931118
10.36 0.1670161829
11.36 3.4805977985
13.36 -0.7297825188
17.36 -0.1510096835
18.36 0.1128895845
20.36 -0.0779623732
22.36 0.1293335934
23.36 -0.1541044583
2.37 0.5054109153
3.37 0.7292914387
4.37 -0.2877865670
7.37 0.4352978680
8.37 0.5408046724
13.37 -0.7738284749
14.37 0.0050214817
16.37 0.6500821981
17.37 -0.2721282342
18.37 0.8181852829
19.37 -0.0380769719
20.37 0.2017381445
22.37 0.2336318157
23.37 -0.3762659514
2.38 0.9995935061
4.38 -0.1975121185
7.38 0.1153423794
8.38 0.4530736411
11.38 0.0813820581
14.38 -0.0987493167
15.38 -0.6179566235
16.38 -0.2807243953
17.38 -0.0159175649
18.38 0.3371742334
20.38 0.3047252399
22.38 0.1615732800
3.39 0.1957122824
4.39 0.2575185193
5.39 0.5270909585
7.39 0.0741292523
10.39 0.4432704726
11.39 0.4319491689
13.39 -0.5860917860
14.39 0.0673322635
15.39 -0.3192479340
16.39 0.1105980029
17.39 -0.2322735462
18.39 0.5899456373
19.39 -0.0969428500
20.39 0.3692689996
22.39 0.2064828899
23.39 -0.1052076232
2.40 -0.1926826300
3.40 0.4155342513
4.40 -0.0495453841
5.40 0.2008912001
8.40 -0.1931883498
10.40 -0.3474091014
11.40 1.8658383450
13.40 -0.9086253968
14.40 0.1941310803
15.40 -0.7738358209
16.40 0.8241619262
17.40 0.2009358816
18.40 0.8848287349
19.40 -0.3367000579
20.40 0.1615352992
22.40 0.0049654780
23.40 0.1828947820
2.41 -0.1962953470
3.41 0.0911133246
4.41 -0.4239122507
5.41 0.2288846733
7.41 -0.6393311330
8.41 -0.8870249648
10.41 -1.1431809213
13.41 -0.9853350874
14.41 -0.2181925174
16.41 0.0702412949
17.41 0.2748372705
19.41 0.2885539121
20.41 0.1811017104
23.41 -0.4112938295
2.42 0.0499839541
3.42 1.5069782471
5.42 0.2067164674
7.42 -0.4352310491
8.42 1.5736331115
10.42 0.0620541383
13.42 -0.9501361993
14.42 0.0461084898
15.42 -0.1583739909
16.42 -0.6057880783
17.42 0.0945881521
18.42 0.6327769597
19.42 0.3096893002
20.42 -0.0673416737
22.42 0.3170001922
23.42 -1.4269655055
2.43 0.3328491647
4.43 -0.5719243818
5.43 0.4943121630
7.43 -0.0215171177
8.43 0.2887327858
10.43 0.5215996844
11.43 -0.5321828896
14.43 -0.1554029106
16.43 0.1647572774
17.43 0.1911432732
18.43 0.2746485465
19.43 -0.2338407563
20.43 0.4670717281
22.43 0.1709235181
23.43 -0.0063676338
2.44 -0.3231601395
3.44 0.2976221523
4.44 0.3580267397
5.44 0.1962188642
7.44 -0.0900638292
10.44 -0.8629342820
11.44 1.7448919210
13.44 0.1000505264
14.44 -0.2561474902
15.44 -0.3247380106
16.44 0.2857868346
17.44 -0.0243737058
18.44 1.2540657757
19.44 0.2784859643
20.44 0.0190501330
22.44 0.2265743082
23.44 0.0387772495
2.45 0.4651265845
3.45 0.9652569584
4.45 0.1433712370
5.45 0.1408454701
7.45 0.3236846353
11.45 -0.8331342360
13.45 -0.0507640407
14.45 -0.0899033212
15.45 1.4503744465
16.45 0.1006895028
17.45 -0.0506977228
18.45 0.8783363075
19.45 0.3717847667
20.45 0.3200929736
23.45 -2.0208740930
2.46 0.2680905937
4.46 -0.0483056484
5.46 0.2335244444
7.46 -0.6776706792
10.46 -1.9799009402
11.46 -1.9877406903
13.46 0.3010983802
14.46 -0.1380034561
15.46 -0.5918493613
17.46 0.2454269405
18.46 1.3781481620
20.46 0.1269030030
22.46 0.3450880314
23.46 -1.1388951888
3.47 0.7742669840
4.47 -0.2387938914
5.47 0.2065771573
7.47 -0.4355986552
8.47 0.9912476599
11.47 -1.1671565985
13.47 -0.0289682982
14.47 -0.2058442197
15.47 0.4656034960
16.47 0.6559052388
18.47 1.2690422602
19.47 0.2988328189
22.47 0.1086211767
23.47 -0.5143409890
5.48 -0.2451955577
23.48 -1.3047209486
$subject
(Intercept)
2 -0.8638459
3 1.0001267
4 -0.6663734
5 -0.9705752
7 0.2207340
8 0.7731093
10 1.0767297
11 2.3399490
13 0.3965736
14 -0.8533339
15 0.0456545
16 -0.5574008
17 -1.2815074
18 0.1110446
19 -0.8987958
20 -0.8513550
22 -0.7623684
23 1.7416345
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
0.21361758 -0.24249548 -0.17088005 -0.17164653 -0.06187026 0.30262151
=============================================================
--- Mixed - Block 2 - Axis X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 16.394 1.4904 11 6049 16.89 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.003946 0.0179 6049 -0.220 1.0000
Step1 - Step3 -0.005719 0.0179 6049 -0.320 1.0000
Step1 - Step4 0.004589 0.0179 6049 0.256 1.0000
Step1 - Step5 0.000053 0.0179 6049 0.003 1.0000
Step1 - Step6 0.031659 0.0179 6049 1.769 0.8349
Step1 - Step7 0.052129 0.0179 6049 2.911 0.1370
Step1 - Step8 0.065019 0.0179 6049 3.633 0.0149
Step1 - Step9 0.090900 0.0179 6049 5.079 <.0001
Step1 - Step10 0.106504 0.0179 6049 5.951 <.0001
Step1 - Step11 0.118291 0.0179 6049 6.610 <.0001
Step1 - Step12 0.131198 0.0179 6049 7.331 <.0001
Step2 - Step3 -0.001773 0.0179 6049 -0.099 1.0000
Step2 - Step4 0.008535 0.0179 6049 0.477 1.0000
Step2 - Step5 0.003999 0.0179 6049 0.223 1.0000
Step2 - Step6 0.035605 0.0179 6049 1.989 0.7008
Step2 - Step7 0.056074 0.0179 6049 3.132 0.0753
Step2 - Step8 0.068965 0.0179 6049 3.854 0.0066
Step2 - Step9 0.094846 0.0179 6049 5.300 <.0001
Step2 - Step10 0.110450 0.0179 6049 6.172 <.0001
Step2 - Step11 0.122237 0.0179 6049 6.830 <.0001
Step2 - Step12 0.135144 0.0179 6049 7.551 <.0001
Step3 - Step4 0.010308 0.0179 6049 0.576 1.0000
Step3 - Step5 0.005772 0.0179 6049 0.323 1.0000
Step3 - Step6 0.037378 0.0179 6049 2.089 0.6311
Step3 - Step7 0.057848 0.0179 6049 3.231 0.0562
Step3 - Step8 0.070738 0.0179 6049 3.953 0.0045
Step3 - Step9 0.096619 0.0179 6049 5.399 <.0001
Step3 - Step10 0.112223 0.0179 6049 6.271 <.0001
Step3 - Step11 0.124010 0.0179 6049 6.929 <.0001
Step3 - Step12 0.136918 0.0179 6049 7.651 <.0001
Step4 - Step5 -0.004536 0.0179 6049 -0.253 1.0000
Step4 - Step6 0.027070 0.0179 6049 1.513 0.9375
Step4 - Step7 0.047540 0.0179 6049 2.655 0.2494
Step4 - Step8 0.060430 0.0179 6049 3.377 0.0356
Step4 - Step9 0.086311 0.0179 6049 4.823 0.0001
Step4 - Step10 0.101915 0.0179 6049 5.695 <.0001
Step4 - Step11 0.113702 0.0179 6049 6.353 <.0001
Step4 - Step12 0.126610 0.0179 6049 7.075 <.0001
Step5 - Step6 0.031606 0.0179 6049 1.766 0.8365
Step5 - Step7 0.052076 0.0179 6049 2.908 0.1380
Step5 - Step8 0.064966 0.0179 6049 3.630 0.0151
Step5 - Step9 0.090847 0.0179 6049 5.076 <.0001
Step5 - Step10 0.106451 0.0179 6049 5.948 <.0001
Step5 - Step11 0.118238 0.0179 6049 6.607 <.0001
Step5 - Step12 0.131145 0.0179 6049 7.328 <.0001
Step6 - Step7 0.020469 0.0179 6049 1.143 0.9927
Step6 - Step8 0.033360 0.0179 6049 1.864 0.7816
Step6 - Step9 0.059241 0.0179 6049 3.310 0.0440
Step6 - Step10 0.074845 0.0179 6049 4.182 0.0017
Step6 - Step11 0.086632 0.0179 6049 4.841 0.0001
Step6 - Step12 0.099539 0.0179 6049 5.562 <.0001
Step7 - Step8 0.012890 0.0179 6049 0.720 0.9999
Step7 - Step9 0.038771 0.0179 6049 2.165 0.5751
Step7 - Step10 0.054375 0.0179 6049 3.037 0.0983
Step7 - Step11 0.066162 0.0179 6049 3.695 0.0119
Step7 - Step12 0.079070 0.0179 6049 4.416 0.0006
Step8 - Step9 0.025881 0.0179 6049 1.446 0.9543
Step8 - Step10 0.041485 0.0179 6049 2.318 0.4636
Step8 - Step11 0.053272 0.0179 6049 2.977 0.1156
Step8 - Step12 0.066179 0.0179 6049 3.698 0.0118
Step9 - Step10 0.015604 0.0179 6049 0.872 0.9994
Step9 - Step11 0.027391 0.0179 6049 1.531 0.9323
Step9 - Step12 0.040299 0.0179 6049 2.252 0.5117
Step10 - Step11 0.011787 0.0179 6049 0.659 1.0000
Step10 - Step12 0.024695 0.0179 6049 1.380 0.9675
Step11 - Step12 0.012908 0.0179 6049 0.721 0.9999
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 12 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5
7.690118e-01 3.945967e-03 5.719087e-03 -4.588718e-03 -5.300349e-05
Step6 Step7 Step8 Step9 Step10
-3.165905e-02 -5.212852e-02 -6.501902e-02 -9.089965e-02 -1.065038e-01
Step11 Step12
-1.182909e-01 -1.311984e-01
Random Effects:
$trial_id
(Intercept)
10.1 -0.1157166863
16.1 0.0413623674
17.1 -0.1182402599
19.1 -0.0643224989
7.2 -0.0935501347
14.2 0.1513759525
17.2 -0.1057685968
20.2 0.0320108893
7.3 0.1407141648
10.3 0.4736570012
17.3 -0.0425401306
18.3 -0.1329886054
20.3 0.0811664389
2.4 -0.4429187272
7.4 -0.0298049015
10.4 0.5116253766
13.4 0.0010251952
14.4 0.2651724958
16.4 0.1261964543
17.4 0.1054406284
18.4 -0.1768597533
19.4 -0.0926396760
20.4 -0.0595823069
22.4 0.0094732814
23.4 0.3599300890
2.5 -0.0973643063
3.5 0.1024058702
5.5 -0.0188821934
7.5 0.1817516475
10.5 0.3991727780
11.5 -0.1772548064
13.5 0.5900176605
14.5 0.2883464537
17.5 -0.0746408421
18.5 -0.1764973200
20.5 0.0308500230
3.6 0.0987352904
8.6 -0.1741153430
10.6 0.7003936893
11.6 -0.4906296194
14.6 0.4699589045
15.6 0.0058249465
16.6 -0.0354586581
17.6 -0.0831518250
20.6 0.1252128827
2.7 -0.4288716734
3.7 0.3877650643
5.7 -0.0386416910
7.7 0.3107652888
8.7 -0.1867284241
11.7 -0.1196989702
13.7 -0.0629869849
14.7 0.1597849032
15.7 0.0409430658
16.7 0.0037903067
17.7 0.0289039036
18.7 -0.0988972744
19.7 -0.0217095158
20.7 0.0341134573
22.7 -0.1025386409
2.8 -0.6120450053
3.8 0.0425450458
5.8 -0.0109579564
7.8 -0.0313613511
10.8 0.1805798612
11.8 -0.1752597622
14.8 0.3721396089
15.8 0.2224787338
17.8 -0.1377964024
18.8 -0.0307355627
19.8 -0.0470732878
22.8 -0.0138962105
2.9 0.1927677047
3.9 0.0177271737
4.9 0.1724928895
5.9 0.0619354379
7.9 0.0597576884
8.9 -0.0966796188
10.9 0.1954028125
13.9 -0.0856635621
14.9 -0.0825108631
16.9 0.0112457613
17.9 0.1807856772
18.9 0.0133245001
19.9 0.0753319744
22.9 0.0603796839
2.10 0.4405235522
8.10 -0.2034545290
10.10 0.5902558287
11.10 1.0032193085
13.10 0.0950693168
14.10 0.0619967482
15.10 -0.0361664635
16.10 -0.0826203999
17.10 -0.1362895456
19.10 -0.0805612432
22.10 -0.0658659971
2.11 -0.1782078590
4.11 -0.0939464180
7.11 -0.0730755223
8.11 -0.1039152568
13.11 -0.1389370738
14.11 -0.0471246352
15.11 -0.0220872096
16.11 0.4933968842
17.11 -0.0463966833
19.11 -0.0500605074
23.11 -0.1448527873
2.12 -0.0976518801
4.12 0.0739214202
5.12 -0.0481057022
8.12 0.9353542827
13.12 -0.1051227757
15.12 0.1034053408
16.12 -0.1997185275
17.12 -0.0097332928
18.12 0.0754192590
19.12 0.1315557013
22.12 -0.1262374608
23.12 -0.2490770349
2.13 -0.2761089399
3.13 -0.0669558656
4.13 -0.1950806582
5.13 -0.1288714510
7.13 -0.1079975654
10.13 0.4584648552
11.13 0.1777700804
13.13 0.0643920080
14.13 0.3547938296
15.13 -0.0847559141
16.13 0.1033234925
17.13 -0.0276871472
18.13 0.0554142048
19.13 0.0152770034
23.13 -0.3969781786
2.14 1.1061694556
3.14 -0.1617149323
4.14 0.2429625021
7.14 -0.1741223545
8.14 -0.0215920536
10.14 0.2903276736
13.14 -0.0467802519
15.14 0.0476904233
16.14 0.1929851161
17.14 -0.0511470004
18.14 -0.1226061879
19.14 -0.0138216427
23.14 -0.2954161446
2.15 -0.0176593823
4.15 0.0082522393
5.15 -0.0463394156
7.15 -0.1022876204
8.15 -0.2984416550
10.15 0.0493125508
11.15 0.0504337730
13.15 -0.0806264096
14.15 0.1774883556
15.15 0.0752695626
16.15 -0.0686160120
17.15 0.0016501764
18.15 -0.1033336909
19.15 -0.0785555570
23.15 0.0632501253
2.16 -0.1114551428
3.16 0.0445978394
7.16 -0.1217589811
8.16 0.0626195185
10.16 -0.0234285294
11.16 0.6898087488
13.16 -0.0716482315
14.16 -0.1118308240
15.16 -0.0565925493
16.16 0.0524161209
17.16 -0.0671832396
18.16 -0.0932245310
19.16 -0.1849308776
2.17 0.1096803658
3.17 0.0600627974
5.17 -0.0185343320
8.17 0.0663428609
13.17 -0.0835888529
14.17 0.1381118260
15.17 -0.2412002011
17.17 -0.0567843545
18.17 -0.0466044599
19.17 0.1524023438
23.17 -0.0696565150
2.18 -0.2570341809
3.18 0.1195530937
4.18 0.0547131175
5.18 -0.0761304291
7.18 0.0506730671
10.18 0.2009004882
13.18 0.0099008125
16.18 -0.0303209461
23.18 0.0210693827
2.19 -0.3501870907
5.19 0.0742901870
7.19 0.1041937416
10.19 -0.1356610365
11.19 -0.1916411433
13.19 0.1616850871
14.19 0.2216379130
15.19 0.1807266132
16.19 -0.2127273897
17.19 -0.0507751331
18.19 -0.1696390724
2.20 -0.0056235469
3.20 0.1139287619
4.20 -0.1132687151
5.20 0.0058629654
7.20 0.0180029940
10.20 0.3704506615
11.20 0.3744587455
13.20 -0.0195095798
15.20 -0.0979514900
17.20 -0.0351716453
18.20 0.2068350811
23.20 0.2390447500
2.21 0.4787799873
3.21 -0.0934215000
4.21 0.0266404927
5.21 -0.0309985051
7.21 -0.1104865231
14.21 0.3000982903
15.21 0.0699703717
16.21 -0.0723288020
17.21 -0.1507414732
18.21 -0.1397760127
3.22 0.1557507582
4.22 -0.1461805808
5.22 -0.0918798362
7.22 0.0319004951
10.22 0.6919691277
13.22 -0.0793129314
14.22 -0.0804202987
16.22 -0.1716473620
17.22 0.0025810139
18.22 0.0859678221
23.22 -0.1560918836
2.23 0.1524633924
3.23 0.1761516233
4.23 0.0171404265
5.23 0.0011274575
7.23 -0.1646502069
10.23 1.3947749408
11.23 0.7012128856
14.23 0.1468793450
16.23 -0.1372913438
18.23 -0.0814167462
2.24 0.0832209360
3.24 0.2749497592
4.24 -0.0676046592
5.24 -0.1458330266
11.24 0.9601554808
13.24 -0.0250941085
15.24 -0.1789824819
17.24 -0.1037708669
18.24 0.0806834487
23.24 -0.0296504812
2.25 -0.1013425655
3.25 -0.0575524372
4.25 0.2651813360
7.25 0.0639610323
13.25 -0.1314078792
15.25 0.3804145851
16.25 -0.0487037816
17.25 0.0902166423
18.25 0.0238041602
23.25 0.3087039280
2.26 -0.0024955306
3.26 0.0029508550
5.26 -0.0786267149
7.26 -0.0375683325
10.26 0.5270576881
11.26 2.0495933423
13.26 -0.0445667827
15.26 0.2479722538
16.26 0.1545948932
17.26 0.1055878128
18.26 -0.1269052051
19.26 -0.1576759395
22.26 -0.0269706698
23.26 -0.0377046145
2.27 -0.1102104886
3.27 -0.1750668404
8.27 -0.2635104738
10.27 0.2434675312
11.27 1.0461757709
13.27 0.1109364394
15.27 0.0579668253
16.27 -0.1211706386
17.27 0.1378388484
18.27 -0.1590207110
22.27 0.1207414654
23.27 0.4679052289
2.28 -0.1576809006
3.28 0.1487516691
5.28 -0.0539774563
7.28 0.0086425812
8.28 -0.0973924131
13.28 0.2944018367
15.28 0.1253604067
16.28 -0.1850178060
17.28 0.0268271667
18.28 -0.0048630481
19.28 -0.2022729843
23.28 0.4180861273
2.29 0.5700499878
3.29 0.3273044458
4.29 -0.1092927755
5.29 -0.0316381969
7.29 0.1957713195
8.29 0.4264894984
10.29 0.3475395488
14.29 0.1794624353
16.29 -0.1058035840
17.29 0.1161009117
18.29 -0.0105532871
23.29 0.3337076421
3.30 0.3653549918
4.30 0.0499938383
5.30 0.1818476983
7.30 -0.0995845424
8.30 0.2913218916
11.30 -0.8289546264
15.30 0.0164932455
16.30 -0.0103611324
18.30 -0.0415966623
23.30 0.2314068104
2.31 0.8277672050
5.31 0.1831151366
7.31 0.3127290650
8.31 0.6483885175
10.31 -0.6181597440
16.31 -0.0739872993
18.31 -0.1138450579
19.31 -0.0135463758
23.31 0.0651167563
3.32 -0.1875127723
4.32 0.0795439144
5.32 0.3645527902
7.32 0.0975259672
8.32 0.0005455159
10.32 -0.4033949520
13.32 -0.0444591401
16.32 0.1812102829
17.32 0.2622652340
19.32 -0.0883222716
23.32 0.1246531643
2.33 -0.0374999191
3.33 -0.0052196847
4.33 -0.0987288509
5.33 0.1527196381
7.33 0.2482029803
8.33 -0.1376325202
10.33 0.1534947072
11.33 -0.6375954602
13.33 0.1735759779
15.33 -0.1381467202
16.33 0.2730230571
17.33 0.2142695933
18.33 -0.2350347511
19.33 -0.1396653018
20.33 -0.0399971992
22.33 -0.0264496631
2.34 -0.0641632079
4.34 0.0055317100
5.34 -0.1049313005
7.34 0.1720111519
8.34 0.1877810741
10.34 0.1330048303
11.34 0.3787606610
13.34 -0.0399152205
15.34 0.4151452744
16.34 0.6571396643
17.34 0.0754640372
18.34 -0.0294723767
23.34 0.2392643425
2.35 0.1350123075
3.35 -0.1717362226
4.35 -0.0186680297
5.35 0.0787918852
7.35 -0.1503587355
8.35 -0.2037961060
10.35 -0.2125869364
13.35 -0.0167713268
15.35 0.0643273266
16.35 0.0470333780
17.35 -0.1561071702
18.35 0.2841378203
19.35 0.0289916914
22.35 -0.0679565454
2.36 0.0249725072
4.36 0.2275668654
8.36 0.5964522522
10.36 -0.6493330217
13.36 -0.1146472554
15.36 0.0762122026
16.36 0.3400981336
17.36 -0.0394866536
18.36 0.3822345763
19.36 -0.0262052346
22.36 0.0983797811
23.36 0.1397277884
2.37 -0.2529071213
4.37 0.1603453914
7.37 0.4254142405
8.37 -0.0966444618
11.37 0.0155566881
13.37 0.1878451297
14.37 -0.2095526090
15.37 -0.0037377448
16.37 0.0920707937
17.37 0.2445504286
18.37 0.5982581488
22.37 0.6853715317
23.37 0.0562506597
2.38 0.1580627494
3.38 -0.0065382751
4.38 0.0222881493
7.38 -0.0285267527
8.38 -0.3679948067
13.38 0.1172328463
14.38 0.5944144467
15.38 0.0721996905
16.38 -0.0231927193
17.38 0.1998138317
18.38 0.4794248122
19.38 0.0440984057
22.38 0.2555767776
23.38 0.1551533725
2.39 0.1456142907
3.39 -0.2021996413
5.39 -0.0562774644
8.39 -0.4137271145
11.39 -0.3618715020
13.39 0.1197002620
14.39 -0.2196995061
15.39 -0.0151295021
16.39 -0.0944111666
17.39 0.2637451765
18.39 0.0659969211
19.39 -0.0602162850
22.39 0.0724092131
23.39 -0.4517626490
3.40 0.2448671313
4.40 0.0253256715
5.40 0.1258546289
7.40 -0.0807297740
10.40 -0.0836173762
14.40 -0.2262000709
15.40 -0.0356711267
16.40 0.0368080471
18.40 0.0969913550
19.40 0.0048016214
20.40 -0.0941897488
23.40 -0.5195312492
3.41 -0.4620837702
4.41 -0.1676594803
5.41 0.2496615840
7.41 0.1181480495
10.41 -1.2028927097
11.41 -0.4196758500
13.41 0.0100243123
14.41 -0.2561963290
15.41 0.0278633749
16.41 -0.1627774356
17.41 0.0053518286
18.41 0.1580458818
19.41 0.3568452864
20.41 -0.0097280786
2.42 0.5319531508
4.42 -0.0323421791
5.42 0.1400780962
7.42 -0.2555518784
8.42 0.1954434099
10.42 -1.2495591954
11.42 -1.1627155108
15.42 -0.1030265116
16.42 -0.0619635525
17.42 -0.0020645088
18.42 0.3246808982
19.42 0.1388130262
20.42 -0.0953350199
22.42 0.0593055133
23.42 -0.2162540395
2.43 -0.6007516838
3.43 0.7346781235
4.43 -0.1300231629
5.43 0.2743050990
7.43 -0.3843938072
10.43 -0.1208832150
13.43 -0.3623775915
14.43 -0.2829834951
15.43 -0.3711894450
18.43 -0.0759081288
19.43 0.1167185524
20.43 -0.0775266282
22.43 -0.2077382882
23.43 -0.1447714170
3.44 -0.3778699939
4.44 -0.2231813976
5.44 -0.1464646354
10.44 -0.8565370306
11.44 -1.1963185215
13.44 -0.3102008319
14.44 -0.3126667212
15.44 -0.3291759793
16.44 0.0937646591
17.44 -0.0628944971
19.44 -0.0299724569
20.44 0.0068125596
22.44 -0.1212349296
23.44 -0.0770878897
2.45 -0.7225459997
3.45 -0.4492201573
4.45 -0.3234884320
5.45 -0.2596713193
7.45 -0.4264378315
13.45 0.0426512097
14.45 -0.6124809525
16.45 -0.3163160253
17.45 -0.2315049344
18.45 -0.0431452333
20.45 -0.0458430679
22.45 -0.3685164078
23.45 -0.4207286990
2.46 0.1803970846
5.46 -0.3194050201
7.46 -0.2161434719
10.46 -0.5174885320
11.46 -0.9294757266
13.46 -0.3650986431
14.46 -0.7741843542
15.46 -0.1813260203
16.46 -0.4829607706
17.46 -0.3822172200
18.46 -0.3212894548
20.46 -0.0245972743
22.46 -0.3528649098
3.47 -0.7902993083
5.47 -0.4284196833
8.47 -0.6383127676
10.47 -1.0336478258
14.47 -0.4609899112
15.47 -0.4554522606
16.47 -0.3544565886
17.47 -0.1566058799
18.47 -0.5099562050
20.47 -0.0732767689
$subject
(Intercept)
2 0.21377542
3 0.21174466
4 -0.28900637
5 -0.24164859
7 -0.14896757
8 0.10733646
10 0.69239747
11 0.75984258
13 -0.25151138
14 0.20584730
15 -0.12093034
16 -0.15215116
17 -0.26864587
18 -0.11351644
19 -0.28815279
20 -0.21096170
22 -0.11922694
23 0.01377527
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.59088941 -0.57079747 -0.57877128 -0.32558864 -0.31688762 0.00181276
=============================================================
--- Mixed - Block 2 - Axis Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 13.929 1.2663 11 6049 11.51 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.00746 0.02 6049 -0.373 1.0000
Step1 - Step3 -0.01002 0.02 6049 -0.501 1.0000
Step1 - Step4 -0.01676 0.02 6049 -0.839 0.9996
Step1 - Step5 -0.00293 0.02 6049 -0.147 1.0000
Step1 - Step6 0.01510 0.02 6049 0.756 0.9998
Step1 - Step7 0.02316 0.02 6049 1.158 0.9919
Step1 - Step8 0.03614 0.02 6049 1.808 0.8137
Step1 - Step9 0.05712 0.02 6049 2.858 0.1563
Step1 - Step10 0.07741 0.02 6049 3.874 0.0061
Step1 - Step11 0.10730 0.02 6049 5.369 <.0001
Step1 - Step12 0.12487 0.02 6049 6.249 <.0001
Step2 - Step3 -0.00256 0.02 6049 -0.128 1.0000
Step2 - Step4 -0.00930 0.02 6049 -0.466 1.0000
Step2 - Step5 0.00452 0.02 6049 0.226 1.0000
Step2 - Step6 0.02256 0.02 6049 1.129 0.9934
Step2 - Step7 0.03061 0.02 6049 1.531 0.9321
Step2 - Step8 0.04360 0.02 6049 2.182 0.5631
Step2 - Step9 0.06458 0.02 6049 3.232 0.0561
Step2 - Step10 0.08487 0.02 6049 4.247 0.0013
Step2 - Step11 0.11476 0.02 6049 5.743 <.0001
Step2 - Step12 0.13233 0.02 6049 6.622 <.0001
Step3 - Step4 -0.00675 0.02 6049 -0.338 1.0000
Step3 - Step5 0.00708 0.02 6049 0.354 1.0000
Step3 - Step6 0.02512 0.02 6049 1.257 0.9841
Step3 - Step7 0.03317 0.02 6049 1.659 0.8865
Step3 - Step8 0.04615 0.02 6049 2.310 0.4696
Step3 - Step9 0.06714 0.02 6049 3.360 0.0376
Step3 - Step10 0.08742 0.02 6049 4.375 0.0008
Step3 - Step11 0.11731 0.02 6049 5.871 <.0001
Step3 - Step12 0.13489 0.02 6049 6.750 <.0001
Step4 - Step5 0.01383 0.02 6049 0.692 0.9999
Step4 - Step6 0.03186 0.02 6049 1.595 0.9114
Step4 - Step7 0.03992 0.02 6049 1.997 0.6960
Step4 - Step8 0.05290 0.02 6049 2.647 0.2535
Step4 - Step9 0.07388 0.02 6049 3.697 0.0118
Step4 - Step10 0.09417 0.02 6049 4.713 0.0002
Step4 - Step11 0.12406 0.02 6049 6.208 <.0001
Step4 - Step12 0.14163 0.02 6049 7.088 <.0001
Step5 - Step6 0.01804 0.02 6049 0.903 0.9991
Step5 - Step7 0.02609 0.02 6049 1.305 0.9787
Step5 - Step8 0.03907 0.02 6049 1.955 0.7239
Step5 - Step9 0.06005 0.02 6049 3.005 0.1071
Step5 - Step10 0.08034 0.02 6049 4.021 0.0034
Step5 - Step11 0.11023 0.02 6049 5.516 <.0001
Step5 - Step12 0.12780 0.02 6049 6.396 <.0001
Step6 - Step7 0.00805 0.02 6049 0.403 1.0000
Step6 - Step8 0.02104 0.02 6049 1.053 0.9964
Step6 - Step9 0.04202 0.02 6049 2.103 0.6209
Step6 - Step10 0.06231 0.02 6049 3.118 0.0784
Step6 - Step11 0.09219 0.02 6049 4.614 0.0003
Step6 - Step12 0.10977 0.02 6049 5.493 <.0001
Step7 - Step8 0.01298 0.02 6049 0.649 1.0000
Step7 - Step9 0.03397 0.02 6049 1.699 0.8692
Step7 - Step10 0.05425 0.02 6049 2.714 0.2195
Step7 - Step11 0.08414 0.02 6049 4.209 0.0016
Step7 - Step12 0.10171 0.02 6049 5.088 <.0001
Step8 - Step9 0.02098 0.02 6049 1.050 0.9965
Step8 - Step10 0.04127 0.02 6049 2.065 0.6478
Step8 - Step11 0.07116 0.02 6049 3.561 0.0192
Step8 - Step12 0.08873 0.02 6049 4.440 0.0006
Step9 - Step10 0.02029 0.02 6049 1.015 0.9974
Step9 - Step11 0.05018 0.02 6049 2.511 0.3330
Step9 - Step12 0.06775 0.02 6049 3.390 0.0341
Step10 - Step11 0.02989 0.02 6049 1.496 0.9421
Step10 - Step12 0.04746 0.02 6049 2.375 0.4232
Step11 - Step12 0.01757 0.02 6049 0.879 0.9993
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 12 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5 Step6
0.807635461 0.007458707 0.010015671 0.016762651 0.002933741 -0.015102056
Step7 Step8 Step9 Step10 Step11 Step12
-0.023155105 -0.036137864 -0.057120342 -0.077408046 -0.107296624 -0.124869748
Random Effects:
$trial_id
(Intercept)
10.1 1.625279e-01
16.1 -8.410986e-02
17.1 -1.293481e-01
19.1 2.591589e-02
7.2 -1.795502e-01
14.2 5.004792e-01
17.2 -2.361149e-01
20.2 2.533687e-01
7.3 -2.597688e-01
10.3 2.492420e-01
17.3 -3.380444e-02
18.3 -2.344076e-01
20.3 4.566332e-03
2.4 -5.064619e-01
7.4 1.850174e-01
10.4 2.915752e-01
13.4 -7.968696e-02
14.4 2.440929e-01
16.4 -2.162242e-01
17.4 -1.372282e-01
18.4 2.915973e-02
19.4 -7.936194e-02
20.4 -2.528895e-02
22.4 -6.959292e-02
23.4 1.618694e-02
2.5 -2.106538e-01
3.5 -1.617750e-01
5.5 5.443467e-04
7.5 -8.844270e-02
10.5 4.944226e-01
11.5 -1.499493e-01
13.5 1.656988e-01
14.5 1.816330e-01
17.5 -6.939578e-02
18.5 -7.766231e-02
20.5 2.388564e-01
3.6 -3.653444e-03
8.6 -2.522389e-01
10.6 1.349992e-01
11.6 -1.659952e-01
14.6 1.533643e-01
15.6 -7.332321e-02
16.6 8.475781e-02
17.6 -1.540071e-01
20.6 1.282472e-01
2.7 -3.831709e-01
3.7 -3.178462e-01
5.7 -1.347005e-02
7.7 -5.025262e-02
8.7 -3.225913e-01
11.7 3.666163e-01
13.7 8.219520e-02
14.7 2.103730e-01
15.7 -4.149650e-02
16.7 -2.373688e-01
17.7 7.064841e-02
18.7 7.246229e-02
19.7 -1.462025e-01
20.7 -1.217714e-01
22.7 1.054214e-01
2.8 -3.926651e-01
3.8 -2.450243e-01
5.8 -8.850291e-02
7.8 2.194067e-01
10.8 1.697195e-01
11.8 3.226263e-01
14.8 1.100034e-01
15.8 -5.957775e-02
17.8 -8.222548e-02
18.8 -1.016952e-01
19.8 -1.913601e-01
22.8 -1.112406e-02
2.9 1.397306e-01
3.9 3.078566e-01
4.9 -5.823958e-03
5.9 -5.846798e-02
7.9 -6.892941e-02
8.9 -4.996795e-02
10.9 5.312459e-01
13.9 -2.065909e-01
14.9 1.164356e-02
16.9 -2.822664e-01
17.9 2.567523e-02
18.9 -1.551140e-01
19.9 -9.299783e-02
22.9 -2.961856e-02
2.10 -1.599579e-01
8.10 -7.040643e-02
10.10 5.887739e-01
11.10 -2.745426e-01
13.10 1.574236e-01
14.10 1.698100e-01
15.10 -9.736024e-02
16.10 -1.510738e-01
17.10 -7.866806e-02
19.10 -9.659672e-02
22.10 2.142604e-02
2.11 -1.642381e-01
4.11 -7.481462e-02
7.11 3.267353e-01
8.11 1.863825e-01
13.11 -2.054442e-01
14.11 6.738078e-02
15.11 1.305021e-01
16.11 -4.237502e-03
17.11 1.098915e-01
19.11 -9.103322e-03
23.11 -2.636468e-01
2.12 1.156718e-01
4.12 2.145919e-02
5.12 1.940601e-02
8.12 3.021191e-01
13.12 -1.155272e-01
15.12 1.118742e-01
16.12 -1.956759e-01
17.12 -6.937313e-02
18.12 -7.927979e-02
19.12 2.059307e-01
22.12 -5.333474e-02
23.12 -3.031976e-01
2.13 -2.025303e-01
3.13 -3.120529e-01
4.13 3.867533e-01
5.13 -4.454852e-02
7.13 -7.186444e-02
10.13 -1.914754e-01
11.13 -1.103972e-01
13.13 3.233692e-02
14.13 -1.585759e-01
15.13 -1.537605e-01
16.13 -2.559477e-01
17.13 -2.722529e-02
18.13 -2.710549e-01
19.13 -5.279204e-02
23.13 -2.570307e-01
2.14 -1.296755e-01
3.14 1.400362e-01
4.14 -2.032693e-02
7.14 -1.787157e-01
8.14 -1.773049e-01
10.14 1.377279e-01
13.14 -8.127785e-02
15.14 1.319314e-01
16.14 -1.491790e-02
17.14 8.289955e-02
18.14 8.342141e-02
19.14 -4.429921e-02
23.14 -3.296499e-01
2.15 -6.128447e-02
4.15 -1.774328e-01
5.15 4.644816e-03
7.15 -6.426144e-02
8.15 5.817713e-02
10.15 9.535212e-01
11.15 -3.667889e-01
13.15 -1.218483e-01
14.15 -4.511846e-02
15.15 6.088226e-02
16.15 1.981124e-01
17.15 1.255664e-01
18.15 5.708268e-02
19.15 -1.595752e-01
23.15 6.374821e-01
2.16 -1.556178e-03
3.16 1.362361e-01
7.16 -4.808323e-03
8.16 -1.844828e-01
10.16 5.190901e-03
11.16 -1.626944e-01
13.16 -4.939273e-02
14.16 2.842984e-01
15.16 -2.876394e-02
16.16 8.438302e-03
17.16 -9.019345e-02
18.16 -4.386403e-03
19.16 6.291769e-02
2.17 2.277239e-01
3.17 -4.085617e-02
5.17 -7.991180e-02
8.17 3.995340e-01
13.17 -1.871624e-01
14.17 9.829641e-02
15.17 -1.653992e-01
17.17 6.448139e-02
18.17 2.939829e-02
19.17 2.074629e-02
23.17 1.526467e-01
2.18 1.538078e-01
3.18 6.543389e-01
4.18 2.884333e-03
5.18 6.880697e-02
7.18 -5.527521e-02
10.18 4.565531e-01
13.18 -5.532149e-02
16.18 -1.896077e-01
23.18 1.048251e-01
2.19 1.190138e-01
5.19 5.279083e-02
7.19 1.755938e-01
10.19 3.308126e-01
11.19 8.001468e-01
13.19 7.399625e-02
14.19 1.538773e-01
15.19 -8.816131e-03
16.19 -1.613407e-01
17.19 -1.918828e-02
18.19 -2.488699e-01
2.20 2.231043e-01
3.20 -2.585580e-01
4.20 8.672556e-02
5.20 4.013659e-02
7.20 3.569197e-01
10.20 6.073295e-01
11.20 9.265194e-01
13.20 -5.969106e-02
15.20 5.758387e-02
17.20 1.445676e-01
18.20 -7.638348e-02
23.20 2.814904e-01
2.21 -9.947993e-03
3.21 1.547086e-01
4.21 -1.226777e-01
5.21 1.251786e-02
7.21 -9.872337e-02
14.21 9.755488e-02
15.21 2.338744e-01
16.21 -4.324381e-03
17.21 -1.641408e-01
18.21 -1.721733e-01
3.22 6.832198e-01
4.22 -8.809003e-02
5.22 -5.887264e-02
7.22 -9.784700e-03
10.22 2.082500e-01
13.22 2.511386e-02
14.22 1.384114e-01
16.22 1.545266e-03
17.22 1.425893e-01
18.22 -1.774554e-03
23.22 -2.461431e-01
2.23 6.055848e-01
3.23 3.590099e-01
4.23 -2.684412e-02
5.23 1.369743e-02
7.23 -6.677548e-02
10.23 8.706235e-01
11.23 8.989852e-01
14.23 -5.789702e-02
16.23 2.141276e-03
18.23 -1.145475e-01
2.24 1.505474e-01
3.24 1.412805e-02
4.24 -1.044834e-02
5.24 7.242025e-02
11.24 8.446670e-01
13.24 -1.037055e-01
15.24 -6.008525e-02
17.24 2.808639e-01
18.24 -8.010205e-02
23.24 -1.162503e-02
2.25 1.114456e-01
3.25 8.350353e-01
4.25 7.649791e-03
7.25 3.630306e-03
13.25 1.517190e-01
15.25 -3.460807e-02
16.25 1.961139e-01
17.25 2.349458e-01
18.25 1.795475e-01
23.25 7.929503e-02
2.26 3.930389e-01
3.26 1.754973e-01
5.26 -1.514588e-02
7.26 -1.104678e-01
10.26 4.132899e-01
11.26 8.015828e-02
13.26 3.819894e-02
15.26 2.304504e-01
16.26 5.903321e-02
17.26 1.493633e-02
18.26 -1.261921e-01
19.26 9.269834e-03
22.26 -1.028124e-01
23.26 -2.037321e-01
2.27 1.313471e-01
3.27 -7.413114e-02
8.27 -2.162982e-01
10.27 8.464512e-01
11.27 1.330642e+00
13.27 4.362413e-01
15.27 -4.026361e-02
16.27 1.555784e-01
17.27 2.688574e-02
18.27 -5.885803e-02
22.27 -9.586072e-02
23.27 -2.807688e-01
2.28 3.080652e-01
3.28 -1.996066e-01
5.28 -6.326669e-03
7.28 4.485525e-02
8.28 8.155925e-02
13.28 -1.291415e-01
15.28 8.257754e-02
16.28 -7.933798e-02
17.28 2.571857e-02
18.28 -1.055981e-01
19.28 -8.332594e-02
23.28 6.860185e-01
2.29 -5.482496e-02
3.29 4.226659e-01
4.29 -1.306841e-01
5.29 5.920903e-02
7.29 7.775391e-02
8.29 2.560549e-01
10.29 6.017574e-01
14.29 -1.069643e-01
16.29 -9.133645e-02
17.29 -4.115194e-02
18.29 -2.449886e-02
23.29 2.632872e-01
3.30 2.198899e-01
4.30 -9.391273e-02
5.30 1.489759e-01
7.30 2.011431e-01
8.30 6.266397e-01
11.30 2.875022e-01
15.30 -2.428414e-02
16.30 -9.836838e-02
18.30 -1.809944e-01
23.30 7.956428e-01
2.31 1.011351e+00
5.31 1.384049e-01
7.31 2.968240e-01
8.31 1.516882e+00
10.31 -5.741489e-01
16.31 7.142275e-01
18.31 4.313314e-02
19.31 -9.302073e-02
23.31 4.336999e-01
3.32 4.115146e-01
4.32 1.624456e-02
5.32 1.992504e-01
7.32 3.751666e-01
8.32 7.379944e-02
10.32 5.917466e-01
13.32 1.681489e-01
16.32 1.169774e-01
17.32 2.220939e-01
19.32 -4.740199e-02
23.32 1.229640e-01
2.33 -1.454486e-01
3.33 1.033756e-01
4.33 3.023871e-03
5.33 2.953877e-01
7.33 2.104749e-01
8.33 -3.516254e-01
10.33 -1.922299e-01
11.33 1.185308e+00
13.33 -1.343605e-01
15.33 2.962174e-01
16.33 9.155210e-01
17.33 3.079209e-01
18.33 -2.001491e-01
19.33 -4.236944e-02
20.33 -2.198308e-01
22.33 -2.872950e-02
2.34 -1.749864e-01
4.34 3.353223e-02
5.34 -4.177164e-02
7.34 8.836799e-02
8.34 1.986283e-01
10.34 -2.213408e-01
11.34 6.598286e-01
13.34 2.456228e-01
15.34 1.514554e-01
16.34 3.282568e-01
17.34 1.076281e-01
18.34 -1.194819e-01
23.34 -8.395137e-02
2.35 2.143999e-01
3.35 -3.504127e-01
4.35 -6.951379e-02
5.35 -1.461034e-01
7.35 -1.984715e-05
8.35 -2.715679e-01
10.35 -3.400211e-01
13.35 2.008890e-01
15.35 1.040166e-01
16.35 2.010181e-01
17.35 2.715794e-01
18.35 2.906584e-01
19.35 4.070754e-02
22.35 2.805385e-02
2.36 1.490677e-01
4.36 1.526212e-01
8.36 8.390304e-01
10.36 -8.924185e-01
13.36 -8.136862e-02
15.36 3.059016e-01
16.36 2.581623e-01
17.36 -4.555461e-02
18.36 1.304873e-01
19.36 2.723239e-01
22.36 -1.799928e-02
23.36 4.526509e-01
2.37 1.589999e-01
4.37 1.684146e-01
7.37 -9.014983e-02
8.37 -5.167720e-01
11.37 -5.376370e-01
13.37 3.345996e-01
14.37 1.647203e-01
15.37 8.020892e-02
16.37 1.365073e-01
17.37 1.853976e-01
18.37 7.090059e-01
22.37 -5.196286e-02
23.37 2.774984e-01
2.38 2.085403e-01
3.38 -1.680876e-01
4.38 -1.609526e-01
7.38 -1.385525e-01
8.38 -1.190788e-02
13.38 3.873248e-01
14.38 3.038799e-01
15.38 2.151135e-01
16.38 2.014189e-01
17.38 -3.885311e-02
18.38 6.053777e-01
19.38 1.232794e-01
22.38 -3.390764e-02
23.38 1.499054e-01
2.39 -2.169543e-01
3.39 -4.992514e-01
5.39 -1.224492e-01
8.39 -4.083059e-01
11.39 -7.763480e-01
13.39 -6.268387e-02
14.39 4.194717e-02
15.39 -2.530920e-02
16.39 -1.288375e-01
17.39 1.259505e-01
18.39 1.287404e-01
19.39 1.945970e-01
22.39 1.893788e-01
23.39 -5.278637e-01
3.40 -3.131109e-01
4.40 1.445025e-01
5.40 -9.899442e-02
7.40 -2.678022e-02
10.40 -2.838367e-01
14.40 7.130188e-02
15.40 -4.782069e-02
16.40 1.156926e-01
18.40 4.122801e-01
19.40 -1.243850e-01
20.40 1.472218e-01
23.40 -5.482892e-01
3.41 1.222089e-01
4.41 -7.844550e-02
5.41 7.150953e-02
7.41 -1.732370e-01
10.41 -1.085120e+00
11.41 -1.036533e+00
13.41 2.270135e-01
14.41 -4.298978e-01
15.41 -1.507530e-01
16.41 -1.225689e-01
17.41 2.449457e-03
18.41 3.788229e-01
19.41 2.416615e-01
20.41 -7.061450e-02
2.42 3.157744e-01
4.42 1.424534e-01
5.42 7.229090e-03
7.42 2.264486e-02
8.42 -3.550870e-01
10.42 -1.225370e+00
11.42 -1.327350e+00
15.42 -1.960981e-01
16.42 -4.247349e-02
17.42 -1.522084e-01
18.42 1.834095e-01
19.42 5.784824e-02
20.42 -4.011526e-02
22.42 2.406516e-01
23.42 -4.583616e-01
2.43 -4.955033e-01
3.43 3.043908e-01
4.43 -1.324206e-01
5.43 2.197565e-01
7.43 -3.010155e-01
10.43 -5.502586e-01
13.43 -3.347626e-01
14.43 -2.574218e-01
15.43 -2.365133e-01
18.43 2.755410e-02
19.43 -1.300450e-01
20.43 -1.510621e-01
22.43 9.629672e-02
23.43 -8.784509e-02
3.44 -1.979293e-01
4.44 -3.790733e-03
5.44 -1.057025e-01
10.44 -1.088026e+00
11.44 -1.352612e+00
13.44 -4.084416e-01
14.44 1.760178e-02
15.44 -3.766214e-01
16.44 -8.349299e-02
17.44 -1.160178e-01
19.44 -7.702399e-02
20.44 -1.171196e-02
22.44 -5.767215e-02
23.44 -2.982057e-01
2.45 -7.244343e-01
3.45 -7.560553e-01
4.45 -2.801754e-01
5.45 -2.964652e-01
7.45 -4.918686e-01
13.45 -5.471189e-02
14.45 -5.909710e-01
16.45 -3.107275e-01
17.45 -3.522778e-01
18.45 -6.831559e-02
20.45 -1.718463e-01
22.45 -1.498191e-01
23.45 -5.457292e-01
2.46 -5.777463e-01
5.46 -1.973379e-01
7.46 -1.810495e-01
10.46 -2.954875e-01
11.46 -7.293406e-01
13.46 -4.397005e-01
14.46 -7.635246e-01
15.46 -9.338278e-02
16.46 -5.370520e-01
17.46 -4.775502e-01
18.46 -4.874875e-01
20.46 5.223916e-03
22.46 -2.936198e-01
3.47 -8.935988e-01
5.47 -3.365190e-01
8.47 -9.398037e-01
10.47 -1.184053e+00
14.47 -5.097907e-01
15.47 -4.402916e-01
16.47 -4.870287e-01
17.47 -2.373490e-01
18.47 -5.028958e-01
20.47 -1.030147e-01
$subject
(Intercept)
2 0.160435851
3 0.323195581
4 -0.397440352
5 -0.366438272
7 -0.161185436
8 0.526067425
10 0.669010310
11 0.913607435
13 -0.236212911
14 0.128820381
15 -0.207556320
16 -0.108708512
17 -0.229662285
18 -0.168390245
19 -0.275132447
20 -0.176581330
22 -0.403510144
23 0.009681272
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
0.04311958 0.22465908 0.07524298 -0.03666331 -0.29926209 -0.26387066
=============================================================
--- Mixed - Block 2 - Axis Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 59.617 5.4198 11 6049 18.11 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.03221 0.033 6049 -0.977 0.9982
Step1 - Step3 -0.00130 0.033 6049 -0.039 1.0000
Step1 - Step4 0.00384 0.033 6049 0.117 1.0000
Step1 - Step5 0.01112 0.033 6049 0.338 1.0000
Step1 - Step6 0.04710 0.033 6049 1.429 0.9580
Step1 - Step7 0.08058 0.033 6049 2.444 0.3764
Step1 - Step8 0.10316 0.033 6049 3.130 0.0757
Step1 - Step9 0.16877 0.033 6049 5.121 <.0001
Step1 - Step10 0.18490 0.033 6049 5.610 <.0001
Step1 - Step11 0.21682 0.033 6049 6.579 <.0001
Step1 - Step12 0.26287 0.033 6049 7.976 <.0001
Step2 - Step3 0.03092 0.033 6049 0.938 0.9987
Step2 - Step4 0.03606 0.033 6049 1.094 0.9950
Step2 - Step5 0.04334 0.033 6049 1.315 0.9774
Step2 - Step6 0.07931 0.033 6049 2.406 0.4016
Step2 - Step7 0.11280 0.033 6049 3.421 0.0309
Step2 - Step8 0.13538 0.033 6049 4.108 0.0024
Step2 - Step9 0.20099 0.033 6049 6.098 <.0001
Step2 - Step10 0.21711 0.033 6049 6.587 <.0001
Step2 - Step11 0.24904 0.033 6049 7.556 <.0001
Step2 - Step12 0.29508 0.033 6049 8.953 <.0001
Step3 - Step4 0.00514 0.033 6049 0.156 1.0000
Step3 - Step5 0.01242 0.033 6049 0.377 1.0000
Step3 - Step6 0.04840 0.033 6049 1.468 0.9491
Step3 - Step7 0.08188 0.033 6049 2.483 0.3507
Step3 - Step8 0.10446 0.033 6049 3.169 0.0675
Step3 - Step9 0.17007 0.033 6049 5.160 <.0001
Step3 - Step10 0.18619 0.033 6049 5.649 <.0001
Step3 - Step11 0.21812 0.033 6049 6.618 <.0001
Step3 - Step12 0.26417 0.033 6049 8.015 <.0001
Step4 - Step5 0.00728 0.033 6049 0.221 1.0000
Step4 - Step6 0.04326 0.033 6049 1.312 0.9777
Step4 - Step7 0.07674 0.033 6049 2.327 0.4571
Step4 - Step8 0.09932 0.033 6049 3.013 0.1048
Step4 - Step9 0.16493 0.033 6049 5.004 <.0001
Step4 - Step10 0.18105 0.033 6049 5.493 <.0001
Step4 - Step11 0.21298 0.033 6049 6.462 <.0001
Step4 - Step12 0.25903 0.033 6049 7.859 <.0001
Step5 - Step6 0.03598 0.033 6049 1.092 0.9951
Step5 - Step7 0.06946 0.033 6049 2.106 0.6182
Step5 - Step8 0.09204 0.033 6049 2.793 0.1832
Step5 - Step9 0.15765 0.033 6049 4.783 0.0001
Step5 - Step10 0.17377 0.033 6049 5.273 <.0001
Step5 - Step11 0.20570 0.033 6049 6.241 <.0001
Step5 - Step12 0.25175 0.033 6049 7.638 <.0001
Step6 - Step7 0.03348 0.033 6049 1.015 0.9974
Step6 - Step8 0.05606 0.033 6049 1.701 0.8682
Step6 - Step9 0.12167 0.033 6049 3.692 0.0121
Step6 - Step10 0.13780 0.033 6049 4.181 0.0018
Step6 - Step11 0.16973 0.033 6049 5.150 <.0001
Step6 - Step12 0.21577 0.033 6049 6.547 <.0001
Step7 - Step8 0.02258 0.033 6049 0.685 0.9999
Step7 - Step9 0.08819 0.033 6049 2.674 0.2392
Step7 - Step10 0.10431 0.033 6049 3.163 0.0687
Step7 - Step11 0.13624 0.033 6049 4.132 0.0022
Step7 - Step12 0.18229 0.033 6049 5.528 <.0001
Step8 - Step9 0.06561 0.033 6049 1.991 0.7000
Step8 - Step10 0.08173 0.033 6049 2.480 0.3528
Step8 - Step11 0.11366 0.033 6049 3.449 0.0282
Step8 - Step12 0.15971 0.033 6049 4.846 0.0001
Step9 - Step10 0.01612 0.033 6049 0.489 1.0000
Step9 - Step11 0.04805 0.033 6049 1.458 0.9515
Step9 - Step12 0.09410 0.033 6049 2.855 0.1576
Step10 - Step11 0.03193 0.033 6049 0.969 0.9983
Step10 - Step12 0.07797 0.033 6049 2.366 0.4297
Step11 - Step12 0.04604 0.033 6049 1.397 0.9643
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 12 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5 Step6
1.649974022 0.032214041 0.001296841 -0.003843850 -0.011123491 -0.047099269
Step7 Step8 Step9 Step10 Step11 Step12
-0.080582242 -0.103163036 -0.168771305 -0.184896148 -0.216824713 -0.262869547
Random Effects:
$trial_id
(Intercept)
10.1 -5.762952e-01
16.1 -4.946757e-01
17.1 -3.900829e-01
19.1 2.240480e-01
7.2 4.444755e-01
14.2 8.071837e-02
17.2 -4.271270e-01
20.2 2.394246e-01
7.3 4.153491e-01
10.3 -2.288072e-02
17.3 -1.910727e-01
18.3 -6.996297e-01
20.3 2.192115e-01
2.4 -6.339821e-01
7.4 4.602146e-01
10.4 1.540343e-01
13.4 3.269232e-01
14.4 2.200370e-01
16.4 -1.661912e-01
17.4 -2.407092e-01
18.4 -3.998786e-01
19.4 -2.409749e-05
20.4 2.482828e-01
22.4 1.565591e-01
23.4 7.817541e-03
2.5 -4.611330e-01
3.5 -6.902964e-01
5.5 -4.673483e-01
7.5 8.017329e-01
10.5 1.786367e+00
11.5 8.226454e-01
13.5 1.195140e+00
14.5 -2.040331e-01
17.5 -1.265584e-01
18.5 -5.512549e-02
20.5 4.362592e-02
3.6 7.653682e-02
8.6 -6.198497e-01
10.6 3.236348e-01
11.6 -5.590400e-01
14.6 -1.672623e-01
15.6 2.486885e-01
16.6 -5.890546e-01
17.6 -9.815769e-02
20.6 1.197783e-01
2.7 -2.790657e-01
3.7 2.589510e-01
5.7 -3.345589e-01
7.7 6.800823e-01
8.7 -6.370732e-01
11.7 1.165848e+00
13.7 -9.562241e-02
14.7 -7.819424e-02
15.7 2.359852e-01
16.7 -6.838414e-01
17.7 1.140800e-01
18.7 -4.930543e-01
19.7 -7.990978e-02
20.7 3.548060e-01
22.7 -2.653525e-02
2.8 -5.065269e-01
3.8 -5.033965e-01
5.8 -4.155798e-01
7.8 3.513861e-01
10.8 4.545563e-01
11.8 -1.710633e-02
14.8 -1.729129e-01
15.8 5.010633e-01
17.8 -3.624280e-02
18.8 6.287850e-02
19.8 -2.476005e-01
22.8 -1.011262e-01
2.9 5.339478e-01
3.9 3.185564e-01
4.9 4.739846e-01
5.9 -9.165268e-02
7.9 3.770034e-01
8.9 -2.348195e-01
10.9 1.965447e+00
13.9 -3.028169e-01
14.9 1.373355e-01
16.9 -2.546720e-01
17.9 2.392330e-01
18.9 5.551927e-01
19.9 -4.759026e-02
22.9 1.103371e-01
2.10 9.307668e-01
8.10 -3.629397e-01
10.10 1.383279e+00
11.10 -3.847529e-01
13.10 7.518416e-02
14.10 -1.196924e-01
15.10 2.912458e-01
16.10 -2.615746e-01
17.10 -1.512677e-01
19.10 -2.975442e-01
22.10 1.503535e-01
2.11 -2.537337e-01
4.11 -3.893225e-02
7.11 1.740838e-01
8.11 -1.558569e-01
13.11 -2.287801e-01
14.11 -5.084816e-01
15.11 -6.871747e-02
16.11 -4.095212e-02
17.11 -1.304641e-02
19.11 -2.482640e-01
23.11 -7.676189e-01
2.12 -6.057563e-01
4.12 4.928881e-01
5.12 -2.672025e-01
8.12 9.101365e-01
13.12 7.559644e-02
15.12 5.653374e-02
16.12 6.260060e-03
17.12 -1.600791e-03
18.12 6.157910e-01
19.12 3.540743e-01
22.12 -9.673680e-02
23.12 -5.397848e-01
2.13 -3.538278e-01
3.13 -6.177271e-01
4.13 -1.171364e-01
5.13 -3.405511e-01
7.13 1.205411e-01
10.13 3.373765e-01
11.13 2.096404e-01
13.13 6.008112e-01
14.13 -1.135225e-01
15.13 -7.639030e-02
16.13 6.020487e-01
17.13 5.066599e-02
18.13 8.708365e-02
19.13 -2.415056e-03
23.13 -4.568808e-01
2.14 3.433186e-02
3.14 6.794463e-02
4.14 1.984456e-01
7.14 1.969918e-01
8.14 -1.341117e-01
10.14 1.804231e+00
13.14 -1.813068e-01
15.14 3.562271e-01
16.14 4.566275e-01
17.14 2.428082e-01
18.14 2.409998e-01
19.14 4.520980e-02
23.14 -2.948417e-01
2.15 7.371081e-02
4.15 1.581960e-01
5.15 3.653941e-02
7.15 1.625747e-01
8.15 -2.234869e-01
10.15 1.737078e+00
11.15 -3.937318e-01
13.15 2.421705e-01
14.15 -2.134675e-01
15.15 1.345743e-01
16.15 1.339099e+00
17.15 1.936351e-01
18.15 6.009865e-01
19.15 -2.179247e-01
23.15 5.804121e-01
2.16 4.587787e-01
3.16 -6.603012e-02
7.16 1.421769e-01
8.16 4.059745e-01
10.16 1.391335e+00
11.16 -1.019313e+00
13.16 8.366097e-02
14.16 -3.435420e-02
15.16 1.631135e-01
16.16 4.819563e-01
17.16 -1.203148e-02
18.16 4.242580e-01
19.16 -6.919681e-02
2.17 5.418413e-01
3.17 6.572685e-01
5.17 -3.006316e-02
8.17 2.828438e-01
13.17 -4.658493e-02
14.17 -4.431834e-01
15.17 -3.372537e-01
17.17 -1.701594e-02
18.17 1.648627e-01
19.17 3.882249e-01
23.17 1.466697e-01
2.18 1.048237e-01
3.18 6.578454e-01
4.18 -2.534890e-01
5.18 -1.152154e-01
7.18 6.887082e-01
10.18 2.460492e+00
13.18 -2.946977e-01
16.18 7.002832e-01
23.18 -1.443846e-01
2.19 4.451786e-01
5.19 1.639125e-01
7.19 5.159478e-01
10.19 4.270903e+00
11.19 1.436388e+00
13.19 -6.757466e-02
14.19 -6.561936e-02
15.19 -9.293844e-02
16.19 3.783138e-01
17.19 1.606631e-01
18.19 -4.089511e-01
2.20 8.221353e-02
3.20 -1.255964e-01
4.20 -2.283002e-01
5.20 9.384798e-02
7.20 5.977932e-01
10.20 2.528309e+00
11.20 1.824785e+00
13.20 -1.612725e-01
15.20 2.145527e-01
17.20 -1.514351e-02
18.20 9.578680e-02
23.20 1.504259e+00
2.21 1.538774e-01
3.21 2.340513e-01
4.21 -7.670148e-02
5.21 -4.709802e-02
7.21 3.281296e-01
14.21 -1.815156e-01
15.21 2.998308e-01
16.21 1.366077e-01
17.21 -3.843567e-02
18.21 -2.662562e-01
3.22 2.447183e-01
4.22 1.603031e-01
5.22 -9.528319e-02
7.22 4.447870e-01
10.22 3.853728e-01
13.22 8.554435e-01
14.22 1.910204e-01
16.22 1.825793e-01
17.22 -5.641862e-02
18.22 1.823506e-01
23.22 2.394371e-01
2.23 9.008256e-01
3.23 3.104125e-01
4.23 5.367506e-02
5.23 -1.561476e-03
7.23 9.878618e-02
10.23 -1.399426e+00
11.23 3.807470e+00
14.23 -2.806280e-01
16.23 2.450044e-01
18.23 -9.303667e-02
2.24 4.745857e-01
3.24 3.090047e-01
4.24 -7.696847e-02
5.24 8.525713e-01
11.24 -7.295868e-02
13.24 4.443394e-01
15.24 1.044267e-01
17.24 -1.053739e-01
18.24 3.397253e-01
23.24 2.570998e-01
2.25 7.076713e-01
3.25 3.109971e-01
4.25 2.401107e-01
7.25 2.705429e-01
13.25 3.360792e-01
15.25 6.822175e-01
16.25 6.326039e-01
17.25 5.297576e-01
18.25 6.652966e-01
23.25 2.241289e-01
2.26 8.137479e-02
3.26 2.212430e-02
5.26 -1.973851e-01
7.26 4.233399e-01
10.26 -1.877871e-01
11.26 2.606609e+00
13.26 5.873863e-01
15.26 3.654354e-01
16.26 6.973264e-01
17.26 2.397283e-01
18.26 2.710363e-01
19.26 1.326203e-01
22.26 -4.792420e-02
23.26 -3.644526e-01
2.27 -1.220952e-01
3.27 -2.025393e-01
8.27 -5.890596e-01
10.27 1.235457e-01
11.27 2.954362e+00
13.27 2.281901e-01
15.27 4.782221e-02
16.27 2.412523e-02
17.27 1.140824e-01
18.27 3.082081e-01
22.27 4.159610e-02
23.27 3.219732e-01
2.28 -5.167071e-02
3.28 4.878315e-01
5.28 1.314930e-01
7.28 -1.340343e-01
8.28 1.462161e-02
13.28 -8.711674e-02
15.28 -2.470921e-02
16.28 3.055318e-01
17.28 1.160773e-01
18.28 1.057800e+00
19.28 -1.080363e-01
23.28 3.212099e-01
2.29 5.760111e-01
3.29 1.195541e+00
4.29 -1.394223e-01
5.29 1.990983e-01
7.29 7.018688e-01
8.29 9.556316e-01
10.29 -5.043632e-01
14.29 -1.712504e-01
16.29 5.496554e-02
17.29 3.559143e-01
18.29 -2.173305e-01
23.29 1.441034e+00
3.30 7.751470e-01
4.30 -6.745634e-02
5.30 2.815233e-01
7.30 4.801839e-01
8.30 6.997778e-01
11.30 -1.029319e+00
15.30 -2.557605e-01
16.30 2.876546e-01
18.30 3.325939e-01
23.30 9.208665e-01
2.31 7.011346e-01
5.31 3.545541e-01
7.31 4.177815e-01
8.31 2.019577e+00
10.31 -7.345579e-02
16.31 1.001328e+00
18.31 4.134707e-01
19.31 -2.028686e-02
23.31 1.477797e+00
3.32 1.789453e-01
4.32 -2.321837e-01
5.32 6.832607e-01
7.32 -8.731090e-03
8.32 9.291684e-01
10.32 -8.339956e-01
13.32 -5.157349e-01
16.32 3.190538e-01
17.32 2.538380e-01
19.32 -3.099989e-01
23.32 -2.664243e-01
2.33 -2.544859e-01
3.33 1.143937e+00
4.33 2.450615e-01
5.33 8.731498e-01
7.33 -4.891193e-01
8.33 7.988446e-01
10.33 -8.942345e-01
11.33 -2.162509e-01
13.33 4.884382e-02
15.33 7.269340e-01
16.33 -2.189528e-01
17.33 2.195126e-01
18.33 6.862785e-01
19.33 -1.863947e-01
20.33 -7.862368e-02
22.33 1.329693e-01
2.34 -3.652041e-01
4.34 1.482481e-01
5.34 1.881359e-01
7.34 -2.236976e-01
8.34 3.683494e-01
10.34 -3.449748e-01
11.34 -4.375102e-01
13.34 -3.434576e-02
15.34 5.104048e-01
16.34 9.661799e-01
17.34 -1.349942e-01
18.34 -2.688691e-01
23.34 2.201117e-01
2.35 -1.691890e-03
3.35 -5.426872e-01
4.35 -2.192411e-01
5.35 -1.957860e-01
7.35 -3.753232e-01
8.35 -8.870854e-01
10.35 -9.019214e-01
13.35 -1.133774e-02
15.35 4.279571e-02
16.35 -7.288418e-02
17.35 -1.639570e-02
18.35 -6.566431e-02
19.35 1.898232e-01
22.35 4.354048e-01
2.36 4.371251e-01
4.36 8.204774e-02
8.36 6.079463e-01
10.36 -2.251401e+00
13.36 -2.961940e-01
15.36 -2.387012e-01
16.36 -1.098005e-01
17.36 3.021779e-02
18.36 3.783137e-02
19.36 -9.549188e-02
22.36 4.770021e-01
23.36 1.865507e-01
2.37 3.526415e-02
4.37 2.102760e-01
7.37 -2.699796e-01
8.37 -7.102640e-01
11.37 -8.270748e-01
13.37 1.919533e-01
14.37 5.407498e-01
15.37 2.813164e-01
16.37 -2.011363e-01
17.37 6.588573e-02
18.37 8.937855e-01
22.37 1.683714e-01
23.37 -4.037380e-01
2.38 2.452780e-01
3.38 -1.015061e-01
4.38 -2.838086e-01
7.38 -1.005559e+00
8.38 6.606729e-01
13.38 1.012536e-01
14.38 1.645333e+00
15.38 -1.127755e-01
16.38 5.371309e-01
17.38 -1.824733e-01
18.38 6.720217e-01
19.38 6.792821e-01
22.38 1.974763e-01
23.38 3.673335e-01
2.39 -4.617617e-01
3.39 -7.875213e-01
5.39 -1.750513e-01
8.39 -1.247234e+00
11.39 -1.520301e+00
13.39 -3.734697e-01
14.39 1.908347e-01
15.39 2.560163e-01
16.39 -3.856305e-01
17.39 7.526613e-02
18.39 5.024491e-02
19.39 -1.301298e-01
22.39 5.478250e-02
23.39 -7.966506e-01
3.40 -4.630603e-01
4.40 -1.961005e-01
5.40 1.872791e-01
7.40 -6.688192e-01
10.40 -8.828748e-01
14.40 1.870468e-01
15.40 2.086685e-01
16.40 7.476341e-01
18.40 -4.066718e-02
19.40 -2.054500e-01
20.40 1.296449e-01
23.40 -1.263565e+00
3.41 -3.811339e-02
4.41 -1.239982e-01
5.41 1.514721e-01
7.41 -5.205745e-01
10.41 -2.296295e+00
11.41 -1.273034e+00
13.41 8.598960e-02
14.41 5.656188e-01
15.41 -8.219461e-01
16.41 -1.221905e+00
17.41 3.681753e-01
18.41 -5.466631e-02
19.41 3.402393e-01
20.41 -4.379527e-01
2.42 -3.464498e-01
4.42 2.635707e-01
5.42 -3.856667e-02
7.42 -1.381776e+00
8.42 -6.594703e-01
10.42 -2.161666e+00
11.42 -2.763358e+00
15.42 -6.613789e-01
16.42 -4.777555e-01
17.42 -1.179115e-02
18.42 -3.365464e-01
19.42 -1.331155e-01
20.42 -3.362749e-01
22.42 -1.307309e-01
23.42 -1.262483e+00
2.43 -1.113114e+00
3.43 7.258629e-01
4.43 -2.711811e-01
5.43 2.725253e-01
7.43 -1.476433e+00
10.43 -6.908694e-01
13.43 -6.876478e-01
14.43 5.094435e-01
15.43 -7.276969e-01
18.43 -3.375536e-01
19.43 -9.643457e-02
20.43 -1.197841e-01
22.43 -3.341212e-01
23.43 3.161366e-02
3.44 -2.192286e-01
4.44 -3.733350e-01
5.44 -5.093450e-01
10.44 -1.564621e+00
11.44 -2.747473e+00
13.44 -1.029536e+00
14.44 -2.653986e-01
15.44 -1.048889e+00
16.44 -5.398193e-02
17.44 -1.757471e-01
19.44 -4.975089e-01
20.44 3.157947e-02
22.44 -4.578675e-01
23.44 -6.982324e-01
2.45 -1.210067e+00
3.45 -1.019670e+00
4.45 -7.603883e-01
5.45 -5.365669e-01
7.45 -1.574471e+00
13.45 -4.325236e-01
14.45 -7.368981e-01
16.45 -1.091200e+00
17.45 -4.110255e-01
18.45 -1.152941e+00
20.45 -1.603551e-01
22.45 -4.648070e-01
23.45 -9.641243e-01
2.46 -6.251847e-01
5.46 -5.683068e-01
7.46 -1.035732e+00
10.46 -1.947732e+00
11.46 2.421844e-01
13.46 -1.069718e+00
14.46 -8.993476e-01
15.46 -3.233279e-01
16.46 -1.850355e+00
17.46 -7.654455e-01
18.46 -1.688757e+00
20.46 -3.526302e-01
22.46 -8.329980e-01
3.47 -2.000627e+00
5.47 -7.916845e-01
8.47 -1.452170e+00
10.47 -2.386592e+00
14.47 -1.388746e-01
15.47 -1.200658e+00
16.47 -1.458712e+00
17.47 -5.340085e-01
18.47 -1.744856e+00
20.47 -4.651090e-01
$subject
(Intercept)
2 -0.12040547
3 0.56659645
4 -0.69377969
5 -0.71047237
7 0.12345981
8 0.70163681
10 1.12297474
11 1.71465337
13 -0.41457475
14 -0.49911989
15 -0.24959957
16 0.44464939
17 -0.74192778
18 0.41209531
19 -0.60652520
20 -0.53502833
22 -0.53845878
23 0.02382593
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
0.33597737 0.07510273 0.01572108 0.25068911 -0.25028643 -0.12556641
=============================================================
--- Mixed - Block 3 - Axis X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 8.7175 0.51279 17 7531 6.7039 2.975e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.003040 0.0186 7531 -0.164 1.0000
Step1 - Step3 -0.017560 0.0186 7531 -0.946 1.0000
Step1 - Step4 -0.022849 0.0186 7531 -1.231 0.9991
Step1 - Step5 -0.013692 0.0186 7531 -0.738 1.0000
Step1 - Step6 -0.004735 0.0186 7531 -0.255 1.0000
Step1 - Step7 -0.011701 0.0186 7531 -0.630 1.0000
Step1 - Step8 -0.005799 0.0186 7531 -0.312 1.0000
Step1 - Step9 -0.002450 0.0186 7531 -0.132 1.0000
Step1 - Step10 0.014070 0.0186 7531 0.758 1.0000
Step1 - Step11 0.022179 0.0186 7531 1.195 0.9994
Step1 - Step12 0.026747 0.0186 7531 1.441 0.9941
Step1 - Step13 0.035647 0.0186 7531 1.920 0.9044
Step1 - Step14 0.050133 0.0186 7531 2.701 0.3798
Step1 - Step15 0.055940 0.0186 7531 3.014 0.1936
Step1 - Step16 0.064677 0.0186 7531 3.484 0.0509
Step1 - Step17 0.068608 0.0186 7531 3.696 0.0249
Step1 - Step18 0.088260 0.0186 7531 4.755 0.0003
Step2 - Step3 -0.014521 0.0186 7531 -0.782 1.0000
Step2 - Step4 -0.019810 0.0186 7531 -1.067 0.9999
Step2 - Step5 -0.010653 0.0186 7531 -0.574 1.0000
Step2 - Step6 -0.001696 0.0186 7531 -0.091 1.0000
Step2 - Step7 -0.008661 0.0186 7531 -0.467 1.0000
Step2 - Step8 -0.002760 0.0186 7531 -0.149 1.0000
Step2 - Step9 0.000589 0.0186 7531 0.032 1.0000
Step2 - Step10 0.017109 0.0186 7531 0.922 1.0000
Step2 - Step11 0.025218 0.0186 7531 1.359 0.9970
Step2 - Step12 0.029787 0.0186 7531 1.605 0.9812
Step2 - Step13 0.038687 0.0186 7531 2.084 0.8257
Step2 - Step14 0.053173 0.0186 7531 2.865 0.2730
Step2 - Step15 0.058980 0.0186 7531 3.177 0.1268
Step2 - Step16 0.067716 0.0186 7531 3.648 0.0295
Step2 - Step17 0.071648 0.0186 7531 3.860 0.0137
Step2 - Step18 0.091300 0.0186 7531 4.919 0.0001
Step3 - Step4 -0.005289 0.0186 7531 -0.285 1.0000
Step3 - Step5 0.003868 0.0186 7531 0.208 1.0000
Step3 - Step6 0.012825 0.0186 7531 0.691 1.0000
Step3 - Step7 0.005860 0.0186 7531 0.316 1.0000
Step3 - Step8 0.011761 0.0186 7531 0.634 1.0000
Step3 - Step9 0.015110 0.0186 7531 0.814 1.0000
Step3 - Step10 0.031630 0.0186 7531 1.704 0.9662
Step3 - Step11 0.039739 0.0186 7531 2.141 0.7921
Step3 - Step12 0.044308 0.0186 7531 2.387 0.6180
Step3 - Step13 0.053207 0.0186 7531 2.866 0.2719
Step3 - Step14 0.067694 0.0186 7531 3.647 0.0296
Step3 - Step15 0.073501 0.0186 7531 3.960 0.0094
Step3 - Step16 0.082237 0.0186 7531 4.430 0.0013
Step3 - Step17 0.086169 0.0186 7531 4.642 0.0005
Step3 - Step18 0.105820 0.0186 7531 5.701 <.0001
Step4 - Step5 0.009157 0.0186 7531 0.493 1.0000
Step4 - Step6 0.018114 0.0186 7531 0.976 1.0000
Step4 - Step7 0.011148 0.0186 7531 0.601 1.0000
Step4 - Step8 0.017050 0.0186 7531 0.919 1.0000
Step4 - Step9 0.020399 0.0186 7531 1.099 0.9998
Step4 - Step10 0.036919 0.0186 7531 1.989 0.8750
Step4 - Step11 0.045028 0.0186 7531 2.426 0.5880
Step4 - Step12 0.049596 0.0186 7531 2.672 0.4004
Step4 - Step13 0.058496 0.0186 7531 3.151 0.1361
Step4 - Step14 0.072982 0.0186 7531 3.932 0.0105
Step4 - Step15 0.078790 0.0186 7531 4.245 0.0029
Step4 - Step16 0.087526 0.0186 7531 4.715 0.0004
Step4 - Step17 0.091458 0.0186 7531 4.927 0.0001
Step4 - Step18 0.111109 0.0186 7531 5.986 <.0001
Step5 - Step6 0.008957 0.0186 7531 0.483 1.0000
Step5 - Step7 0.001991 0.0186 7531 0.107 1.0000
Step5 - Step8 0.007893 0.0186 7531 0.425 1.0000
Step5 - Step9 0.011242 0.0186 7531 0.606 1.0000
Step5 - Step10 0.027762 0.0186 7531 1.496 0.9911
Step5 - Step11 0.035871 0.0186 7531 1.932 0.8996
Step5 - Step12 0.040439 0.0186 7531 2.179 0.7681
Step5 - Step13 0.049339 0.0186 7531 2.658 0.4105
Step5 - Step14 0.063825 0.0186 7531 3.438 0.0589
Step5 - Step15 0.069633 0.0186 7531 3.751 0.0205
Step5 - Step16 0.078369 0.0186 7531 4.222 0.0032
Step5 - Step17 0.082301 0.0186 7531 4.434 0.0013
Step5 - Step18 0.101952 0.0186 7531 5.492 <.0001
Step6 - Step7 -0.006966 0.0186 7531 -0.375 1.0000
Step6 - Step8 -0.001064 0.0186 7531 -0.057 1.0000
Step6 - Step9 0.002285 0.0186 7531 0.123 1.0000
Step6 - Step10 0.018805 0.0186 7531 1.013 0.9999
Step6 - Step11 0.026914 0.0186 7531 1.450 0.9937
Step6 - Step12 0.031482 0.0186 7531 1.696 0.9676
Step6 - Step13 0.040382 0.0186 7531 2.176 0.7701
Step6 - Step14 0.054869 0.0186 7531 2.956 0.2222
Step6 - Step15 0.060676 0.0186 7531 3.269 0.0982
Step6 - Step16 0.069412 0.0186 7531 3.739 0.0214
Step6 - Step17 0.073344 0.0186 7531 3.951 0.0097
Step6 - Step18 0.092995 0.0186 7531 5.010 0.0001
Step7 - Step8 0.005902 0.0186 7531 0.318 1.0000
Step7 - Step9 0.009251 0.0186 7531 0.498 1.0000
Step7 - Step10 0.025771 0.0186 7531 1.388 0.9961
Step7 - Step11 0.033880 0.0186 7531 1.825 0.9372
Step7 - Step12 0.038448 0.0186 7531 2.071 0.8329
Step7 - Step13 0.047348 0.0186 7531 2.551 0.4910
Step7 - Step14 0.061834 0.0186 7531 3.331 0.0818
Step7 - Step15 0.067641 0.0186 7531 3.644 0.0299
Step7 - Step16 0.076377 0.0186 7531 4.115 0.0051
Step7 - Step17 0.080309 0.0186 7531 4.326 0.0021
Step7 - Step18 0.099961 0.0186 7531 5.385 <.0001
Step8 - Step9 0.003349 0.0186 7531 0.180 1.0000
Step8 - Step10 0.019869 0.0186 7531 1.070 0.9999
Step8 - Step11 0.027978 0.0186 7531 1.507 0.9903
Step8 - Step12 0.032546 0.0186 7531 1.753 0.9559
Step8 - Step13 0.041446 0.0186 7531 2.233 0.7316
Step8 - Step14 0.055932 0.0186 7531 3.013 0.1938
Step8 - Step15 0.061740 0.0186 7531 3.326 0.0830
Step8 - Step16 0.070476 0.0186 7531 3.797 0.0174
Step8 - Step17 0.074408 0.0186 7531 4.009 0.0078
Step8 - Step18 0.094059 0.0186 7531 5.067 0.0001
Step9 - Step10 0.016520 0.0186 7531 0.890 1.0000
Step9 - Step11 0.024629 0.0186 7531 1.327 0.9977
Step9 - Step12 0.029197 0.0186 7531 1.573 0.9847
Step9 - Step13 0.038097 0.0186 7531 2.052 0.8432
Step9 - Step14 0.052583 0.0186 7531 2.833 0.2922
Step9 - Step15 0.058391 0.0186 7531 3.146 0.1381
Step9 - Step16 0.067127 0.0186 7531 3.616 0.0329
Step9 - Step17 0.071059 0.0186 7531 3.828 0.0155
Step9 - Step18 0.090710 0.0186 7531 4.887 0.0002
Step10 - Step11 0.008109 0.0186 7531 0.437 1.0000
Step10 - Step12 0.012677 0.0186 7531 0.683 1.0000
Step10 - Step13 0.021577 0.0186 7531 1.162 0.9996
Step10 - Step14 0.036063 0.0186 7531 1.943 0.8953
Step10 - Step15 0.041870 0.0186 7531 2.256 0.7155
Step10 - Step16 0.050607 0.0186 7531 2.726 0.3620
Step10 - Step17 0.054539 0.0186 7531 2.938 0.2316
Step10 - Step18 0.074190 0.0186 7531 3.997 0.0081
Step11 - Step12 0.004568 0.0186 7531 0.246 1.0000
Step11 - Step13 0.013468 0.0186 7531 0.726 1.0000
Step11 - Step14 0.027954 0.0186 7531 1.506 0.9904
Step11 - Step15 0.033762 0.0186 7531 1.819 0.9390
Step11 - Step16 0.042498 0.0186 7531 2.289 0.6912
Step11 - Step17 0.046430 0.0186 7531 2.501 0.5293
Step11 - Step18 0.066081 0.0186 7531 3.560 0.0397
Step12 - Step13 0.008900 0.0186 7531 0.479 1.0000
Step12 - Step14 0.023386 0.0186 7531 1.260 0.9988
Step12 - Step15 0.029193 0.0186 7531 1.573 0.9847
Step12 - Step16 0.037929 0.0186 7531 2.043 0.8480
Step12 - Step17 0.041861 0.0186 7531 2.255 0.7159
Step12 - Step18 0.061513 0.0186 7531 3.314 0.0861
Step13 - Step14 0.014486 0.0186 7531 0.780 1.0000
Step13 - Step15 0.020293 0.0186 7531 1.093 0.9998
Step13 - Step16 0.029029 0.0186 7531 1.564 0.9856
Step13 - Step17 0.032961 0.0186 7531 1.776 0.9506
Step13 - Step18 0.052613 0.0186 7531 2.834 0.2913
Step14 - Step15 0.005807 0.0186 7531 0.313 1.0000
Step14 - Step16 0.014543 0.0186 7531 0.783 1.0000
Step14 - Step17 0.018475 0.0186 7531 0.995 0.9999
Step14 - Step18 0.038127 0.0186 7531 2.054 0.8423
Step15 - Step16 0.008736 0.0186 7531 0.471 1.0000
Step15 - Step17 0.012668 0.0186 7531 0.682 1.0000
Step15 - Step18 0.032320 0.0186 7531 1.741 0.9587
Step16 - Step17 0.003932 0.0186 7531 0.212 1.0000
Step16 - Step18 0.023584 0.0186 7531 1.271 0.9987
Step17 - Step18 0.019652 0.0186 7531 1.059 0.9999
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5 Step6
0.573935086 0.003039570 0.017560376 0.022849226 0.013692158 0.004735284
Step7 Step8 Step9 Step10 Step11 Step12
0.011700832 0.005799236 0.002450190 -0.014069897 -0.022178848 -0.026747217
Step13 Step14 Step15 Step16 Step17 Step18
-0.035647009 -0.050133220 -0.055940383 -0.064676513 -0.068608426 -0.088260045
Random Effects:
$trial_id
(Intercept)
7.1 -2.335607e-02
20.1 -3.829123e-02
2.2 4.985558e-02
7.2 9.683537e-02
8.2 3.664373e-02
10.2 -2.767938e-02
16.2 -1.535171e-01
17.2 1.330471e-02
23.2 -8.754236e-04
2.3 -1.775388e-01
3.3 -1.490626e-01
8.3 7.726058e-02
10.3 6.171820e-01
11.3 3.207178e-01
16.3 6.446475e-02
17.3 2.976752e-02
20.3 2.791485e-02
2.4 -1.523157e-01
4.4 -4.984816e-02
7.4 1.285154e-01
11.4 1.228496e+00
13.4 -5.833870e-02
14.4 1.011565e-01
16.4 -7.466675e-02
17.4 2.081310e-02
18.4 1.143406e-01
20.4 6.649547e-02
23.4 -1.976532e-02
2.5 2.933885e-01
3.5 3.048811e-01
4.5 -2.226687e-02
7.5 1.106321e-01
8.5 9.746312e-02
11.5 2.159504e-01
13.5 1.132292e-03
14.5 3.955169e-01
15.5 -6.768340e-03
16.5 -4.936514e-02
17.5 -8.934942e-03
18.5 2.311829e-01
23.5 8.261424e-02
4.6 3.679917e-02
5.6 -6.016862e-02
7.6 -3.906979e-02
8.6 4.907929e-01
10.6 1.038269e-01
13.6 -8.383704e-03
14.6 7.756142e-02
17.6 -9.449678e-04
18.6 2.087440e-01
23.6 7.281036e-02
2.7 -1.114305e-01
3.7 1.063079e-01
4.7 6.757103e-02
5.7 -1.216000e-02
7.7 2.113227e-02
8.7 9.251647e-02
13.7 6.104475e-02
14.7 1.434729e-01
16.7 -6.564502e-03
17.7 2.588989e-02
18.7 2.443697e-01
19.7 -5.401857e-02
23.7 1.670962e-01
2.8 -9.306763e-02
3.8 3.115532e-01
4.8 2.917588e-02
7.8 -4.448666e-02
8.8 2.119312e-01
13.8 1.626328e-01
16.8 -1.265928e-02
17.8 1.753349e-02
22.8 5.930584e-02
23.8 1.106949e-01
3.9 -8.018701e-02
4.9 1.124859e-01
7.9 7.042934e-02
13.9 1.217052e-02
16.9 8.329397e-02
17.9 -5.355779e-02
20.9 2.510633e-03
22.9 4.483594e-02
23.9 1.274942e-01
2.10 -2.405668e-01
4.10 7.693016e-02
7.10 -5.406640e-02
8.10 5.706069e-02
14.10 8.717836e-03
18.10 -4.496919e-02
19.10 2.080222e-02
20.10 1.026436e-01
2.11 -1.409571e-01
3.11 1.763941e-01
7.11 8.189480e-03
8.11 2.160734e-01
10.11 1.918960e-01
13.11 -7.981494e-02
16.11 1.428354e-01
17.11 -6.692893e-02
18.11 3.061765e-01
20.11 9.060499e-03
22.11 5.398324e-02
23.11 -2.145993e-02
2.12 -6.453447e-02
5.12 -6.438386e-02
7.12 4.646491e-02
10.12 2.650344e-01
13.12 2.102328e-01
14.12 9.147681e-02
15.12 -8.331124e-02
17.12 8.205763e-03
19.12 -2.960274e-02
20.12 1.045725e-01
3.13 2.388061e-01
7.13 1.253026e-01
8.13 2.103647e-02
10.13 4.840353e-01
13.13 -8.559146e-02
14.13 1.001258e-01
16.13 -4.040974e-03
17.13 -8.794404e-02
18.13 1.833797e-01
23.13 1.390910e-02
2.14 -9.703724e-02
3.14 4.446701e-01
5.14 -3.658603e-02
7.14 7.626023e-02
10.14 6.372946e-01
11.14 1.806901e-01
13.14 -1.123166e-01
14.14 -8.798625e-02
15.14 7.772532e-02
16.14 1.229647e-02
17.14 -1.169461e-02
20.14 1.030571e-01
2.15 7.093261e-02
3.15 8.212825e-02
5.15 -7.673090e-02
7.15 8.257986e-03
8.15 1.261070e-01
10.15 5.403865e-01
14.15 -6.295603e-02
15.15 -5.771015e-02
17.15 4.680894e-02
18.15 2.484816e-01
23.15 3.176935e-03
3.16 1.093257e-01
5.16 -8.073741e-02
8.16 -3.234977e-02
10.16 4.443045e-01
13.16 7.485617e-02
15.16 5.322951e-02
16.16 3.297148e-02
17.16 1.651260e-02
2.17 6.593076e-02
4.17 1.464055e-01
5.17 -3.336657e-02
10.17 1.322136e+00
11.17 -1.903993e-01
13.17 -3.977722e-02
14.17 1.370752e-01
15.17 1.279010e-02
17.17 -2.075142e-02
19.17 -7.313310e-02
22.17 9.176479e-02
23.17 4.585032e-02
2.18 6.838359e-01
4.18 -3.147741e-02
5.18 1.920503e-01
7.18 1.729800e-01
8.18 -3.499606e-03
11.18 -1.720582e-01
15.18 -5.988280e-02
16.18 -8.768607e-02
17.18 2.386662e-01
3.19 2.555460e-01
7.19 1.475173e-01
10.19 6.271371e-01
11.19 9.656652e-01
14.19 2.187104e-02
15.19 -1.179264e-01
18.19 2.470923e-01
23.19 4.111884e-02
3.20 2.028860e-01
4.20 1.795270e-02
7.20 -1.811566e-01
13.20 1.400701e-01
14.20 7.295095e-02
15.20 -1.705829e-01
16.20 1.080926e-01
17.20 -8.010370e-03
2.21 9.145506e-01
7.21 5.764673e-01
8.21 2.415469e-01
10.21 3.491096e-01
13.21 3.205124e-01
14.21 -7.498749e-02
15.21 1.589583e-01
16.21 5.644464e-02
17.21 1.957349e-02
19.21 -9.810996e-02
2.22 5.425540e-02
4.22 4.663387e-02
5.22 2.652691e-02
7.22 1.382674e-01
11.22 1.081741e+00
13.22 1.260307e-01
15.22 2.046497e-01
16.22 1.443747e-01
17.22 2.354863e-01
19.22 -5.650731e-02
20.22 8.965419e-02
23.22 -1.802164e-02
2.23 -4.860093e-02
3.23 2.222834e-01
4.23 -2.105988e-02
5.23 -1.459395e-02
7.23 -1.799776e-03
8.23 5.731839e-01
10.23 6.347894e-01
14.23 4.519421e-02
15.23 -3.789432e-02
16.23 3.294556e-01
17.23 1.770544e-01
2.24 -1.420670e-01
3.24 3.819579e-01
5.24 8.885220e-03
7.24 -6.996151e-02
8.24 1.717492e-01
13.24 2.401780e-01
14.24 1.108463e-01
15.24 9.410666e-02
16.24 3.056438e-01
17.24 1.904579e-01
20.24 9.602439e-02
23.24 2.161924e-01
2.25 6.168487e-01
7.25 7.635570e-02
10.25 -1.520205e-01
13.25 4.854703e-02
15.25 -9.230187e-02
16.25 3.292727e-01
17.25 3.450870e-02
18.25 1.389139e-01
23.25 4.173327e-01
2.26 2.777023e-01
3.26 1.133586e-01
4.26 3.022509e-02
5.26 2.681334e-01
8.26 6.599524e-01
10.26 2.793744e-01
13.26 1.390350e-01
14.26 2.772928e-01
17.26 2.813527e-01
18.26 2.143551e-01
23.26 2.676592e-01
2.27 4.509857e-01
3.27 -7.236866e-02
5.27 2.786739e-01
7.27 3.430897e-01
8.27 -2.061128e-01
10.27 4.499428e-02
11.27 -2.140472e-01
13.27 2.010817e-01
14.27 1.163858e-01
16.27 2.335994e-02
17.27 2.671166e-01
18.27 1.196504e-01
20.27 -9.045738e-03
3.28 9.666980e-03
5.28 5.494226e-02
7.28 6.838048e-02
8.28 2.466468e-02
10.28 -7.027592e-01
11.28 -3.011600e-01
15.28 1.118830e-01
16.28 2.089070e-01
17.28 5.417975e-01
19.28 -9.073435e-02
23.28 -2.249606e-02
2.29 6.976720e-01
4.29 1.228996e-02
5.29 -1.021097e-02
7.29 7.359381e-02
13.29 -6.815044e-02
14.29 -5.505172e-02
15.29 4.340292e-02
17.29 -7.400565e-03
19.29 1.572997e-01
20.29 -3.308530e-02
22.29 9.786260e-02
23.29 -3.303617e-01
2.30 3.917524e-01
3.30 3.839822e-02
5.30 2.035907e-02
7.30 -1.127518e-02
8.30 -2.120963e-01
10.30 -6.794155e-01
13.30 -2.209398e-01
14.30 -1.239073e-02
16.30 1.583832e-01
17.30 -5.810958e-02
18.30 4.117600e-01
20.30 7.046863e-02
22.30 -5.865689e-02
23.30 -4.927774e-01
2.31 -4.528331e-01
3.31 -1.694989e-01
4.31 1.049221e-01
7.31 -1.595373e-01
8.31 -1.840100e-01
10.31 -2.271838e-01
11.31 -4.802663e-01
13.31 -3.260652e-01
15.31 3.749078e-01
16.31 1.887850e-01
17.31 -9.469422e-02
2.32 -5.927549e-01
3.32 -3.124888e-01
5.32 7.581213e-02
7.32 -3.567568e-01
8.32 -3.160681e-01
10.32 -7.667982e-01
11.32 -1.745786e-01
14.32 -4.562580e-01
16.32 -1.540240e-01
17.32 -4.204791e-01
18.32 6.813490e-02
23.32 -1.163530e-02
2.33 -2.289930e-01
5.33 -5.841953e-02
8.33 -2.445306e-01
11.33 -5.309810e-01
13.33 -1.204716e-01
14.33 -2.019227e-01
16.33 -3.927573e-01
17.33 -2.537379e-01
18.33 -8.033687e-02
19.33 -1.339754e-02
20.33 6.003617e-05
23.33 -8.317317e-02
2.34 -1.947785e-01
3.34 -5.712544e-02
8.34 -1.703891e-02
10.34 -4.837973e-01
11.34 -6.606526e-01
13.34 -2.091265e-01
16.34 -1.325570e-01
17.34 3.464884e-02
20.34 5.902390e-02
2.35 -5.117213e-01
3.35 -5.102956e-01
4.35 -9.139917e-02
5.35 -4.337307e-02
7.35 -1.851823e-01
8.35 -2.928341e-01
10.35 -2.032058e-01
13.35 -3.324739e-01
15.35 -2.804768e-01
16.35 1.871538e-01
18.35 -5.018244e-01
20.35 -2.077496e-01
23.35 7.255620e-02
2.36 4.963549e-02
4.36 -2.445491e-01
5.36 -2.274821e-01
7.36 -1.883538e-01
8.36 -3.246451e-01
10.36 -7.084059e-02
14.36 -1.552405e-01
16.36 -3.950054e-01
17.36 -4.376357e-01
18.36 -3.985466e-01
20.36 -1.040184e-01
23.36 -2.262280e-01
2.37 -5.872658e-01
4.37 -1.991461e-01
5.37 -1.029798e-01
7.37 -1.730910e-01
8.37 -2.568665e-01
10.37 -5.595482e-01
13.37 3.369398e-02
14.37 -1.244436e-01
15.37 5.716470e-02
16.37 -2.103962e-01
18.37 -1.639139e-01
20.37 -5.962695e-02
2.38 -1.144929e-01
3.38 -3.860386e-01
5.38 -1.459266e-01
7.38 -3.462066e-01
10.38 -4.510306e-01
13.38 2.156186e-02
16.38 -3.510096e-01
17.38 -2.166637e-01
18.38 -2.832986e-01
20.38 -1.269162e-01
23.38 -2.982747e-01
3.39 -6.303909e-02
4.39 -2.366205e-01
7.39 -2.362986e-01
8.39 -3.445676e-01
10.39 -6.318351e-01
13.39 -1.076285e-01
14.39 -2.319721e-01
16.39 -1.113506e-01
17.39 -1.275577e-01
18.39 -1.489229e-01
23.39 -1.147026e-01
2.40 -2.055013e-01
3.40 -1.175514e-01
5.40 -1.709050e-01
8.40 -2.961354e-01
10.40 -5.190695e-01
13.40 -8.540245e-02
14.40 -2.741729e-01
16.40 2.010489e-02
17.40 -2.468102e-01
2.41 -2.121076e-01
3.41 -3.142841e-01
5.41 -3.073108e-02
7.41 -2.888370e-01
8.41 -2.694136e-01
13.41 -3.182496e-01
15.41 -2.560510e-01
16.41 -1.260782e-02
17.41 -1.746050e-01
18.41 -2.884066e-01
20.41 -2.843816e-01
3.42 -4.148775e-01
4.42 -1.556659e-01
5.42 -1.399914e-01
7.42 -2.185229e-02
11.42 -6.004708e-01
15.42 5.246048e-02
16.42 -3.168850e-01
18.42 -4.688914e-01
20.42 -1.295100e-01
3.43 -2.820617e-01
5.43 -1.909922e-01
15.43 -3.089918e-01
20.43 -1.141558e-01
22.43 -3.522124e-01
15.44 2.090072e-02
$subject
(Intercept)
2 0.1054959813
3 0.0293800412
4 -0.1571709217
5 -0.2435560774
7 -0.0392739947
8 0.0414781743
10 0.4521724422
11 0.2835402497
13 -0.1611183786
14 -0.0160025974
15 -0.0889312661
16 -0.0293667667
17 -0.0411168339
18 0.1515859178
19 -0.1006703373
20 -0.1591441671
22 -0.0267647522
23 -0.0005367133
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
1.1065321 0.7563700 0.6937123 0.4274399 0.2254632 0.3009317
=============================================================
--- Mixed - Block 3 - Axis Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 13.173 0.77489 17 7531 9.2083 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.004258 0.0195 7531 -0.219 1.0000
Step1 - Step3 -0.028112 0.0195 7531 -1.444 0.9939
Step1 - Step4 -0.031987 0.0195 7531 -1.643 0.9762
Step1 - Step5 -0.031731 0.0195 7531 -1.630 0.9780
Step1 - Step6 -0.021659 0.0195 7531 -1.112 0.9998
Step1 - Step7 -0.032485 0.0195 7531 -1.669 0.9723
Step1 - Step8 -0.039907 0.0195 7531 -2.050 0.8446
Step1 - Step9 -0.041413 0.0195 7531 -2.127 0.8006
Step1 - Step10 -0.015332 0.0195 7531 -0.788 1.0000
Step1 - Step11 -0.005256 0.0195 7531 -0.270 1.0000
Step1 - Step12 0.000724 0.0195 7531 0.037 1.0000
Step1 - Step13 0.011942 0.0195 7531 0.613 1.0000
Step1 - Step14 0.031394 0.0195 7531 1.612 0.9803
Step1 - Step15 0.049294 0.0195 7531 2.532 0.5056
Step1 - Step16 0.069784 0.0195 7531 3.584 0.0366
Step1 - Step17 0.076068 0.0195 7531 3.907 0.0115
Step1 - Step18 0.088536 0.0195 7531 4.547 0.0008
Step2 - Step3 -0.023854 0.0195 7531 -1.225 0.9992
Step2 - Step4 -0.027729 0.0195 7531 -1.424 0.9948
Step2 - Step5 -0.027473 0.0195 7531 -1.411 0.9953
Step2 - Step6 -0.017401 0.0195 7531 -0.894 1.0000
Step2 - Step7 -0.028228 0.0195 7531 -1.450 0.9937
Step2 - Step8 -0.035649 0.0195 7531 -1.831 0.9354
Step2 - Step9 -0.037155 0.0195 7531 -1.908 0.9091
Step2 - Step10 -0.011075 0.0195 7531 -0.569 1.0000
Step2 - Step11 -0.000998 0.0195 7531 -0.051 1.0000
Step2 - Step12 0.004981 0.0195 7531 0.256 1.0000
Step2 - Step13 0.016200 0.0195 7531 0.832 1.0000
Step2 - Step14 0.035652 0.0195 7531 1.831 0.9354
Step2 - Step15 0.053552 0.0195 7531 2.751 0.3454
Step2 - Step16 0.074042 0.0195 7531 3.803 0.0170
Step2 - Step17 0.080326 0.0195 7531 4.126 0.0048
Step2 - Step18 0.092794 0.0195 7531 4.766 0.0003
Step3 - Step4 -0.003875 0.0195 7531 -0.199 1.0000
Step3 - Step5 -0.003619 0.0195 7531 -0.186 1.0000
Step3 - Step6 0.006453 0.0195 7531 0.331 1.0000
Step3 - Step7 -0.004373 0.0195 7531 -0.225 1.0000
Step3 - Step8 -0.011795 0.0195 7531 -0.606 1.0000
Step3 - Step9 -0.013301 0.0195 7531 -0.683 1.0000
Step3 - Step10 0.012780 0.0195 7531 0.656 1.0000
Step3 - Step11 0.022856 0.0195 7531 1.174 0.9995
Step3 - Step12 0.028836 0.0195 7531 1.481 0.9920
Step3 - Step13 0.040054 0.0195 7531 2.057 0.8406
Step3 - Step14 0.059506 0.0195 7531 3.056 0.1742
Step3 - Step15 0.077406 0.0195 7531 3.976 0.0088
Step3 - Step16 0.097896 0.0195 7531 5.028 0.0001
Step3 - Step17 0.104180 0.0195 7531 5.351 <.0001
Step3 - Step18 0.116648 0.0195 7531 5.991 <.0001
Step4 - Step5 0.000256 0.0195 7531 0.013 1.0000
Step4 - Step6 0.010328 0.0195 7531 0.530 1.0000
Step4 - Step7 -0.000499 0.0195 7531 -0.026 1.0000
Step4 - Step8 -0.007920 0.0195 7531 -0.407 1.0000
Step4 - Step9 -0.009426 0.0195 7531 -0.484 1.0000
Step4 - Step10 0.016655 0.0195 7531 0.855 1.0000
Step4 - Step11 0.026731 0.0195 7531 1.373 0.9966
Step4 - Step12 0.032710 0.0195 7531 1.680 0.9704
Step4 - Step13 0.043929 0.0195 7531 2.256 0.7151
Step4 - Step14 0.063381 0.0195 7531 3.255 0.1020
Step4 - Step15 0.081281 0.0195 7531 4.175 0.0040
Step4 - Step16 0.101771 0.0195 7531 5.227 <.0001
Step4 - Step17 0.108055 0.0195 7531 5.550 <.0001
Step4 - Step18 0.120523 0.0195 7531 6.190 <.0001
Step5 - Step6 0.010072 0.0195 7531 0.517 1.0000
Step5 - Step7 -0.000754 0.0195 7531 -0.039 1.0000
Step5 - Step8 -0.008176 0.0195 7531 -0.420 1.0000
Step5 - Step9 -0.009682 0.0195 7531 -0.497 1.0000
Step5 - Step10 0.016399 0.0195 7531 0.842 1.0000
Step5 - Step11 0.026475 0.0195 7531 1.360 0.9970
Step5 - Step12 0.032455 0.0195 7531 1.667 0.9726
Step5 - Step13 0.043674 0.0195 7531 2.243 0.7243
Step5 - Step14 0.063125 0.0195 7531 3.242 0.1059
Step5 - Step15 0.081026 0.0195 7531 4.162 0.0042
Step5 - Step16 0.101515 0.0195 7531 5.214 <.0001
Step5 - Step17 0.107799 0.0195 7531 5.537 <.0001
Step5 - Step18 0.120267 0.0195 7531 6.177 <.0001
Step6 - Step7 -0.010827 0.0195 7531 -0.556 1.0000
Step6 - Step8 -0.018248 0.0195 7531 -0.937 1.0000
Step6 - Step9 -0.019754 0.0195 7531 -1.015 0.9999
Step6 - Step10 0.006327 0.0195 7531 0.325 1.0000
Step6 - Step11 0.016403 0.0195 7531 0.843 1.0000
Step6 - Step12 0.022382 0.0195 7531 1.150 0.9996
Step6 - Step13 0.033601 0.0195 7531 1.726 0.9619
Step6 - Step14 0.053053 0.0195 7531 2.725 0.3629
Step6 - Step15 0.070953 0.0195 7531 3.644 0.0299
Step6 - Step16 0.091443 0.0195 7531 4.697 0.0004
Step6 - Step17 0.097727 0.0195 7531 5.020 0.0001
Step6 - Step18 0.110195 0.0195 7531 5.660 <.0001
Step7 - Step8 -0.007422 0.0195 7531 -0.381 1.0000
Step7 - Step9 -0.008928 0.0195 7531 -0.459 1.0000
Step7 - Step10 0.017153 0.0195 7531 0.881 1.0000
Step7 - Step11 0.027230 0.0195 7531 1.399 0.9958
Step7 - Step12 0.033209 0.0195 7531 1.706 0.9659
Step7 - Step13 0.044428 0.0195 7531 2.282 0.6967
Step7 - Step14 0.063879 0.0195 7531 3.281 0.0948
Step7 - Step15 0.081780 0.0195 7531 4.200 0.0036
Step7 - Step16 0.102269 0.0195 7531 5.253 <.0001
Step7 - Step17 0.108554 0.0195 7531 5.576 <.0001
Step7 - Step18 0.121021 0.0195 7531 6.216 <.0001
Step8 - Step9 -0.001506 0.0195 7531 -0.077 1.0000
Step8 - Step10 0.024575 0.0195 7531 1.262 0.9988
Step8 - Step11 0.034652 0.0195 7531 1.780 0.9496
Step8 - Step12 0.040631 0.0195 7531 2.087 0.8242
Step8 - Step13 0.051850 0.0195 7531 2.663 0.4068
Step8 - Step14 0.071301 0.0195 7531 3.662 0.0281
Step8 - Step15 0.089202 0.0195 7531 4.582 0.0007
Step8 - Step16 0.109691 0.0195 7531 5.634 <.0001
Step8 - Step17 0.115975 0.0195 7531 5.957 <.0001
Step8 - Step18 0.128443 0.0195 7531 6.597 <.0001
Step9 - Step10 0.026081 0.0195 7531 1.340 0.9975
Step9 - Step11 0.036157 0.0195 7531 1.857 0.9272
Step9 - Step12 0.042137 0.0195 7531 2.164 0.7774
Step9 - Step13 0.053355 0.0195 7531 2.740 0.3523
Step9 - Step14 0.072807 0.0195 7531 3.740 0.0214
Step9 - Step15 0.090707 0.0195 7531 4.659 0.0005
Step9 - Step16 0.111197 0.0195 7531 5.711 <.0001
Step9 - Step17 0.117481 0.0195 7531 6.034 <.0001
Step9 - Step18 0.129949 0.0195 7531 6.675 <.0001
Step10 - Step11 0.010077 0.0195 7531 0.518 1.0000
Step10 - Step12 0.016056 0.0195 7531 0.825 1.0000
Step10 - Step13 0.027275 0.0195 7531 1.401 0.9957
Step10 - Step14 0.046726 0.0195 7531 2.400 0.6080
Step10 - Step15 0.064627 0.0195 7531 3.319 0.0847
Step10 - Step16 0.085116 0.0195 7531 4.372 0.0017
Step10 - Step17 0.091401 0.0195 7531 4.695 0.0004
Step10 - Step18 0.103868 0.0195 7531 5.335 <.0001
Step11 - Step12 0.005979 0.0195 7531 0.307 1.0000
Step11 - Step13 0.017198 0.0195 7531 0.883 1.0000
Step11 - Step14 0.036650 0.0195 7531 1.882 0.9186
Step11 - Step15 0.054550 0.0195 7531 2.802 0.3117
Step11 - Step16 0.075040 0.0195 7531 3.854 0.0140
Step11 - Step17 0.081324 0.0195 7531 4.177 0.0039
Step11 - Step18 0.093792 0.0195 7531 4.817 0.0002
Step12 - Step13 0.011219 0.0195 7531 0.576 1.0000
Step12 - Step14 0.030670 0.0195 7531 1.575 0.9845
Step12 - Step15 0.048571 0.0195 7531 2.495 0.5344
Step12 - Step16 0.069060 0.0195 7531 3.547 0.0415
Step12 - Step17 0.075345 0.0195 7531 3.870 0.0132
Step12 - Step18 0.087812 0.0195 7531 4.510 0.0009
Step13 - Step14 0.019451 0.0195 7531 0.999 0.9999
Step13 - Step15 0.037352 0.0195 7531 1.918 0.9052
Step13 - Step16 0.057842 0.0195 7531 2.971 0.2146
Step13 - Step17 0.064126 0.0195 7531 3.294 0.0914
Step13 - Step18 0.076594 0.0195 7531 3.934 0.0104
Step14 - Step15 0.017900 0.0195 7531 0.919 1.0000
Step14 - Step16 0.038390 0.0195 7531 1.972 0.8828
Step14 - Step17 0.044674 0.0195 7531 2.295 0.6875
Step14 - Step18 0.057142 0.0195 7531 2.935 0.2333
Step15 - Step16 0.020490 0.0195 7531 1.052 0.9999
Step15 - Step17 0.026774 0.0195 7531 1.375 0.9965
Step15 - Step18 0.039242 0.0195 7531 2.016 0.8622
Step16 - Step17 0.006284 0.0195 7531 0.323 1.0000
Step16 - Step18 0.018752 0.0195 7531 0.963 1.0000
Step17 - Step18 0.012468 0.0195 7531 0.640 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5
0.5818702923 0.0042578243 0.0281119978 0.0319868630 0.0317312185
Step6 Step7 Step8 Step9 Step10
0.0216588747 0.0324854247 0.0399072551 0.0414130280 0.0153323310
Step11 Step12 Step13 Step14 Step15
0.0052557342 -0.0007235145 -0.0119424616 -0.0313939079 -0.0492944033
Step16 Step17 Step18
-0.0697840157 -0.0760682167 -0.0885359871
Random Effects:
$trial_id
(Intercept)
7.1 -0.1000286271
20.1 0.0484526173
2.2 0.0350216045
7.2 -0.0078730734
8.2 0.1217083693
10.2 -0.0408219349
16.2 -0.0344031787
17.2 0.0402472921
23.2 0.0185062397
2.3 -0.0906566813
3.3 -0.0934769553
8.3 0.2844708019
10.3 0.4692248435
11.3 0.2542355927
16.3 0.0558365736
17.3 0.1885213993
20.3 0.0492817469
2.4 0.0879322039
4.4 -0.1458730308
7.4 0.2947379841
11.4 0.8794311289
13.4 0.0841541444
14.4 0.2937701302
16.4 0.0826598977
17.4 -0.0468197251
18.4 -0.1133937686
20.4 0.1417657630
23.4 0.1187421927
2.5 0.0107376231
3.5 0.0633307419
4.5 -0.0915695828
7.5 0.0976054781
8.5 0.2802815930
11.5 0.5179338045
13.5 0.1320685922
14.5 0.2984876512
15.5 -0.0593356793
16.5 0.2415048603
17.5 0.5234476381
18.5 0.0207016396
23.5 0.0761560559
4.6 -0.0598919320
5.6 0.1781790857
7.6 0.2440990335
8.6 0.5724340825
10.6 0.4954900709
13.6 0.1630647090
14.6 -0.0256392031
17.6 -0.0221608801
18.6 -0.1357701640
23.6 -0.0614977729
2.7 -0.2419532409
3.7 0.3261942333
4.7 -0.0792912830
5.7 0.0293355808
7.7 -0.0157331200
8.7 0.1982406025
13.7 0.0884039280
14.7 0.2154196981
16.7 0.2000117832
17.7 0.0677722156
18.7 0.0758088161
19.7 -0.0976159016
23.7 0.1323084903
2.8 0.0957455424
3.8 0.2009116330
4.8 -0.1050682614
7.8 -0.0188606372
8.8 0.6970003007
13.8 0.1829094658
16.8 0.0534339692
17.8 0.2006261197
22.8 -0.1161787945
23.8 0.1739267501
3.9 0.1128437246
4.9 -0.1576736878
7.9 -0.0134450406
13.9 0.2058694431
16.9 0.1584785940
17.9 0.2088344599
20.9 0.0010125061
22.9 -0.0112267464
23.9 -0.0115131567
2.10 0.0049948276
4.10 -0.1932045079
7.10 -0.0525839781
8.10 0.2119100543
14.10 0.2391775462
18.10 0.2198761826
19.10 0.0444639190
20.10 0.1890583374
2.11 -0.0975515058
3.11 0.1980215420
7.11 0.1298555585
8.11 0.5440510690
10.11 0.0730094818
13.11 -0.0199663077
16.11 -0.0026781111
17.11 -0.0110935270
18.11 0.0269188710
20.11 -0.0027638843
22.11 -0.0534501520
23.11 -0.0346724485
2.12 0.0547511918
5.12 -0.0849692103
7.12 0.0213621456
10.12 0.6013981487
13.12 -0.0323748318
14.12 0.1635940259
15.12 0.0944775356
17.12 0.1261201220
19.12 -0.0803815807
20.12 0.0366714259
3.13 0.0120737139
7.13 0.0047150349
8.13 0.1105476371
10.13 0.4159926765
13.13 0.0022776369
14.13 0.1769834852
16.13 0.0422768616
17.13 0.2055891916
18.13 0.0658063032
23.13 -0.0074836125
2.14 -0.0481707856
3.14 0.4549938353
5.14 0.0047294263
7.14 0.1481569528
10.14 0.4201614060
11.14 0.1337291358
13.14 -0.0088645777
14.14 0.1044300940
15.14 0.3689635724
16.14 -0.0323330309
17.14 -0.0526635857
20.14 0.3992919936
2.15 0.0007901201
3.15 0.1502762802
5.15 0.0446724223
7.15 -0.1015939185
8.15 -0.0428978136
10.15 0.3410012020
14.15 -0.0934778663
15.15 0.2829031351
17.15 0.0072477422
18.15 -0.0832520343
23.15 -0.2406606575
3.16 0.4075160479
5.16 0.0639222016
8.16 0.3250725903
10.16 0.4579414573
13.16 -0.0113916546
15.16 0.2541419860
16.16 -0.0487088801
17.16 0.0732111349
2.17 0.2560744270
4.17 0.0616466762
5.17 0.1852782235
10.17 0.7674487704
11.17 0.0586949568
13.17 -0.0168749481
14.17 0.1345836091
15.17 0.1422350334
17.17 0.0454432962
19.17 -0.0970609332
22.17 -0.1180744873
23.17 0.1614106376
2.18 0.5343794088
4.18 0.0146831049
5.18 0.1261874828
7.18 0.0968583015
8.18 0.1242183009
11.18 -0.0774737939
15.18 0.3977213394
16.18 0.0742783806
17.18 0.0623301876
3.19 0.3314051587
7.19 0.0586929330
10.19 0.4735117645
11.19 0.2289138813
14.19 -0.0620517796
15.19 0.0269425902
18.19 -0.1381616444
23.19 0.2449549010
3.20 0.2005916831
4.20 0.1298680040
7.20 -0.0588589051
13.20 0.1891770109
14.20 -0.0089964120
15.20 -0.2057643759
16.20 0.0638402292
17.20 0.2589473509
2.21 0.6614565500
7.21 0.2326129722
8.21 -0.0635641231
10.21 0.6394352165
13.21 0.3732632364
14.21 -0.0049209247
15.21 -0.0007423095
16.21 0.0102500637
17.21 0.1567476042
19.21 -0.0912284789
2.22 0.3773416498
4.22 -0.0059116716
5.22 -0.1236383255
7.22 -0.0080616566
11.22 0.2627308452
13.22 -0.0282971557
15.22 -0.0471335453
16.22 -0.0775266880
17.22 0.1634679549
19.22 -0.0386283662
20.22 -0.1346912570
23.22 0.4244663440
2.23 -0.1397172128
3.23 0.0856633695
4.23 0.4013614738
5.23 0.0233270438
7.23 -0.0361857832
8.23 0.0120414425
10.23 0.3011276399
14.23 0.0768157212
15.23 0.1085126504
16.23 0.0776924971
17.23 0.1844253330
2.24 0.2620071173
3.24 0.3256715507
5.24 -0.0940193224
7.24 0.5424883530
8.24 -0.1056356999
13.24 0.4097117434
14.24 -0.1101040110
15.24 0.0459074834
16.24 0.0264830158
17.24 0.2659184500
20.24 0.0147146909
23.24 -0.0180129785
2.25 0.3367898463
7.25 0.1864897263
10.25 0.3047334823
13.25 0.1125661811
15.25 -0.0101816613
16.25 0.1477367962
17.25 0.0455196522
18.25 -0.0839433302
23.25 0.5288764501
2.26 0.4231018311
3.26 0.0736754048
4.26 0.1302548971
5.26 0.2392536669
8.26 0.3501802778
10.26 -0.0402178222
13.26 0.0115158479
14.26 -0.0429087805
17.26 0.0109491070
18.26 -0.0344055409
23.26 0.0919971539
2.27 0.5122989586
3.27 0.1354111737
5.27 0.3476852593
7.27 0.2035021759
8.27 -0.2474201070
10.27 0.1920885474
11.27 0.0916565495
13.27 0.1751046299
14.27 0.0918410175
16.27 0.1212427950
17.27 -0.0763334827
18.27 0.2081045359
20.27 -0.0257388057
3.28 0.0698211398
5.28 0.0470425171
7.28 0.0702151605
8.28 -0.2068434724
10.28 -0.8289818719
11.28 -0.2017393948
15.28 0.0528471586
16.28 -0.0417501393
17.28 0.2260204954
19.28 -0.1343996597
23.28 -0.0034659221
2.29 0.2269350605
4.29 0.2385086999
5.29 -0.0081390543
7.29 0.1595161770
13.29 -0.2137771415
14.29 0.2962651616
15.29 0.1001800212
17.29 -0.0381928785
19.29 0.2370471993
20.29 0.0951926408
22.29 0.1468618602
23.29 -0.2640003264
2.30 0.4925856239
3.30 -0.2496421855
5.30 0.0199640948
7.30 0.0492513183
8.30 -0.0478321565
10.30 -0.7271872500
13.30 -0.1447905815
14.30 0.0643720991
16.30 -0.0903654023
17.30 -0.1225416750
18.30 0.6786888194
20.30 0.0345883205
22.30 0.0479557361
23.30 -0.5485761337
2.31 -0.4048043033
3.31 -0.1469561945
4.31 0.2638413563
7.31 -0.2726810624
8.31 -0.4207139589
10.31 -0.0015431007
11.31 -0.3658082115
13.31 -0.3494637350
15.31 -0.1093178552
16.31 0.1916842263
17.31 -0.1941723414
2.32 -0.5293055995
3.32 -0.3499898007
5.32 0.0991191946
7.32 -0.3792431905
8.32 -0.0107769158
10.32 -0.8798255623
11.32 -0.1788135102
14.32 -0.4945057255
16.32 -0.0514182808
17.32 -0.4815165371
18.32 0.1425393446
23.32 0.0065237328
2.33 -0.1700501212
5.33 0.1399718630
8.33 -0.0403578978
11.33 -0.4156771945
13.33 -0.1195894862
14.33 -0.2337477432
16.33 -0.3442240739
17.33 -0.2571319135
18.33 0.2538085885
19.33 -0.0307235542
20.33 -0.0715753642
23.33 -0.1717378447
2.34 -0.2136058800
3.34 -0.1673195703
8.34 0.0724943865
10.34 0.0439829548
11.34 -0.5428847949
13.34 -0.1846182242
16.34 -0.0713651598
17.34 -0.2419618938
20.34 -0.0900363665
2.35 -0.5001925677
3.35 -0.6261918799
4.35 -0.1058535024
5.35 -0.0054771935
7.35 -0.1783787415
8.35 -0.4990492811
10.35 -0.3957570303
13.35 -0.3638345776
15.35 -0.0377062117
16.35 -0.1337186410
18.35 -0.3928095992
20.35 -0.3173330053
23.35 0.1960742370
2.36 -0.1854229581
4.36 -0.2496800877
5.36 -0.3252577997
7.36 -0.2648808046
8.36 -0.4576013132
10.36 -0.0157943173
14.36 -0.1274183029
16.36 -0.2618227825
17.36 -0.4172271455
18.36 -0.2607334724
20.36 -0.2269270657
23.36 -0.2845455348
2.37 -0.5377794258
4.37 -0.1419579297
5.37 -0.1771393712
7.37 -0.1718909654
8.37 -0.2436610411
10.37 -0.4811778520
13.37 -0.1795591530
14.37 -0.3594629192
15.37 -0.2087700415
16.37 0.2638364289
18.37 -0.1105059333
20.37 0.1002784888
2.38 -0.3045904004
3.38 -0.4468449285
5.38 -0.2537544785
7.38 -0.4142690441
10.38 -0.3630331732
13.38 -0.2098018819
16.38 -0.3560149811
17.38 -0.2338966028
18.38 -0.0166245377
20.38 -0.1053805600
23.38 -0.2374167668
3.39 0.2314956142
4.39 -0.1787615650
7.39 -0.2084488281
8.39 -0.3547535841
10.39 -0.6239251170
13.39 -0.1431779762
14.39 -0.1807789504
16.39 -0.1973666533
17.39 -0.3287169275
18.39 -0.0186936566
23.39 -0.1886496947
2.40 -0.3857802189
3.40 -0.3869343869
5.40 -0.2672235127
8.40 -0.4705712683
10.40 -0.5049290807
13.40 -0.0916841734
14.40 -0.3592177334
16.40 -0.0585978375
17.40 -0.1928209719
2.41 -0.3807526698
3.41 -0.3818531453
5.41 -0.1225902523
7.41 -0.2232755312
8.41 -0.2831213855
13.41 -0.3696459183
15.41 -0.3095052091
16.41 0.1773718841
17.41 -0.3437602143
18.41 -0.0902467172
20.41 -0.2647646972
3.42 -0.4740770913
4.42 -0.0836628665
5.42 -0.2103827188
7.42 -0.1958904493
11.42 -0.3732277061
15.42 -0.2493476201
16.42 -0.3805379454
18.42 -0.3596027450
20.42 -0.0963098266
3.43 0.3402342521
5.43 -0.2566185700
15.43 -0.4000771223
20.43 -0.0783479650
22.43 -0.2335575600
15.44 -0.1576208585
$subject
(Intercept)
2 0.0638350426
3 0.1776355950
4 -0.1603533305
5 -0.1703379564
7 -0.0814775389
8 0.1834575730
10 0.4894065133
11 0.1216188308
13 -0.1600803088
14 0.0235044564
15 0.0355096724
16 -0.0869335198
17 0.0001685041
18 -0.0653032472
19 -0.1291505089
20 -0.1358795347
22 -0.1511477854
23 0.0455275428
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
1.1844903 0.8088531 0.4877690 0.3625828 0.4619440 0.3403525
=============================================================
--- Mixed - Block 3 - Axis Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 25.198 1.4822 17 7531 5.0548 3.931e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.024279 0.0363 7531 -0.668 1.0000
Step1 - Step3 -0.045689 0.0363 7531 -1.257 0.9988
Step1 - Step4 -0.047983 0.0363 7531 -1.320 0.9979
Step1 - Step5 -0.061631 0.0363 7531 -1.696 0.9677
Step1 - Step6 -0.025692 0.0363 7531 -0.707 1.0000
Step1 - Step7 -0.037183 0.0363 7531 -1.023 0.9999
Step1 - Step8 -0.031626 0.0363 7531 -0.870 1.0000
Step1 - Step9 -0.018463 0.0363 7531 -0.508 1.0000
Step1 - Step10 -0.025220 0.0363 7531 -0.694 1.0000
Step1 - Step11 -0.016567 0.0363 7531 -0.456 1.0000
Step1 - Step12 -0.000650 0.0363 7531 -0.018 1.0000
Step1 - Step13 0.009126 0.0363 7531 0.251 1.0000
Step1 - Step14 0.037930 0.0363 7531 1.044 0.9999
Step1 - Step15 0.047521 0.0363 7531 1.308 0.9981
Step1 - Step16 0.074831 0.0363 7531 2.059 0.8397
Step1 - Step17 0.115652 0.0363 7531 3.182 0.1252
Step1 - Step18 0.146428 0.0363 7531 4.029 0.0072
Step2 - Step3 -0.021410 0.0363 7531 -0.589 1.0000
Step2 - Step4 -0.023704 0.0363 7531 -0.652 1.0000
Step2 - Step5 -0.037352 0.0363 7531 -1.028 0.9999
Step2 - Step6 -0.001414 0.0363 7531 -0.039 1.0000
Step2 - Step7 -0.012904 0.0363 7531 -0.355 1.0000
Step2 - Step8 -0.007348 0.0363 7531 -0.202 1.0000
Step2 - Step9 0.005816 0.0363 7531 0.160 1.0000
Step2 - Step10 -0.000941 0.0363 7531 -0.026 1.0000
Step2 - Step11 0.007711 0.0363 7531 0.212 1.0000
Step2 - Step12 0.023629 0.0363 7531 0.650 1.0000
Step2 - Step13 0.033405 0.0363 7531 0.919 1.0000
Step2 - Step14 0.062209 0.0363 7531 1.712 0.9647
Step2 - Step15 0.071800 0.0363 7531 1.976 0.8811
Step2 - Step16 0.099109 0.0363 7531 2.727 0.3615
Step2 - Step17 0.139931 0.0363 7531 3.850 0.0142
Step2 - Step18 0.170707 0.0363 7531 4.697 0.0004
Step3 - Step4 -0.002294 0.0363 7531 -0.063 1.0000
Step3 - Step5 -0.015942 0.0363 7531 -0.439 1.0000
Step3 - Step6 0.019997 0.0363 7531 0.550 1.0000
Step3 - Step7 0.008506 0.0363 7531 0.234 1.0000
Step3 - Step8 0.014062 0.0363 7531 0.387 1.0000
Step3 - Step9 0.027226 0.0363 7531 0.749 1.0000
Step3 - Step10 0.020469 0.0363 7531 0.563 1.0000
Step3 - Step11 0.029122 0.0363 7531 0.801 1.0000
Step3 - Step12 0.045039 0.0363 7531 1.239 0.9990
Step3 - Step13 0.054815 0.0363 7531 1.508 0.9902
Step3 - Step14 0.083619 0.0363 7531 2.301 0.6829
Step3 - Step15 0.093210 0.0363 7531 2.565 0.4804
Step3 - Step16 0.120519 0.0363 7531 3.316 0.0855
Step3 - Step17 0.161341 0.0363 7531 4.439 0.0013
Step3 - Step18 0.192117 0.0363 7531 5.286 <.0001
Step4 - Step5 -0.013648 0.0363 7531 -0.376 1.0000
Step4 - Step6 0.022290 0.0363 7531 0.613 1.0000
Step4 - Step7 0.010800 0.0363 7531 0.297 1.0000
Step4 - Step8 0.016356 0.0363 7531 0.450 1.0000
Step4 - Step9 0.029520 0.0363 7531 0.812 1.0000
Step4 - Step10 0.022763 0.0363 7531 0.626 1.0000
Step4 - Step11 0.031415 0.0363 7531 0.864 1.0000
Step4 - Step12 0.047333 0.0363 7531 1.302 0.9982
Step4 - Step13 0.057109 0.0363 7531 1.571 0.9849
Step4 - Step14 0.085913 0.0363 7531 2.364 0.6356
Step4 - Step15 0.095504 0.0363 7531 2.628 0.4327
Step4 - Step16 0.122813 0.0363 7531 3.379 0.0708
Step4 - Step17 0.163635 0.0363 7531 4.502 0.0009
Step4 - Step18 0.194411 0.0363 7531 5.349 <.0001
Step5 - Step6 0.035939 0.0363 7531 0.989 1.0000
Step5 - Step7 0.024448 0.0363 7531 0.673 1.0000
Step5 - Step8 0.030005 0.0363 7531 0.826 1.0000
Step5 - Step9 0.043168 0.0363 7531 1.188 0.9994
Step5 - Step10 0.036411 0.0363 7531 1.002 0.9999
Step5 - Step11 0.045064 0.0363 7531 1.240 0.9990
Step5 - Step12 0.060981 0.0363 7531 1.678 0.9708
Step5 - Step13 0.070757 0.0363 7531 1.947 0.8936
Step5 - Step14 0.099561 0.0363 7531 2.739 0.3530
Step5 - Step15 0.109152 0.0363 7531 3.003 0.1985
Step5 - Step16 0.136462 0.0363 7531 3.755 0.0202
Step5 - Step17 0.177283 0.0363 7531 4.878 0.0002
Step5 - Step18 0.208060 0.0363 7531 5.725 <.0001
Step6 - Step7 -0.011491 0.0363 7531 -0.316 1.0000
Step6 - Step8 -0.005934 0.0363 7531 -0.163 1.0000
Step6 - Step9 0.007229 0.0363 7531 0.199 1.0000
Step6 - Step10 0.000472 0.0363 7531 0.013 1.0000
Step6 - Step11 0.009125 0.0363 7531 0.251 1.0000
Step6 - Step12 0.025042 0.0363 7531 0.689 1.0000
Step6 - Step13 0.034818 0.0363 7531 0.958 1.0000
Step6 - Step14 0.063622 0.0363 7531 1.751 0.9566
Step6 - Step15 0.073214 0.0363 7531 2.014 0.8627
Step6 - Step16 0.100523 0.0363 7531 2.766 0.3351
Step6 - Step17 0.141344 0.0363 7531 3.889 0.0123
Step6 - Step18 0.172121 0.0363 7531 4.736 0.0003
Step7 - Step8 0.005557 0.0363 7531 0.153 1.0000
Step7 - Step9 0.018720 0.0363 7531 0.515 1.0000
Step7 - Step10 0.011963 0.0363 7531 0.329 1.0000
Step7 - Step11 0.020616 0.0363 7531 0.567 1.0000
Step7 - Step12 0.036533 0.0363 7531 1.005 0.9999
Step7 - Step13 0.046309 0.0363 7531 1.274 0.9986
Step7 - Step14 0.075113 0.0363 7531 2.067 0.8354
Step7 - Step15 0.084704 0.0363 7531 2.331 0.6608
Step7 - Step16 0.112014 0.0363 7531 3.082 0.1631
Step7 - Step17 0.152835 0.0363 7531 4.205 0.0035
Step7 - Step18 0.183611 0.0363 7531 5.052 0.0001
Step8 - Step9 0.013163 0.0363 7531 0.362 1.0000
Step8 - Step10 0.006406 0.0363 7531 0.176 1.0000
Step8 - Step11 0.015059 0.0363 7531 0.414 1.0000
Step8 - Step12 0.030976 0.0363 7531 0.852 1.0000
Step8 - Step13 0.040752 0.0363 7531 1.121 0.9997
Step8 - Step14 0.069556 0.0363 7531 1.914 0.9070
Step8 - Step15 0.079148 0.0363 7531 2.178 0.7686
Step8 - Step16 0.106457 0.0363 7531 2.929 0.2364
Step8 - Step17 0.147278 0.0363 7531 4.052 0.0065
Step8 - Step18 0.178055 0.0363 7531 4.899 0.0001
Step9 - Step10 -0.006757 0.0363 7531 -0.186 1.0000
Step9 - Step11 0.001896 0.0363 7531 0.052 1.0000
Step9 - Step12 0.017813 0.0363 7531 0.490 1.0000
Step9 - Step13 0.027589 0.0363 7531 0.759 1.0000
Step9 - Step14 0.056393 0.0363 7531 1.552 0.9867
Step9 - Step15 0.065985 0.0363 7531 1.816 0.9400
Step9 - Step16 0.093294 0.0363 7531 2.567 0.4786
Step9 - Step17 0.134115 0.0363 7531 3.690 0.0255
Step9 - Step18 0.164892 0.0363 7531 4.537 0.0008
Step10 - Step11 0.008653 0.0363 7531 0.238 1.0000
Step10 - Step12 0.024570 0.0363 7531 0.676 1.0000
Step10 - Step13 0.034346 0.0363 7531 0.945 1.0000
Step10 - Step14 0.063150 0.0363 7531 1.738 0.9594
Step10 - Step15 0.072741 0.0363 7531 2.002 0.8690
Step10 - Step16 0.100051 0.0363 7531 2.753 0.3438
Step10 - Step17 0.140872 0.0363 7531 3.876 0.0129
Step10 - Step18 0.171648 0.0363 7531 4.723 0.0003
Step11 - Step12 0.015917 0.0363 7531 0.438 1.0000
Step11 - Step13 0.025693 0.0363 7531 0.707 1.0000
Step11 - Step14 0.054497 0.0363 7531 1.500 0.9908
Step11 - Step15 0.064089 0.0363 7531 1.763 0.9536
Step11 - Step16 0.091398 0.0363 7531 2.515 0.5188
Step11 - Step17 0.132219 0.0363 7531 3.638 0.0305
Step11 - Step18 0.162996 0.0363 7531 4.485 0.0010
Step12 - Step13 0.009776 0.0363 7531 0.269 1.0000
Step12 - Step14 0.038580 0.0363 7531 1.062 0.9999
Step12 - Step15 0.048171 0.0363 7531 1.325 0.9978
Step12 - Step16 0.075481 0.0363 7531 2.077 0.8298
Step12 - Step17 0.116302 0.0363 7531 3.200 0.1192
Step12 - Step18 0.147078 0.0363 7531 4.047 0.0067
Step13 - Step14 0.028804 0.0363 7531 0.793 1.0000
Step13 - Step15 0.038395 0.0363 7531 1.056 0.9999
Step13 - Step16 0.065705 0.0363 7531 1.808 0.9421
Step13 - Step17 0.106526 0.0363 7531 2.931 0.2354
Step13 - Step18 0.137302 0.0363 7531 3.778 0.0186
Step14 - Step15 0.009591 0.0363 7531 0.264 1.0000
Step14 - Step16 0.036901 0.0363 7531 1.015 0.9999
Step14 - Step17 0.077722 0.0363 7531 2.139 0.7935
Step14 - Step18 0.108498 0.0363 7531 2.985 0.2073
Step15 - Step16 0.027309 0.0363 7531 0.751 1.0000
Step15 - Step17 0.068131 0.0363 7531 1.875 0.9213
Step15 - Step18 0.098907 0.0363 7531 2.721 0.3653
Step16 - Step17 0.040821 0.0363 7531 1.123 0.9997
Step16 - Step18 0.071598 0.0363 7531 1.970 0.8836
Step17 - Step18 0.030776 0.0363 7531 0.847 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5
1.2427887961 0.0242788233 0.0456889513 0.0479827935 0.0616312115
Step6 Step7 Step8 Step9 Step10
0.0256923599 0.0371830744 0.0316264700 0.0184632835 0.0252200895
Step11 Step12 Step13 Step14 Step15
0.0165674486 0.0006500407 -0.0091259572 -0.0379298941 -0.0475212771
Step16 Step17 Step18
-0.0748305068 -0.1156519288 -0.1464283426
Random Effects:
$trial_id
(Intercept)
7.1 0.103206024
20.1 -0.082297144
2.2 0.055144936
7.2 0.340257711
8.2 -0.085750219
10.2 1.071488248
16.2 0.570079026
17.2 0.344618393
23.2 0.056333909
2.3 0.182062363
3.3 0.032124380
8.3 0.087466236
10.3 1.956693699
11.3 1.264912591
16.3 0.777952893
17.3 0.154315498
20.3 0.232934528
2.4 0.360004017
4.4 0.117159921
7.4 0.417071225
11.4 1.624614409
13.4 0.126562175
14.4 1.037519357
16.4 0.878248561
17.4 0.034315429
18.4 0.487647849
20.4 0.278116642
23.4 0.206161889
2.5 -0.022621425
3.5 0.828982570
4.5 -0.073212122
7.5 0.469254181
8.5 0.195500705
11.5 1.183737045
13.5 0.653885043
14.5 0.997649995
15.5 0.169237257
16.5 0.715174504
17.5 0.332974580
18.5 0.106068117
23.5 0.233094209
4.6 -0.009674351
5.6 0.338911587
7.6 0.687347775
8.6 0.863954861
10.6 1.482882071
13.6 0.463861720
14.6 0.434634721
17.6 -0.075000679
18.6 0.680656041
23.6 0.519062920
2.7 -0.456596223
3.7 0.699524443
4.7 0.087672965
5.7 -0.010653245
7.7 0.212394083
8.7 0.727630923
13.7 0.492485855
14.7 0.584824752
16.7 0.649590268
17.7 0.197726255
18.7 0.291909294
19.7 0.213717572
23.7 0.679337767
2.8 0.390248137
3.8 0.854076552
4.8 -0.187574985
7.8 0.605151417
8.8 0.711429448
13.8 0.754529858
16.8 0.639414549
17.8 0.116821335
22.8 0.150005037
23.8 0.281237318
3.9 0.452477702
4.9 -0.050607538
7.9 0.319956492
13.9 0.393120965
16.9 0.043963055
17.9 0.130153706
20.9 0.025092881
22.9 0.194805581
23.9 0.003887667
2.10 -0.140283201
4.10 -0.075101458
7.10 0.348196197
8.10 0.113555303
14.10 0.580793312
18.10 1.001614305
19.10 0.233073418
20.10 0.304128445
2.11 0.242837866
3.11 0.547149229
7.11 0.301194792
8.11 1.335006727
10.11 2.538506959
13.11 0.101503918
16.11 0.533560126
17.11 -0.065605246
18.11 0.565325584
20.11 0.319638677
22.11 0.189900689
23.11 -0.071744616
2.12 -0.008892200
5.12 -0.129379294
7.12 0.457859404
10.12 2.149821264
13.12 0.465406940
14.12 0.641119775
15.12 0.150809973
17.12 -0.053742652
19.12 -0.008206379
20.12 0.172754990
3.13 0.854933190
7.13 -0.104160228
8.13 -0.013440919
10.13 1.786069815
13.13 -0.013083826
14.13 0.727625836
16.13 0.856846017
17.13 0.119006354
18.13 0.317929860
23.13 -0.117512292
2.14 0.287800660
3.14 1.107535015
5.14 -0.024764105
7.14 0.302290061
10.14 0.977338488
11.14 0.961093830
13.14 -0.008881537
14.14 0.162612272
15.14 0.660906410
16.14 0.251766753
17.14 -0.136479164
20.14 0.551868019
2.15 0.180534717
3.15 0.456290758
5.15 0.095530347
7.15 0.059009047
8.15 -0.305391800
10.15 2.101327590
14.15 0.171732860
15.15 1.034964604
17.15 -0.010655066
18.15 0.150342530
23.15 0.124937461
3.16 0.873189864
5.16 0.083583527
8.16 0.504591141
10.16 2.437964367
13.16 -0.041667503
15.16 0.476820224
16.16 -0.061844126
17.16 0.138230053
2.17 0.554911078
4.17 0.164716832
5.17 0.378013096
10.17 1.379389067
11.17 0.354659685
13.17 0.139987317
14.17 0.287214700
15.17 0.480374623
17.17 0.039258226
19.17 -0.189448987
22.17 0.032771499
23.17 0.734969268
2.18 0.537254755
4.18 -0.191812950
5.18 0.460610192
7.18 0.254387888
8.18 0.437135004
11.18 0.179078459
15.18 0.216835537
16.18 0.306389374
17.18 0.222855210
3.19 0.452602876
7.19 0.514980616
10.19 1.653698830
11.19 0.631818776
14.19 0.066201174
15.19 -0.118872215
18.19 0.513610738
23.19 0.805394771
3.20 0.239362657
4.20 -0.049250803
7.20 0.237791003
13.20 0.273270190
14.20 0.023576985
15.20 -0.515226857
16.20 0.298675236
17.20 0.141772360
2.21 2.054352650
7.21 0.958359105
8.21 -0.096267215
10.21 1.821436235
13.21 0.214588138
14.21 0.211802469
15.21 0.349190190
16.21 0.347802414
17.21 0.165146912
19.21 -0.124815458
2.22 1.137697038
4.22 -0.039212536
5.22 -0.004725778
7.22 0.763308024
11.22 0.049711680
13.22 0.000526622
15.22 -0.149426942
16.22 0.492874898
17.22 0.134792649
19.22 -0.178356460
20.22 0.010720432
23.22 0.037230592
2.23 0.344526859
3.23 0.768151641
4.23 0.474571874
5.23 0.083918732
7.23 0.552589689
8.23 0.549159364
10.23 0.550620156
14.23 0.086880657
15.23 0.050889063
16.23 0.641642008
17.23 0.161014376
2.24 0.596229257
3.24 0.054595112
5.24 0.025649922
7.24 0.819841900
8.24 0.078410531
13.24 0.371832119
14.24 -0.564907035
15.24 -0.093117445
16.24 0.437772228
17.24 0.183145237
20.24 0.235228173
23.24 0.503105755
2.25 0.551415683
7.25 -0.074704109
10.25 0.151424894
13.25 0.121843741
15.25 -0.192109399
16.25 0.589689507
17.25 0.464268957
18.25 0.215342477
23.25 0.696027519
2.26 0.832569578
3.26 0.168897030
4.26 0.216318197
5.26 0.137421396
8.26 0.631842279
10.26 -0.786820332
13.26 -0.219009696
14.26 -0.422786610
17.26 0.129903399
18.26 0.067756818
23.26 -0.016839609
2.27 0.205950652
3.27 -0.366385655
5.27 0.452599958
7.27 0.114163071
8.27 -0.053400156
10.27 -0.820900184
11.27 -0.685775121
13.27 0.544601810
14.27 -0.298902475
16.27 -0.287683651
17.27 0.137314909
18.27 0.769985856
20.27 -0.014225657
3.28 0.164878697
5.28 -0.043097797
7.28 -0.022402593
8.28 -0.169598757
10.28 -2.087552341
11.28 -0.434447084
15.28 0.034312611
16.28 -0.201631239
17.28 0.839431037
19.28 -0.213247131
23.28 -0.381665863
2.29 -0.125998653
4.29 0.057343318
5.29 -0.302651454
7.29 -0.506417062
13.29 -0.080884444
14.29 -0.119511935
15.29 0.125566070
17.29 0.002100625
19.29 -0.112412827
20.29 0.217540637
22.29 -0.134348625
23.29 -0.852249939
2.30 -0.357418551
3.30 -0.475934661
5.30 0.148411677
7.30 -0.503352343
8.30 0.239464859
10.30 -2.265645517
13.30 -0.604823812
14.30 -0.334400585
16.30 0.253606754
17.30 -0.210297586
18.30 0.555289019
20.30 0.096369599
22.30 -0.304941531
23.30 -1.077594315
2.31 -0.780465030
3.31 0.047150479
4.31 -0.007578294
7.31 -0.608919843
8.31 -0.853575845
10.31 -1.230817833
11.31 -0.748289102
13.31 -0.666801058
15.31 0.178878718
16.31 -0.162757025
17.31 -0.231206033
2.32 -1.138213816
3.32 -1.180950248
5.32 -0.190569788
7.32 -1.297321784
8.32 -0.366300998
10.32 -2.471915755
11.32 -0.632092250
14.32 -1.207542259
16.32 -0.421341795
17.32 -0.778706745
18.32 -0.067076196
23.32 -0.323484999
2.33 -0.638152095
5.33 -0.241869468
8.33 -0.417273450
11.33 -1.094425455
13.33 -0.175952606
14.33 -0.643015530
16.33 -1.187621561
17.33 -0.449820120
18.33 -0.566991930
19.33 -0.309351392
20.33 -0.049833014
23.33 -0.713335546
2.34 -0.239551541
3.34 -0.680744229
8.34 -0.401777804
10.34 -1.693715585
11.34 -1.123174044
13.34 -0.753859572
16.34 -0.646373057
17.34 -0.231798235
20.34 -0.387431213
2.35 -0.908945499
3.35 -1.443744273
4.35 -0.027669897
5.35 0.033883389
7.35 -0.509720214
8.35 -0.615986872
10.35 -1.691020087
13.35 -0.855164574
15.35 -0.529951250
16.35 -0.162326143
18.35 -1.119299831
20.35 -0.715802443
23.35 0.043121529
2.36 -0.364991282
4.36 -0.359986971
5.36 -0.544879146
7.36 -0.929915971
8.36 -0.621846609
10.36 -0.770957592
14.36 -0.189850151
16.36 -1.166655147
17.36 -0.802736318
18.36 0.045500041
20.36 -0.359092221
23.36 -0.599717367
2.37 -1.174536126
4.37 -0.437167878
5.37 -0.239810505
7.37 -0.623784868
8.37 -0.401632462
10.37 -0.491056331
13.37 -0.069360596
14.37 -0.535342336
15.37 -0.185354823
16.37 -0.731390814
18.37 -0.625881713
20.37 -0.289234657
2.38 -0.736745627
3.38 -0.924232171
5.38 -0.459052946
7.38 -1.370728596
10.38 -1.723225967
13.38 -0.518109050
16.38 -1.164875653
17.38 -0.452749269
18.38 -0.519054301
20.38 -0.266631430
23.38 -0.619818179
3.39 -0.141229692
4.39 -0.348434317
7.39 -0.328080712
8.39 -0.568863001
10.39 -2.146281497
13.39 -0.563918816
14.39 -0.624284865
16.39 -0.393276307
17.39 -0.507650496
18.39 -0.410907284
23.39 -0.285755265
2.40 -0.676944782
3.40 -0.135233249
5.40 -0.456531749
8.40 -0.746171403
10.40 -1.148666235
13.40 -0.317671731
14.40 -0.896833093
16.40 -0.389000425
17.40 -0.337709896
2.41 -0.692987859
3.41 -1.072801981
5.41 0.076834470
7.41 -1.056985305
8.41 -0.583038624
13.41 -0.853277181
15.41 -0.753959188
16.41 -0.953441627
17.41 -0.405127140
18.41 -0.325598618
20.41 -0.684347318
3.42 -1.086268501
4.42 -0.217707300
5.42 -0.393066648
7.42 -0.400700600
11.42 -1.044068171
15.42 -0.434870245
16.42 -1.057436082
18.42 -1.418821687
20.42 -0.367088483
3.43 -0.397352317
5.43 -0.406060440
15.43 -0.767936568
20.43 -0.044536572
22.43 -0.697501124
15.44 -0.534613021
$subject
(Intercept)
2 0.02686604
3 0.37307192
4 -0.51231616
5 -0.60573105
7 0.26836714
8 0.09357302
10 1.46119441
11 0.26084184
13 -0.33422270
14 0.09463316
15 -0.18553513
16 0.15917069
17 -0.29978647
18 0.38286737
19 -0.36879146
20 -0.43680683
22 -0.30470477
23 -0.07269101
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
1.3658409 0.9949617 0.7114777 0.6610985 0.5108105 0.3576602
=============================================================
--- Mixed - Block 4 - Axis X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 17.592 1.0348 17 8793.6 14.144 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.021265 0.0154 8840 -1.383 0.9963
Step1 - Step3 -0.056343 0.0194 8831 -2.901 0.2520
Step1 - Step4 -0.014225 0.0136 8780 -1.046 0.9999
Step1 - Step5 -0.034370 0.0154 8840 -2.234 0.7309
Step1 - Step6 -0.035046 0.0194 8831 -1.807 0.9424
Step1 - Step7 -0.024882 0.0154 8840 -1.617 0.9797
Step1 - Step8 0.006222 0.0154 8811 0.405 1.0000
Step1 - Step9 -0.012173 0.0154 8840 -0.792 1.0000
Step1 - Step10 -0.001225 0.0154 8840 -0.080 1.0000
Step1 - Step11 0.016541 0.0154 8811 1.077 0.9998
Step1 - Step12 0.004309 0.0154 8840 0.280 1.0000
Step1 - Step13 0.034696 0.0194 8831 1.789 0.9473
Step1 - Step14 0.036691 0.0154 8840 2.385 0.6198
Step1 - Step15 0.043765 0.0136 8780 3.218 0.1134
Step1 - Step16 0.094803 0.0194 8831 4.881 0.0002
Step1 - Step17 0.088066 0.0154 8840 5.727 <.0001
Step1 - Step18 0.100357 0.0136 8780 7.379 <.0001
Step2 - Step3 -0.035078 0.0204 8803 -1.720 0.9631
Step2 - Step4 0.007040 0.0154 8840 0.458 1.0000
Step2 - Step5 -0.013105 0.0165 8780 -0.792 1.0000
Step2 - Step6 -0.013781 0.0204 8803 -0.677 1.0000
Step2 - Step7 -0.003617 0.0165 8780 -0.219 1.0000
Step2 - Step8 0.027487 0.0172 8906 1.600 0.9818
Step2 - Step9 0.009092 0.0165 8780 0.550 1.0000
Step2 - Step10 0.020040 0.0165 8780 1.211 0.9993
Step2 - Step11 0.037806 0.0172 8906 2.201 0.7536
Step2 - Step12 0.025574 0.0165 8780 1.546 0.9873
Step2 - Step13 0.055961 0.0204 8803 2.748 0.3475
Step2 - Step14 0.057956 0.0165 8780 3.503 0.0480
Step2 - Step15 0.065030 0.0154 8840 4.229 0.0031
Step2 - Step16 0.116069 0.0204 8803 5.692 <.0001
Step2 - Step17 0.109331 0.0165 8780 6.611 <.0001
Step2 - Step18 0.121622 0.0154 8840 7.909 <.0001
Step3 - Step4 0.042118 0.0194 8831 2.169 0.7747
Step3 - Step5 0.021973 0.0204 8803 1.077 0.9998
Step3 - Step6 0.021297 0.0232 8780 0.919 1.0000
Step3 - Step7 0.031462 0.0204 8803 1.542 0.9876
Step3 - Step8 0.062565 0.0206 8852 3.036 0.1833
Step3 - Step9 0.044171 0.0204 8803 2.166 0.7763
Step3 - Step10 0.055118 0.0204 8803 2.702 0.3790
Step3 - Step11 0.072884 0.0206 8852 3.537 0.0429
Step3 - Step12 0.060653 0.0204 8803 2.973 0.2133
Step3 - Step13 0.091039 0.0232 8780 3.928 0.0106
Step3 - Step14 0.093034 0.0204 8803 4.561 0.0007
Step3 - Step15 0.100108 0.0194 8831 5.154 <.0001
Step3 - Step16 0.151147 0.0232 8780 6.517 <.0001
Step3 - Step17 0.144409 0.0204 8803 7.081 <.0001
Step3 - Step18 0.156700 0.0194 8831 8.068 <.0001
Step4 - Step5 -0.020145 0.0154 8840 -1.309 0.9981
Step4 - Step6 -0.020822 0.0194 8831 -1.074 0.9999
Step4 - Step7 -0.010657 0.0154 8840 -0.693 1.0000
Step4 - Step8 0.020447 0.0154 8811 1.332 0.9976
Step4 - Step9 0.002052 0.0154 8840 0.133 1.0000
Step4 - Step10 0.013000 0.0154 8840 0.845 1.0000
Step4 - Step11 0.030766 0.0154 8811 2.004 0.8678
Step4 - Step12 0.018534 0.0154 8840 1.205 0.9993
Step4 - Step13 0.048920 0.0194 8831 2.522 0.5131
Step4 - Step14 0.050916 0.0154 8840 3.309 0.0873
Step4 - Step15 0.057990 0.0136 8780 4.264 0.0027
Step4 - Step16 0.109028 0.0194 8831 5.613 <.0001
Step4 - Step17 0.102291 0.0154 8840 6.652 <.0001
Step4 - Step18 0.114582 0.0136 8780 8.425 <.0001
Step5 - Step6 -0.000676 0.0204 8803 -0.033 1.0000
Step5 - Step7 0.009489 0.0166 8780 0.573 1.0000
Step5 - Step8 0.040592 0.0172 8906 2.362 0.6375
Step5 - Step9 0.022198 0.0165 8780 1.342 0.9974
Step5 - Step10 0.033145 0.0166 8780 2.002 0.8686
Step5 - Step11 0.050911 0.0172 8906 2.962 0.2191
Step5 - Step12 0.038680 0.0166 8780 2.337 0.6563
Step5 - Step13 0.069066 0.0204 8803 3.390 0.0685
Step5 - Step14 0.071061 0.0166 8780 4.293 0.0024
Step5 - Step15 0.078135 0.0154 8840 5.078 0.0001
Step5 - Step16 0.129174 0.0204 8803 6.332 <.0001
Step5 - Step17 0.122436 0.0165 8780 7.400 <.0001
Step5 - Step18 0.134727 0.0154 8840 8.756 <.0001
Step6 - Step7 0.010165 0.0204 8803 0.499 1.0000
Step6 - Step8 0.041269 0.0206 8852 2.005 0.8673
Step6 - Step9 0.022874 0.0204 8803 1.123 0.9997
Step6 - Step10 0.033821 0.0204 8803 1.660 0.9737
Step6 - Step11 0.051588 0.0206 8852 2.506 0.5254
Step6 - Step12 0.039356 0.0204 8803 1.931 0.9000
Step6 - Step13 0.069742 0.0232 8780 3.012 0.1941
Step6 - Step14 0.071738 0.0204 8803 3.521 0.0452
Step6 - Step15 0.078811 0.0194 8831 4.063 0.0062
Step6 - Step16 0.129850 0.0232 8780 5.603 <.0001
Step6 - Step17 0.123112 0.0204 8803 6.044 <.0001
Step6 - Step18 0.135403 0.0194 8831 6.981 <.0001
Step7 - Step8 0.031104 0.0172 8906 1.810 0.9417
Step7 - Step9 0.012709 0.0165 8780 0.768 1.0000
Step7 - Step10 0.023656 0.0166 8780 1.429 0.9946
Step7 - Step11 0.041423 0.0172 8906 2.410 0.6004
Step7 - Step12 0.029191 0.0166 8780 1.763 0.9536
Step7 - Step13 0.059577 0.0204 8803 2.924 0.2392
Step7 - Step14 0.061573 0.0166 8780 3.720 0.0229
Step7 - Step15 0.068646 0.0154 8840 4.461 0.0011
Step7 - Step16 0.119685 0.0204 8803 5.867 <.0001
Step7 - Step17 0.112947 0.0165 8780 6.826 <.0001
Step7 - Step18 0.125238 0.0154 8840 8.139 <.0001
Step8 - Step9 -0.018395 0.0172 8906 -1.071 0.9999
Step8 - Step10 -0.007448 0.0172 8906 -0.433 1.0000
Step8 - Step11 0.010319 0.0166 8780 0.620 1.0000
Step8 - Step12 -0.001913 0.0172 8906 -0.111 1.0000
Step8 - Step13 0.028473 0.0206 8852 1.383 0.9963
Step8 - Step14 0.030469 0.0172 8906 1.773 0.9514
Step8 - Step15 0.037543 0.0154 8811 2.446 0.5727
Step8 - Step16 0.088581 0.0206 8852 4.298 0.0023
Step8 - Step17 0.081844 0.0172 8906 4.764 0.0003
Step8 - Step18 0.094134 0.0154 8811 6.132 <.0001
Step9 - Step10 0.010947 0.0165 8780 0.662 1.0000
Step9 - Step11 0.028714 0.0172 8906 1.671 0.9719
Step9 - Step12 0.016482 0.0165 8780 0.996 0.9999
Step9 - Step13 0.046868 0.0204 8803 2.301 0.6827
Step9 - Step14 0.048864 0.0165 8780 2.953 0.2236
Step9 - Step15 0.055937 0.0154 8840 3.638 0.0305
Step9 - Step16 0.106976 0.0204 8803 5.246 <.0001
Step9 - Step17 0.100238 0.0165 8780 6.061 <.0001
Step9 - Step18 0.112529 0.0154 8840 7.318 <.0001
Step10 - Step11 0.017767 0.0172 8906 1.034 0.9999
Step10 - Step12 0.005535 0.0166 8780 0.334 1.0000
Step10 - Step13 0.035921 0.0204 8803 1.763 0.9537
Step10 - Step14 0.037916 0.0166 8780 2.291 0.6904
Step10 - Step15 0.044990 0.0154 8840 2.924 0.2392
Step10 - Step16 0.096029 0.0204 8803 4.707 0.0004
Step10 - Step17 0.089291 0.0165 8780 5.396 <.0001
Step10 - Step18 0.101582 0.0154 8840 6.602 <.0001
Step11 - Step12 -0.012232 0.0172 8906 -0.712 1.0000
Step11 - Step13 0.018154 0.0206 8852 0.882 1.0000
Step11 - Step14 0.020150 0.0172 8906 1.172 0.9995
Step11 - Step15 0.027224 0.0154 8811 1.773 0.9512
Step11 - Step16 0.078262 0.0206 8852 3.798 0.0173
Step11 - Step17 0.071525 0.0172 8906 4.163 0.0041
Step11 - Step18 0.083815 0.0154 8811 5.460 <.0001
Step12 - Step13 0.030386 0.0204 8803 1.491 0.9913
Step12 - Step14 0.032382 0.0166 8780 1.956 0.8897
Step12 - Step15 0.039455 0.0154 8840 2.564 0.4807
Step12 - Step16 0.090494 0.0204 8803 4.436 0.0013
Step12 - Step17 0.083756 0.0165 8780 5.062 0.0001
Step12 - Step18 0.096047 0.0154 8840 6.242 <.0001
Step13 - Step14 0.001996 0.0204 8803 0.098 1.0000
Step13 - Step15 0.009069 0.0194 8831 0.468 1.0000
Step13 - Step16 0.060108 0.0232 8780 2.594 0.4583
Step13 - Step17 0.053370 0.0204 8803 2.620 0.4383
Step13 - Step18 0.065661 0.0194 8831 3.385 0.0694
Step14 - Step15 0.007074 0.0154 8840 0.460 1.0000
Step14 - Step16 0.058112 0.0204 8803 2.849 0.2825
Step14 - Step17 0.051375 0.0165 8780 3.105 0.1538
Step14 - Step18 0.063666 0.0154 8840 4.138 0.0046
Step15 - Step16 0.051039 0.0194 8831 2.628 0.4327
Step15 - Step17 0.044301 0.0154 8840 2.881 0.2635
Step15 - Step18 0.056592 0.0136 8780 4.161 0.0042
Step16 - Step17 -0.006738 0.0204 8803 -0.330 1.0000
Step16 - Step18 0.005553 0.0194 8831 0.286 1.0000
Step17 - Step18 0.012291 0.0154 8840 0.799 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5 Step6
0.685918450 0.021265083 0.056343256 0.014224924 0.034370192 0.035046474
Step7 Step8 Step9 Step10 Step11 Step12
0.024881695 -0.006222184 0.012172586 0.001225363 -0.016541212 -0.004309323
Step13 Step14 Step15 Step16 Step17 Step18
-0.034695577 -0.036691120 -0.043764745 -0.094803460 -0.088065762 -0.100356659
Random Effects:
$trial_id
(Intercept)
3.1 -0.216925117
4.1 -0.071844243
5.1 -0.012428991
10.1 0.154278311
11.1 -0.261608870
14.1 -0.044546824
16.1 -0.164398687
17.1 -0.131078081
18.1 -0.081189249
19.1 0.111284539
20.1 0.080196681
22.1 0.322068732
23.1 0.100704458
3.2 -0.092274091
4.2 0.014961738
5.2 -0.023358723
8.2 -0.328762978
10.2 0.333388030
13.2 -0.153771263
14.2 0.195856080
16.2 -0.268752285
17.2 -0.026524093
20.2 0.050821132
22.2 0.236544391
2.3 0.102485500
3.3 0.130850493
4.3 -0.040169738
5.3 -0.054271937
7.3 -0.066531170
10.3 0.437868713
11.3 -0.636778661
13.3 0.055092916
14.3 0.139593688
15.3 -0.061942477
17.3 -0.179071809
22.3 -0.016866759
23.3 -0.053909794
2.4 -0.260483901
3.4 -0.181519578
4.4 -0.028932131
5.4 -0.094717350
7.4 0.085770657
8.4 -0.038067362
11.4 -0.433784453
13.4 -0.092644403
14.4 -0.009749129
15.4 -0.261163278
16.4 -0.236584746
17.4 -0.196787730
18.4 -0.066990716
19.4 -0.051088230
20.4 -0.052827619
22.4 0.081609030
23.4 0.033374465
2.5 0.117554671
3.5 -0.045839675
4.5 0.062623099
5.5 -0.094430953
7.5 -0.038199748
8.5 0.191856543
10.5 0.495825311
11.5 -0.400681062
13.5 -0.025241389
14.5 -0.105418112
15.5 0.172711507
16.5 -0.035077757
17.5 0.035036909
18.5 -0.039650320
20.5 -0.009369365
22.5 0.103603312
23.5 -0.288201569
2.6 -0.030993119
3.6 -0.123394091
4.6 -0.123069867
5.6 -0.013886026
7.6 0.466975783
8.6 0.892775839
10.6 0.473883391
11.6 0.019603645
13.6 0.313862104
14.6 -0.211339645
15.6 -0.125200052
16.6 -0.228396116
17.6 -0.071591095
18.6 0.279772623
19.6 -0.076281075
20.6 -0.006812939
23.6 0.109756200
2.7 -0.106234636
3.7 -0.008411090
4.7 0.061513318
5.7 -0.010140702
7.7 0.024167229
8.7 0.139137769
10.7 0.893201329
11.7 -0.040386495
13.7 0.047625047
14.7 0.123329113
15.7 0.348486275
16.7 0.041143574
17.7 -0.013230075
18.7 -0.048777757
19.7 -0.072933189
20.7 0.009436122
22.7 -0.074951069
23.7 0.160638114
2.8 -0.047799555
3.8 -0.138165321
4.8 -0.101131969
5.8 -0.022639743
7.8 0.005493937
8.8 0.485292816
10.8 0.202444743
11.8 -0.412205835
13.8 0.039259484
14.8 -0.126715791
15.8 -0.096382611
16.8 -0.266027049
17.8 0.249934447
18.8 -0.149674053
19.8 0.067451906
20.8 -0.061591708
22.8 -0.025828797
23.8 -0.001980403
2.9 -0.332653708
3.9 -0.070096507
4.9 0.040271654
5.9 -0.144427567
7.9 -0.028396097
8.9 -0.168372639
10.9 0.587484028
11.9 -0.092089218
13.9 -0.166916032
14.9 -0.106625713
15.9 0.061815365
16.9 -0.350222138
17.9 0.069480472
18.9 -0.278332164
19.9 0.063814543
20.9 -0.094358601
22.9 -0.114749658
23.9 -0.015020668
2.10 -0.155533656
3.10 0.031540145
4.10 0.161837544
5.10 -0.032898257
7.10 -0.017703135
8.10 -0.067867526
10.10 1.010736411
11.10 0.259882216
13.10 0.010074304
14.10 -0.026601336
16.10 -0.243861764
17.10 0.089263130
18.10 0.025256351
19.10 0.004788612
20.10 0.031198616
22.10 -0.009545464
23.10 -0.039280719
2.11 -0.203954598
3.11 -0.167793859
4.11 0.021222903
5.11 0.156824368
7.11 -0.234824874
8.11 -0.014468668
10.11 0.685922678
11.11 -0.277224260
13.11 -0.067320322
14.11 -0.029089798
15.11 -0.105255386
16.11 -0.173159963
17.11 -0.018122344
18.11 -0.113148942
19.11 -0.047130376
20.11 -0.095130961
22.11 0.041321808
23.11 -0.117641263
2.12 0.124508432
3.12 0.071082361
5.12 0.042915100
7.12 -0.093284640
8.12 0.359275557
10.12 0.196246631
11.12 -0.060195780
13.12 0.067011995
14.12 -0.054343054
15.12 0.007015840
16.12 -0.312301052
17.12 -0.046387235
18.12 -0.105191935
19.12 -0.004299392
20.12 -0.150373747
22.12 0.020609272
23.12 0.200396706
2.13 0.027418001
3.13 0.156873430
4.13 0.050196499
5.13 -0.047665912
7.13 -0.110036652
8.13 0.305237407
10.13 0.052181032
11.13 -0.233031497
13.13 -0.090938324
14.13 0.053183170
15.13 -0.125011113
16.13 -0.215270615
17.13 0.368739655
18.13 0.008931532
19.13 -0.059170641
20.13 0.239574796
22.13 0.086867989
23.13 0.146226898
2.14 0.253723097
3.14 -0.178954827
4.14 -0.070389293
5.14 -0.082377135
7.14 -0.199717139
8.14 0.183238921
10.14 0.639601696
11.14 -0.297694742
13.14 -0.007063300
14.14 -0.171230675
15.14 -0.194033478
16.14 -0.224501988
17.14 0.090564516
18.14 -0.070832835
19.14 0.018686139
20.14 -0.143936432
22.14 0.052001754
23.14 -0.125601260
2.15 0.025986042
3.15 0.001704563
4.15 0.045819441
5.15 -0.044585279
7.15 -0.089924032
8.15 -0.336875987
10.15 0.819921958
11.15 -0.553427351
14.15 -0.161234382
15.15 -0.155227039
16.15 -0.271991025
17.15 -0.115992258
18.15 -0.075584492
19.15 -0.117196549
20.15 0.303096005
22.15 0.071227525
23.15 0.175071183
2.16 -0.210152271
3.16 -0.212380711
4.16 -0.032057435
5.16 -0.094909904
7.16 0.035256158
8.16 -0.175938323
10.16 0.525969815
11.16 -0.112613055
13.16 0.160778354
14.16 0.071688528
15.16 -0.104234141
16.16 -0.321814230
18.16 0.082908202
19.16 -0.070101254
20.16 -0.087334088
22.16 -0.038622962
23.16 0.094985359
2.17 -0.112381058
3.17 -0.172837777
4.17 0.125081568
7.17 0.412905744
8.17 0.454885014
10.17 0.672750098
11.17 -0.159827683
13.17 -0.044006976
14.17 0.182415232
15.17 -0.186510561
17.17 0.161486230
18.17 -0.014352540
19.17 0.171160571
20.17 0.039534013
22.17 0.019947023
23.17 -0.008466890
2.18 -0.070294450
3.18 -0.151950873
4.18 -0.029971488
5.18 -0.044341333
7.18 -0.223773680
8.18 0.337068588
10.18 0.797901596
11.18 -0.341505332
13.18 -0.002724674
14.18 -0.210494507
15.18 -0.191479152
16.18 -0.264799333
17.18 -0.076191288
18.18 0.027253654
19.18 0.025127065
20.18 -0.049101851
22.18 0.102469419
23.18 -0.335233874
2.19 -0.172484358
3.19 0.157643607
4.19 -0.009716813
5.19 -0.046663836
7.19 0.518543628
8.19 -0.480894903
10.19 0.754699000
11.19 0.439275930
13.19 0.306640947
14.19 0.154152364
15.19 0.339939965
16.19 -0.226565562
17.19 -0.072170438
18.19 -0.192690779
19.19 -0.157137895
20.19 -0.016102159
22.19 -0.061753261
23.19 0.098436696
2.20 -0.335311871
3.20 -0.097076698
4.20 -0.060367106
5.20 0.043109085
7.20 -0.071387033
8.20 -0.276717355
10.20 0.524978846
11.20 -0.213719220
13.20 0.033596462
14.20 0.062072981
15.20 -0.052774058
16.20 -0.075086604
17.20 0.182074204
18.20 0.116991284
19.20 0.259190319
20.20 0.043681161
22.20 -0.061633497
23.20 0.153907566
2.21 -0.028331382
3.21 -0.059262183
4.21 0.232139853
5.21 -0.009762090
7.21 0.186942258
8.21 0.706001391
10.21 0.942469111
11.21 -0.462609175
13.21 0.106479037
14.21 0.132809580
15.21 -0.121938914
16.21 0.248144060
17.21 -0.018187914
18.21 -0.186274030
19.21 0.251984368
20.21 -0.087931151
23.21 -0.142389180
2.22 -0.366996833
3.22 0.089915027
4.22 0.018503585
5.22 -0.052536753
7.22 -0.043066582
8.22 -0.093173970
10.22 0.541702174
11.22 -0.376196700
13.22 -0.119341574
14.22 0.140573843
15.22 -0.162400928
16.22 0.506267605
17.22 -0.031500157
18.22 0.465423855
19.22 -0.021135099
20.22 -0.083618268
22.22 0.049126706
23.22 0.136713152
2.23 -0.142751999
3.23 -0.127977549
4.23 -0.038924663
5.23 0.034367137
7.23 -0.074213898
8.23 0.335415817
10.23 0.795208447
11.23 -0.167754825
13.23 -0.076376124
14.23 -0.240128876
15.23 0.199245475
16.23 0.711650242
17.23 0.140055945
18.23 -0.130019884
19.23 0.038596039
20.23 -0.016163480
22.23 -0.083921910
23.23 0.031172449
2.24 -0.304867228
3.24 0.138939561
4.24 -0.047070297
5.24 0.091883793
7.24 0.035135475
8.24 0.092843526
10.24 0.664652976
11.24 -0.281985985
13.24 0.041832781
15.24 -0.019916509
16.24 0.780635942
17.24 0.022467932
18.24 0.138142041
19.24 0.083916511
20.24 0.137171041
22.24 -0.025814973
2.25 -0.031233523
3.25 0.169013604
4.25 0.003126410
5.25 0.034216132
7.25 -0.190564285
8.25 -0.271686395
10.25 -0.310503835
11.25 0.386192581
14.25 -0.160400737
15.25 0.044332031
16.25 0.249754149
17.25 0.059658006
18.25 -0.115354635
19.25 0.026006710
20.25 -0.129374992
22.25 0.120028135
23.25 0.210223372
2.26 -0.029818850
3.26 -0.242446110
4.26 -0.037773535
5.26 -0.008380341
7.26 -0.120390538
8.26 0.427452024
10.26 0.514975714
11.26 -0.155959376
15.26 0.011934001
16.26 -0.061369617
17.26 -0.062516511
18.26 0.041294890
19.26 0.127299442
20.26 0.174241173
22.26 0.109904900
23.26 0.005598387
2.27 -0.082444844
3.27 -0.179709988
4.27 0.049582324
5.27 -0.023316082
7.27 0.633220596
8.27 2.170777167
10.27 -0.106304283
11.27 0.368963244
13.27 0.195276117
14.27 -0.023789129
15.27 0.017534720
16.27 0.661932814
17.27 -0.018237807
18.27 -0.142817780
19.27 -0.039597107
20.27 -0.012231840
22.27 -0.090318605
23.27 0.391135238
2.28 -0.232519398
3.28 -0.206183728
4.28 0.031959178
5.28 0.308901188
7.28 -0.091004921
8.28 0.974503043
10.28 -0.049251198
11.28 0.066727533
13.28 0.332316750
14.28 -0.200522652
15.28 -0.013009841
16.28 0.448862280
19.28 0.043596625
20.28 -0.130755823
22.28 -0.077146033
23.28 0.298632302
2.29 -0.017914611
3.29 0.474078799
4.29 0.078775116
5.29 -0.075388754
7.29 0.087597955
8.29 -0.084328859
10.29 -0.264758777
11.29 1.449855726
13.29 0.300153926
14.29 0.401652161
15.29 -0.161110800
16.29 1.318111021
17.29 0.301905477
18.29 -0.277067949
19.29 0.000665542
20.29 -0.112052969
22.29 0.094357143
23.29 -0.095689190
2.30 -0.258000594
3.30 0.557629907
4.30 0.092988887
5.30 -0.057478929
7.30 0.257775947
8.30 -0.415375686
10.30 -0.606525189
11.30 0.879949331
13.30 0.082572904
14.30 0.202057467
15.30 0.244337040
16.30 0.409824209
17.30 0.087099055
18.30 -0.197678675
19.30 -0.175837027
20.30 -0.035211532
22.30 -0.047065862
23.30 0.703154081
2.31 0.480422176
3.31 0.395911959
4.31 0.194808486
5.31 -0.039698283
7.31 0.129485364
8.31 -0.201242959
10.31 0.425464275
11.31 0.449878465
13.31 -0.088049418
14.31 0.189609855
15.31 0.032074514
16.31 0.702257243
17.31 0.046795054
18.31 -0.003636726
19.31 0.096883765
20.31 -0.045137362
22.31 0.080856876
23.31 0.198613325
2.32 -0.075843599
3.32 0.462479273
4.32 0.222981444
5.32 -0.078622340
7.32 -0.223608471
8.32 0.177137502
10.32 -0.318087744
11.32 0.898997742
13.32 -0.146271499
14.32 0.431826018
15.32 0.031453267
16.32 0.027229764
17.32 0.144088347
18.32 -0.181001944
19.32 -0.086076613
20.32 -0.204933088
22.32 0.025161974
23.32 0.243739342
2.33 -0.166367929
3.33 0.580827716
4.33 0.134436393
5.33 0.056138538
7.33 0.174047695
8.33 0.269748512
10.33 -0.018257492
11.33 1.050136510
13.33 0.386528965
14.33 0.285138583
15.33 0.022585743
16.33 0.317983965
17.33 0.126627701
18.33 0.130169352
19.33 -0.213627841
20.33 0.238617602
22.33 0.261011115
23.33 -0.126742497
2.34 0.508570510
3.34 -0.229545720
4.34 -0.098936815
5.34 0.134062006
7.34 0.011126597
8.34 -0.423748023
10.34 -0.464557464
11.34 0.574317551
13.34 0.205111488
14.34 0.143226993
15.34 0.120873735
16.34 0.337345093
17.34 -0.001895797
18.34 -0.249322347
19.34 0.119440200
20.34 0.093671587
22.34 0.330571111
23.34 0.213690142
2.35 0.331164794
3.35 0.243665864
4.35 0.070612217
5.35 0.028164411
7.35 0.011334206
8.35 -0.223318487
10.35 -0.412615623
11.35 0.426052811
13.35 0.485265340
14.35 0.325393704
15.35 0.129441596
16.35 0.014660436
17.35 -0.040457582
18.35 0.227258932
19.35 -0.064084136
20.35 0.009810246
22.35 0.158155313
23.35 -0.235105516
2.36 0.303269201
3.36 0.202067460
4.36 0.098543088
5.36 -0.032380907
7.36 0.114448278
8.36 -0.532776200
10.36 -1.296623972
11.36 0.968612597
13.36 0.120060966
14.36 0.268338486
15.36 0.077516021
16.36 0.353837165
17.36 0.147378511
18.36 -0.275832944
19.36 0.016984426
20.36 0.283182035
22.36 -0.136793067
23.36 0.127281434
2.37 0.137252342
3.37 -0.067682748
4.37 -0.061177160
5.37 -0.120678120
7.37 0.235763089
8.37 0.010574286
10.37 -1.165678146
11.37 0.152403214
13.37 -0.289371994
14.37 0.292205504
15.37 0.116543202
16.37 0.080442153
17.37 0.188309524
18.37 0.180794674
20.37 -0.022633366
22.37 0.241780279
23.37 -0.139333660
2.38 0.379399581
3.38 0.141909864
4.38 -0.133518841
5.38 -0.013500269
7.38 -0.357669144
8.38 0.385960499
10.38 -1.390483566
11.38 -0.076243714
13.38 -0.330385842
14.38 -0.032102819
15.38 0.095473246
16.38 -0.182381877
17.38 0.073730691
18.38 0.088624553
19.38 0.160272163
20.38 -0.045716176
22.38 -0.169341201
23.38 -0.162960737
2.39 2.076930901
3.39 0.169171128
4.39 -0.057457245
5.39 0.040253329
7.39 0.044378915
8.39 -0.524409673
10.39 -1.103426673
11.39 0.356927970
14.39 -0.164678471
15.39 0.162529560
16.39 -0.330622260
17.39 0.021919336
18.39 0.371867301
19.39 -0.167332732
20.39 0.031228066
22.39 0.049245003
23.39 -0.520459286
2.40 0.514874722
3.40 -0.210610578
4.40 -0.076419096
5.40 0.020477056
7.40 -0.010831949
8.40 -0.478714131
10.40 -0.966782653
13.40 -0.248709197
14.40 -0.178302977
15.40 -0.124683543
17.40 -0.247464343
18.40 -0.232071170
19.40 0.210726633
20.40 -0.119468380
22.40 0.083365800
23.40 -0.437872067
3.41 -0.028687989
4.41 -0.048525411
5.41 0.156775409
7.41 -0.233290921
10.41 -1.078471340
11.41 -0.297792009
13.41 -0.374245002
14.41 -0.013011697
16.41 -0.287575530
17.41 -0.265624806
18.41 0.435443932
19.41 0.022221789
20.41 -0.161524287
22.41 0.021216393
23.41 -0.227890684
2.42 -0.261601026
3.42 0.133039015
4.42 -0.114893889
5.42 -0.046515582
7.42 0.273546982
8.42 -0.383131403
10.42 -0.754290015
11.42 -0.400740087
13.42 -0.269129464
14.42 0.344065316
15.42 -0.161342406
16.42 0.196184777
17.42 -0.431976691
18.42 0.351413905
19.42 -0.050757209
20.42 0.207877789
22.42 -0.187341931
23.42 -0.302716291
2.43 -0.448381195
3.43 -0.197608304
4.43 -0.262020878
5.43 -0.080276603
7.43 -0.095870124
8.43 -0.851688870
10.43 0.111520039
11.43 -0.223144022
13.43 -0.362175169
14.43 -0.305824839
15.43 0.427004365
16.43 -0.463322441
17.43 -0.434451349
18.43 0.181160895
19.43 -0.101272753
20.43 -0.060361008
22.43 -0.415572106
23.43 -0.338636448
3.44 -0.327482853
4.44 -0.228844138
5.44 0.072903013
7.44 -0.168488423
8.44 -0.890905906
10.44 -0.135986460
11.44 0.008062322
13.44 -0.138188505
14.44 -0.506448144
16.44 -0.717952547
17.44 -0.169465091
18.44 0.732994091
19.44 -0.127039053
20.44 -0.069019935
22.44 -0.431260881
23.44 -0.088680543
2.45 0.047241261
3.45 -0.494815340
4.45 -0.278169350
5.45 -0.017531930
7.45 -0.466405165
8.45 -0.506510638
10.45 -1.331574309
11.45 -0.811784327
13.45 -0.107842110
14.45 -0.529212621
15.45 -0.051022790
16.45 -0.582289422
17.45 0.002096155
18.45 0.208088666
20.45 -0.006965024
22.45 -0.366763754
23.45 -0.121824865
2.46 -0.631467624
3.46 -0.184798045
4.46 -0.043063076
7.46 -0.460504306
8.46 -0.718136303
10.46 -1.389478978
11.46 0.122074945
13.46 -0.358282192
14.46 -0.305532034
15.46 -0.159185946
16.46 -0.672730975
17.46 -0.121308320
18.46 -0.430985035
19.46 -0.015417913
22.46 -0.160830560
2.47 -0.239665527
5.47 -0.118860615
7.47 -0.103717527
11.47 -0.614223705
14.47 -0.271463480
15.47 -0.193385482
18.47 -0.472589611
19.47 -0.401101244
20.47 -0.124284928
22.47 -0.327209031
$subject
(Intercept)
2 0.06213576
3 -0.08869779
4 -0.23615918
5 -0.34921483
7 -0.05809756
8 0.34452299
10 0.91268145
11 0.40358200
13 -0.22528607
14 -0.04148531
15 -0.13742079
16 0.19163885
17 -0.15176666
18 -0.03116724
19 -0.16597829
20 -0.21820458
22 -0.13400736
23 -0.07707540
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
0.75509016 0.31253208 0.08523483 0.13105091 -0.37971441 -0.20467934
=============================================================
--- Mixed - Block 4 - Axis Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 48.681 2.8636 17 8798.3 35.117 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.028744 0.0162 8841 -1.771 0.9519
Step1 - Step3 -0.029159 0.0205 8831 -1.422 0.9949
Step1 - Step4 0.011218 0.0144 8780 0.781 1.0000
Step1 - Step5 -0.007764 0.0162 8841 -0.478 1.0000
Step1 - Step6 0.038185 0.0205 8832 1.865 0.9247
Step1 - Step7 0.025516 0.0162 8841 1.571 0.9849
Step1 - Step8 0.071732 0.0162 8811 4.426 0.0013
Step1 - Step9 0.070764 0.0162 8841 4.359 0.0018
Step1 - Step10 0.082423 0.0162 8841 5.074 0.0001
Step1 - Step11 0.085694 0.0162 8811 5.287 <.0001
Step1 - Step12 0.111452 0.0162 8841 6.861 <.0001
Step1 - Step13 0.121563 0.0205 8832 5.937 <.0001
Step1 - Step14 0.142356 0.0162 8841 8.764 <.0001
Step1 - Step15 0.135288 0.0144 8780 9.422 <.0001
Step1 - Step16 0.174182 0.0205 8831 8.495 <.0001
Step1 - Step17 0.196352 0.0162 8841 12.095 <.0001
Step1 - Step18 0.186383 0.0144 8780 12.980 <.0001
Step2 - Step3 -0.000415 0.0215 8803 -0.019 1.0000
Step2 - Step4 0.039962 0.0162 8841 2.462 0.5602
Step2 - Step5 0.020980 0.0175 8780 1.201 0.9993
Step2 - Step6 0.066929 0.0215 8803 3.113 0.1507
Step2 - Step7 0.054261 0.0175 8780 3.106 0.1533
Step2 - Step8 0.100477 0.0181 8908 5.540 <.0001
Step2 - Step9 0.099508 0.0175 8780 5.699 <.0001
Step2 - Step10 0.111167 0.0175 8780 6.364 <.0001
Step2 - Step11 0.114438 0.0181 8908 6.310 <.0001
Step2 - Step12 0.140196 0.0175 8780 8.026 <.0001
Step2 - Step13 0.150307 0.0215 8803 6.990 <.0001
Step2 - Step14 0.171100 0.0175 8780 9.795 <.0001
Step2 - Step15 0.164032 0.0162 8841 10.104 <.0001
Step2 - Step16 0.202926 0.0215 8803 9.425 <.0001
Step2 - Step17 0.225096 0.0175 8780 12.892 <.0001
Step2 - Step18 0.215128 0.0162 8841 13.251 <.0001
Step3 - Step4 0.040377 0.0205 8831 1.969 0.8840
Step3 - Step5 0.021395 0.0215 8803 0.993 0.9999
Step3 - Step6 0.067344 0.0245 8780 2.753 0.3441
Step3 - Step7 0.054675 0.0215 8803 2.539 0.5002
Step3 - Step8 0.100892 0.0218 8853 4.637 0.0005
Step3 - Step9 0.099923 0.0215 8803 4.641 0.0005
Step3 - Step10 0.111582 0.0215 8803 5.181 <.0001
Step3 - Step11 0.114853 0.0218 8853 5.279 <.0001
Step3 - Step12 0.140611 0.0215 8803 6.529 <.0001
Step3 - Step13 0.150722 0.0245 8780 6.160 <.0001
Step3 - Step14 0.171515 0.0215 8803 7.964 <.0001
Step3 - Step15 0.164447 0.0205 8831 8.020 <.0001
Step3 - Step16 0.203341 0.0245 8780 8.304 <.0001
Step3 - Step17 0.225511 0.0215 8803 10.474 <.0001
Step3 - Step18 0.215542 0.0205 8831 10.512 <.0001
Step4 - Step5 -0.018982 0.0162 8841 -1.169 0.9995
Step4 - Step6 0.026967 0.0205 8832 1.317 0.9979
Step4 - Step7 0.014298 0.0162 8841 0.880 1.0000
Step4 - Step8 0.060514 0.0162 8811 3.734 0.0218
Step4 - Step9 0.059546 0.0162 8841 3.668 0.0275
Step4 - Step10 0.071205 0.0162 8841 4.383 0.0016
Step4 - Step11 0.074475 0.0162 8811 4.595 0.0006
Step4 - Step12 0.100234 0.0162 8841 6.171 <.0001
Step4 - Step13 0.110345 0.0205 8832 5.389 <.0001
Step4 - Step14 0.131138 0.0162 8841 8.073 <.0001
Step4 - Step15 0.124070 0.0144 8780 8.641 <.0001
Step4 - Step16 0.162964 0.0205 8831 7.948 <.0001
Step4 - Step17 0.185134 0.0162 8841 11.404 <.0001
Step4 - Step18 0.175165 0.0144 8780 12.199 <.0001
Step5 - Step6 0.045949 0.0215 8803 2.136 0.7951
Step5 - Step7 0.033280 0.0175 8780 1.904 0.9106
Step5 - Step8 0.079496 0.0181 8908 4.381 0.0016
Step5 - Step9 0.078528 0.0175 8780 4.495 0.0010
Step5 - Step10 0.090187 0.0175 8780 5.161 <.0001
Step5 - Step11 0.093457 0.0181 8908 5.150 <.0001
Step5 - Step12 0.119216 0.0175 8780 6.822 <.0001
Step5 - Step13 0.129327 0.0215 8803 6.012 <.0001
Step5 - Step14 0.150120 0.0175 8780 8.590 <.0001
Step5 - Step15 0.143051 0.0162 8841 8.806 <.0001
Step5 - Step16 0.181945 0.0215 8803 8.448 <.0001
Step5 - Step17 0.204116 0.0175 8780 11.685 <.0001
Step5 - Step18 0.194147 0.0162 8841 11.952 <.0001
Step6 - Step7 -0.012669 0.0215 8803 -0.589 1.0000
Step6 - Step8 0.033547 0.0217 8853 1.544 0.9874
Step6 - Step9 0.032579 0.0215 8803 1.515 0.9897
Step6 - Step10 0.044238 0.0215 8803 2.056 0.8410
Step6 - Step11 0.047508 0.0217 8853 2.186 0.7630
Step6 - Step12 0.073267 0.0215 8803 3.406 0.0651
Step6 - Step13 0.083378 0.0244 8780 3.411 0.0641
Step6 - Step14 0.104171 0.0215 8803 4.843 0.0002
Step6 - Step15 0.097102 0.0205 8832 4.742 0.0003
Step6 - Step16 0.135997 0.0245 8780 5.559 <.0001
Step6 - Step17 0.158167 0.0215 8803 7.356 <.0001
Step6 - Step18 0.148198 0.0205 8832 7.238 <.0001
Step7 - Step8 0.046216 0.0181 8908 2.547 0.4940
Step7 - Step9 0.045247 0.0175 8780 2.590 0.4609
Step7 - Step10 0.056906 0.0175 8780 3.256 0.1017
Step7 - Step11 0.060177 0.0181 8908 3.316 0.0854
Step7 - Step12 0.085936 0.0175 8780 4.917 0.0001
Step7 - Step13 0.096047 0.0215 8803 4.465 0.0011
Step7 - Step14 0.116840 0.0175 8780 6.686 <.0001
Step7 - Step15 0.109771 0.0162 8841 6.758 <.0001
Step7 - Step16 0.148665 0.0215 8803 6.903 <.0001
Step7 - Step17 0.170836 0.0175 8780 9.780 <.0001
Step7 - Step18 0.160867 0.0162 8841 9.903 <.0001
Step8 - Step9 -0.000969 0.0181 8908 -0.053 1.0000
Step8 - Step10 0.010690 0.0181 8908 0.589 1.0000
Step8 - Step11 0.013961 0.0176 8780 0.795 1.0000
Step8 - Step12 0.039720 0.0181 8908 2.189 0.7613
Step8 - Step13 0.049831 0.0217 8853 2.293 0.6885
Step8 - Step14 0.070624 0.0181 8908 3.892 0.0122
Step8 - Step15 0.063555 0.0162 8811 3.921 0.0109
Step8 - Step16 0.102449 0.0218 8853 4.709 0.0004
Step8 - Step17 0.124620 0.0181 8908 6.871 <.0001
Step8 - Step18 0.114651 0.0162 8811 7.074 <.0001
Step9 - Step10 0.011659 0.0175 8780 0.667 1.0000
Step9 - Step11 0.014930 0.0181 8908 0.823 1.0000
Step9 - Step12 0.040689 0.0175 8780 2.329 0.6618
Step9 - Step13 0.050799 0.0215 8803 2.362 0.6367
Step9 - Step14 0.071593 0.0175 8780 4.098 0.0054
Step9 - Step15 0.064524 0.0162 8841 3.975 0.0089
Step9 - Step16 0.103418 0.0215 8803 4.804 0.0002
Step9 - Step17 0.125588 0.0175 8780 7.193 <.0001
Step9 - Step18 0.115620 0.0162 8841 7.122 <.0001
Step10 - Step11 0.003271 0.0181 8908 0.180 1.0000
Step10 - Step12 0.029030 0.0175 8780 1.661 0.9735
Step10 - Step13 0.039140 0.0215 8803 1.820 0.9388
Step10 - Step14 0.059934 0.0175 8780 3.429 0.0606
Step10 - Step15 0.052865 0.0162 8841 3.254 0.1023
Step10 - Step16 0.091759 0.0215 8803 4.261 0.0027
Step10 - Step17 0.113929 0.0175 8780 6.522 <.0001
Step10 - Step18 0.103961 0.0162 8841 6.400 <.0001
Step11 - Step12 0.025759 0.0181 8908 1.420 0.9950
Step11 - Step13 0.035870 0.0217 8853 1.651 0.9751
Step11 - Step14 0.056663 0.0181 8908 3.123 0.1468
Step11 - Step15 0.049594 0.0162 8811 3.060 0.1725
Step11 - Step16 0.088488 0.0218 8853 4.067 0.0061
Step11 - Step17 0.110659 0.0181 8908 6.101 <.0001
Step11 - Step18 0.100690 0.0162 8811 6.213 <.0001
Step12 - Step13 0.010111 0.0215 8803 0.470 1.0000
Step12 - Step14 0.030904 0.0175 8780 1.768 0.9524
Step12 - Step15 0.023835 0.0162 8841 1.467 0.9928
Step12 - Step16 0.062729 0.0215 8803 2.913 0.2454
Step12 - Step17 0.084900 0.0175 8780 4.860 0.0002
Step12 - Step18 0.074931 0.0162 8841 4.613 0.0006
Step13 - Step14 0.020793 0.0215 8803 0.967 1.0000
Step13 - Step15 0.013724 0.0205 8832 0.670 1.0000
Step13 - Step16 0.052618 0.0245 8780 2.151 0.7860
Step13 - Step17 0.074789 0.0215 8803 3.478 0.0519
Step13 - Step18 0.064820 0.0205 8832 3.166 0.1309
Step14 - Step15 -0.007069 0.0162 8841 -0.435 1.0000
Step14 - Step16 0.031825 0.0215 8803 1.478 0.9922
Step14 - Step17 0.053996 0.0175 8780 3.091 0.1594
Step14 - Step18 0.044027 0.0162 8841 2.710 0.3730
Step15 - Step16 0.038894 0.0205 8831 1.897 0.9134
Step15 - Step17 0.061065 0.0162 8841 3.761 0.0197
Step15 - Step18 0.051096 0.0144 8780 3.558 0.0399
Step16 - Step17 0.022171 0.0215 8803 1.030 0.9999
Step16 - Step18 0.012202 0.0205 8831 0.595 1.0000
Step17 - Step18 -0.009969 0.0162 8841 -0.614 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5 Step6
0.797763635 0.028744148 0.029159036 -0.011218043 0.007763756 -0.038185112
Step7 Step8 Step9 Step10 Step11 Step12
-0.025516404 -0.071732474 -0.070763810 -0.082422803 -0.085693502 -0.111452312
Step13 Step14 Step15 Step16 Step17 Step18
-0.121563267 -0.142356308 -0.135287552 -0.174181682 -0.196352277 -0.186383373
Random Effects:
$trial_id
(Intercept)
3.1 -0.1472809108
4.1 -0.1561970128
5.1 -0.0494788187
10.1 -0.1561626307
11.1 -0.3898361783
14.1 -0.0297435825
16.1 -0.2448464177
17.1 -0.0223464032
18.1 -0.1928776813
19.1 -0.0293703890
20.1 0.0325820641
22.1 0.0731428036
23.1 -0.0051136906
3.2 -0.0849517956
4.2 -0.2168601413
5.2 -0.0077217718
8.2 -0.1418423937
10.2 0.1787806910
13.2 -0.0145501383
14.2 0.6597548648
16.2 -0.0509675936
17.2 -0.0375529686
20.2 0.1618901301
22.2 0.2543791388
2.3 0.0530945987
3.3 -0.1477334799
4.3 0.0032239491
5.3 0.0378920189
7.3 0.0759407571
10.3 -0.2562909375
11.3 -0.5892009732
13.3 -0.0721185035
14.3 0.2437600673
15.3 -0.1750415609
17.3 0.0427712194
22.3 0.0982769910
23.3 0.0419177141
2.4 -0.0201610567
3.4 -0.0565807828
4.4 0.0078059074
5.4 0.0512921680
7.4 -0.1325768578
8.4 0.1837697061
11.4 -0.2369419862
13.4 0.0149445469
14.4 -0.0215882665
15.4 -0.0172468202
16.4 -0.2854274465
17.4 -0.0917588095
18.4 -0.1266929011
19.4 -0.0704385213
20.4 -0.1000976255
22.4 -0.1823566854
23.4 -0.0234359300
2.5 0.1350616317
3.5 -0.1063267497
4.5 -0.1321516563
5.5 -0.1616181373
7.5 0.0316110640
8.5 -0.1078756278
10.5 0.6533707561
11.5 -0.3384625417
13.5 0.1696681980
14.5 0.0375446337
15.5 0.0683442225
16.5 -0.1177632787
17.5 0.1344744998
18.5 -0.1448984168
20.5 -0.0805132592
22.5 -0.1143956488
23.5 -0.2819130639
2.6 -0.3740522223
3.6 0.0136917126
4.6 0.1209084159
5.6 0.0106658681
7.6 0.2132853090
8.6 -0.2728104976
10.6 0.4136726633
11.6 0.0651308250
13.6 0.5964131911
14.6 0.0743064046
15.6 -0.0401501989
16.6 -0.2721864626
17.6 -0.0161938461
18.6 0.2104938447
19.6 -0.0597210366
20.6 0.1072333980
23.6 0.0584420288
2.7 -0.3262882825
3.7 -0.0136765150
4.7 -0.0945832467
5.7 0.0779418615
7.7 0.0678964155
8.7 -0.2878866419
10.7 0.5195253009
11.7 -0.1878754376
13.7 -0.2453361928
14.7 0.1767216654
15.7 -0.0370582181
16.7 -0.2689187847
17.7 0.2343707219
18.7 -0.0791618492
19.7 0.0082302102
20.7 -0.2537780568
22.7 -0.0566707996
23.7 -0.0019410576
2.8 0.2851828513
3.8 -0.1580320339
4.8 -0.0259086303
5.8 -0.1806867300
7.8 -0.0513875109
8.8 0.0971900531
10.8 0.7369715982
11.8 -0.2856853583
13.8 -0.0862929877
14.8 -0.0309301659
15.8 0.1037227836
16.8 -0.2499581212
17.8 -0.2772181998
18.8 -0.0403062592
19.8 0.0548885919
20.8 -0.1521530605
22.8 -0.0801202460
23.8 0.0668645960
2.9 -0.1599547524
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4.23 0.2661153417
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5.26 0.1518786400
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8.27 1.0170570059
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2.28 0.0731450608
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8.28 1.2175949017
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2.29 0.0868071876
3.29 1.3781467228
4.29 0.0122971843
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2.30 -0.1408429194
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2.31 0.0752749838
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2.32 0.3315199939
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11.32 2.9595310436
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4.37 0.3338402747
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2.38 -0.1669365813
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4.38 0.2312038599
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8.38 0.3295626801
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19.38 0.0270092518
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22.38 0.2740344728
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2.39 0.1668831440
3.39 0.3645885833
4.39 0.1483721309
5.39 -0.0192706045
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2.40 0.5286603700
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4.40 -0.0416230055
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13.40 -0.0363070807
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3.41 -0.4916575359
4.41 0.0119318399
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10.41 -1.2665769373
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22.41 0.1311436778
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3.42 0.0331706015
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13.42 -0.3145034417
14.42 -0.0716449602
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16.42 1.1178527835
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19.42 0.3525510010
20.42 0.1218437347
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23.42 0.4167124520
2.43 -0.5810325844
3.43 -0.3204736211
4.43 -0.2219002731
5.43 0.0853249918
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10.43 -0.3110309684
11.43 -0.2803971741
13.43 -0.3724355780
14.43 -0.0620535295
15.43 0.3516576566
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17.43 -0.3595486968
18.43 -0.0622262575
19.43 0.0150838574
20.43 0.1129585471
22.43 -0.2722136108
23.43 -0.4027066803
3.44 -0.4857879218
4.44 -0.2623676950
5.44 -0.1701960996
7.44 -0.1471511943
8.44 -0.9257720266
10.44 -0.4264868718
11.44 -0.3964618231
13.44 -0.0134661412
14.44 -0.5048136752
16.44 -0.5829778975
17.44 -0.2738377316
18.44 0.0177265183
19.44 0.0691377071
20.44 0.0545784547
22.44 -0.3615156361
23.44 -0.3552367196
2.45 -0.3352269427
3.45 -0.8068048269
4.45 -0.2888518042
5.45 0.0405794155
7.45 -0.3940792687
8.45 -0.2004453720
10.45 -1.2720264825
11.45 -0.7754857595
13.45 -0.2298394562
14.45 -0.5404445800
15.45 -0.2138688966
16.45 -0.5030564851
17.45 -0.0257649060
18.45 -0.3308760750
20.45 0.0565218335
22.45 -0.2540892606
23.45 -0.5502021129
2.46 -0.8991020738
3.46 -0.5045688769
4.46 -0.1899633416
7.46 -0.3654387474
8.46 -0.2954884640
10.46 -1.3569454095
11.46 -0.3710593667
13.46 -0.3452901680
14.46 -0.4184417966
15.46 0.0398856730
16.46 -0.5048918337
17.46 -0.1530779846
18.46 -0.4085736304
19.46 -0.2834847131
22.46 -0.1642896102
2.47 -0.6183288965
5.47 -0.0821202178
7.47 -0.1804333374
11.47 -0.6974626078
14.47 0.1724652850
15.47 0.0895602448
18.47 -0.7367423851
19.47 -0.3189146441
20.47 -0.1544235856
22.47 -0.0505034501
$subject
(Intercept)
2 0.30467735
3 0.20314125
4 -0.28187408
5 -0.28092875
7 -0.21491342
8 0.34189234
10 0.83157393
11 0.33401033
13 -0.26701182
14 -0.08795784
15 -0.19133591
16 0.06200322
17 -0.13354906
18 0.17498883
19 -0.32219269
20 -0.21162992
22 -0.28135904
23 0.02046528
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.7868819 -1.1650990 -1.1501612 -0.9935119 -1.0933710 -1.2482428
=============================================================
--- Mixed - Block 4 - Axis Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 178.76 10.515 17 8794 42.826 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.038131 0.0282 8829 -1.353 0.9971
Step1 - Step3 -0.067648 0.0356 8821 -1.901 0.9120
Step1 - Step4 0.020943 0.0249 8779 0.841 1.0000
Step1 - Step5 -0.003266 0.0282 8829 -0.116 1.0000
Step1 - Step6 0.050076 0.0355 8821 1.409 0.9954
Step1 - Step7 0.040692 0.0282 8829 1.443 0.9940
Step1 - Step8 0.108923 0.0281 8805 3.872 0.0131
Step1 - Step9 0.105409 0.0282 8829 3.741 0.0213
Step1 - Step10 0.128474 0.0282 8829 4.556 0.0007
Step1 - Step11 0.135271 0.0281 8805 4.809 0.0002
Step1 - Step12 0.161255 0.0282 8829 5.719 <.0001
Step1 - Step13 0.185531 0.0355 8821 5.220 <.0001
Step1 - Step14 0.245110 0.0282 8829 8.693 <.0001
Step1 - Step15 0.262021 0.0249 8779 10.516 <.0001
Step1 - Step16 0.368015 0.0356 8821 10.340 <.0001
Step1 - Step17 0.377622 0.0282 8829 13.400 <.0001
Step1 - Step18 0.367760 0.0249 8779 14.760 <.0001
Step2 - Step3 -0.029517 0.0374 8798 -0.790 1.0000
Step2 - Step4 0.059074 0.0282 8829 2.096 0.8188
Step2 - Step5 0.034865 0.0303 8779 1.150 0.9996
Step2 - Step6 0.088206 0.0373 8798 2.364 0.6358
Step2 - Step7 0.078823 0.0303 8779 2.600 0.4532
Step2 - Step8 0.147054 0.0315 8884 4.669 0.0004
Step2 - Step9 0.143540 0.0303 8779 4.738 0.0003
Step2 - Step10 0.166605 0.0303 8779 5.496 <.0001
Step2 - Step11 0.173402 0.0315 8884 5.506 <.0001
Step2 - Step12 0.199386 0.0303 8779 6.578 <.0001
Step2 - Step13 0.223662 0.0373 8798 5.994 <.0001
Step2 - Step14 0.283241 0.0303 8779 9.344 <.0001
Step2 - Step15 0.300152 0.0282 8829 10.651 <.0001
Step2 - Step16 0.406146 0.0374 8798 10.870 <.0001
Step2 - Step17 0.415753 0.0303 8779 13.723 <.0001
Step2 - Step18 0.405891 0.0282 8829 14.403 <.0001
Step3 - Step4 0.088590 0.0356 8821 2.489 0.5388
Step3 - Step5 0.064382 0.0374 8798 1.723 0.9626
Step3 - Step6 0.117723 0.0425 8780 2.773 0.3305
Step3 - Step7 0.108339 0.0374 8798 2.899 0.2532
Step3 - Step8 0.176570 0.0378 8839 4.675 0.0004
Step3 - Step9 0.173057 0.0374 8798 4.632 0.0005
Step3 - Step10 0.196122 0.0374 8798 5.247 <.0001
Step3 - Step11 0.202918 0.0378 8839 5.373 <.0001
Step3 - Step12 0.228903 0.0374 8798 6.125 <.0001
Step3 - Step13 0.253178 0.0425 8780 5.964 <.0001
Step3 - Step14 0.312758 0.0374 8798 8.368 <.0001
Step3 - Step15 0.329668 0.0356 8821 9.263 <.0001
Step3 - Step16 0.435663 0.0425 8779 10.253 <.0001
Step3 - Step17 0.445270 0.0374 8798 11.917 <.0001
Step3 - Step18 0.435408 0.0356 8821 12.234 <.0001
Step4 - Step5 -0.024208 0.0282 8829 -0.859 1.0000
Step4 - Step6 0.029133 0.0355 8821 0.820 1.0000
Step4 - Step7 0.019749 0.0282 8829 0.700 1.0000
Step4 - Step8 0.087980 0.0281 8805 3.128 0.1448
Step4 - Step9 0.084467 0.0282 8829 2.997 0.2014
Step4 - Step10 0.107532 0.0282 8829 3.814 0.0163
Step4 - Step11 0.114328 0.0281 8805 4.065 0.0062
Step4 - Step12 0.140313 0.0282 8829 4.976 0.0001
Step4 - Step13 0.164588 0.0355 8821 4.631 0.0005
Step4 - Step14 0.224168 0.0282 8829 7.950 <.0001
Step4 - Step15 0.241078 0.0249 8779 9.676 <.0001
Step4 - Step16 0.347072 0.0356 8821 9.752 <.0001
Step4 - Step17 0.356679 0.0282 8829 12.657 <.0001
Step4 - Step18 0.346818 0.0249 8779 13.919 <.0001
Step5 - Step6 0.053341 0.0373 8798 1.429 0.9946
Step5 - Step7 0.043957 0.0303 8779 1.450 0.9937
Step5 - Step8 0.112188 0.0315 8885 3.560 0.0397
Step5 - Step9 0.108675 0.0303 8779 3.585 0.0365
Step5 - Step10 0.131740 0.0303 8779 4.344 0.0019
Step5 - Step11 0.138536 0.0315 8885 4.396 0.0015
Step5 - Step12 0.164521 0.0303 8779 5.425 <.0001
Step5 - Step13 0.188796 0.0373 8798 5.057 0.0001
Step5 - Step14 0.248376 0.0303 8779 8.191 <.0001
Step5 - Step15 0.265286 0.0282 8829 9.408 <.0001
Step5 - Step16 0.371281 0.0374 8798 9.934 <.0001
Step5 - Step17 0.380887 0.0303 8779 12.566 <.0001
Step5 - Step18 0.371026 0.0282 8829 13.158 <.0001
Step6 - Step7 -0.009384 0.0373 8798 -0.251 1.0000
Step6 - Step8 0.058847 0.0377 8839 1.560 0.9860
Step6 - Step9 0.055334 0.0373 8798 1.483 0.9919
Step6 - Step10 0.078399 0.0373 8798 2.100 0.8166
Step6 - Step11 0.085195 0.0377 8839 2.259 0.7135
Step6 - Step12 0.111180 0.0373 8798 2.978 0.2109
Step6 - Step13 0.135455 0.0424 8779 3.194 0.1212
Step6 - Step14 0.195035 0.0373 8798 5.224 <.0001
Step6 - Step15 0.211945 0.0355 8821 5.963 <.0001
Step6 - Step16 0.317939 0.0425 8780 7.489 <.0001
Step6 - Step17 0.327547 0.0373 8798 8.777 <.0001
Step6 - Step18 0.317685 0.0355 8821 8.938 <.0001
Step7 - Step8 0.068231 0.0315 8885 2.165 0.7767
Step7 - Step9 0.064718 0.0303 8779 2.135 0.7957
Step7 - Step10 0.087783 0.0303 8779 2.895 0.2555
Step7 - Step11 0.094579 0.0315 8885 3.001 0.1994
Step7 - Step12 0.120564 0.0303 8779 3.976 0.0088
Step7 - Step13 0.144839 0.0373 8798 3.880 0.0127
Step7 - Step14 0.204419 0.0303 8779 6.741 <.0001
Step7 - Step15 0.221329 0.0282 8829 7.849 <.0001
Step7 - Step16 0.327323 0.0374 8798 8.758 <.0001
Step7 - Step17 0.336930 0.0303 8779 11.115 <.0001
Step7 - Step18 0.327069 0.0282 8829 11.599 <.0001
Step8 - Step9 -0.003513 0.0315 8884 -0.112 1.0000
Step8 - Step10 0.019552 0.0315 8885 0.620 1.0000
Step8 - Step11 0.026348 0.0305 8779 0.865 1.0000
Step8 - Step12 0.052333 0.0315 8885 1.661 0.9736
Step8 - Step13 0.076608 0.0377 8839 2.031 0.8545
Step8 - Step14 0.136188 0.0315 8885 4.322 0.0021
Step8 - Step15 0.153098 0.0281 8805 5.443 <.0001
Step8 - Step16 0.259092 0.0378 8839 6.860 <.0001
Step8 - Step17 0.268699 0.0315 8884 8.531 <.0001
Step8 - Step18 0.258838 0.0281 8805 9.202 <.0001
Step9 - Step10 0.023065 0.0303 8779 0.761 1.0000
Step9 - Step11 0.029861 0.0315 8884 0.948 1.0000
Step9 - Step12 0.055846 0.0303 8779 1.842 0.9319
Step9 - Step13 0.080121 0.0373 8798 2.147 0.7883
Step9 - Step14 0.139701 0.0303 8779 4.609 0.0006
Step9 - Step15 0.156611 0.0282 8829 5.557 <.0001
Step9 - Step16 0.262606 0.0374 8798 7.028 <.0001
Step9 - Step17 0.272213 0.0303 8779 8.985 <.0001
Step9 - Step18 0.262351 0.0282 8829 9.310 <.0001
Step10 - Step11 0.006796 0.0315 8885 0.216 1.0000
Step10 - Step12 0.032781 0.0303 8779 1.081 0.9998
Step10 - Step13 0.057056 0.0373 8798 1.528 0.9887
Step10 - Step14 0.116636 0.0303 8779 3.846 0.0144
Step10 - Step15 0.133546 0.0282 8829 4.736 0.0003
Step10 - Step16 0.239541 0.0374 8798 6.409 <.0001
Step10 - Step17 0.249148 0.0303 8779 8.219 <.0001
Step10 - Step18 0.239286 0.0282 8829 8.486 <.0001
Step11 - Step12 0.025985 0.0315 8885 0.825 1.0000
Step11 - Step13 0.050260 0.0377 8839 1.332 0.9976
Step11 - Step14 0.109840 0.0315 8885 3.486 0.0507
Step11 - Step15 0.126750 0.0281 8805 4.506 0.0009
Step11 - Step16 0.232744 0.0378 8839 6.162 <.0001
Step11 - Step17 0.242351 0.0315 8884 7.695 <.0001
Step11 - Step18 0.232490 0.0281 8805 8.265 <.0001
Step12 - Step13 0.024275 0.0373 8798 0.650 1.0000
Step12 - Step14 0.083855 0.0303 8779 2.765 0.3356
Step12 - Step15 0.100765 0.0282 8829 3.574 0.0380
Step12 - Step16 0.206759 0.0374 8798 5.532 <.0001
Step12 - Step17 0.216366 0.0303 8779 7.138 <.0001
Step12 - Step18 0.206505 0.0282 8829 7.324 <.0001
Step13 - Step14 0.059580 0.0373 8798 1.596 0.9822
Step13 - Step15 0.076490 0.0355 8821 2.152 0.7851
Step13 - Step16 0.182484 0.0425 8780 4.298 0.0023
Step13 - Step17 0.192091 0.0373 8798 5.148 <.0001
Step13 - Step18 0.182230 0.0355 8821 5.127 <.0001
Step14 - Step15 0.016910 0.0282 8829 0.600 1.0000
Step14 - Step16 0.122905 0.0374 8798 3.288 0.0927
Step14 - Step17 0.132512 0.0303 8779 4.372 0.0017
Step14 - Step18 0.122650 0.0282 8829 4.350 0.0019
Step15 - Step16 0.105994 0.0356 8821 2.978 0.2109
Step15 - Step17 0.115601 0.0282 8829 4.102 0.0053
Step15 - Step18 0.105740 0.0249 8779 4.244 0.0030
Step16 - Step17 0.009607 0.0374 8798 0.257 1.0000
Step16 - Step18 -0.000255 0.0356 8821 -0.007 1.0000
Step17 - Step18 -0.009862 0.0282 8829 -0.350 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5 Step6
1.634061155 0.038130982 0.067647717 -0.020942606 0.003265586 -0.050075489
Step7 Step8 Step9 Step10 Step11 Step12
-0.040691647 -0.108922721 -0.105409319 -0.128474294 -0.135270620 -0.161255439
Step13 Step14 Step15 Step16 Step17 Step18
-0.185530594 -0.245110280 -0.262020646 -0.368014876 -0.377621948 -0.367760303
Random Effects:
$trial_id
(Intercept)
3.1 6.649688e-02
4.1 -1.794264e-01
5.1 3.092627e-01
10.1 1.501899e-01
11.1 4.219736e-01
14.1 4.716877e-01
16.1 2.070883e-01
17.1 4.896103e-02
18.1 -2.581335e-01
19.1 -5.198416e-02
20.1 4.119882e-01
22.1 4.979588e-01
23.1 -3.095162e-01
3.2 -4.248837e-01
4.2 -2.225416e-01
5.2 1.676832e-01
8.2 -6.501593e-01
10.2 -4.053554e-01
13.2 3.440621e-01
14.2 9.030892e-01
16.2 1.161119e-01
17.2 8.227111e-02
20.2 1.289941e-01
22.2 -9.410870e-03
2.3 1.300568e-01
3.3 5.174294e-02
4.3 4.618047e-02
5.3 -2.203588e-01
7.3 6.580582e-01
10.3 5.966766e-01
11.3 -7.397333e-01
13.3 1.993205e-01
14.3 5.678560e-01
15.3 -3.456600e-01
17.3 -1.352474e-01
22.3 9.230932e-02
23.3 3.265950e-01
2.4 -4.187892e-01
3.4 -2.051750e-01
4.4 -7.338545e-02
5.4 4.156510e-02
7.4 3.403689e-01
8.4 -3.633391e-01
11.4 -3.668022e-01
13.4 1.781929e-01
14.4 7.067554e-01
15.4 1.752950e-01
16.4 1.240879e-01
17.4 2.274747e-01
18.4 1.503061e-01
19.4 -8.899614e-02
20.4 -2.777897e-01
22.4 6.627817e-02
23.4 -4.999774e-01
2.5 -1.874579e-01
3.5 -5.915322e-01
4.5 2.113093e-01
5.5 -2.717731e-01
7.5 2.149122e-01
8.5 -2.096603e-01
10.5 1.993253e+00
11.5 -1.494423e-02
13.5 7.354995e-01
14.5 5.463147e-01
15.5 3.322080e-01
16.5 1.119159e-01
17.5 -1.667059e-01
18.5 1.099837e-01
20.5 -2.543796e-01
22.5 1.815883e-01
23.5 -2.962743e-01
2.6 -6.639366e-01
3.6 -1.811017e-01
4.6 1.772201e-01
5.6 -3.658359e-02
7.6 1.188753e+00
8.6 1.233392e+00
10.6 4.654715e-01
11.6 1.199178e-01
13.6 4.499286e-01
14.6 3.077286e-01
15.6 1.682428e-02
16.6 -1.797764e-01
17.6 -1.681001e-01
18.6 5.035487e-01
19.6 -5.873447e-02
20.6 -2.093033e-01
23.6 6.417615e-01
2.7 -3.480955e-01
3.7 -1.357538e-01
4.7 3.477559e-01
5.7 3.986973e-02
7.7 8.908735e-01
8.7 -1.816151e-01
10.7 3.024938e+00
11.7 -1.467370e-01
13.7 9.341641e-01
14.7 6.470777e-01
15.7 2.164358e-01
16.7 8.390946e-03
17.7 -5.214857e-02
18.7 -6.879403e-01
19.7 -9.110882e-02
20.7 -4.413691e-01
22.7 -5.116617e-02
23.7 1.714817e-01
2.8 -1.385489e-01
3.8 -3.731025e-01
4.8 1.109603e-01
5.8 -2.000350e-01
7.8 3.593210e-01
8.8 -4.189290e-01
10.8 8.154979e-02
11.8 9.262382e-02
13.8 -9.634490e-02
14.8 2.292667e-01
15.8 -1.493214e-02
16.8 -1.558541e-01
17.8 1.969258e-01
18.8 -8.597118e-02
19.8 4.222962e-01
20.8 -2.533141e-01
22.8 1.399279e-01
23.8 1.355504e-01
2.9 -3.907913e-01
3.9 -3.024887e-01
4.9 -1.285806e-01
5.9 -2.361650e-01
7.9 7.854387e-01
8.9 -9.903078e-02
10.9 1.629110e+00
11.9 -3.015622e-01
13.9 1.302658e-01
14.9 4.203701e-01
15.9 1.528124e-01
16.9 -1.555840e-01
17.9 1.349424e-01
18.9 -4.853178e-01
19.9 -9.728594e-02
20.9 -4.608637e-01
22.9 2.724601e-01
23.9 -1.100140e-01
2.10 -2.626408e-01
3.10 4.225382e-01
4.10 4.236656e-01
5.10 1.686651e-01
7.10 3.632238e-01
8.10 -4.431350e-01
10.10 1.853113e-01
11.10 -9.599217e-02
13.10 -1.817667e-01
14.10 1.968688e-01
16.10 2.713556e-01
17.10 1.079219e-01
18.10 -8.092177e-02
19.10 -2.486252e-01
20.10 -3.269483e-01
22.10 1.430185e-01
23.10 -3.519910e-01
2.11 -1.430011e-01
3.11 -1.667465e-01
4.11 8.780503e-03
5.11 9.706077e-02
7.11 1.493620e-01
8.11 -2.751768e-02
10.11 2.259782e+00
11.11 -6.241579e-01
13.11 5.053247e-02
14.11 6.166272e-01
15.11 1.399893e-01
16.11 2.777435e-01
17.11 -1.983313e-01
18.11 -2.195053e-01
19.11 -3.082203e-01
20.11 6.250231e-02
22.11 3.214420e-01
23.11 -3.961403e-02
2.12 1.676990e-01
3.12 1.075314e+00
5.12 3.420003e-01
7.12 6.376508e-01
8.12 5.379727e-01
10.12 5.167425e-01
11.12 2.474994e-01
13.12 2.288568e-01
14.12 2.604687e-01
15.12 3.578882e-02
16.12 2.661987e-01
17.12 -3.506975e-02
18.12 8.806282e-01
19.12 6.508466e-01
20.12 -5.659430e-01
22.12 2.585335e-01
23.12 5.172453e-01
2.13 -5.420496e-01
3.13 5.895573e-01
4.13 -2.587682e-01
5.13 -1.329172e-01
7.13 2.570483e-01
8.13 -1.027989e-01
10.13 4.062246e-01
11.13 -6.039253e-02
13.13 2.555135e-01
14.13 5.100565e-01
15.13 -1.097122e-01
16.13 -2.929665e-04
17.13 -5.421540e-02
18.13 -3.373815e-01
19.13 -1.622247e-01
20.13 -1.632551e-02
22.13 2.145229e-01
23.13 8.364730e-02
2.14 3.300854e-01
3.14 2.445965e-01
4.14 -3.332990e-01
5.14 -1.962867e-01
7.14 3.072432e-01
8.14 2.083490e-01
10.14 6.401151e-01
11.14 3.607107e-02
13.14 3.418764e-01
14.14 2.809959e-01
15.14 -1.734396e-01
16.14 -1.002322e-01
17.14 2.428209e-02
18.14 -5.782864e-02
19.14 6.237059e-02
20.14 -2.749903e-01
22.14 1.777209e-02
23.14 1.956361e-01
2.15 1.986138e-01
3.15 -1.173456e-01
4.15 -2.426683e-01
5.15 -6.355056e-02
7.15 -1.082753e-02
8.15 3.856795e-01
10.15 1.053337e+00
11.15 -1.212690e+00
14.15 -1.555326e-01
15.15 -4.414879e-01
16.15 1.086110e-01
17.15 4.343743e-01
18.15 2.360197e-01
19.15 -1.675092e-01
20.15 1.380558e-01
22.15 1.329587e-01
23.15 5.276670e-01
2.16 -4.205606e-01
3.16 -8.735538e-02
4.16 2.281403e-02
5.16 -3.258416e-01
7.16 -2.259680e-01
8.16 6.015595e-01
10.16 1.771548e-01
11.16 -3.649432e-01
13.16 9.275351e-03
14.16 2.905375e-02
15.16 -3.216091e-01
16.16 -1.341053e-01
18.16 3.794938e-01
19.16 -1.265189e-01
20.16 -1.674493e-01
22.16 6.982632e-02
23.16 -1.556780e-02
2.17 -1.910253e-01
3.17 2.557325e-01
4.17 2.292756e-01
7.17 -4.935710e-01
8.17 8.686354e-01
10.17 2.067948e+00
11.17 -1.575812e-01
13.17 1.142745e-01
14.17 4.622892e-03
15.17 -2.755240e-01
17.17 -9.868857e-02
18.17 1.435220e-01
19.17 1.361992e-01
20.17 -3.642017e-01
22.17 2.216444e-01
23.17 2.032781e-01
2.18 1.965966e-01
3.18 -1.792881e-01
4.18 -1.576905e-01
5.18 -1.280382e-01
7.18 -2.441822e-01
8.18 -3.547939e-01
10.18 1.583955e+00
11.18 -6.662715e-01
13.18 -8.714903e-02
14.18 -6.006591e-02
15.18 -3.865148e-01
16.18 1.807053e-01
17.18 6.190174e-02
18.18 2.514425e-02
19.18 8.288928e-02
20.18 2.227073e-01
22.18 4.217493e-01
23.18 3.314536e-01
2.19 2.536484e-01
3.19 -1.436023e-01
4.19 3.590887e-01
5.19 -1.102411e-03
7.19 4.752135e-01
8.19 8.105420e-01
10.19 1.611252e+00
11.19 -3.957014e-01
13.19 5.011061e-01
14.19 -2.585255e-01
15.19 -3.178375e-02
16.19 -8.144600e-01
17.19 1.961243e-02
18.19 1.259491e-01
19.19 5.745309e-02
20.19 3.576905e-02
22.19 1.158617e-01
23.19 -6.912956e-03
2.20 7.306612e-03
3.20 -5.327672e-02
4.20 2.111957e-02
5.20 1.336743e-01
7.20 -1.325460e-01
8.20 -2.695632e-01
10.20 7.902016e-01
11.20 -3.478849e-01
13.20 3.048240e-01
14.20 3.164487e-01
15.20 -6.837047e-02
16.20 -4.328481e-01
17.20 2.302716e-01
18.20 -2.932651e-01
19.20 6.459433e-01
20.20 8.608949e-02
22.20 9.919956e-02
23.20 6.183426e-01
2.21 3.967220e-01
3.21 3.560317e-01
4.21 2.233102e-01
5.21 -1.790333e-01
7.21 4.495961e-01
8.21 6.957941e-01
10.21 1.677002e+00
11.21 1.742425e-01
13.21 4.221226e-01
14.21 2.571362e-01
15.21 3.818521e-02
16.21 -8.809781e-01
17.21 7.709859e-02
18.21 2.153112e-02
19.21 4.112846e-01
20.21 1.314728e-01
23.21 2.759568e-01
2.22 6.577642e-01
3.22 1.052348e-01
4.22 3.250076e-01
5.22 1.750109e-01
7.22 1.058893e+00
8.22 2.092197e-01
10.22 2.875224e+00
11.22 3.439811e-01
13.22 -1.323214e-01
14.22 -1.408436e-01
15.22 -9.304063e-03
16.22 -5.422087e-01
17.22 2.327910e-01
18.22 3.002425e-01
19.22 -1.192951e-02
20.22 2.528365e-01
22.22 -8.857363e-02
23.22 1.424360e+00
2.23 -5.400186e-02
3.23 3.422830e-01
4.23 -2.997765e-02
5.23 2.499487e-02
7.23 1.054143e-01
8.23 1.468765e+00
10.23 3.692164e-01
11.23 -4.966599e-01
13.23 6.680685e-05
14.23 -5.466351e-01
15.23 7.412714e-02
16.23 6.048262e-01
17.23 3.536884e-01
18.23 1.921268e-01
19.23 2.777758e-01
20.23 2.230871e-01
22.23 4.478350e-02
23.23 6.147635e-03
2.24 8.576775e-02
3.24 -8.585620e-02
4.24 4.968942e-01
5.24 2.613547e-01
7.24 4.931969e-02
8.24 1.631700e-01
10.24 1.612760e+00
11.24 -9.168266e-01
13.24 1.659631e-01
15.24 -3.237183e-01
16.24 2.329857e+00
17.24 -2.663226e-02
18.24 4.714102e-01
19.24 9.525542e-02
20.24 2.822963e-01
22.24 -4.580814e-02
2.25 3.723946e-01
3.25 -1.819894e-01
4.25 5.347696e-02
5.25 1.197815e-01
7.25 5.797663e-02
8.25 -7.798693e-01
10.25 2.459685e-01
11.25 3.082818e-01
14.25 -3.439875e-01
15.25 1.978182e-01
16.25 1.773503e+00
17.25 2.024901e-01
18.25 -2.143058e-03
19.25 1.053564e-01
20.25 5.391479e-02
22.25 7.748638e-04
23.25 5.251377e-01
2.26 5.180672e-01
3.26 -1.225202e-01
4.26 -3.154343e-01
5.26 -3.713833e-02
7.26 1.131056e-01
8.26 8.649926e-01
10.26 1.242023e+00
11.26 1.551067e+00
15.26 3.267341e-02
16.26 1.656860e-01
17.26 -6.327951e-02
18.26 1.834241e-01
19.26 2.677287e-01
20.26 3.718764e-01
22.26 1.832099e-01
23.26 -1.896234e-01
2.27 1.847312e-01
3.27 4.307156e-01
4.27 -3.524204e-02
5.27 -2.086483e-02
7.27 2.453012e-01
8.27 2.108453e+00
10.27 5.784280e-01
11.27 -1.125918e-01
13.27 6.855898e-02
14.27 6.578551e-02
15.27 1.365634e-01
16.27 2.757207e+00
17.27 -4.928568e-02
18.27 2.890736e-01
19.27 5.815493e-02
20.27 1.494644e-01
22.27 -2.655920e-01
23.27 2.957452e-01
2.28 -7.947089e-02
3.28 -2.697752e-01
4.28 -9.788284e-02
5.28 -9.499890e-02
7.28 5.827997e-01
8.28 1.634997e+00
10.28 1.979664e+00
11.28 -2.556853e-01
13.28 4.537229e-01
14.28 -1.048785e-01
15.28 -1.467792e-01
16.28 6.498535e-02
19.28 -3.201975e-02
20.28 2.943786e-01
22.28 -2.030664e-01
23.28 2.378594e-01
2.29 1.765313e-01
3.29 2.468067e+00
4.29 -4.023559e-02
5.29 1.137454e-01
7.29 6.422706e-01
8.29 4.657129e-04
10.29 -2.433755e-01
11.29 2.402671e+00
13.29 3.311982e-02
14.29 7.483318e-01
15.29 4.640635e-02
16.29 1.039954e+00
17.29 7.183354e-01
18.29 -3.812033e-01
19.29 1.210544e-01
20.29 8.659024e-02
22.29 4.923551e-02
23.29 1.000682e+00
2.30 -9.564542e-02
3.30 2.925521e-01
4.30 3.420123e-01
5.30 1.606349e-01
7.30 1.267504e-01
8.30 6.970248e-01
10.30 -8.254565e-01
11.30 1.432219e+00
13.30 4.096126e-01
14.30 6.377931e-01
15.30 2.071786e-01
16.30 -1.029989e-01
17.30 2.172547e-01
18.30 2.070488e-01
19.30 -1.274043e-01
20.30 -3.915474e-01
22.30 -2.379942e-01
23.30 1.467803e+00
2.31 9.230442e-01
3.31 9.617089e-01
4.31 4.518800e-01
5.31 -2.672713e-01
7.31 5.839957e-01
8.31 -3.128861e-01
10.31 -1.083959e+00
11.31 1.647537e+00
13.31 -3.144203e-01
14.31 -1.786941e-01
15.31 -7.742959e-02
16.31 -1.736862e-01
17.31 5.818749e-04
18.31 4.480879e-01
19.31 3.532643e-01
20.31 -2.957750e-01
22.31 3.743965e-02
23.31 3.290035e-01
2.32 4.841997e-01
3.32 -2.241184e-01
4.32 2.662246e-01
5.32 7.568137e-02
7.32 -9.655827e-02
8.32 -2.180572e-01
10.32 -2.756011e-01
11.32 3.642102e+00
13.32 -4.185685e-01
14.32 -6.334892e-01
15.32 -1.035006e-01
16.32 -3.007858e-01
17.32 7.801189e-01
18.32 2.289548e-01
19.32 -1.617760e-01
20.32 -2.011113e-02
22.32 2.616935e-01
23.32 1.652371e-01
2.33 4.149576e-01
3.33 8.402469e-01
4.33 2.548205e-01
5.33 -6.167382e-02
7.33 -6.772800e-02
8.33 2.546449e-01
10.33 -1.109037e+00
11.33 1.124823e+00
13.33 7.062560e-01
14.33 -3.332131e-02
15.33 1.768745e-02
16.33 -2.092587e-01
17.33 1.077133e-02
18.33 1.062988e+00
19.33 -1.015973e-01
20.33 2.273709e-01
22.33 2.339447e-01
23.33 5.005165e-01
2.34 2.571818e-01
3.34 -4.338550e-01
4.34 -1.098769e-01
5.34 4.081709e-02
7.34 -2.867808e-01
8.34 8.607674e-01
10.34 -8.405128e-01
11.34 1.111541e+00
13.34 2.904423e-02
14.34 -8.486014e-02
15.34 3.100620e-01
16.34 -5.147728e-01
17.34 -9.714371e-02
18.34 6.377359e-01
19.34 3.700495e-01
20.34 2.264278e-02
22.34 2.464668e-01
23.34 -4.756895e-02
2.35 7.298307e-01
3.35 -5.359684e-01
4.35 -3.809150e-01
5.35 2.323275e-02
7.35 -8.219831e-01
8.35 -3.391173e-01
10.35 -5.162309e-01
11.35 3.112163e-01
13.35 2.190202e-01
14.35 9.023382e-01
15.35 -2.021985e-01
16.35 -5.421526e-02
17.35 -1.024831e-01
18.35 6.153610e-01
19.35 1.851686e-01
20.35 3.510076e-01
22.35 6.280581e-02
23.35 -5.617402e-01
2.36 1.694570e-01
3.36 1.277960e-02
4.36 1.073675e-01
5.36 -1.491985e-02
7.36 -1.972413e-01
8.36 -1.086240e+00
10.36 -2.628456e+00
11.36 6.441611e-01
13.36 5.166547e-01
14.36 -3.799198e-01
15.36 3.471562e-01
16.36 1.607207e-01
17.36 -3.866924e-01
18.36 8.315529e-02
19.36 -3.583384e-01
20.36 5.068719e-01
22.36 -3.415060e-01
23.36 1.044491e-01
2.37 -2.312620e-01
3.37 5.550032e-01
4.37 2.348593e-01
5.37 -3.579966e-01
7.37 -2.751250e-01
8.37 6.609885e-01
10.37 -2.070417e+00
11.37 -5.287867e-01
13.37 -8.403688e-01
14.37 -1.174867e-01
15.37 -1.879522e-01
16.37 -4.793504e-01
17.37 2.579216e-01
18.37 6.678317e-02
20.37 -4.289742e-02
22.37 5.470599e-02
23.37 1.166396e-01
2.38 7.434145e-01
3.38 8.138774e-02
4.38 -1.289407e-01
5.38 7.047359e-02
7.38 -3.499093e-01
8.38 1.330768e-01
10.38 -2.761641e+00
11.38 -1.028584e+00
13.38 -8.490001e-01
14.38 -8.106730e-01
15.38 7.606028e-03
16.38 4.588586e-02
17.38 2.220956e-01
18.38 6.897755e-01
19.38 2.374147e-03
20.38 4.771614e-02
22.38 -1.935075e-01
23.38 -9.810532e-01
2.39 6.583581e-01
3.39 5.718631e-01
4.39 4.551873e-02
5.39 1.698898e-01
7.39 2.161819e-01
8.39 -1.284937e+00
10.39 -2.282574e+00
11.39 6.056899e-01
14.39 -6.306166e-02
15.39 4.681108e-02
16.39 -4.092055e-02
17.39 -8.884965e-02
18.39 -6.219994e-02
19.39 -3.318649e-01
20.39 4.159083e-01
22.39 2.710940e-01
23.39 -1.697830e+00
2.40 -6.823599e-02
3.40 -7.081715e-02
4.40 -2.999765e-01
5.40 1.059044e-02
7.40 -8.182382e-01
8.40 -3.263709e-01
10.40 -2.012371e+00
13.40 -5.227856e-01
14.40 -3.639133e-01
15.40 -1.962683e-01
17.40 -2.611189e-01
18.40 -1.131017e-01
19.40 -1.929404e-01
20.40 -6.777545e-02
22.40 -1.594270e-01
23.40 -1.633666e+00
3.41 -6.664285e-01
4.41 -2.822162e-01
5.41 2.707917e-01
7.41 -4.883351e-01
10.41 -2.587195e+00
11.41 -1.011707e+00
13.41 -9.737583e-01
14.41 -5.969473e-01
16.41 -3.019221e-01
17.41 -5.710200e-01
18.41 1.926900e+00
19.41 -3.756081e-01
20.41 -1.067503e-01
22.41 6.220591e-02
23.41 -6.161055e-01
2.42 -5.370888e-01
3.42 -5.822179e-01
4.42 -2.347136e-02
5.42 4.235116e-02
7.42 -2.363059e-01
8.42 -4.253755e-01
10.42 -8.260810e-01
11.42 -1.062037e+00
13.42 -8.777848e-01
14.42 1.376297e-01
15.42 -1.791270e-01
16.42 -9.790471e-01
17.42 -9.171857e-01
18.42 -1.082045e+00
19.42 -1.595821e-01
20.42 1.181895e-01
22.42 -3.113483e-01
23.42 -5.417156e-01
2.43 -7.508514e-01
3.43 -6.076369e-01
4.43 -5.958863e-01
5.43 -5.528891e-02
7.43 -9.887363e-01
8.43 -1.915336e+00
10.43 -1.633373e-01
11.43 -9.948728e-01
13.43 -9.645290e-01
14.43 -5.572363e-01
15.43 1.546597e+00
16.43 -3.430582e-01
17.43 -8.384876e-01
18.43 -3.131296e-01
19.43 -2.384985e-01
20.43 1.872013e-01
22.43 -7.828864e-01
23.43 -4.120641e-01
3.44 -4.978565e-01
4.44 -5.011333e-01
5.44 -1.354029e-01
7.44 -1.401858e+00
8.44 -1.975043e+00
10.44 -1.981624e+00
11.44 -1.063501e+00
13.44 -3.113562e-01
14.44 -1.262003e+00
16.44 -1.474263e+00
17.44 -7.566043e-01
18.44 5.745371e-01
19.44 -3.558347e-01
20.44 -5.271339e-01
22.44 -7.775055e-01
23.44 -3.929250e-01
2.45 -5.390877e-01
3.45 -1.452349e+00
4.45 -6.728953e-01
5.45 -3.562821e-01
7.45 -1.607348e+00
8.45 -6.987001e-01
10.45 -2.673132e+00
11.45 -1.734791e+00
13.45 -8.029467e-01
14.45 -1.283594e+00
15.45 -5.033530e-01
16.45 -8.917725e-01
17.45 -2.170249e-01
18.45 -1.152811e+00
20.45 1.071394e-01
22.45 -7.995185e-01
23.45 -1.100712e+00
2.46 -1.267193e+00
3.46 -8.900292e-01
4.46 -4.108348e-01
7.46 -1.435197e+00
8.46 -1.055985e+00
10.46 -2.669664e+00
11.46 6.159935e-01
13.46 -9.576368e-01
14.46 -1.107583e+00
15.46 -4.254195e-02
16.46 -1.155271e+00
17.46 9.107736e-02
18.46 -1.958683e+00
19.46 -5.743551e-01
22.46 -6.665421e-01
2.47 -8.385538e-01
5.47 -3.856708e-01
7.47 -3.179505e-01
11.47 -1.366401e+00
14.47 -7.650381e-01
15.47 -4.454095e-01
18.47 -2.017225e+00
19.47 -7.287485e-01
20.47 -3.412955e-01
22.47 -5.853094e-01
$subject
(Intercept)
2 -0.08744244
3 0.11005212
4 -0.59546570
5 -0.71923183
7 0.28351700
8 0.67230377
10 1.45203095
11 0.59862100
13 -0.41345397
14 -0.06565358
15 -0.39741961
16 0.15413401
17 -0.43084598
18 0.75430134
19 -0.66152366
20 -0.38311542
22 -0.58140526
23 0.31059725
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
1.0917119 1.3440337 0.8723332 1.0387849 0.6172025 0.5908217
=============================================================
--- Mixed - Block 5 - Axis X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 5.0285 0.29579 17 8785.5 6.2651 6.92e-15 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.009146 0.0123 8833 -0.741 1.0000
Step1 - Step3 -0.025092 0.0158 8820 -1.584 0.9836
Step1 - Step4 -0.003653 0.0108 8760 -0.338 1.0000
Step1 - Step5 0.000900 0.0123 8833 0.073 1.0000
Step1 - Step6 0.005297 0.0158 8820 0.334 1.0000
Step1 - Step7 0.010789 0.0123 8833 0.874 1.0000
Step1 - Step8 0.012855 0.0122 8797 1.050 0.9999
Step1 - Step9 0.023289 0.0123 8833 1.886 0.9172
Step1 - Step10 0.031182 0.0123 8833 2.526 0.5103
Step1 - Step11 0.025230 0.0122 8797 2.060 0.8392
Step1 - Step12 0.013618 0.0123 8833 1.103 0.9998
Step1 - Step13 0.021546 0.0158 8820 1.360 0.9970
Step1 - Step14 0.030816 0.0123 8833 2.496 0.5333
Step1 - Step15 0.049277 0.0108 8760 4.553 0.0008
Step1 - Step16 0.049699 0.0158 8820 3.137 0.1413
Step1 - Step17 0.050760 0.0123 8833 4.112 0.0051
Step1 - Step18 0.061632 0.0108 8760 5.694 <.0001
Step2 - Step3 -0.015946 0.0167 8787 -0.957 1.0000
Step2 - Step4 0.005493 0.0123 8833 0.445 1.0000
Step2 - Step5 0.010046 0.0133 8760 0.753 1.0000
Step2 - Step6 0.014443 0.0167 8787 0.867 1.0000
Step2 - Step7 0.019935 0.0133 8760 1.495 0.9911
Step2 - Step8 0.022001 0.0138 8913 1.593 0.9826
Step2 - Step9 0.032435 0.0133 8760 2.432 0.5829
Step2 - Step10 0.040328 0.0133 8760 3.024 0.1886
Step2 - Step11 0.034376 0.0138 8913 2.488 0.5393
Step2 - Step12 0.022764 0.0133 8760 1.707 0.9656
Step2 - Step13 0.030691 0.0167 8787 1.842 0.9320
Step2 - Step14 0.039962 0.0133 8760 2.997 0.2017
Step2 - Step15 0.058422 0.0123 8833 4.732 0.0003
Step2 - Step16 0.058845 0.0167 8787 3.532 0.0436
Step2 - Step17 0.059906 0.0133 8760 4.492 0.0010
Step2 - Step18 0.070778 0.0123 8833 5.733 <.0001
Step3 - Step4 0.021438 0.0158 8820 1.353 0.9971
Step3 - Step5 0.025992 0.0167 8787 1.560 0.9860
Step3 - Step6 0.030389 0.0191 8760 1.595 0.9824
Step3 - Step7 0.035881 0.0167 8787 2.154 0.7841
Step3 - Step8 0.037947 0.0168 8846 2.260 0.7126
Step3 - Step9 0.048381 0.0167 8787 2.904 0.2502
Step3 - Step10 0.056274 0.0167 8787 3.378 0.0710
Step3 - Step11 0.050322 0.0168 8846 2.997 0.2017
Step3 - Step12 0.038709 0.0167 8787 2.324 0.6661
Step3 - Step13 0.046637 0.0191 8760 2.447 0.5714
Step3 - Step14 0.055908 0.0167 8787 3.356 0.0759
Step3 - Step15 0.074368 0.0158 8820 4.694 0.0004
Step3 - Step16 0.074790 0.0191 8760 3.925 0.0107
Step3 - Step17 0.075852 0.0167 8787 4.553 0.0008
Step3 - Step18 0.086724 0.0158 8820 5.474 <.0001
Step4 - Step5 0.004554 0.0123 8833 0.369 1.0000
Step4 - Step6 0.008951 0.0158 8820 0.565 1.0000
Step4 - Step7 0.014442 0.0123 8833 1.170 0.9995
Step4 - Step8 0.016509 0.0122 8797 1.348 0.9973
Step4 - Step9 0.026943 0.0123 8833 2.182 0.7656
Step4 - Step10 0.034836 0.0123 8833 2.822 0.2991
Step4 - Step11 0.028883 0.0122 8797 2.358 0.6401
Step4 - Step12 0.017271 0.0123 8833 1.399 0.9958
Step4 - Step13 0.025199 0.0158 8820 1.591 0.9828
Step4 - Step14 0.034469 0.0123 8833 2.792 0.3180
Step4 - Step15 0.052930 0.0108 8760 4.890 0.0001
Step4 - Step16 0.053352 0.0158 8820 3.368 0.0733
Step4 - Step17 0.054413 0.0123 8833 4.408 0.0015
Step4 - Step18 0.065285 0.0108 8760 6.032 <.0001
Step5 - Step6 0.004397 0.0167 8787 0.264 1.0000
Step5 - Step7 0.009889 0.0133 8760 0.742 1.0000
Step5 - Step8 0.011955 0.0138 8913 0.865 1.0000
Step5 - Step9 0.022389 0.0133 8760 1.679 0.9706
Step5 - Step10 0.030282 0.0133 8760 2.271 0.7047
Step5 - Step11 0.024330 0.0138 8913 1.761 0.9541
Step5 - Step12 0.012717 0.0133 8760 0.954 1.0000
Step5 - Step13 0.020645 0.0167 8787 1.239 0.9990
Step5 - Step14 0.029916 0.0133 8760 2.243 0.7242
Step5 - Step15 0.048376 0.0123 8833 3.919 0.0110
Step5 - Step16 0.048798 0.0167 8787 2.929 0.2364
Step5 - Step17 0.049860 0.0133 8760 3.739 0.0214
Step5 - Step18 0.060731 0.0123 8833 4.919 0.0001
Step6 - Step7 0.005492 0.0167 8787 0.330 1.0000
Step6 - Step8 0.007558 0.0168 8846 0.450 1.0000
Step6 - Step9 0.017992 0.0167 8787 1.080 0.9998
Step6 - Step10 0.025885 0.0167 8787 1.554 0.9865
Step6 - Step11 0.019933 0.0168 8846 1.187 0.9994
Step6 - Step12 0.008320 0.0167 8787 0.499 1.0000
Step6 - Step13 0.016248 0.0191 8760 0.853 1.0000
Step6 - Step14 0.025519 0.0167 8787 1.532 0.9884
Step6 - Step15 0.043979 0.0158 8820 2.776 0.3285
Step6 - Step16 0.044401 0.0191 8760 2.330 0.6613
Step6 - Step17 0.045463 0.0167 8787 2.729 0.3602
Step6 - Step18 0.056335 0.0158 8820 3.556 0.0403
Step7 - Step8 0.002066 0.0138 8913 0.150 1.0000
Step7 - Step9 0.012500 0.0133 8760 0.937 1.0000
Step7 - Step10 0.020393 0.0133 8760 1.529 0.9886
Step7 - Step11 0.014441 0.0138 8913 1.045 0.9999
Step7 - Step12 0.002829 0.0133 8760 0.212 1.0000
Step7 - Step13 0.010756 0.0167 8787 0.646 1.0000
Step7 - Step14 0.020027 0.0133 8760 1.502 0.9906
Step7 - Step15 0.038487 0.0123 8833 3.118 0.1488
Step7 - Step16 0.038909 0.0167 8787 2.336 0.6571
Step7 - Step17 0.039971 0.0133 8760 2.997 0.2013
Step7 - Step18 0.050843 0.0123 8833 4.118 0.0050
Step8 - Step9 0.010434 0.0138 8913 0.755 1.0000
Step8 - Step10 0.018327 0.0138 8913 1.327 0.9977
Step8 - Step11 0.012375 0.0133 8760 0.931 1.0000
Step8 - Step12 0.000762 0.0138 8913 0.055 1.0000
Step8 - Step13 0.008690 0.0168 8846 0.518 1.0000
Step8 - Step14 0.017961 0.0138 8913 1.300 0.9982
Step8 - Step15 0.036421 0.0122 8797 2.973 0.2132
Step8 - Step16 0.036843 0.0168 8846 2.194 0.7579
Step8 - Step17 0.037905 0.0138 8913 2.744 0.3499
Step8 - Step18 0.048777 0.0122 8797 3.982 0.0086
Step9 - Step10 0.007893 0.0133 8760 0.592 1.0000
Step9 - Step11 0.001941 0.0138 8913 0.140 1.0000
Step9 - Step12 -0.009672 0.0133 8760 -0.725 1.0000
Step9 - Step13 -0.001744 0.0167 8787 -0.105 1.0000
Step9 - Step14 0.007527 0.0133 8760 0.564 1.0000
Step9 - Step15 0.025987 0.0123 8833 2.105 0.8138
Step9 - Step16 0.026409 0.0167 8787 1.585 0.9834
Step9 - Step17 0.027471 0.0133 8760 2.060 0.8391
Step9 - Step18 0.038342 0.0123 8833 3.106 0.1534
Step10 - Step11 -0.005952 0.0138 8913 -0.431 1.0000
Step10 - Step12 -0.017565 0.0133 8760 -1.317 0.9979
Step10 - Step13 -0.009637 0.0167 8787 -0.578 1.0000
Step10 - Step14 -0.000366 0.0133 8760 -0.027 1.0000
Step10 - Step15 0.018094 0.0123 8833 1.466 0.9928
Step10 - Step16 0.018516 0.0167 8787 1.111 0.9998
Step10 - Step17 0.019578 0.0133 8760 1.468 0.9927
Step10 - Step18 0.030449 0.0123 8833 2.466 0.5564
Step11 - Step12 -0.011612 0.0138 8913 -0.841 1.0000
Step11 - Step13 -0.003684 0.0168 8846 -0.219 1.0000
Step11 - Step14 0.005586 0.0138 8913 0.404 1.0000
Step11 - Step15 0.024046 0.0122 8797 1.963 0.8866
Step11 - Step16 0.024469 0.0168 8846 1.457 0.9933
Step11 - Step17 0.025530 0.0138 8913 1.848 0.9301
Step11 - Step18 0.036402 0.0122 8797 2.972 0.2140
Step12 - Step13 0.007928 0.0167 8787 0.476 1.0000
Step12 - Step14 0.017198 0.0133 8760 1.290 0.9984
Step12 - Step15 0.035659 0.0123 8833 2.888 0.2591
Step12 - Step16 0.036081 0.0167 8787 2.166 0.7764
Step12 - Step17 0.037142 0.0133 8760 2.785 0.3223
Step12 - Step18 0.048014 0.0123 8833 3.889 0.0123
Step13 - Step14 0.009271 0.0167 8787 0.556 1.0000
Step13 - Step15 0.027731 0.0158 8820 1.750 0.9566
Step13 - Step16 0.028153 0.0191 8760 1.477 0.9922
Step13 - Step17 0.029215 0.0167 8787 1.754 0.9559
Step13 - Step18 0.040086 0.0158 8820 2.530 0.5068
Step14 - Step15 0.018460 0.0123 8833 1.495 0.9911
Step14 - Step16 0.018882 0.0167 8787 1.133 0.9997
Step14 - Step17 0.019944 0.0133 8760 1.496 0.9911
Step14 - Step18 0.030816 0.0123 8833 2.496 0.5333
Step15 - Step16 0.000422 0.0158 8820 0.027 1.0000
Step15 - Step17 0.001484 0.0123 8833 0.120 1.0000
Step15 - Step18 0.012355 0.0108 8760 1.142 0.9997
Step16 - Step17 0.001062 0.0167 8787 0.064 1.0000
Step16 - Step18 0.011933 0.0158 8820 0.753 1.0000
Step17 - Step18 0.010872 0.0123 8833 0.881 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5
0.5980705625 0.0091459673 0.0250916759 0.0036531961 -0.0009004748
Step6 Step7 Step8 Step9 Step10
-0.0052973793 -0.0107891506 -0.0128553888 -0.0232894873 -0.0311824198
Step11 Step12 Step13 Step14 Step15
-0.0252300197 -0.0136177881 -0.0215455200 -0.0308162875 -0.0492765080
Step16 Step17 Step18
-0.0496985660 -0.0507600754 -0.0616318880
Random Effects:
$trial_id
(Intercept)
3.1 -0.0287539542
4.1 0.2463641090
7.1 0.1992907673
8.1 -0.2045945448
10.1 -0.0503935199
11.1 -0.4207903715
13.1 0.0275004454
15.1 0.0014765254
18.1 -0.0203189478
19.1 -0.0354188907
20.1 0.0212764874
22.1 -0.0938323205
2.2 0.0426641631
3.2 -0.0439393622
4.2 0.1855733626
7.2 -0.1076081242
11.2 0.2156256120
13.2 -0.0179803890
14.2 -0.1414334134
15.2 0.3092954028
16.2 0.0805121689
19.2 -0.0321604245
22.2 -0.0946005770
23.2 1.8309123065
2.3 0.2937846139
3.3 0.0467345456
4.3 -0.0969256408
7.3 -0.0966690314
11.3 0.1647663081
13.3 0.0616100937
14.3 -0.1260527991
15.3 0.0942098823
16.3 0.0346761342
17.3 0.0424239151
18.3 -0.0684676981
19.3 -0.0267971305
22.3 -0.0948970753
23.3 0.3520493702
2.4 -0.2785701355
3.4 0.1578938333
4.4 -0.0970853042
5.4 -0.0725688891
7.4 -0.0020764648
8.4 -0.2778863012
10.4 -0.5356753500
11.4 -0.1088587876
13.4 0.0485123445
14.4 -0.2868037764
15.4 0.0219590533
16.4 -0.1347454616
17.4 -0.0684734831
18.4 -0.0075103893
19.4 0.1246213700
20.4 -0.1386598822
22.4 0.0071270436
23.4 0.0009170076
2.5 0.0571692087
3.5 0.0233434832
4.5 0.1056762054
5.5 -0.0649712809
7.5 -0.0853611513
8.5 -0.3907507080
10.5 -0.3184521476
11.5 -0.2561851662
13.5 -0.0381724960
14.5 -0.1144681165
15.5 0.1719602332
16.5 -0.1368470895
17.5 0.0718300384
18.5 -0.0529848920
19.5 -0.1371237554
20.5 -0.0860311357
22.5 0.0793815794
23.5 0.0917559276
2.6 -0.1214468891
4.6 -0.1075379570
5.6 0.0065522875
7.6 -0.1129811221
8.6 -0.1763818340
10.6 0.0074274394
11.6 0.3697784638
13.6 -0.0139218744
14.6 0.1022724146
15.6 0.0320551576
18.6 0.0298315514
19.6 -0.0033790417
20.6 -0.0959553259
22.6 0.0067917556
23.6 0.4345684717
2.7 -0.1541261804
3.7 0.1232695765
4.7 -0.0454036742
5.7 0.0011960658
7.7 -0.0421810306
8.7 0.0476511263
10.7 -0.0464926827
13.7 0.0438475342
14.7 0.0964174658
15.7 -0.2567891819
16.7 -0.1360157688
17.7 -0.0631637195
18.7 0.0322625400
19.7 -0.0178827223
20.7 -0.0001412558
22.7 0.0994578553
23.7 -0.2650599343
2.8 -0.0844745314
3.8 0.0249558276
4.8 0.1403972257
5.8 -0.0379272244
7.8 -0.0900312913
8.8 -0.2157057874
10.8 -0.0788220196
11.8 -0.1150899814
13.8 0.0659569650
14.8 -0.2091524876
15.8 -0.2179837050
16.8 0.0394534087
17.8 -0.0889351394
18.8 -0.0727681560
19.8 0.1154257570
20.8 0.0112527798
22.8 -0.0806242761
23.8 -0.0278876823
2.9 -0.0274672275
3.9 0.1953848568
4.9 -0.2387148124
5.9 -0.0013381881
7.9 -0.1679814647
8.9 -0.2043679228
10.9 0.0599180874
11.9 -0.2233956035
13.9 -0.1015387867
14.9 -0.1005024025
15.9 0.1131169534
16.9 -0.0808654377
17.9 -0.2146457207
18.9 -0.2067205240
19.9 -0.0221286663
20.9 -0.0843382644
22.9 -0.0141741600
23.9 -0.0926085716
2.10 -0.1183489253
3.10 -0.1332192704
4.10 -0.0212214993
5.10 -0.0309409754
7.10 -0.1640530476
8.10 -0.1448558152
10.10 -0.1358497782
11.10 -0.0361495225
13.10 -0.0515088589
14.10 0.0556863819
15.10 -0.2108798790
16.10 0.0105959343
17.10 -0.1718319705
18.10 -0.0242228088
19.10 0.0425403964
20.10 -0.0077084161
22.10 0.0750945932
23.10 -0.2374905706
2.11 0.3160332453
3.11 0.0239172961
4.11 0.0011713405
5.11 -0.0300834081
7.11 -0.0975080441
8.11 -0.1337218302
10.11 0.2276736220
11.11 -0.2096624130
13.11 0.0155558201
14.11 -0.1996848221
15.11 -0.0350240810
16.11 -0.1424818428
17.11 -0.0580524908
18.11 -0.1819075535
19.11 0.0801827259
20.11 0.1498200944
22.11 0.0321659026
23.11 -0.2084386817
2.12 -0.0677448193
3.12 0.2036147939
4.12 0.0759869164
7.12 -0.0512570258
8.12 0.1259332447
10.12 -0.3970019535
11.12 -0.1306138593
13.12 0.0740842675
14.12 -0.2067102827
15.12 0.3105762328
16.12 -0.1545665271
17.12 -0.1357701144
18.12 -0.1560442513
19.12 0.1686512228
20.12 -0.0557455833
22.12 -0.0300239764
23.12 -0.2956564473
2.13 -0.1151835036
3.13 -0.1209975707
4.13 -0.0437839811
5.13 0.0311757059
7.13 -0.1479204439
8.13 -0.1885976924
10.13 0.0002560870
11.13 0.1223058508
13.13 0.1814346042
14.13 0.0623371412
15.13 -0.1344108837
16.13 -0.1892876018
17.13 -0.1252749796
18.13 0.0130002815
19.13 0.0629294158
20.13 0.0044765775
22.13 0.0335567673
23.13 -0.2240466361
2.14 -0.0203599552
3.14 0.0960840720
4.14 0.0910918656
5.14 -0.0851538451
7.14 0.0231127288
8.14 0.6918241626
10.14 -0.0209188686
11.14 -0.1574188726
13.14 -0.1311054302
14.14 0.0289061586
15.14 -0.0867019324
16.14 -0.2974232891
17.14 0.1677152177
18.14 -0.1887479851
19.14 0.0271447194
20.14 0.0087948383
22.14 -0.0257902388
23.14 -0.0327255969
2.15 0.0063751128
3.15 0.0041680797
4.15 -0.1194975950
5.15 0.0460077108
7.15 -0.0557011872
8.15 -0.1379493191
10.15 -0.0160425740
11.15 0.2755474175
13.15 0.0885055897
15.15 -0.0457011774
16.15 -0.0557205700
17.15 0.1473444175
18.15 -0.0007365889
19.15 -0.0949459267
20.15 -0.1433872983
22.15 0.0956793810
23.15 -0.2201746714
2.16 -0.0003737360
3.16 0.1912772321
4.16 -0.2110072801
5.16 0.0822034851
7.16 0.0375568864
8.16 0.0415084613
10.16 -0.0469651419
11.16 0.2300067302
14.16 -0.0616040021
15.16 0.0391632906
16.16 -0.1301284512
17.16 -0.1447414667
18.16 0.0523862419
19.16 -0.0137645494
20.16 0.0339017673
22.16 0.3019489534
23.16 -0.2326740299
2.17 -0.1301480271
3.17 -0.0366149046
4.17 -0.1335634668
5.17 0.0310518944
7.17 -0.1642759466
8.17 0.3337771873
10.17 -0.2229658530
11.17 0.1882153487
13.17 0.1723308983
14.17 0.0406820350
15.17 0.1019617989
16.17 -0.1315924992
17.17 -0.2756534507
18.17 0.0983028342
19.17 -0.0920070083
20.17 -0.0550176120
22.17 -0.0828532353
23.17 -0.3136316983
2.18 -0.1037063124
3.18 -0.0292733094
4.18 0.1017976150
5.18 0.0156143433
7.18 -0.0406031661
8.18 0.2679085186
10.18 0.3677631800
11.18 0.0333662371
13.18 0.0302669020
14.18 -0.1732442528
15.18 0.1028400581
16.18 -0.0508684129
17.18 -0.0256879974
18.18 0.2495012677
19.18 -0.0736800887
20.18 0.2164398997
22.18 0.0303298150
23.18 -0.1302484116
2.19 -0.1511778797
3.19 -0.1805912039
4.19 -0.0155788168
5.19 0.2110464710
7.19 -0.0919163192
8.19 -0.1729355545
10.19 0.3312224229
11.19 -0.0043789697
13.19 0.0225693434
14.19 -0.0025537323
15.19 -0.0496657334
16.19 0.1034925686
17.19 -0.0102467622
18.19 -0.0914515951
19.19 -0.0283692472
20.19 -0.0159595087
22.19 0.1546261846
23.19 -0.1288538439
2.20 -0.2112424998
3.20 -0.0773830331
4.20 0.0374319166
5.20 -0.0804305884
7.20 -0.0378892006
8.20 -0.2239184492
10.20 0.2175790728
11.20 -0.0852915991
13.20 -0.0058112035
14.20 -0.0854498370
15.20 -0.0635859124
16.20 -0.2020140452
17.20 -0.2378836905
18.20 0.0861044958
19.20 -0.0057762010
20.20 0.1130087897
22.20 0.0143318095
23.20 -0.2886610391
2.21 -0.0976850414
3.21 0.1527323828
4.21 -0.0342732605
5.21 -0.0607554169
7.21 -0.0973454390
8.21 -0.1741701337
10.21 0.1260175668
11.21 0.0637147946
13.21 0.0805426600
14.21 -0.1157832649
15.21 -0.0830387222
16.21 0.0402089757
17.21 0.0610873113
18.21 0.0963429418
19.21 -0.1202715868
20.21 0.0168732227
22.21 0.1829210048
23.21 -0.2412368675
2.22 -0.0576850938
3.22 -0.1960398411
4.22 -0.0742299515
5.22 0.0602505008
7.22 -0.1008239036
8.22 0.3139380045
10.22 0.0350771597
11.22 -0.0937591266
13.22 0.2011677825
14.22 0.0054885333
15.22 -0.1456760405
16.22 -0.0222131485
17.22 0.0101793599
18.22 -0.0054592552
19.22 -0.0508516743
20.22 -0.1616601975
22.22 -0.0654368721
23.22 -0.2918126191
2.23 0.1410750774
3.23 -0.0410636442
4.23 0.2026396811
5.23 -0.0693298588
7.23 -0.0149751432
8.23 -0.0822648832
10.23 0.0486076933
11.23 -0.0206984407
13.23 0.0359875725
14.23 0.0065478675
15.23 -0.0108467440
16.23 -0.1130560417
17.23 0.0385978135
18.23 -0.2252677030
19.23 -0.1228816556
20.23 0.0426691069
22.23 0.1261342941
2.24 -0.0278314833
3.24 -0.0467515503
4.24 0.0233119214
5.24 -0.1107239136
7.24 -0.1353622656
8.24 -0.2160625777
10.24 -0.2040032363
11.24 -0.0824354920
13.24 0.0083048187
14.24 -0.1849951462
15.24 -0.1390597301
16.24 0.0102292457
17.24 0.1971954344
18.24 -0.0414593134
19.24 -0.0551458318
20.24 0.0019146449
22.24 0.0229104493
23.24 -0.1918364365
2.25 -0.0823998281
3.25 -0.1336043156
4.25 -0.1431353745
5.25 -0.0589429056
7.25 -0.1062849394
8.25 0.3262652601
10.25 0.6250634490
11.25 -0.0896620142
13.25 0.1406761568
14.25 0.1983416805
16.25 -0.0216779869
17.25 -0.0377891454
18.25 0.1475530622
19.25 -0.0479756828
20.25 -0.0111631856
22.25 0.0419065801
23.25 0.0739234805
2.26 0.0471966557
3.26 -0.1521908818
4.26 -0.2165157632
5.26 0.1148602087
7.26 -0.0484644499
8.26 0.2959632150
10.26 0.8441427054
11.26 0.4335242982
13.26 0.1308119546
14.26 -0.1721897744
15.26 0.0507882599
16.26 -0.0498147392
17.26 -0.2042839788
18.26 0.1991443408
19.26 -0.0874000129
20.26 -0.0742691990
22.26 0.1055926883
23.26 -0.1341190439
2.27 0.5323078866
3.27 -0.1063330975
4.27 -0.1416240448
5.27 0.0289590911
7.27 -0.1575870553
8.27 0.9945566306
10.27 0.4161643348
11.27 0.0613093848
13.27 0.2732536802
14.27 0.0797781101
15.27 -0.1496023042
16.27 0.2212674575
17.27 0.0526912514
18.27 0.1843516587
19.27 -0.0588710557
20.27 -0.0923729398
22.27 0.0639389001
23.27 -0.2515357233
2.28 0.1614678273
3.28 -0.1179814797
4.28 -0.1955740889
5.28 0.0761507119
7.28 -0.1513483036
8.28 0.8512699326
10.28 0.0687700055
11.28 -0.0051964668
13.28 0.3840756723
14.28 -0.1231208096
15.28 -0.0158820621
16.28 -0.3027994257
17.28 -0.0695089775
18.28 0.0650231070
19.28 0.0132611489
20.28 0.0556312627
22.28 -0.0733985814
23.28 0.2719710876
2.29 -0.0778528232
3.29 0.0677995611
4.29 0.0786967970
5.29 -0.0076918754
7.29 -0.0522724382
8.29 -0.1705263389
10.29 0.4231125580
11.29 0.0180383372
13.29 0.5008833472
14.29 -0.1541892114
15.29 0.0009229771
16.29 -0.0566060814
17.29 -0.0413715989
18.29 -0.1253940245
19.29 -0.0538519143
20.29 0.1410954266
22.29 0.0239063137
23.29 -0.2552417591
2.30 -0.0759182281
3.30 0.1700672399
4.30 -0.1025226338
5.30 0.0225175153
7.30 -0.0901169003
8.30 0.2027638623
10.30 0.7089878950
11.30 0.3601873723
13.30 0.2229687075
14.30 -0.1970158112
15.30 -0.0172549397
16.30 -0.2149391051
17.30 -0.0029304475
18.30 0.1462534800
19.30 0.0931051005
20.30 -0.0898313506
22.30 0.0491402599
23.30 -0.2970084781
2.31 -0.1644345850
3.31 0.0509615699
4.31 -0.1080977972
5.31 -0.0290752635
7.31 0.0656033437
8.31 0.1242582409
10.31 0.5077332071
11.31 0.6991337768
13.31 0.0554358518
14.31 0.2641731994
15.31 0.2439097928
16.31 -0.0222610282
17.31 0.0975423527
18.31 0.0468172247
19.31 0.0831106517
20.31 0.1584441341
23.31 0.2063342452
2.32 0.3489206762
3.32 -0.0131879321
4.32 0.1114590390
7.32 0.2922315752
8.32 0.5046071158
10.32 0.3677141558
11.32 0.5704497502
13.32 0.2427721636
14.32 -0.1511533808
15.32 0.5785939805
16.32 -0.1679766714
17.32 0.1744767570
18.32 -0.0220605075
19.32 -0.1315912154
20.32 0.1654293534
22.32 0.0116965445
23.32 0.8193020817
2.33 -0.0966732362
3.33 -0.0771846681
4.33 -0.0987481842
5.33 -0.0373093935
7.33 0.0286406474
8.33 -0.5661339843
10.33 0.2504122098
11.33 0.0428135828
13.33 0.0437299910
14.33 -0.1770081489
15.33 0.0473875817
17.33 -0.0412340421
18.33 -0.0167718130
19.33 -0.0336313983
20.33 0.0494670705
22.33 0.2945296911
23.33 0.5122443831
2.34 0.0829995877
3.34 0.0997912748
4.34 0.5934241385
5.34 -0.0592825165
7.34 0.0764685765
8.34 -0.1321968237
11.34 0.3396566484
13.34 0.5095183044
14.34 -0.2302481766
15.34 0.0966487170
17.34 0.0045699359
18.34 -0.0515410718
19.34 -0.1324638706
20.34 0.3989642636
22.34 0.1100932355
23.34 -0.0707036740
2.35 0.3602534129
4.35 -0.1752053289
5.35 0.0228314793
7.35 0.4402027679
8.35 -0.3524292641
10.35 -0.0326081799
14.35 0.1185600836
15.35 -0.0189781979
16.35 0.3773627415
17.35 0.3315076141
18.35 -0.2296446189
19.35 0.1660264273
20.35 0.1963913850
22.35 -0.0274865539
23.35 -0.0319872617
2.36 0.1920197561
3.36 0.1704905313
4.36 0.0264107436
5.36 -0.1176554092
7.36 0.3610949528
10.36 -0.1276531869
11.36 0.0020609078
13.36 0.2305302005
14.36 0.3948838256
15.36 0.2585293938
16.36 0.3088437715
17.36 0.4507949346
18.36 0.2155061494
19.36 0.0230538042
20.36 0.0344413371
22.36 0.0672013615
23.36 0.4479029564
2.37 0.0901913670
3.37 0.1674633792
4.37 0.0022208608
5.37 -0.0801714302
7.37 0.2925129733
8.37 0.3897971986
10.37 0.9590308511
11.37 -0.0408512240
13.37 -0.3725139343
14.37 0.3019890968
15.37 -0.1363658724
16.37 0.4770474869
17.37 0.0538819323
18.37 0.0405513997
19.37 0.0016929439
20.37 -0.1022769311
22.37 0.0628552262
23.37 0.0925325592
2.38 0.1918978301
3.38 -0.1321865515
4.38 0.0457242846
5.38 -0.1539691862
7.38 0.2382693079
8.38 -0.2132451235
10.38 0.1931568821
11.38 -0.1826849897
13.38 -0.3756008876
14.38 0.4390029104
15.38 -0.0469490717
16.38 0.1041285129
17.38 0.2925683616
18.38 -0.0975381794
19.38 0.0232111902
20.38 -0.1386526695
22.38 0.0092812821
23.38 0.0632652813
2.39 0.5441685177
3.39 -0.0508783166
4.39 -0.0562538621
5.39 -0.0254773784
7.39 0.3050904652
8.39 -0.4971541861
10.39 -0.1378591468
11.39 -0.3030093016
13.39 -0.3448337533
14.39 0.2623875096
15.39 0.2622089103
16.39 0.4759801900
18.39 -0.1271625671
19.39 -0.0132703014
20.39 0.0408789161
22.39 -0.1637576954
23.39 0.2206996323
2.40 0.3317437318
3.40 -0.0597274419
4.40 0.1643182132
5.40 0.0322730287
7.40 0.1851690091
8.40 0.5089283698
10.40 0.4944780286
11.40 -0.2886917732
13.40 -0.3957844116
14.40 0.9285488867
15.40 0.0016681969
16.40 0.2741291799
17.40 0.0859602575
18.40 -0.1070303929
19.40 -0.0512234538
20.40 -0.1435299232
22.40 0.0473867353
23.40 0.0507806998
2.41 0.2097522978
3.41 -0.1632016580
4.41 0.1756895648
5.41 -0.0265689808
7.41 -0.0266482081
8.41 -0.4463228273
10.41 -0.9044844344
11.41 -0.1173771103
13.41 -0.3526169989
14.41 -0.0873656107
15.41 -0.1758388859
16.41 0.4762773414
17.41 0.1141825803
18.41 -0.0336468777
19.41 0.0456553556
20.41 -0.0557611128
22.41 0.0055623956
23.41 0.4379824698
2.42 0.0576564642
3.42 0.0344347401
5.42 0.2689997260
7.42 -0.0472684675
8.42 -0.4468642971
10.42 -0.3100715431
11.42 -0.0811508534
13.42 -0.3953257775
14.42 0.4916393207
15.42 -0.2993913386
16.42 0.0719362510
17.42 -0.0988151143
18.42 0.2664421486
19.42 0.0194627905
20.42 -0.0508372051
22.42 -0.1482968915
23.42 0.0603029288
2.43 -0.2261178969
3.43 -0.1719881047
4.43 -0.0170695199
5.43 -0.0541694634
7.43 0.0964718726
8.43 -0.5662581044
10.43 -0.7591768861
11.43 -0.2780332393
13.43 -0.3755389933
14.43 0.1436064618
15.43 0.1427844723
16.43 -0.0476659754
17.43 -0.1116666990
18.43 -0.2763789528
19.43 -0.0262389826
20.43 -0.0144100736
22.43 -0.4388463063
23.43 -0.1234602412
2.44 -0.4827147831
3.44 0.0585357354
4.44 0.1634692837
5.44 -0.0257647134
7.44 0.0869232582
8.44 0.1248754283
10.44 -0.1295203851
11.44 -0.0201925572
13.44 -0.3287565867
14.44 0.0243181227
15.44 -0.0036080127
16.44 0.1729694392
17.44 0.0600146132
18.44 -0.1234134816
19.44 0.2641321281
22.44 -0.4063021513
23.44 -0.2917202597
3.45 0.5895450761
4.45 -0.2585959081
5.45 0.0831462826
7.45 -0.1206872156
8.45 2.0049221511
10.45 -0.3949500404
11.45 0.0347212801
13.45 -0.2275523567
14.45 -0.0775460860
15.45 0.1062737466
16.45 -0.0509262869
17.45 -0.0960778577
18.45 0.7637856190
19.45 0.0611890240
20.45 0.0324237918
22.45 -0.4277943933
23.45 -0.4687997135
2.46 -0.3134914776
3.46 -0.2737707418
4.46 0.0763301326
5.46 -0.0749818840
7.46 -0.1838118383
8.46 -0.7662696465
10.46 -0.6751466834
11.46 -0.3823024124
13.46 -0.2267020881
14.46 -0.2383073582
15.46 -0.1963539787
16.46 -0.0917028520
17.46 -0.2260658406
19.46 -0.1158977053
20.46 -0.2764588433
22.46 0.1147560377
23.46 -0.4942580884
2.47 -0.1381996483
3.47 -0.3228608108
4.47 -0.0927636783
5.47 -0.1145088365
7.47 -0.1791608478
8.47 -0.4814516153
10.47 -0.9303899886
11.47 -0.4109476767
13.47 -0.3287479939
14.47 -0.4689249882
15.47 -0.2535117931
16.47 -0.3951473835
17.47 -0.0733716442
18.47 -0.1482720172
19.47 -0.0246605133
20.47 -0.1948755161
2.48 -0.5110505563
$subject
(Intercept)
2 0.046131737
3 -0.037071688
4 -0.126733339
5 -0.242763078
7 -0.219828053
8 0.367574818
10 0.521104724
11 0.052050762
13 -0.123777722
14 -0.025859213
15 0.191145065
16 -0.077844035
17 -0.109361149
18 0.019577516
19 -0.190519569
20 -0.126541744
22 0.001417471
23 0.081297494
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.1472849 -0.5075371 -0.5098736 -0.6479743 0.8708004 0.9327042
=============================================================
--- Mixed - Block 5 - Axis Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 15.67 0.92174 17 8799 12.146 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.012113 0.0157 8806 -0.773 1.0000
Step1 - Step3 -0.005411 0.0201 8797 -0.269 1.0000
Step1 - Step4 0.020049 0.0137 8759 1.461 0.9931
Step1 - Step5 0.020367 0.0157 8806 1.300 0.9982
Step1 - Step6 0.036533 0.0201 8797 1.818 0.9394
Step1 - Step7 0.045993 0.0157 8806 2.936 0.2327
Step1 - Step8 0.044658 0.0155 8783 2.875 0.2672
Step1 - Step9 0.063012 0.0157 8806 4.022 0.0073
Step1 - Step10 0.066358 0.0157 8806 4.236 0.0031
Step1 - Step11 0.047716 0.0155 8783 3.071 0.1676
Step1 - Step12 0.066172 0.0157 8806 4.224 0.0032
Step1 - Step13 0.064536 0.0201 8797 3.211 0.1156
Step1 - Step14 0.088290 0.0157 8806 5.636 <.0001
Step1 - Step15 0.093962 0.0137 8759 6.847 <.0001
Step1 - Step16 0.126133 0.0201 8797 6.276 <.0001
Step1 - Step17 0.112662 0.0157 8806 7.192 <.0001
Step1 - Step18 0.115790 0.0137 8759 8.438 <.0001
Step2 - Step3 0.006701 0.0211 8776 0.317 1.0000
Step2 - Step4 0.032162 0.0157 8806 2.053 0.8428
Step2 - Step5 0.032480 0.0169 8759 1.921 0.9041
Step2 - Step6 0.048646 0.0211 8776 2.302 0.6817
Step2 - Step7 0.058106 0.0169 8759 3.437 0.0592
Step2 - Step8 0.056770 0.0175 8858 3.236 0.1078
Step2 - Step9 0.075124 0.0169 8759 4.443 0.0012
Step2 - Step10 0.078471 0.0169 8759 4.641 0.0005
Step2 - Step11 0.059828 0.0175 8858 3.410 0.0643
Step2 - Step12 0.078285 0.0169 8759 4.630 0.0005
Step2 - Step13 0.076648 0.0211 8776 3.628 0.0316
Step2 - Step14 0.100402 0.0169 8759 5.939 <.0001
Step2 - Step15 0.106075 0.0157 8806 6.771 <.0001
Step2 - Step16 0.138246 0.0211 8776 6.543 <.0001
Step2 - Step17 0.124775 0.0169 8759 7.380 <.0001
Step2 - Step18 0.127902 0.0157 8806 8.165 <.0001
Step3 - Step4 0.025461 0.0201 8797 1.267 0.9987
Step3 - Step5 0.025779 0.0211 8776 1.220 0.9992
Step3 - Step6 0.041944 0.0242 8759 1.736 0.9598
Step3 - Step7 0.051404 0.0211 8776 2.433 0.5824
Step3 - Step8 0.050069 0.0213 8814 2.349 0.6466
Step3 - Step9 0.068423 0.0211 8776 3.238 0.1070
Step3 - Step10 0.071770 0.0211 8776 3.397 0.0670
Step3 - Step11 0.053127 0.0213 8814 2.493 0.5358
Step3 - Step12 0.071584 0.0211 8776 3.388 0.0688
Step3 - Step13 0.069947 0.0242 8759 2.895 0.2553
Step3 - Step14 0.093701 0.0211 8776 4.435 0.0013
Step3 - Step15 0.099374 0.0201 8797 4.944 0.0001
Step3 - Step16 0.131545 0.0242 8759 5.445 <.0001
Step3 - Step17 0.118073 0.0211 8776 5.588 <.0001
Step3 - Step18 0.121201 0.0201 8797 6.030 <.0001
Step4 - Step5 0.000318 0.0157 8806 0.020 1.0000
Step4 - Step6 0.016484 0.0201 8797 0.820 1.0000
Step4 - Step7 0.025943 0.0157 8806 1.656 0.9743
Step4 - Step8 0.024608 0.0155 8783 1.584 0.9836
Step4 - Step9 0.042962 0.0157 8806 2.743 0.3508
Step4 - Step10 0.046309 0.0157 8806 2.956 0.2220
Step4 - Step11 0.027666 0.0155 8783 1.781 0.9493
Step4 - Step12 0.046123 0.0157 8806 2.944 0.2283
Step4 - Step13 0.044486 0.0201 8797 2.213 0.7450
Step4 - Step14 0.068240 0.0157 8806 4.356 0.0018
Step4 - Step15 0.073913 0.0137 8759 5.386 <.0001
Step4 - Step16 0.106084 0.0201 8797 5.278 <.0001
Step4 - Step17 0.092613 0.0157 8806 5.912 <.0001
Step4 - Step18 0.095740 0.0137 8759 6.977 <.0001
Step5 - Step6 0.016166 0.0211 8776 0.765 1.0000
Step5 - Step7 0.025626 0.0169 8759 1.516 0.9897
Step5 - Step8 0.024291 0.0175 8858 1.384 0.9963
Step5 - Step9 0.042644 0.0169 8759 2.522 0.5130
Step5 - Step10 0.045991 0.0169 8759 2.720 0.3661
Step5 - Step11 0.027348 0.0175 8858 1.559 0.9861
Step5 - Step12 0.045805 0.0169 8759 2.709 0.3738
Step5 - Step13 0.044168 0.0211 8776 2.091 0.8221
Step5 - Step14 0.067922 0.0169 8759 4.018 0.0075
Step5 - Step15 0.073595 0.0157 8806 4.698 0.0004
Step5 - Step16 0.105766 0.0211 8776 5.006 0.0001
Step5 - Step17 0.092295 0.0169 8759 5.459 <.0001
Step5 - Step18 0.095422 0.0157 8806 6.091 <.0001
Step6 - Step7 0.009460 0.0211 8776 0.448 1.0000
Step6 - Step8 0.008125 0.0213 8814 0.381 1.0000
Step6 - Step9 0.026478 0.0211 8776 1.253 0.9989
Step6 - Step10 0.029825 0.0211 8776 1.412 0.9953
Step6 - Step11 0.011183 0.0213 8814 0.525 1.0000
Step6 - Step12 0.029639 0.0211 8776 1.403 0.9956
Step6 - Step13 0.028003 0.0242 8759 1.159 0.9996
Step6 - Step14 0.051757 0.0211 8776 2.450 0.5695
Step6 - Step15 0.057429 0.0201 8797 2.857 0.2774
Step6 - Step16 0.089600 0.0242 8759 3.708 0.0238
Step6 - Step17 0.076129 0.0211 8776 3.603 0.0344
Step6 - Step18 0.079256 0.0201 8797 3.943 0.0100
Step7 - Step8 -0.001335 0.0175 8858 -0.076 1.0000
Step7 - Step9 0.017019 0.0169 8759 1.007 0.9999
Step7 - Step10 0.020365 0.0169 8759 1.205 0.9993
Step7 - Step11 0.001723 0.0175 8858 0.098 1.0000
Step7 - Step12 0.020179 0.0169 8759 1.194 0.9994
Step7 - Step13 0.018543 0.0211 8776 0.878 1.0000
Step7 - Step14 0.042297 0.0169 8759 2.502 0.5289
Step7 - Step15 0.047969 0.0157 8806 3.062 0.1716
Step7 - Step16 0.080140 0.0211 8776 3.793 0.0176
Step7 - Step17 0.066669 0.0169 8759 3.943 0.0100
Step7 - Step18 0.069797 0.0157 8806 4.456 0.0012
Step8 - Step9 0.018354 0.0175 8858 1.046 0.9999
Step8 - Step10 0.021701 0.0175 8858 1.237 0.9991
Step8 - Step11 0.003058 0.0168 8759 0.182 1.0000
Step8 - Step12 0.021514 0.0175 8858 1.226 0.9991
Step8 - Step13 0.019878 0.0213 8814 0.933 1.0000
Step8 - Step14 0.043632 0.0175 8858 2.487 0.5405
Step8 - Step15 0.049304 0.0155 8783 3.174 0.1281
Step8 - Step16 0.081475 0.0213 8814 3.823 0.0157
Step8 - Step17 0.068004 0.0175 8858 3.876 0.0129
Step8 - Step18 0.071132 0.0155 8783 4.579 0.0007
Step9 - Step10 0.003347 0.0169 8759 0.198 1.0000
Step9 - Step11 -0.015296 0.0175 8858 -0.872 1.0000
Step9 - Step12 0.003161 0.0169 8759 0.187 1.0000
Step9 - Step13 0.001524 0.0211 8776 0.072 1.0000
Step9 - Step14 0.025278 0.0169 8759 1.495 0.9911
Step9 - Step15 0.030951 0.0157 8806 1.976 0.8810
Step9 - Step16 0.063122 0.0211 8776 2.988 0.2062
Step9 - Step17 0.049651 0.0169 8759 2.937 0.2323
Step9 - Step18 0.052778 0.0157 8806 3.369 0.0729
Step10 - Step11 -0.018643 0.0175 8858 -1.063 0.9999
Step10 - Step12 -0.000186 0.0169 8759 -0.011 1.0000
Step10 - Step13 -0.001823 0.0211 8776 -0.086 1.0000
Step10 - Step14 0.021931 0.0169 8759 1.297 0.9983
Step10 - Step15 0.027604 0.0157 8806 1.762 0.9539
Step10 - Step16 0.059775 0.0211 8776 2.829 0.2945
Step10 - Step17 0.046304 0.0169 8759 2.739 0.3534
Step10 - Step18 0.049431 0.0157 8806 3.156 0.1345
Step11 - Step12 0.018457 0.0175 8858 1.052 0.9999
Step11 - Step13 0.016820 0.0213 8814 0.789 1.0000
Step11 - Step14 0.040574 0.0175 8858 2.313 0.6743
Step11 - Step15 0.046247 0.0155 8783 2.977 0.2115
Step11 - Step16 0.078418 0.0213 8814 3.680 0.0264
Step11 - Step17 0.064946 0.0175 8858 3.702 0.0244
Step11 - Step18 0.068074 0.0155 8783 4.382 0.0016
Step12 - Step13 -0.001636 0.0211 8776 -0.077 1.0000
Step12 - Step14 0.022117 0.0169 8759 1.308 0.9981
Step12 - Step15 0.027790 0.0157 8806 1.774 0.9510
Step12 - Step16 0.059961 0.0211 8776 2.838 0.2890
Step12 - Step17 0.046490 0.0169 8759 2.750 0.3459
Step12 - Step18 0.049617 0.0157 8806 3.167 0.1303
Step13 - Step14 0.023754 0.0211 8776 1.124 0.9997
Step13 - Step15 0.029427 0.0201 8797 1.464 0.9929
Step13 - Step16 0.061598 0.0242 8759 2.549 0.4920
Step13 - Step17 0.048126 0.0211 8776 2.278 0.6997
Step13 - Step18 0.051254 0.0201 8797 2.550 0.4916
Step14 - Step15 0.005673 0.0157 8806 0.362 1.0000
Step14 - Step16 0.037844 0.0211 8776 1.791 0.9467
Step14 - Step17 0.024372 0.0169 8759 1.442 0.9941
Step14 - Step18 0.027500 0.0157 8806 1.756 0.9554
Step15 - Step16 0.032171 0.0201 8797 1.601 0.9817
Step15 - Step17 0.018700 0.0157 8806 1.194 0.9994
Step15 - Step18 0.021827 0.0137 8759 1.591 0.9828
Step16 - Step17 -0.013471 0.0211 8776 -0.638 1.0000
Step16 - Step18 -0.010344 0.0201 8797 -0.515 1.0000
Step17 - Step18 0.003127 0.0157 8806 0.200 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5 Step6
0.669178115 0.012112588 0.005411322 -0.020049485 -0.020367345 -0.036533120
Step7 Step8 Step9 Step10 Step11 Step12
-0.045992948 -0.044657892 -0.063011578 -0.066358420 -0.047715769 -0.066172302
Step13 Step14 Step15 Step16 Step17 Step18
-0.064535811 -0.088289712 -0.093962368 -0.126133367 -0.112662134 -0.115789547
Random Effects:
$trial_id
(Intercept)
3.1 -0.109095693
4.1 0.089141669
7.1 0.125556156
8.1 -0.034731156
10.1 0.214904331
11.1 -0.124235229
13.1 -0.157681239
15.1 -0.131875575
18.1 0.190137426
19.1 -0.092284707
20.1 -0.013661483
22.1 -0.115892154
2.2 -0.141654023
3.2 -0.242445707
4.2 0.135094890
7.2 -0.185841882
11.2 -0.023479398
13.2 0.060392382
14.2 -0.190198190
15.2 -0.011408420
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3.44 -0.249168923
4.44 -0.001531582
5.44 -0.071600937
7.44 0.253504534
8.44 -0.169027098
10.44 -0.524063690
11.44 0.045857405
13.44 -0.376086784
14.44 0.050987635
15.44 0.229974926
16.44 -0.308320971
17.44 -0.070310158
18.44 -0.338305189
19.44 0.244609181
22.44 -0.126602500
23.44 -0.442038619
3.45 0.213804974
4.45 0.397053540
5.45 -0.001485982
7.45 -0.169541905
8.45 -0.027519961
10.45 0.120645793
11.45 0.350362408
13.45 -0.170583709
14.45 -0.276378171
15.45 0.001345022
16.45 -0.248230032
17.45 0.193388311
18.45 -0.080477891
19.45 -0.031660760
20.45 -0.061154620
22.45 -0.233343823
23.45 -0.627286031
2.46 -0.553577669
3.46 -0.619667524
4.46 0.085749365
5.46 -0.051207515
7.46 -0.308530609
8.46 -0.783626686
10.46 -0.618543154
11.46 -0.323218969
13.46 -0.283475124
14.46 -0.262621189
15.46 -0.043605431
16.46 -0.489992452
17.46 -0.343012756
19.46 0.241846944
20.46 -0.142105995
22.46 -0.293376992
23.46 -0.713672787
2.47 -0.448562609
3.47 -0.635122108
4.47 -0.171239616
5.47 -0.010584754
7.47 -0.294301570
8.47 -0.308503276
10.47 -0.838820160
11.47 -0.253284897
13.47 -0.302765369
14.47 -0.457856926
15.47 0.130241903
16.47 -0.650184604
17.47 -0.312001443
18.47 -0.163635914
19.47 -0.098767469
20.47 -0.125720976
2.48 -0.919748827
$subject
(Intercept)
2 0.41969076
3 0.18564384
4 -0.14116067
5 -0.26141832
7 -0.07803407
8 0.30081720
10 0.34591740
11 -0.13040688
13 -0.15447948
14 -0.04910504
15 0.00702148
16 0.13207982
17 -0.05452659
18 -0.02347743
19 -0.23101781
20 -0.25311661
22 -0.20743358
23 0.19300599
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.3457690 -0.6940378 -0.4944567 -0.4220265 -0.3277490 0.2953130
=============================================================
--- Mixed - Block 5 - Axis Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 56.095 3.2997 17 8781.5 19.843 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Step1 - Step2 -0.01630 0.0232 8807 -0.703 1.0000
Step1 - Step3 -0.01774 0.0298 8798 -0.596 1.0000
Step1 - Step4 0.02164 0.0203 8759 1.065 0.9999
Step1 - Step5 0.01017 0.0232 8807 0.439 1.0000
Step1 - Step6 0.06255 0.0298 8798 2.102 0.8152
Step1 - Step7 0.06040 0.0232 8807 2.605 0.4497
Step1 - Step8 0.10560 0.0230 8784 4.592 0.0006
Step1 - Step9 0.11656 0.0232 8807 5.027 0.0001
Step1 - Step10 0.12949 0.0232 8807 5.584 <.0001
Step1 - Step11 0.12334 0.0230 8784 5.363 <.0001
Step1 - Step12 0.13500 0.0232 8807 5.822 <.0001
Step1 - Step13 0.12182 0.0298 8798 4.095 0.0055
Step1 - Step14 0.15977 0.0232 8807 6.890 <.0001
Step1 - Step15 0.16317 0.0203 8759 8.032 <.0001
Step1 - Step16 0.17180 0.0298 8798 5.774 <.0001
Step1 - Step17 0.20635 0.0232 8807 8.899 <.0001
Step1 - Step18 0.22575 0.0203 8759 11.114 <.0001
Step2 - Step3 -0.00144 0.0313 8777 -0.046 1.0000
Step2 - Step4 0.03794 0.0232 8807 1.636 0.9772
Step2 - Step5 0.02647 0.0250 8759 1.058 0.9999
Step2 - Step6 0.07885 0.0313 8777 2.521 0.5138
Step2 - Step7 0.07671 0.0250 8759 3.065 0.1703
Step2 - Step8 0.12190 0.0260 8861 4.694 0.0004
Step2 - Step9 0.13287 0.0250 8759 5.309 <.0001
Step2 - Step10 0.14579 0.0250 8759 5.825 <.0001
Step2 - Step11 0.13964 0.0260 8861 5.377 <.0001
Step2 - Step12 0.15130 0.0250 8759 6.046 <.0001
Step2 - Step13 0.13813 0.0313 8777 4.416 0.0014
Step2 - Step14 0.17607 0.0250 8759 7.036 <.0001
Step2 - Step15 0.17947 0.0232 8807 7.740 <.0001
Step2 - Step16 0.18810 0.0313 8777 6.014 <.0001
Step2 - Step17 0.22265 0.0250 8759 8.896 <.0001
Step2 - Step18 0.24206 0.0232 8807 10.439 <.0001
Step3 - Step4 0.03938 0.0298 8798 1.324 0.9978
Step3 - Step5 0.02791 0.0313 8777 0.892 1.0000
Step3 - Step6 0.08029 0.0358 8759 2.245 0.7231
Step3 - Step7 0.07815 0.0313 8777 2.499 0.5313
Step3 - Step8 0.12334 0.0315 8816 3.910 0.0114
Step3 - Step9 0.13431 0.0313 8777 4.294 0.0024
Step3 - Step10 0.14723 0.0313 8777 4.707 0.0004
Step3 - Step11 0.14108 0.0315 8816 4.472 0.0011
Step3 - Step12 0.15274 0.0313 8777 4.884 0.0002
Step3 - Step13 0.13956 0.0358 8759 3.902 0.0117
Step3 - Step14 0.17751 0.0313 8777 5.676 <.0001
Step3 - Step15 0.18091 0.0298 8798 6.081 <.0001
Step3 - Step16 0.18954 0.0358 8759 5.300 <.0001
Step3 - Step17 0.22409 0.0313 8777 7.165 <.0001
Step3 - Step18 0.24349 0.0298 8798 8.184 <.0001
Step4 - Step5 -0.01147 0.0232 8807 -0.495 1.0000
Step4 - Step6 0.04091 0.0298 8798 1.375 0.9965
Step4 - Step7 0.03876 0.0232 8807 1.672 0.9718
Step4 - Step8 0.08395 0.0230 8784 3.651 0.0292
Step4 - Step9 0.09492 0.0232 8807 4.094 0.0055
Step4 - Step10 0.10784 0.0232 8807 4.651 0.0005
Step4 - Step11 0.10169 0.0230 8784 4.422 0.0014
Step4 - Step12 0.11335 0.0232 8807 4.889 0.0002
Step4 - Step13 0.10018 0.0298 8798 3.367 0.0734
Step4 - Step14 0.13813 0.0232 8807 5.957 <.0001
Step4 - Step15 0.14152 0.0203 8759 6.967 <.0001
Step4 - Step16 0.15016 0.0298 8798 5.047 0.0001
Step4 - Step17 0.18470 0.0232 8807 7.966 <.0001
Step4 - Step18 0.20411 0.0203 8759 10.048 <.0001
Step5 - Step6 0.05238 0.0313 8777 1.675 0.9713
Step5 - Step7 0.05023 0.0250 8759 2.007 0.8663
Step5 - Step8 0.09542 0.0260 8861 3.674 0.0269
Step5 - Step9 0.10639 0.0250 8759 4.251 0.0029
Step5 - Step10 0.11931 0.0250 8759 4.768 0.0003
Step5 - Step11 0.11316 0.0260 8861 4.358 0.0018
Step5 - Step12 0.12482 0.0250 8759 4.988 0.0001
Step5 - Step13 0.11165 0.0313 8777 3.570 0.0384
Step5 - Step14 0.14960 0.0250 8759 5.978 <.0001
Step5 - Step15 0.15299 0.0232 8807 6.598 <.0001
Step5 - Step16 0.16163 0.0313 8777 5.168 <.0001
Step5 - Step17 0.19617 0.0250 8759 7.839 <.0001
Step5 - Step18 0.21558 0.0232 8807 9.297 <.0001
Step6 - Step7 -0.00215 0.0313 8777 -0.069 1.0000
Step6 - Step8 0.04304 0.0315 8816 1.365 0.9968
Step6 - Step9 0.05401 0.0313 8777 1.727 0.9617
Step6 - Step10 0.06693 0.0313 8777 2.140 0.7926
Step6 - Step11 0.06078 0.0315 8816 1.927 0.9018
Step6 - Step12 0.07244 0.0313 8777 2.316 0.6714
Step6 - Step13 0.05927 0.0358 8759 1.657 0.9741
Step6 - Step14 0.09722 0.0313 8777 3.109 0.1523
Step6 - Step15 0.10061 0.0298 8798 3.382 0.0702
Step6 - Step16 0.10925 0.0358 8759 3.055 0.1749
Step6 - Step17 0.14379 0.0313 8777 4.598 0.0006
Step6 - Step18 0.16320 0.0298 8798 5.485 <.0001
Step7 - Step8 0.04519 0.0260 8861 1.740 0.9589
Step7 - Step9 0.05616 0.0250 8759 2.244 0.7238
Step7 - Step10 0.06908 0.0250 8759 2.760 0.3388
Step7 - Step11 0.06293 0.0260 8861 2.423 0.5900
Step7 - Step12 0.07459 0.0250 8759 2.981 0.2097
Step7 - Step13 0.06142 0.0313 8777 1.964 0.8864
Step7 - Step14 0.09937 0.0250 8759 3.971 0.0090
Step7 - Step15 0.10276 0.0232 8807 4.432 0.0013
Step7 - Step16 0.11140 0.0313 8777 3.562 0.0395
Step7 - Step17 0.14594 0.0250 8759 5.831 <.0001
Step7 - Step18 0.16535 0.0232 8807 7.131 <.0001
Step8 - Step9 0.01097 0.0260 8861 0.422 1.0000
Step8 - Step10 0.02389 0.0260 8861 0.920 1.0000
Step8 - Step11 0.01774 0.0249 8759 0.712 1.0000
Step8 - Step12 0.02940 0.0260 8861 1.132 0.9997
Step8 - Step13 0.01623 0.0315 8816 0.514 1.0000
Step8 - Step14 0.05418 0.0260 8861 2.086 0.8246
Step8 - Step15 0.05757 0.0230 8784 2.503 0.5277
Step8 - Step16 0.06620 0.0315 8816 2.099 0.8174
Step8 - Step17 0.10075 0.0260 8861 3.879 0.0128
Step8 - Step18 0.12016 0.0230 8784 5.225 <.0001
Step9 - Step10 0.01292 0.0250 8759 0.516 1.0000
Step9 - Step11 0.00677 0.0260 8861 0.261 1.0000
Step9 - Step12 0.01843 0.0250 8759 0.736 1.0000
Step9 - Step13 0.00526 0.0313 8777 0.168 1.0000
Step9 - Step14 0.04321 0.0250 8759 1.726 0.9618
Step9 - Step15 0.04660 0.0232 8807 2.010 0.8650
Step9 - Step16 0.05524 0.0313 8777 1.766 0.9530
Step9 - Step17 0.08978 0.0250 8759 3.587 0.0362
Step9 - Step18 0.10919 0.0232 8807 4.709 0.0004
Step10 - Step11 -0.00615 0.0260 8861 -0.237 1.0000
Step10 - Step12 0.00551 0.0250 8759 0.220 1.0000
Step10 - Step13 -0.00766 0.0313 8777 -0.245 1.0000
Step10 - Step14 0.03029 0.0250 8759 1.210 0.9993
Step10 - Step15 0.03368 0.0232 8807 1.452 0.9935
Step10 - Step16 0.04231 0.0313 8777 1.353 0.9971
Step10 - Step17 0.07686 0.0250 8759 3.071 0.1677
Step10 - Step18 0.09627 0.0232 8807 4.152 0.0043
Step11 - Step12 0.01166 0.0260 8861 0.449 1.0000
Step11 - Step13 -0.00151 0.0315 8816 -0.048 1.0000
Step11 - Step14 0.03644 0.0260 8861 1.403 0.9956
Step11 - Step15 0.03983 0.0230 8784 1.732 0.9606
Step11 - Step16 0.04846 0.0315 8816 1.536 0.9881
Step11 - Step17 0.08301 0.0260 8861 3.196 0.1204
Step11 - Step18 0.10242 0.0230 8784 4.454 0.0012
Step12 - Step13 -0.01317 0.0313 8777 -0.421 1.0000
Step12 - Step14 0.02478 0.0250 8759 0.990 1.0000
Step12 - Step15 0.02817 0.0232 8807 1.215 0.9992
Step12 - Step16 0.03680 0.0313 8777 1.177 0.9995
Step12 - Step17 0.07135 0.0250 8759 2.851 0.2811
Step12 - Step18 0.09076 0.0232 8807 3.914 0.0112
Step13 - Step14 0.03795 0.0313 8777 1.213 0.9993
Step13 - Step15 0.04134 0.0298 8798 1.390 0.9961
Step13 - Step16 0.04998 0.0358 8759 1.397 0.9958
Step13 - Step17 0.08452 0.0313 8777 2.703 0.3786
Step13 - Step18 0.10393 0.0298 8798 3.493 0.0494
Step14 - Step15 0.00339 0.0232 8807 0.146 1.0000
Step14 - Step16 0.01203 0.0313 8777 0.385 1.0000
Step14 - Step17 0.04657 0.0250 8759 1.861 0.9260
Step14 - Step18 0.06598 0.0232 8807 2.846 0.2844
Step15 - Step16 0.00863 0.0298 8798 0.290 1.0000
Step15 - Step17 0.04318 0.0232 8807 1.862 0.9256
Step15 - Step18 0.06259 0.0203 8759 3.081 0.1635
Step16 - Step17 0.03455 0.0313 8777 1.105 0.9998
Step16 - Step18 0.05395 0.0298 8798 1.813 0.9406
Step17 - Step18 0.01941 0.0232 8807 0.837 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates
Fixed Effects:
(Intercept) Step2 Step3 Step4 Step5 Step6
1.37070549 0.01630225 0.01774092 -0.02164139 -0.01017177 -0.06255194
Step7 Step8 Step9 Step10 Step11 Step12
-0.06040450 -0.10559566 -0.11656455 -0.12948579 -0.12333639 -0.13499599
Step13 Step14 Step15 Step16 Step17 Step18
-0.12182327 -0.15977194 -0.16316541 -0.17180002 -0.20634521 -0.22575395
Random Effects:
$trial_id
(Intercept)
3.1 -0.220426794
4.1 0.043893046
7.1 0.380571927
8.1 -0.288813752
10.1 0.574773484
11.1 -0.406079307
13.1 -0.434724084
15.1 -0.169374994
18.1 1.037244905
19.1 -0.200851387
20.1 -0.258285085
22.1 -0.190067587
2.2 0.195090742
3.2 -0.523368070
4.2 0.209701776
7.2 -0.029166174
11.2 -0.006445543
13.2 -0.208434284
14.2 -0.312914676
15.2 -0.263326866
16.2 0.167917019
19.2 -0.162862972
22.2 -0.091016638
23.2 3.570359491
2.3 -0.225995123
3.3 0.547790507
4.3 0.191637828
7.3 0.146296854
11.3 0.404381630
13.3 0.473492363
14.3 -0.408374651
15.3 1.097267094
16.3 1.300099508
17.3 0.107825214
18.3 0.572819980
19.3 0.143975940
22.3 -0.220418882
23.3 -0.723657540
2.4 -0.316802094
3.4 -0.132830427
4.4 -0.179530015
5.4 -0.143030303
7.4 -0.096829201
8.4 -0.718553038
10.4 0.692187160
11.4 -0.274865741
13.4 -0.095513976
14.4 -0.252338467
15.4 -0.461419154
16.4 0.155968232
17.4 -0.029363915
18.4 1.123715441
19.4 -0.067393841
20.4 -0.388608100
22.4 0.117109558
23.4 -0.687303061
2.5 -0.134228659
3.5 0.030731795
4.5 -0.110076678
5.5 -0.248702254
7.5 -0.203859830
8.5 -0.476587053
10.5 1.161525473
11.5 -0.283238245
13.5 0.293613674
14.5 -0.089966880
15.5 0.086730049
16.5 0.463472546
17.5 -0.105517082
18.5 0.727008508
19.5 -0.181606920
20.5 -0.458007249
22.5 0.228393079
23.5 -0.347576989
2.6 -0.179425390
4.6 -0.170109605
5.6 0.029677256
7.6 0.211104213
8.6 1.107369263
10.6 0.540586302
11.6 0.158592162
13.6 0.327711453
14.6 0.019215903
15.6 -0.328146394
18.6 1.173580544
19.6 -0.123151142
20.6 -0.139188567
22.6 -0.069601095
23.6 -0.274290483
2.7 -0.492368286
3.7 -0.204631885
4.7 -0.142673830
5.7 0.087676875
7.7 0.165403823
8.7 -0.486351964
10.7 0.477387259
13.7 0.792463838
14.7 -0.051906554
15.7 -0.611953050
16.7 1.165240806
17.7 -0.164744533
18.7 0.993061663
19.7 -0.240868563
20.7 0.035699662
22.7 -0.065003294
23.7 -0.371083422
2.8 -0.063850314
3.8 0.064630344
4.8 -0.144038158
5.8 -0.198173933
7.8 0.227257026
8.8 -0.561443739
10.8 0.803407899
11.8 -0.232887742
13.8 1.318765539
14.8 -0.350100603
15.8 -0.732574266
16.8 0.225278829
17.8 -0.220034999
18.8 0.659452558
19.8 -0.023111955
20.8 0.114410316
22.8 -0.216818536
23.8 -0.432781188
2.9 -0.275538767
3.9 0.282924245
4.9 -0.065876384
5.9 0.181231420
7.9 0.354168321
8.9 -0.612644770
10.9 0.784806382
11.9 0.311156123
13.9 0.251212125
14.9 -0.174590774
15.9 -0.169137761
16.9 -0.127110722
17.9 -0.209562978
18.9 0.228988647
19.9 0.113655107
20.9 0.056713924
22.9 -0.047921478
23.9 -0.274455691
2.10 0.139035622
3.10 0.144025576
4.10 -0.161633994
5.10 -0.042037300
7.10 -0.184562975
8.10 -0.523120216
10.10 0.789351087
11.10 0.364098531
13.10 0.587851850
14.10 -0.238585897
15.10 -0.367258048
16.10 0.135775531
17.10 0.103593934
18.10 0.830554617
19.10 0.167360022
20.10 0.566304993
22.10 -0.010748786
23.10 -0.532017593
2.11 -0.454464123
3.11 -0.075256972
4.11 0.254438261
5.11 -0.118074558
7.11 -0.244460134
8.11 -0.089248863
10.11 0.350166611
11.11 0.230929237
13.11 0.524881910
14.11 -0.215077957
15.11 0.096345325
16.11 -0.371178097
17.11 -0.210988445
18.11 0.555166244
19.11 -0.225248176
20.11 0.627928662
22.11 0.070004607
23.11 -0.320871839
2.12 0.146233826
3.12 0.694079579
4.12 -0.038930544
7.12 -0.120768536
8.12 0.583938385
10.12 0.235877033
11.12 -0.219435376
13.12 0.114117661
14.12 -0.437084967
15.12 0.170033458
16.12 -0.400682576
17.12 -0.038610790
18.12 0.182473159
19.12 0.177615100
20.12 0.498413039
22.12 0.152040360
23.12 -0.469763697
2.13 -0.318891772
3.13 -0.189018301
4.13 -0.086056465
5.13 -0.098275432
7.13 -0.292062076
8.13 -0.187461235
10.13 0.436955805
11.13 -0.427580856
13.13 0.172348415
14.13 0.993864072
15.13 0.481116696
16.13 -0.412774085
17.13 -0.128682270
18.13 0.456764565
19.13 -0.052439793
20.13 0.231151044
22.13 -0.067093931
23.13 -0.283477369
2.14 -0.327398359
3.14 0.217470066
4.14 -0.046814124
5.14 -0.189066748
7.14 0.756012085
8.14 0.739921683
10.14 -0.263706425
11.14 -0.113396749
13.14 0.326556610
14.14 -0.045709373
15.14 0.313566347
16.14 0.222878338
17.14 0.031824753
18.14 0.175225753
19.14 -0.134254875
20.14 0.287158747
22.14 0.207538014
23.14 -0.392960253
2.15 -0.609816246
3.15 -0.233812311
4.15 -0.377499497
5.15 -0.089026480
7.15 0.134385923
8.15 0.034558170
10.15 -0.048766325
11.15 0.346840342
13.15 0.150356257
15.15 0.233895421
16.15 0.350650968
17.15 0.080137036
18.15 0.363706010
19.15 0.224667563
20.15 -0.226692549
22.15 0.142298628
23.15 0.195179985
2.16 0.009417257
3.16 0.171941316
4.16 -0.225148410
5.16 0.495708731
7.16 0.027755661
8.16 0.480522025
10.16 -0.661683580
11.16 0.077089281
14.16 -0.059146598
15.16 1.523550219
16.16 -0.027940156
17.16 -0.150356051
18.16 0.173808683
19.16 -0.276856974
20.16 0.118386293
22.16 0.135775071
23.16 -0.495573187
2.17 -0.526472113
3.17 0.113996634
4.17 -0.480811846
5.17 0.428889096
7.17 -0.393084967
8.17 0.935340918
10.17 -0.004089431
11.17 -0.057813936
13.17 0.387541901
14.17 0.143526043
15.17 -0.080859126
16.17 -0.333733942
17.17 -0.202445482
18.17 -0.512969630
19.17 -0.173542621
20.17 0.187428098
22.17 0.157897811
23.17 -0.530313502
2.18 -0.621788760
3.18 0.024887007
4.18 0.250255072
5.18 0.138948070
7.18 -0.062937375
8.18 -0.020019328
10.18 0.484661055
11.18 0.772945886
13.18 -0.091145699
14.18 -0.301832793
15.18 1.062102953
16.18 -0.383516328
17.18 0.037924186
18.18 -0.139672231
19.18 -0.438441404
20.18 0.078693847
22.18 0.125464735
23.18 -0.333922384
2.19 -0.391800584
3.19 0.234633077
4.19 -0.184589938
5.19 0.196561154
7.19 0.007296737
8.19 -0.026149339
10.19 0.642273614
11.19 0.118480824
13.19 0.532059186
14.19 -0.244012774
15.19 1.161067921
16.19 1.266390219
17.19 -0.283464057
18.19 -0.630293825
19.19 -0.115045095
20.19 0.092722673
22.19 0.222763128
23.19 -0.031054547
2.20 -0.421270886
3.20 0.209477264
4.20 0.072230685
5.20 -0.176161413
7.20 -0.085825456
8.20 -0.123206028
10.20 1.968924624
11.20 0.215873820
13.20 0.451506936
14.20 -0.008939750
15.20 0.654757760
16.20 -0.546141615
17.20 -0.150849686
18.20 -0.361550265
19.20 -0.108432663
20.20 -0.045893315
22.20 0.064181466
23.20 -0.311638361
2.21 -0.433366734
3.21 0.115042427
4.21 -0.204549160
5.21 -0.219527447
7.21 0.332061173
8.21 -0.247794072
10.21 3.093979408
11.21 0.043334540
13.21 0.482738906
14.21 -0.268750380
15.21 -0.554870982
16.21 -0.519985844
17.21 -0.060844166
18.21 -0.261009874
19.21 0.089367196
20.21 -0.020164590
22.21 0.084529563
23.21 0.994553886
2.22 -0.580401962
3.22 -0.173357237
4.22 0.499616988
5.22 0.190082325
7.22 0.482960433
8.22 0.263529968
10.22 0.997984875
11.22 -0.194531890
13.22 0.533488030
14.22 -0.113248536
15.22 -0.035667765
16.22 -0.329052436
17.22 -0.169537659
18.22 -0.469431701
19.22 -0.284783529
20.22 -0.048746691
22.22 0.016581509
23.22 -0.435733675
2.23 -0.044581842
3.23 -0.115518869
4.23 -0.161786695
5.23 -0.108747566
7.23 0.231393910
8.23 0.106954575
10.23 1.949816560
11.23 0.068944581
13.23 0.229023606
14.23 -0.239480753
15.23 -0.359891745
16.23 -0.389157575
17.23 -0.195102131
18.23 -0.287081457
19.23 -0.021111462
20.23 -0.153470723
22.23 0.358015686
2.24 -0.314078441
3.24 -0.018170840
4.24 0.148988653
5.24 0.060262093
7.24 0.099727473
8.24 0.367992031
10.24 1.010693143
11.24 0.192280663
13.24 0.158300560
14.24 0.028896565
15.24 -0.457745710
16.24 -0.088450593
17.24 0.026672425
18.24 -0.373269977
19.24 -0.022151719
20.24 -0.036856380
22.24 0.213582926
23.24 0.005381008
2.25 -0.034008924
3.25 0.262671488
4.25 0.175830563
5.25 0.523920019
7.25 -0.010816578
8.25 0.746065778
10.25 0.636632980
11.25 -0.402190023
13.25 1.420906145
14.25 0.305702050
16.25 -0.265767739
17.25 -0.011958147
18.25 -0.350451742
19.25 -0.021316109
20.25 0.108748707
22.25 0.183660424
23.25 -0.111528096
2.26 -0.248027777
3.26 -0.045401632
4.26 -0.219664727
5.26 -0.060222197
7.26 0.096818982
8.26 1.065126984
10.26 0.767455448
11.26 0.545086516
13.26 0.633275007
14.26 -0.296903516
15.26 0.924899447
16.26 -0.208170925
17.26 -0.275531608
18.26 -0.182270478
19.26 0.023408335
20.26 -0.055740373
22.26 0.223400281
23.26 -0.035774418
2.27 0.899340775
3.27 -0.200868762
4.27 -0.189584406
5.27 0.054499379
7.27 0.269988439
8.27 0.773877396
10.27 0.135078038
11.27 0.843098473
13.27 0.741760013
14.27 0.273497292
15.27 0.007873188
16.27 -0.223488804
17.27 0.121957097
18.27 -0.351286924
19.27 0.061187198
20.27 -0.016995885
22.27 0.432441946
23.27 0.056879480
2.28 1.474179835
3.28 0.154131901
4.28 -0.217383851
5.28 -0.054743709
7.28 0.494155256
8.28 1.459757372
10.28 1.392634027
11.28 -0.070401238
13.28 1.345561599
14.28 0.108804709
15.28 -0.074029580
16.28 -0.358949492
17.28 -0.077647411
18.28 0.008291862
19.28 -0.040174562
20.28 -0.257272297
22.28 0.184652929
23.28 0.522891220
2.29 0.073959998
3.29 0.323290623
4.29 -0.059945370
5.29 -0.111657613
7.29 0.475513385
8.29 0.284381269
10.29 0.341505930
11.29 -0.097151706
13.29 0.514646859
14.29 -0.228774858
15.29 0.221039916
16.29 -0.008336674
17.29 -0.029352485
18.29 -0.452996338
19.29 0.139825549
20.29 -0.055479779
22.29 0.142435851
23.29 0.165436257
2.30 0.243616391
3.30 0.110517559
4.30 -0.268560084
5.30 -0.082405087
7.30 0.241740868
8.30 0.425999959
10.30 1.147851206
11.30 0.038085987
13.30 -0.095139831
14.30 0.080274027
15.30 0.323427085
16.30 0.429759775
17.30 -0.167892717
18.30 0.403004050
19.30 0.027872061
20.30 -0.015693347
22.30 0.054580437
23.30 0.174211850
2.31 -0.101607761
3.31 0.522826612
4.31 0.126174806
5.31 0.019025648
7.31 0.023119439
8.31 0.581356434
10.31 0.049837684
11.31 0.530739099
13.31 0.333720256
14.31 0.118030301
15.31 0.795532453
16.31 0.023434105
17.31 -0.112544410
18.31 -0.380723200
19.31 0.081213118
20.31 -0.052148643
23.31 1.199028926
2.32 0.268348883
3.32 -0.063588024
4.32 0.484411559
7.32 0.425879304
8.32 0.581222374
10.32 0.606958912
11.32 0.541719648
13.32 0.315133497
14.32 0.062404174
15.32 0.729643077
16.32 0.183168102
17.32 -0.286001759
18.32 0.021918506
19.32 -0.023235487
20.32 0.256515391
22.32 0.248191667
23.32 1.520279721
2.33 -0.058213002
3.33 0.346456871
4.33 -0.250401350
5.33 -0.186931283
7.33 -0.059269403
8.33 -0.064304673
10.33 -0.934217296
11.33 0.063431853
13.33 -0.187488715
14.33 0.279444671
15.33 0.642293827
17.33 0.320561804
18.33 -0.501993266
19.33 0.076028006
20.33 0.232214323
22.33 0.076580929
23.33 0.633474529
2.34 2.585493538
3.34 0.257196629
4.34 0.095536102
5.34 -0.139498938
7.34 -0.404890279
8.34 -0.025024846
11.34 1.008932658
13.34 1.924872506
14.34 -0.114757008
15.34 -0.181527464
17.34 -0.016001897
18.34 -0.285005902
19.34 -0.043810883
20.34 0.045340195
22.34 -0.012948458
23.34 -0.557925515
2.35 1.317546424
4.35 -0.316158993
5.35 -0.049876655
7.35 0.026395748
8.35 0.025725916
10.35 0.906830330
14.35 0.106472266
15.35 0.817184192
16.35 0.014627075
17.35 0.425405444
18.35 -0.618182881
19.35 0.165727857
20.35 0.117662271
22.35 0.024884818
23.35 1.060532873
2.36 -0.067070596
3.36 0.142150974
4.36 -0.122362972
5.36 -0.210845222
7.36 0.009927089
10.36 -0.603605940
11.36 0.279183113
13.36 -0.133669450
14.36 0.692293793
15.36 0.398349822
16.36 0.145818165
17.36 0.277697517
18.36 -0.474099540
19.36 -0.137107097
20.36 -0.177745904
22.36 -0.276594134
23.36 0.084051393
2.37 0.541248112
3.37 0.306017665
4.37 0.387449002
5.37 -0.104720949
7.37 0.840465872
8.37 0.622603236
10.37 0.031211381
11.37 -0.422874995
13.37 -1.321470653
14.37 0.436041750
15.37 -0.015780723
16.37 -0.219395340
17.37 0.692494716
18.37 -0.061354126
19.37 0.179432184
20.37 -0.216914154
22.37 -0.098119090
23.37 0.043217288
2.38 1.167098946
3.38 0.003798746
4.38 0.110350592
5.38 -0.126340291
7.38 -0.482930796
8.38 -0.514290536
10.38 -0.851559927
11.38 -0.115221154
13.38 -1.325458560
14.38 0.370740290
15.38 -0.009022789
16.38 4.125154427
17.38 0.487081803
18.38 -0.210289365
19.38 -0.151007941
20.38 -0.247207360
22.38 0.177541979
23.38 -0.358203979
2.39 0.586291034
3.39 -0.322249847
4.39 -0.037934181
5.39 0.067305754
7.39 -0.529230250
8.39 0.163450896
10.39 -1.139616711
11.39 -0.085662075
13.39 -1.314931101
14.39 0.651435447
15.39 -0.416581796
16.39 0.507830153
18.39 -0.489277515
19.39 0.045974645
20.39 0.049557033
22.39 -0.205311370
23.39 0.849493311
2.40 -0.037043617
3.40 -0.067793970
4.40 0.167195453
5.40 -0.191576787
7.40 0.153051037
8.40 -0.301171132
10.40 -1.327074701
11.40 -0.583367010
13.40 -1.379721702
14.40 0.187109997
15.40 0.556892258
16.40 -1.089633241
17.40 0.279531922
18.40 0.113661351
19.40 0.517038719
20.40 -0.088918233
22.40 -0.007154665
23.40 1.022686182
2.41 0.377827916
3.41 0.042930368
4.41 0.230577869
5.41 -0.133551037
7.41 -0.354397542
8.41 -1.115142687
10.41 -2.855029677
11.41 0.219648312
13.41 -1.346091958
14.41 0.259472679
15.41 -0.427116296
16.41 0.246837433
17.41 0.085639693
18.41 -0.290632887
19.41 0.260411775
20.41 -0.157055303
22.41 -0.299810373
23.41 0.026552819
2.42 0.595809531
3.42 -0.358832448
5.42 0.496455456
7.42 0.049792589
8.42 -0.523554582
10.42 -1.845366435
11.42 -0.323472380
13.42 -1.366639746
14.42 0.557986211
15.42 -1.117252463
16.42 0.715820048
17.42 -0.052786611
18.42 -0.021273588
19.42 0.073214949
20.42 -0.097736991
22.42 -0.312600404
23.42 -0.464767631
2.43 -0.256613209
3.43 -0.242197516
4.43 -0.126065831
5.43 -0.130358402
7.43 -0.228684072
8.43 -1.422134076
10.43 -2.515439395
11.43 -0.653181018
13.43 -1.321937501
14.43 -0.076908095
15.43 -0.468232774
16.43 -0.647610916
17.43 0.358660183
18.43 -0.836714563
19.43 -0.129237512
20.43 0.128488270
22.43 -0.707033360
23.43 -0.170699423
2.44 -1.107466128
3.44 -0.243956763
4.44 0.404618379
5.44 -0.153945164
7.44 -0.516117102
8.44 -0.450071690
10.44 -2.047452532
11.44 -0.224854265
13.44 -1.261134001
14.44 -0.035320816
15.44 -1.299374842
16.44 -0.705050778
17.44 0.125147196
18.44 -0.918773200
19.44 -0.047896363
22.44 -0.628947739
23.44 -0.596693061
3.45 -0.248713286
4.45 0.377343151
5.45 0.219411827
7.45 -0.748242043
8.45 0.391728354
10.45 -1.187144789
11.45 -0.746076736
13.45 -1.078273683
14.45 -0.339952496
15.45 -1.124109864
16.45 -0.852338479
17.45 0.282831958
18.45 -0.096873152
19.45 0.383011245
20.45 -0.401359155
22.45 -0.650163913
23.45 -1.080889719
2.46 -0.326564715
3.46 -0.814515202
4.46 0.102958560
5.46 -0.206981684
7.46 -0.679398795
8.46 -1.539279178
10.46 -2.046301347
11.46 -0.972091889
13.46 -1.045257627
14.46 -0.512049252
15.46 -0.804693175
16.46 -1.370643836
17.46 -0.528810659
19.46 -0.065579650
20.46 -0.295692585
22.46 -0.235010425
23.46 -1.292799362
2.47 -0.466057859
3.47 -0.774937172
4.47 -0.214561780
5.47 -0.261562626
7.47 -0.903785975
8.47 -1.026973656
10.47 -2.864968533
11.47 -0.592982445
13.47 -1.155584358
14.47 -0.674781410
15.47 -1.188086270
16.47 -1.554512124
17.47 -0.537534254
18.47 -0.349069717
19.47 -0.025263996
20.47 -0.325967011
2.48 -1.160242079
$subject
(Intercept)
2 0.02582024
3 0.05576585
4 -0.48334695
5 -0.66539236
7 0.03286270
8 0.40978779
10 1.86870879
11 -0.13477889
13 0.18044401
14 -0.42853845
15 0.59657529
16 0.42462811
17 -0.58797433
18 -0.10922008
19 -0.60302298
20 -0.41619093
22 -0.37865266
23 0.21252483
with conditional variances for "trial_id" "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-1.6806686 -0.8158509 0.8663228 0.9826814 1.0118361 1.1474065
=============================================================4. Additional models
4.1 SD of Acceleration - changes over time
# --- Compute SD per trial and assign trial index ---
compute_sd <- function(df) {
df %>%
group_by(subject, Block, trial, phase) %>%
summarise(
sd_x = sd(CoM.acc.x, na.rm = TRUE),
sd_y = sd(CoM.acc.y, na.rm = TRUE),
sd_z = sd(CoM.acc.z, na.rm = TRUE),
.groups = "drop"
) %>%
group_by(subject, Block, phase) %>%
arrange(trial) %>%
mutate(TrialInBlock = row_number()) %>%
ungroup()
}
# --- Run LMMs for SD and extract ANOVA p-values ---
run_sd_model_analysis <- function(tagged_df, label) {
sd_df <- compute_sd(tagged_df) %>%
mutate(
Block = factor(Block),
subject = factor(subject),
phase = factor(phase)
)
get_anova <- function(axis) {
model <- lmer(as.formula(paste0("sd_", axis, " ~ TrialInBlock * Block * phase + (1 + Block | subject)")),
data = sd_df)
anova(model)
}
an_x <- get_anova("x")
an_y <- get_anova("y")
an_z <- get_anova("z")
tibble(
Dataset = label,
Axis = c("X", "Y", "Z"),
`TrialInBlock p-value` = c(an_x["TrialInBlock", "Pr(>F)"], an_y["TrialInBlock", "Pr(>F)"], an_z["TrialInBlock", "Pr(>F)"]),
`Block p-value` = c(an_x["Block", "Pr(>F)"], an_y["Block", "Pr(>F)"], an_z["Block", "Pr(>F)"]),
`Phase p-value` = c(an_x["phase", "Pr(>F)"], an_y["phase", "Pr(>F)"], an_z["phase", "Pr(>F)"]),
`TrialInBlock:Block p` = c(an_x["TrialInBlock:Block", "Pr(>F)"], an_y["TrialInBlock:Block", "Pr(>F)"], an_z["TrialInBlock:Block", "Pr(>F)"]),
`TrialInBlock:Phase p` = c(an_x["TrialInBlock:phase", "Pr(>F)"], an_y["TrialInBlock:phase", "Pr(>F)"], an_z["TrialInBlock:phase", "Pr(>F)"]),
`Block:Phase p` = c(an_x["Block:phase", "Pr(>F)"], an_y["Block:phase", "Pr(>F)"], an_z["Block:phase", "Pr(>F)"]),
`3-way p-value` = c(an_x["TrialInBlock:Block:phase", "Pr(>F)"],
an_y["TrialInBlock:Block:phase", "Pr(>F)"],
an_z["TrialInBlock:Block:phase", "Pr(>F)"])
)
}
# Use already-tagged data if available
sd_mixed_pvals <- run_sd_model_analysis(tagged_data, "Mixed")Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.00251375 (tol = 0.002, component 1)print(sd_mixed_pvals)# A tibble: 3 × 9
Dataset Axis `TrialInBlock p-value` `Block p-value` `Phase p-value`
<chr> <chr> <dbl> <dbl> <dbl>
1 Mixed X 0.125 0.0000460 0
2 Mixed Y 0.0904 0.00158 0
3 Mixed Z 0.000567 0.00182 0
# ℹ 4 more variables: `TrialInBlock:Block p` <dbl>,
# `TrialInBlock:Phase p` <dbl>, `Block:Phase p` <dbl>, `3-way p-value` <dbl># --- Suppress emmeans/pbkrtest warnings globally ---
emmeans::emm_options(
lmerTest.limit = Inf,
pbkrtest.limit = Inf
)
# --- Compute SD per trial and assign trial index ---
compute_sd <- function(df) {
df %>%
group_by(subject, Block, trial, phase) %>%
summarise(
sd_x = sd(CoM.acc.x, na.rm = TRUE),
sd_y = sd(CoM.acc.y, na.rm = TRUE),
sd_z = sd(CoM.acc.z, na.rm = TRUE),
.groups = "drop"
) %>%
group_by(subject, Block, phase) %>%
arrange(trial) %>%
mutate(TrialInBlock = row_number()) %>%
ungroup()
}
# --- Extended: Run SD LMM with Full Output per Axis ---
run_sd_model_diagnostics <- function(tagged_df, label) {
sd_df <- compute_sd(tagged_df) %>%
mutate(
Block = factor(Block),
subject = factor(subject),
phase = factor(phase)
)
axes <- c("x", "y", "z")
results <- list()
for (axis in axes) {
model <- lmer(
as.formula(paste0("sd_", axis, " ~ TrialInBlock * Block * phase + (1 + Block | subject)")),
data = sd_df
)
key <- paste0(label, "_", toupper(axis))
results[[key]] <- list(
ANOVA = anova(model),
Emmeans = emmeans(model, ~ Block * phase),
FixedEffects = fixef(model),
RandomEffects = ranef(model),
ScaledResiduals = resid(model, scaled = TRUE),
Model = model
)
}
return(results)
}
# --- Run Extended SD Diagnostics ---
sd_mixed_diagnostics <- run_sd_model_diagnostics(tagged_data, "Mixed")NOTE: Results may be misleading due to involvement in interactionsWarning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.00251375 (tol = 0.002, component 1)NOTE: Results may be misleading due to involvement in interactions
NOTE: Results may be misleading due to involvement in interactions# --- Print Diagnostics Example (Axis X) ---
cat("\n=== SD LMM: Axis X ===\n")
=== SD LMM: Axis X ===print(sd_mixed_diagnostics$Mixed_X$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
TrialInBlock 0.135 0.135 1 5746.5 2.3590 0.1246
Block 1.972 0.493 4 38.6 8.5982 4.597e-05 ***
phase 249.104 249.104 1 6434.0 4345.2038 < 2.2e-16 ***
TrialInBlock:Block 1.553 0.388 4 4790.3 6.7711 1.975e-05 ***
TrialInBlock:phase 17.954 17.954 1 6434.8 313.1832 < 2.2e-16 ***
Block:phase 6.784 1.696 4 6434.0 29.5819 < 2.2e-16 ***
TrialInBlock:Block:phase 7.883 1.971 4 6434.8 34.3752 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(sd_mixed_diagnostics$Mixed_X$Emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 0.8589 0.0463 17.8 0.7616 0.956
2 Execution 0.7722 0.0460 18.1 0.6757 0.869
3 Execution 0.5958 0.0348 21.3 0.5235 0.668
4 Execution 0.7512 0.0372 18.0 0.6730 0.829
5 Execution 0.5829 0.0234 19.8 0.5341 0.632
1 Preparation 0.0665 0.0462 17.7 -0.0307 0.164
2 Preparation 0.1416 0.0459 18.0 0.0451 0.238
3 Preparation 0.2170 0.0346 20.9 0.1450 0.289
4 Preparation 0.1057 0.0372 18.0 0.0275 0.184
5 Preparation 0.1058 0.0233 19.8 0.0570 0.154
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(sd_mixed_diagnostics$Mixed_X$FixedEffects) (Intercept) TrialInBlock
0.8670157681 -0.0004070869
Block2 Block3
0.0559088391 -0.0360016658
Block4 Block5
-0.0267292326 -0.2966271553
phasePreparation TrialInBlock:Block2
-0.8045860884 -0.0071820838
TrialInBlock:Block3 TrialInBlock:Block4
-0.0114413892 -0.0040774891
TrialInBlock:Block5 TrialInBlock:phasePreparation
0.0010358221 0.0006132538
Block2:phasePreparation Block3:phasePreparation
-0.1321684047 -0.0065997845
Block4:phasePreparation Block5:phasePreparation
-0.0350077883 0.2389905765
TrialInBlock:Block2:phasePreparation TrialInBlock:Block3:phasePreparation
0.0148035495 0.0211674507
TrialInBlock:Block4:phasePreparation TrialInBlock:Block5:phasePreparation
0.0091604473 0.0038433568 print(sd_mixed_diagnostics$Mixed_X$RandomEffects)$subject
(Intercept) Block2 Block3 Block4 Block5
2 -0.002988795 0.14156712 0.1581594223 0.08769465 0.059652684
3 0.212609300 -0.20029327 -0.2619025889 -0.27899585 -0.250960985
4 -0.085181378 -0.05349970 -0.0210815156 -0.01082793 0.060289294
5 -0.114621143 -0.06346916 -0.0538648037 -0.09673877 -0.035267752
7 -0.082730012 0.03177071 0.0723551492 0.06539204 0.005344579
8 0.023389807 0.04016877 -0.0008738165 0.05538312 0.048245656
10 0.320251535 0.17728157 0.0556958401 0.09787794 -0.082884933
11 0.420755543 -0.04364919 -0.2389522326 -0.17238057 -0.343794765
13 -0.105300030 -0.01409427 0.0304672747 0.08532433 0.075952557
14 0.121575104 -0.04358855 -0.0843247333 -0.08775479 -0.063264006
15 -0.120445885 0.01060097 0.0514848796 0.01798462 0.187721830
16 -0.103117330 0.06730267 0.1317134148 0.17176740 0.072982024
17 -0.192825214 0.04684707 0.1279847332 0.09152784 0.153400768
18 -0.097032137 0.05689834 0.1338593646 0.13412624 0.065676588
19 -0.199767968 0.01788578 0.0733258041 0.05520932 0.084107052
20 -0.126808714 -0.00562211 0.0126196789 -0.02084153 0.061604516
22 -0.156404295 0.02848354 0.0755602517 0.05215784 0.130604689
23 0.288641611 -0.19459030 -0.2622261227 -0.24690591 -0.229409796
with conditional variances for "subject" print(head(sd_mixed_diagnostics$Mixed_X$ScaledResiduals)) 1 2 3 4 5 6
-0.2362797 -0.1681371 0.1413259 -1.0167938 0.3667212 -0.5659619 # --- Print Diagnostics Example (Axis X) ---
cat("\n=== SD LMM: Axis Y ===\n")
=== SD LMM: Axis Y ===print(sd_mixed_diagnostics$Mixed_Y$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
TrialInBlock 0.232 0.232 1 5188.0 2.8680 0.090415 .
Block 1.718 0.430 4 39.9 5.3171 0.001578 **
phase 279.561 279.561 1 6435.5 3460.2860 < 2.2e-16 ***
TrialInBlock:Block 0.663 0.166 4 5224.0 2.0511 0.084541 .
TrialInBlock:phase 20.396 20.396 1 6436.2 252.4581 < 2.2e-16 ***
Block:phase 5.904 1.476 4 6435.5 18.2685 6.184e-15 ***
TrialInBlock:Block:phase 9.118 2.279 4 6436.2 28.2133 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(sd_mixed_diagnostics$Mixed_Y$Emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 0.9118 0.0586 17.7 0.7885 1.035
2 Execution 0.8268 0.0548 18.1 0.7116 0.942
3 Execution 0.6162 0.0367 22.7 0.5402 0.692
4 Execution 0.7805 0.0422 18.1 0.6918 0.869
5 Execution 0.6118 0.0242 20.8 0.5614 0.662
1 Preparation 0.0672 0.0585 17.6 -0.0560 0.190
2 Preparation 0.1630 0.0548 18.0 0.0479 0.278
3 Preparation 0.2246 0.0364 22.1 0.1490 0.300
4 Preparation 0.1009 0.0422 18.1 0.0123 0.190
5 Preparation 0.1000 0.0242 20.7 0.0496 0.150
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(sd_mixed_diagnostics$Mixed_Y$FixedEffects) (Intercept) TrialInBlock
0.8897810297 0.0011082715
Block2 Block3
0.0822160265 -0.0101378448
Block4 Block5
-0.0282883665 -0.2550414971
phasePreparation TrialInBlock:Block2
-0.8348514013 -0.0084211023
TrialInBlock:Block3 TrialInBlock:Block4
-0.0143766421 -0.0051883951
TrialInBlock:Block5 TrialInBlock:phasePreparation
-0.0022635025 -0.0004895652
Block2:phasePreparation Block3:phasePreparation
-0.1570715557 -0.0244418559
Block4:phasePreparation Block5:phasePreparation
-0.0371109447 0.2002025356
TrialInBlock:Block2:phasePreparation TrialInBlock:Block3:phasePreparation
0.0170130632 0.0240441539
TrialInBlock:Block4:phasePreparation TrialInBlock:Block5:phasePreparation
0.0101794347 0.0066785128 print(sd_mixed_diagnostics$Mixed_Y$RandomEffects)$subject
(Intercept) Block2 Block3 Block4 Block5
2 -0.08234594 0.185087259 0.21785201 0.17962949 0.17679332
3 0.04611270 0.030726485 0.03928753 0.02114883 -0.01357963
4 -0.18744612 -0.017242124 0.08289313 0.04887136 0.15646520
5 -0.22431229 -0.007321099 0.06374761 0.02113200 0.08861165
7 -0.11122260 0.029650181 0.06720228 0.03336942 0.12308974
8 0.14526321 0.086655267 -0.05307747 0.01753172 -0.09464450
10 0.45010656 0.015506627 -0.07733778 -0.01851272 -0.22497245
11 0.50219223 0.052184107 -0.38386833 -0.19761004 -0.53817922
13 0.08345246 -0.174887857 -0.19853261 -0.17321943 -0.11352360
14 0.13354972 -0.095393264 -0.13814968 -0.12584080 -0.13187409
15 -0.12732127 -0.048888855 0.07609548 0.03206445 0.16208994
16 -0.12626597 0.073484805 0.15052722 0.13297897 0.16768904
17 -0.25109265 0.127282488 0.21870252 0.16863979 0.23151458
18 -0.09146300 0.015964763 0.13349575 0.08206185 0.06999104
19 -0.21066210 0.020881983 0.03255722 0.01429839 0.06453651
20 -0.15620016 0.052418546 0.04419543 0.01924245 0.03648449
22 -0.20574993 0.010400748 0.08228253 0.04755815 0.15636494
23 0.41340515 -0.356510062 -0.35787284 -0.30334390 -0.31685696
with conditional variances for "subject" print(head(sd_mixed_diagnostics$Mixed_Y$ScaledResiduals)) 1 2 3 4 5 6
-0.1962739 -0.2278271 0.4396876 -0.6653148 -0.1065930 -0.6182290 # --- Print Diagnostics Example (Axis X) ---
cat("\n=== SD LMM: Axis Z ===\n")
=== SD LMM: Axis Z ===print(sd_mixed_diagnostics$Mixed_Z$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
TrialInBlock 2.52 2.52 1 5510.2 11.8939 0.0005674 ***
Block 4.44 1.11 4 38.5 5.2332 0.0018246 **
phase 782.80 782.80 1 6435.6 3691.2368 < 2.2e-16 ***
TrialInBlock:Block 3.26 0.81 4 5576.1 3.8377 0.0040594 **
TrialInBlock:phase 50.32 50.32 1 6436.2 237.2811 < 2.2e-16 ***
Block:phase 16.84 4.21 4 6435.7 19.8532 2.922e-16 ***
TrialInBlock:Block:phase 26.47 6.62 4 6436.3 31.1994 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(sd_mixed_diagnostics$Mixed_Z$Emmeans)) Block phase emmean SE df lower.CL upper.CL
1 Execution 1.4196 0.0878 17.8 1.2351 1.604
2 Execution 1.3741 0.0888 18.1 1.1877 1.561
3 Execution 1.0528 0.0687 21.1 0.9100 1.196
4 Execution 1.3081 0.0666 18.2 1.1682 1.448
5 Execution 1.0221 0.0508 19.1 0.9158 1.128
1 Preparation 0.0748 0.0876 17.7 -0.1095 0.259
2 Preparation 0.2279 0.0887 18.0 0.0415 0.414
3 Preparation 0.3327 0.0684 20.7 0.1904 0.475
4 Preparation 0.1288 0.0666 18.2 -0.0111 0.269
5 Preparation 0.1267 0.0508 19.1 0.0204 0.233
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(sd_mixed_diagnostics$Mixed_Z$FixedEffects) (Intercept) TrialInBlock
1.341741268 0.003922163
Block2 Block3
0.185823520 0.149101971
Block4 Block5
0.120401517 -0.321050371
phasePreparation TrialInBlock:Block2
-1.298535171 -0.011649781
TrialInBlock:Block3 TrialInBlock:Block4
-0.025982147 -0.011679048
TrialInBlock:Block5 TrialInBlock:phasePreparation
-0.003849036 -0.002329397
Block2:phasePreparation Block3:phasePreparation
-0.307614345 -0.216104710
Block4:phasePreparation Block5:phasePreparation
-0.226894068 0.227467258
TrialInBlock:Block2:phasePreparation TrialInBlock:Block3:phasePreparation
0.025491280 0.042344641
TrialInBlock:Block4:phasePreparation TrialInBlock:Block5:phasePreparation
0.019758505 0.011175646 print(sd_mixed_diagnostics$Mixed_Z$RandomEffects)$subject
(Intercept) Block2 Block3 Block4 Block5
2 -0.30814638 0.2294529029 0.355770427 0.26626784 0.32513676
3 0.33931050 -0.2224618321 -0.274188557 -0.26835375 -0.34185102
4 -0.16498059 -0.1098020214 -0.044309010 -0.03275700 0.12512196
5 -0.32110439 -0.0249757532 0.007379763 -0.01210276 -0.01133951
7 -0.01433727 0.0191648020 0.113187192 0.10448933 -0.01003884
8 0.23908248 0.1732785798 -0.045565883 0.04541524 -0.04813707
10 0.48215997 0.1960701192 0.173182408 0.10486479 0.03346474
11 0.86180585 0.0216850999 -0.638736567 -0.45985346 -0.84410570
13 0.12409308 -0.2496958168 -0.280339405 -0.22561690 0.01598564
14 -0.03086219 -0.0048061321 0.090539945 0.05475269 -0.03247776
15 -0.13856395 -0.1082509979 -0.045030319 -0.06627008 0.31934134
16 -0.08506709 0.2409337594 0.259708867 0.22973424 0.19159645
17 -0.45334855 0.0935573501 0.313503133 0.24907473 0.31135975
18 -0.08421651 0.0808597662 0.349342671 0.25776675 0.04866049
19 -0.31123439 -0.0307044060 -0.024431129 -0.02492679 0.03281403
20 -0.25232766 0.0136265854 0.020800977 0.01380094 0.07789839
22 -0.39354146 -0.0009274911 0.047353663 0.03946954 0.19078152
23 0.51127856 -0.3170045144 -0.378168178 -0.27575534 -0.38421115
with conditional variances for "subject" print(head(sd_mixed_diagnostics$Mixed_Z$ScaledResiduals)) 1 2 3 4 5 6
0.405538236 0.082510827 -0.253608734 -1.000084912 0.003132928 -0.709006436 4.2 LMM step level
# Extract Step-Level RMS with ±3 Line Buffer Around Markers
extract_step_rms <- function(df, label) {
buffer <- 3
step_markers <- c(14, 15, 16, 17)
step_data <- df %>%
filter(phase == "Execution", Marker.Text %in% step_markers) %>%
assign_steps_by_block() %>%
arrange(subject, Block, trial, ms) %>%
group_by(subject, Block, trial) %>%
mutate(row_id = row_number()) %>%
ungroup()
step_indices <- step_data %>%
select(subject, Block, trial, row_id, Step)
# Extract ±3 rows around each marker
window_data <- map_dfr(1:nrow(step_indices), function(i) {
step <- step_indices[i, ]
rows <- (step$row_id - buffer):(step$row_id + buffer)
step_data %>%
filter(subject == step$subject,
Block == step$Block,
trial == step$trial,
row_id %in% rows) %>%
mutate(Step = step$Step)
})
# Compute RMS over buffered region
window_data %>%
group_by(subject, Block, Step) %>%
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"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = gsub("rms_", "", Axis),
Dataset = label,
Step = factor(Step), # ✅ Now a factor
subject = factor(subject),
Block = factor(Block)
)
}
# --- Run Step-Level LMMs and Extract ANOVA p-values ---
run_step_model <- function(df, label) {
get_anova <- function(axis_label) {
model <- lmer(RMS ~ Step * Block + (1 | subject), data = filter(df, Axis == axis_label))
anova(model)
}
ax <- get_anova("x")
ay <- get_anova("y")
az <- get_anova("z")
tibble(
Dataset = label,
Axis = c("X", "Y", "Z"),
`Step p-value` = c(ax["Step", "Pr(>F)"], ay["Step", "Pr(>F)"], az["Step", "Pr(>F)"]),
`Block p-value` = c(ax["Block", "Pr(>F)"], ay["Block", "Pr(>F)"], az["Block", "Pr(>F)"]),
`Interaction p-value` = c(ax["Step:Block", "Pr(>F)"], ay["Step:Block", "Pr(>F)"], az["Step:Block", "Pr(>F)"])
)
}
# --- Execute Updated Step-Level RMS Analysis ---
step_rms_data <- extract_step_rms(tagged_data, "Mixed")
step_model_results <- run_step_model(step_rms_data, "Mixed")fixed-effect model matrix is rank deficient so dropping 18 columns / coefficientsMissing cells for: Step7:Block1, Step8:Block1, Step9:Block1, Step10:Block1, Step11:Block1, Step12:Block1, Step13:Block1, Step14:Block1, Step15:Block1, Step16:Block1, Step17:Block1, Step18:Block1, Step13:Block2, Step14:Block2, Step15:Block2, Step16:Block2, Step17:Block2, Step18:Block2.
Interpret type III hypotheses with care.fixed-effect model matrix is rank deficient so dropping 18 columns / coefficientsMissing cells for: Step7:Block1, Step8:Block1, Step9:Block1, Step10:Block1, Step11:Block1, Step12:Block1, Step13:Block1, Step14:Block1, Step15:Block1, Step16:Block1, Step17:Block1, Step18:Block1, Step13:Block2, Step14:Block2, Step15:Block2, Step16:Block2, Step17:Block2, Step18:Block2.
Interpret type III hypotheses with care.fixed-effect model matrix is rank deficient so dropping 18 columns / coefficientsMissing cells for: Step7:Block1, Step8:Block1, Step9:Block1, Step10:Block1, Step11:Block1, Step12:Block1, Step13:Block1, Step14:Block1, Step15:Block1, Step16:Block1, Step17:Block1, Step18:Block1, Step13:Block2, Step14:Block2, Step15:Block2, Step16:Block2, Step17:Block2, Step18:Block2.
Interpret type III hypotheses with care.# --- Output ---
print(step_model_results)# A tibble: 3 × 5
Dataset Axis `Step p-value` `Block p-value` `Interaction p-value`
<chr> <chr> <dbl> <dbl> <dbl>
1 Mixed X 0.0125 5.77e-36 1.00
2 Mixed Y 0.00339 7.44e-15 1.00
3 Mixed Z 0.0537 2.89e-28 1.00# -------- Suppress Emmeans Warnings Globally --------
emmeans::emm_options(
lmerTest.limit = Inf,
pbkrtest.limit = Inf
)
# --- Extract Step-Level RMS with ±3 Row Buffer Around Markers ---
extract_step_rms <- function(df, label) {
buffer <- 3
step_markers <- c(14, 15, 16, 17)
step_data <- df %>%
filter(phase == "Execution", Marker.Text %in% step_markers) %>%
assign_steps_by_block() %>%
arrange(subject, Block, trial, ms) %>%
group_by(subject, Block, trial) %>%
mutate(row_id = row_number()) %>%
ungroup()
step_indices <- step_data %>%
select(subject, Block, trial, row_id, Step)
window_data <- map_dfr(1:nrow(step_indices), function(i) {
step <- step_indices[i, ]
rows <- (step$row_id - buffer):(step$row_id + buffer)
step_data %>%
filter(subject == step$subject,
Block == step$Block,
trial == step$trial,
row_id %in% rows) %>%
mutate(Step = step$Step)
})
window_data %>%
group_by(subject, Block, Step) %>%
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"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = gsub("rms_", "", Axis),
Dataset = label,
Step = as.numeric(Step),
subject = factor(subject),
Block = factor(Block)
)
}
# --- Run Step-Level LMMs with Full Diagnostics ---
run_step_model_diagnostics <- function(df, label) {
axes <- c("x", "y", "z")
results <- list()
for (axis in axes) {
data_sub <- df %>% filter(Axis == axis)
model <- lmer(RMS ~ Step * Block + (1 | subject), data = data_sub)
key <- paste0(label, "_", toupper(axis))
results[[key]] <- list(
ANOVA = anova(model),
Emmeans = emmeans(model, ~ Step * Block),
FixedEffects = fixef(model),
RandomEffects = ranef(model),
ScaledResiduals = resid(model, scaled = TRUE),
Model = model
)
}
return(results)
}
# --- Run Analysis and Extract Diagnostics ---
step_rms_data <- extract_step_rms(tagged_data, "Mixed")
step_model_diag_results <- run_step_model_diagnostics(step_rms_data, "Mixed")
# --- Print Diagnostics Example for Axis X ---
cat("\n=== STEP-LEVEL RMS LMM: Axis X ===\n")
=== STEP-LEVEL RMS LMM: Axis X ===print(step_model_diag_results$Mixed_X$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 0.23877 0.23877 1 1269 6.9077 0.008686 **
Block 2.41418 0.60355 4 1269 17.4610 5.874e-14 ***
Step:Block 0.31355 0.07839 4 1269 2.2678 0.059955 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(step_model_diag_results$Mixed_X$Emmeans)) Step Block emmean SE df lower.CL upper.CL
8.5 1 0.926 0.0896 43.8 0.745 1.106
8.5 2 0.780 0.0720 18.3 0.629 0.931
8.5 3 0.655 0.0713 17.6 0.505 0.805
8.5 4 0.759 0.0713 17.6 0.609 0.909
8.5 5 0.654 0.0713 17.6 0.504 0.804
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(step_model_diag_results$Mixed_X$FixedEffects) (Intercept) Step Block2 Block3 Block4 Block5
0.915119362 0.001237707 -0.001022194 -0.210534955 -0.100201149 -0.229933792
Step:Block2 Step:Block3 Step:Block4 Step:Block5
-0.017027599 -0.007057896 -0.007769410 -0.004876759 print(step_model_diag_results$Mixed_X$RandomEffects)$subject
(Intercept)
2 0.11002898
3 0.03724100
4 -0.24933085
5 -0.34342046
7 -0.15999779
8 0.31398331
10 0.81534049
11 0.52169432
13 -0.18853320
14 0.03463009
15 -0.06972254
16 -0.06112373
17 -0.18563684
18 -0.03037388
19 -0.28291659
20 -0.24510198
22 -0.14256874
23 0.12580842
with conditional variances for "subject" print(head(step_model_diag_results$Mixed_X$ScaledResiduals)) 1 2 3 4 5 6
-2.407074 -2.365733 -2.359757 -2.366414 -2.201504 -2.278840 # --- Print Diagnostics Example for Axis Y ---
cat("\n=== STEP-LEVEL RMS LMM: Axis Y ===\n")
=== STEP-LEVEL RMS LMM: Axis Y ===print(step_model_diag_results$Mixed_Y$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 0.44036 0.44036 1 1269 6.1963 0.01293 *
Block 2.38367 0.59592 4 1269 8.3853 1.123e-06 ***
Step:Block 0.36458 0.09115 4 1269 1.2825 0.27483
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(step_model_diag_results$Mixed_Y$Emmeans)) Step Block emmean SE df lower.CL upper.CL
8.5 1 0.920 0.1120 67.1 0.697 1.143
8.5 2 0.852 0.0814 19.2 0.682 1.022
8.5 3 0.676 0.0801 17.9 0.507 0.844
8.5 4 0.812 0.0801 17.9 0.644 0.980
8.5 5 0.750 0.0801 17.9 0.582 0.918
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(step_model_diag_results$Mixed_Y$FixedEffects) (Intercept) Step Block2 Block3 Block4 Block5
0.935720192 -0.001859348 0.011416990 -0.219819184 -0.009199095 -0.098823885
Step:Block2 Step:Block3 Step:Block4 Step:Block5
-0.009354914 -0.002880084 -0.011599384 -0.008347937 print(step_model_diag_results$Mixed_Y$RandomEffects)$subject
(Intercept)
2 0.215930304
3 0.181924219
4 -0.295210899
5 -0.351153879
7 -0.199510787
8 0.404200990
10 0.767464280
11 0.428233663
13 -0.188243262
14 0.005818085
15 -0.130100848
16 -0.078256254
17 -0.160586355
18 -0.075812111
19 -0.347035341
20 -0.274834330
22 -0.351687924
23 0.448860452
with conditional variances for "subject" print(head(step_model_diag_results$Mixed_Y$ScaledResiduals)) 1 2 3 4 5 6
-2.024183 -2.162425 -2.129534 -2.122559 -2.028701 -1.911946 # --- Print Diagnostics Example for Axis Z ---
cat("\n=== STEP-LEVEL RMS LMM: Axis Z ===\n")
=== STEP-LEVEL RMS LMM: Axis Z ===print(step_model_diag_results$Mixed_Z$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 0.5721 0.57209 1 1269 3.6005 0.05799 .
Block 9.0610 2.26524 4 1269 14.2565 2.181e-11 ***
Step:Block 1.4199 0.35499 4 1269 2.2341 0.06333 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(step_model_diag_results$Mixed_Z$Emmeans)) Step Block emmean SE df lower.CL upper.CL
8.5 1 1.94 0.201 39.4 1.54 2.35
8.5 2 1.72 0.166 18.1 1.37 2.06
8.5 3 1.44 0.164 17.5 1.09 1.78
8.5 4 1.65 0.164 17.5 1.30 2.00
8.5 5 1.43 0.164 17.5 1.09 1.78
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(step_model_diag_results$Mixed_Z$FixedEffects) (Intercept) Step Block2 Block3 Block4 Block5
1.907241742 0.004300091 -0.062824207 -0.428918866 -0.072468609 -0.387961951
Step:Block2 Step:Block3 Step:Block4 Step:Block5
-0.019328358 -0.009085057 -0.026036291 -0.014328949 print(step_model_diag_results$Mixed_Z$RandomEffects)$subject
(Intercept)
2 -0.09776719
3 0.29920593
4 -0.65956180
5 -0.81472718
7 0.13898391
8 0.55945688
10 1.96205535
11 0.83171422
13 -0.13930132
14 -0.26992210
15 -0.04939117
16 0.15528059
17 -0.64051400
18 0.33940562
19 -0.73185992
20 -0.59496903
22 -0.59578675
23 0.30769796
with conditional variances for "subject" print(head(step_model_diag_results$Mixed_Z$ScaledResiduals)) 1 2 3 4 5 6
-1.707922 -1.851905 -2.013106 -2.023894 -2.268428 -2.198652 #4.3 Model: Does Sequence Length Influence RMS?
# --- Compute RMS with Sequence Length per Trial ---
compute_step_rms_with_sequence_length <- function(df, label) {
df %>%
filter(phase == "Execution", Marker.Text %in% c(14, 15, 16, 17)) %>%
group_by(subject, Block, trial) %>%
mutate(SequenceLength = n()) %>%
group_by(subject, Block, trial, Marker.Text, SequenceLength) %>%
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"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = gsub("rms_", "", Axis),
Dataset = label,
subject = factor(subject),
SequenceLength = factor(SequenceLength)
)
}
# --- LMM per Axis for Sequence Length Effect ---
run_sequence_length_model <- function(df, label) {
get_anova <- function(axis_label) {
model <- lmer(RMS ~ SequenceLength + (1 | subject), data = filter(df, Axis == axis_label))
anova(model)
}
ax <- get_anova("x")
ay <- get_anova("y")
az <- get_anova("z")
tibble(
Dataset = label,
Axis = c("X", "Y", "Z"),
`SequenceLength p-value` = c(ax["SequenceLength", "Pr(>F)"],
ay["SequenceLength", "Pr(>F)"],
az["SequenceLength", "Pr(>F)"])
)
}
# --- Run Sequence Length Analysis on Mixed Data ---
step_rms_seq_mixed <- compute_step_rms_with_sequence_length(tagged_data, "Mixed")
seq_length_pvals <- run_sequence_length_model(step_rms_seq_mixed, "Mixed")
# --- Display Results ---
print(seq_length_pvals)# A tibble: 3 × 3
Dataset Axis `SequenceLength p-value`
<chr> <chr> <dbl>
1 Mixed X 0.00000000547
2 Mixed Y 0.0000863
3 Mixed Z 0.000000826 # --- Suppress lmerTest/pbkrtest warnings globally ---
emmeans::emm_options(
lmerTest.limit = Inf,
pbkrtest.limit = Inf
)
# --- Compute RMS with Sequence Length per Trial ---
compute_step_rms_with_sequence_length <- function(df, label) {
df %>%
filter(phase == "Execution", Marker.Text %in% c(14, 15, 16, 17)) %>%
group_by(subject, Block, trial) %>%
mutate(SequenceLength = n()) %>%
group_by(subject, Block, trial, Marker.Text, SequenceLength) %>%
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"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = gsub("rms_", "", Axis),
Dataset = label,
subject = factor(subject),
SequenceLength = factor(SequenceLength)
)
}
# --- Extended: LMM per Axis for Sequence Length Effect + Diagnostics ---
run_sequence_length_model_diagnostics <- function(df, label) {
axes <- c("x", "y", "z")
results <- list()
for (axis in axes) {
model <- lmer(RMS ~ SequenceLength + (1 | subject), data = filter(df, Axis == axis))
key <- paste0(label, "_", toupper(axis))
results[[key]] <- list(
ANOVA = anova(model),
Emmeans = emmeans(model, ~ SequenceLength),
FixedEffects = fixef(model),
RandomEffects = ranef(model),
ScaledResiduals = resid(model, scaled = TRUE),
Model = model
)
}
return(results)
}
# --- Run Model and Extract Diagnostics ---
step_rms_seq_mixed <- compute_step_rms_with_sequence_length(tagged_data, "Mixed")
seq_length_diag <- run_sequence_length_model_diagnostics(step_rms_seq_mixed, "Mixed")
# --- Display Diagnostics for Axis X ---
cat("\n=== SEQUENCE LENGTH RMS MODEL: Axis X ===\n")
=== SEQUENCE LENGTH RMS MODEL: Axis X ===print(seq_length_diag$Mixed_X$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
SequenceLength 12.509 2.5017 5 12832 9.4326 5.467e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(seq_length_diag$Mixed_X$Emmeans)) SequenceLength emmean SE df lower.CL upper.CL
4 0.7637 0.1930 1641.3 0.386 1.141
5 0.8554 0.1360 445.2 0.588 1.123
6 0.6433 0.0600 17.4 0.517 0.770
11 -0.0672 0.1920 1626.5 -0.444 0.309
12 0.6300 0.0601 17.4 0.504 0.757
18 0.5853 0.0601 17.5 0.459 0.712
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(seq_length_diag$Mixed_X$FixedEffects) (Intercept) SequenceLength5 SequenceLength6 SequenceLength11
0.76370009 0.09171029 -0.12044732 -0.83088215
SequenceLength12 SequenceLength18
-0.13366738 -0.17843204 print(seq_length_diag$Mixed_X$RandomEffects)$subject
(Intercept)
2 0.02489508
3 0.08326921
4 -0.21935031
5 -0.28237119
7 -0.15103986
8 0.26310369
10 0.67778730
11 0.41232158
13 -0.18595566
14 0.06469506
15 -0.01953860
16 -0.03857193
17 -0.18366675
18 -0.06357062
19 -0.25103141
20 -0.19254560
22 -0.11384027
23 0.17541029
with conditional variances for "subject" print(head(seq_length_diag$Mixed_X$ScaledResiduals)) 1 2 3 4 5 6
-0.6827629 -0.8548025 0.0423730 -0.1269665 -0.7462673 -0.5825483 # --- Display Diagnostics for Axis Y ---
cat("\n=== SEQUENCE LENGTH RMS MODEL: Axis Y ===\n")
=== SEQUENCE LENGTH RMS MODEL: Axis Y ===print(seq_length_diag$Mixed_Y$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
SequenceLength 8.5664 1.7133 5 12832 5.2197 8.629e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(seq_length_diag$Mixed_Y$Emmeans)) SequenceLength emmean SE df lower.CL upper.CL
4 0.661 0.2140 1654.1 0.241 1.081
5 0.462 0.1510 448.7 0.165 0.759
6 0.661 0.0666 17.4 0.520 0.801
11 0.275 0.2140 1639.3 -0.144 0.694
12 0.669 0.0667 17.4 0.529 0.810
18 0.615 0.0667 17.5 0.475 0.756
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(seq_length_diag$Mixed_Y$FixedEffects) (Intercept) SequenceLength5 SequenceLength6 SequenceLength11
0.6613243056 -0.1994382630 -0.0007259362 -0.3866337575
SequenceLength12 SequenceLength18
0.0081033492 -0.0460787019 print(seq_length_diag$Mixed_Y$RandomEffects)$subject
(Intercept)
2 0.16437097
3 0.16331899
4 -0.27385113
5 -0.29253679
7 -0.15742241
8 0.34376582
10 0.70260269
11 0.37257745
13 -0.18343453
14 0.08767554
15 -0.12664871
16 -0.04886466
17 -0.14491544
18 -0.04644085
19 -0.27856427
20 -0.21896539
22 -0.28502361
23 0.22235631
with conditional variances for "subject" print(head(seq_length_diag$Mixed_Y$ScaledResiduals)) 1 2 3 4 5 6
-0.9029029 -1.3907004 -0.9201271 -1.0519783 -1.3013022 -1.2657374 # --- Display Diagnostics for Axis Z ---
cat("\n=== SEQUENCE LENGTH RMS MODEL: Axis Z ===\n")
=== SEQUENCE LENGTH RMS MODEL: Axis Z ===print(seq_length_diag$Mixed_Z$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
SequenceLength 38.542 7.7084 5 12832 7.2702 8.257e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(seq_length_diag$Mixed_Z$Emmeans)) SequenceLength emmean SE df lower.CL upper.CL
4 1.6260 0.390 1124.9 0.861 2.391
5 1.2101 0.279 310.6 0.662 1.758
6 1.4232 0.135 17.3 1.139 1.707
11 0.0696 0.389 1114.3 -0.693 0.832
12 1.3848 0.135 17.3 1.101 1.669
18 1.3171 0.135 17.4 1.033 1.601
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(seq_length_diag$Mixed_Z$FixedEffects) (Intercept) SequenceLength5 SequenceLength6 SequenceLength11
1.6259910 -0.4159359 -0.2028338 -1.5564279
SequenceLength12 SequenceLength18
-0.2411451 -0.3089131 print(seq_length_diag$Mixed_Z$RandomEffects)$subject
(Intercept)
2 -0.14574863
3 0.29801432
4 -0.60504327
5 -0.72630042
7 0.17801680
8 0.52524867
10 1.41134086
11 0.69413886
13 -0.10292345
14 -0.32161570
15 0.03962644
16 0.19739584
17 -0.61375376
18 0.33765739
19 -0.63553399
20 -0.48683277
22 -0.46516627
23 0.42147909
with conditional variances for "subject" print(head(seq_length_diag$Mixed_Z$ScaledResiduals)) 1 2 3 4 5 6
-0.73126265 -0.36255661 -0.95756250 -0.10941782 0.18974358 -0.08769942 #4.4 Model: Does Sequence Length Influence SD?
# --- Compute SD with Sequence Length per Trial ---
compute_step_sd_with_sequence_length <- function(df, label) {
df %>%
filter(phase == "Execution", Marker.Text %in% c(14, 15, 16, 17)) %>%
group_by(subject, Block, trial) %>%
mutate(SequenceLength = n()) %>%
group_by(subject, Block, trial, Marker.Text, SequenceLength) %>%
summarise(
sd_x = sd(CoM.acc.x, na.rm = TRUE),
sd_y = sd(CoM.acc.y, na.rm = TRUE),
sd_z = sd(CoM.acc.z, na.rm = TRUE),
.groups = "drop"
) %>%
pivot_longer(cols = starts_with("sd_"), names_to = "Axis", values_to = "SD") %>%
mutate(
Axis = gsub("sd_", "", Axis),
Dataset = label,
subject = factor(subject),
SequenceLength = factor(SequenceLength)
)
}
# --- LMM per Axis for Sequence Length Effect on SD ---
run_sequence_length_sd_model <- function(df, label) {
get_anova <- function(axis_label) {
model <- lmer(SD ~ SequenceLength + (1 | subject), data = filter(df, Axis == axis_label))
anova(model)
}
ax <- get_anova("x")
ay <- get_anova("y")
az <- get_anova("z")
tibble(
Dataset = label,
Axis = c("X", "Y", "Z"),
`SequenceLength p-value` = c(ax["SequenceLength", "Pr(>F)"],
ay["SequenceLength", "Pr(>F)"],
az["SequenceLength", "Pr(>F)"])
)
}
# --- Run Sequence Length SD Analysis on Mixed Data ---
step_sd_seq_mixed <- compute_step_sd_with_sequence_length(tagged_data, "Mixed")
seq_length_sd_pvals <- run_sequence_length_sd_model(step_sd_seq_mixed, "Mixed")
# --- Display SD Model Results ---
print(seq_length_sd_pvals)# A tibble: 3 × 3
Dataset Axis `SequenceLength p-value`
<chr> <chr> <dbl>
1 Mixed X 0.000210
2 Mixed Y 0.0214
3 Mixed Z 0.0000533# --- Suppress emmeans/pbkrtest warnings globally ---
emmeans::emm_options(
lmerTest.limit = Inf,
pbkrtest.limit = Inf
)
# --- Compute SD with Sequence Length per Trial ---
compute_step_sd_with_sequence_length <- function(df, label) {
df %>%
filter(phase == "Execution", Marker.Text %in% c(14, 15, 16, 17)) %>%
group_by(subject, Block, trial) %>%
mutate(SequenceLength = n()) %>%
group_by(subject, Block, trial, Marker.Text, SequenceLength) %>%
summarise(
sd_x = sd(CoM.acc.x, na.rm = TRUE),
sd_y = sd(CoM.acc.y, na.rm = TRUE),
sd_z = sd(CoM.acc.z, na.rm = TRUE),
.groups = "drop"
) %>%
pivot_longer(cols = starts_with("sd_"), names_to = "Axis", values_to = "SD") %>%
mutate(
Axis = gsub("sd_", "", Axis),
Dataset = label,
subject = factor(subject),
SequenceLength = factor(SequenceLength)
)
}
# --- Extended: LMM per Axis for Sequence Length Effect on SD ---
run_sequence_length_sd_model_diagnostics <- function(df, label) {
axes <- c("x", "y", "z")
results <- list()
for (axis in axes) {
model <- lmer(SD ~ SequenceLength + (1 | subject), data = filter(df, Axis == axis))
key <- paste0(label, "_", toupper(axis))
results[[key]] <- list(
ANOVA = anova(model),
Emmeans = emmeans(model, ~ SequenceLength),
FixedEffects = fixef(model),
RandomEffects = ranef(model),
ScaledResiduals = resid(model, scaled = TRUE),
Model = model
)
}
return(results)
}
# --- Run Model and Extract Diagnostics ---
step_sd_seq_mixed <- compute_step_sd_with_sequence_length(tagged_data, "Mixed")
seq_length_sd_diag <- run_sequence_length_sd_model_diagnostics(step_sd_seq_mixed, "Mixed")
# --- Display Diagnostics for Axis X ---
cat("\n=== SEQUENCE LENGTH SD MODEL: Axis X ===\n")
=== SEQUENCE LENGTH SD MODEL: Axis X ===print(seq_length_sd_diag$Mixed_X$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
SequenceLength 5.1399 1.285 4 10494 5.479 0.0002103 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(seq_length_sd_diag$Mixed_X$Emmeans)) SequenceLength emmean SE df lower.CL upper.CL
5 0.865 0.1910 2419.6 0.491 1.239
6 0.526 0.0530 18.0 0.415 0.637
11 -0.079 0.1790 1985.8 -0.431 0.273
12 0.547 0.0526 17.4 0.436 0.658
18 0.522 0.0526 17.5 0.411 0.633
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(seq_length_sd_diag$Mixed_X$FixedEffects) (Intercept) SequenceLength6 SequenceLength11 SequenceLength12
0.8647651 -0.3388310 -0.9437831 -0.3178414
SequenceLength18
-0.3424989 print(seq_length_sd_diag$Mixed_X$RandomEffects)$subject
(Intercept)
2 0.05357079
3 0.05179505
4 -0.17197087
5 -0.23596647
7 -0.09540464
8 0.16549144
10 0.63696777
11 0.33174416
13 -0.13389309
14 0.15327494
15 -0.09959260
16 -0.02519785
17 -0.12802594
18 -0.05634306
19 -0.20015367
20 -0.16863686
22 -0.17314635
23 0.09548725
with conditional variances for "subject" print(head(seq_length_sd_diag$Mixed_X$ScaledResiduals)) 1 3 5 7 9 11
-0.8944736 -1.0235233 -1.1320258 -0.8847848 -0.8272984 -1.0565356 # --- Display Diagnostics for Axis Y ---
cat("\n=== SEQUENCE LENGTH SD MODEL: Axis Y ===\n")
=== SEQUENCE LENGTH SD MODEL: Axis Y ===print(seq_length_sd_diag$Mixed_Y$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
SequenceLength 3.436 0.859 4 10494 2.8777 0.02143 *
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(seq_length_sd_diag$Mixed_Y$Emmeans)) SequenceLength emmean SE df lower.CL upper.CL
5 0.374 0.2150 2605.1 -0.0469 0.796
6 0.554 0.0583 18.1 0.4319 0.677
11 0.224 0.2020 2144.8 -0.1721 0.620
12 0.576 0.0578 17.4 0.4541 0.698
18 0.543 0.0579 17.5 0.4207 0.664
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(seq_length_sd_diag$Mixed_Y$FixedEffects) (Intercept) SequenceLength6 SequenceLength11 SequenceLength12
0.3743739 0.1801059 -0.1502666 0.2014899
SequenceLength18
0.1681699 print(seq_length_sd_diag$Mixed_Y$RandomEffects)$subject
(Intercept)
2 0.10951141
3 0.12664285
4 -0.19684764
5 -0.25913656
7 -0.06860125
8 0.26833537
10 0.61703214
11 0.32411261
13 -0.14357021
14 0.12144695
15 -0.12156601
16 -0.05942662
17 -0.11835536
18 -0.08892038
19 -0.26722993
20 -0.19137652
22 -0.26257925
23 0.21052840
with conditional variances for "subject" print(head(seq_length_sd_diag$Mixed_Y$ScaledResiduals)) 1 3 5 7 9 11
-0.4348516 -1.0671381 -1.1047401 -1.0053693 -0.2228061 -0.3297785 # --- Display Diagnostics for Axis Z ---
cat("\n=== SEQUENCE LENGTH SD MODEL: Axis Z ===\n")
=== SEQUENCE LENGTH SD MODEL: Axis Z ===print(seq_length_sd_diag$Mixed_Z$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
SequenceLength 22.772 5.6931 4 10494 6.2253 5.333e-05 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(seq_length_sd_diag$Mixed_Z$Emmeans)) SequenceLength emmean SE df lower.CL upper.CL
5 1.2085 0.3740 3203.6 0.475 1.942
6 0.8894 0.0950 18.2 0.690 1.089
11 0.0981 0.3520 2666.3 -0.591 0.788
12 0.9910 0.0940 17.5 0.793 1.189
18 0.9749 0.0941 17.6 0.777 1.173
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(seq_length_sd_diag$Mixed_Z$FixedEffects) (Intercept) SequenceLength6 SequenceLength11 SequenceLength12
1.2085449 -0.3191928 -1.1104780 -0.2175467
SequenceLength18
-0.2336458 print(seq_length_sd_diag$Mixed_Z$RandomEffects)$subject
(Intercept)
2 -5.668181e-03
3 2.204648e-01
4 -3.089863e-01
5 -4.538521e-01
7 9.664095e-02
8 5.206798e-01
10 9.713584e-01
11 5.823385e-01
13 -9.885303e-02
14 -1.358140e-01
15 -2.836437e-02
16 -4.906112e-03
17 -3.613736e-01
18 -4.733085e-05
19 -3.992486e-01
20 -3.788382e-01
22 -4.349800e-01
23 2.194495e-01
with conditional variances for "subject" print(head(seq_length_sd_diag$Mixed_Z$ScaledResiduals)) 1 3 5 7 9 11
-0.77140783 -0.84919395 0.04769161 -0.89023568 1.36427252 -0.80744817 #5 top 50% vs bottom 50%
# --- Tag Top vs Bottom Performers ---
tag_performance_group <- function(df) {
top_ids <- c(17, 7, 23, 16, 10, 14, 13, 2, 8)
df %>%
mutate(
PerformanceGroup = ifelse(subject %in% top_ids, "Top", "Bottom"),
PerformanceGroup = factor(PerformanceGroup, levels = c("Top", "Bottom"))
)
}
# --- Compute Step-Level RMS with Buffer + Performance Group ---
compute_step_rms_grouped <- function(df) {
buffer <- 3
step_markers <- c(14, 15, 16, 17)
step_data <- df %>%
filter(phase == "Execution", Marker.Text %in% step_markers) %>%
assign_steps_by_block() %>%
arrange(subject, Block, trial, ms) %>%
group_by(subject, Block, trial) %>%
mutate(row_id = row_number()) %>%
ungroup()
step_indices <- step_data %>%
select(subject, Block, trial, row_id, Step)
window_data <- map_dfr(1:nrow(step_indices), function(i) {
step <- step_indices[i, ]
rows <- (step$row_id - buffer):(step$row_id + buffer)
step_data %>%
filter(subject == step$subject,
Block == step$Block,
trial == step$trial,
row_id %in% rows) %>%
mutate(Step = step$Step)
})
window_data %>%
group_by(subject, Block, Step) %>%
summarise(
rms_x = sqrt(mean(CoM.acc.x^2, na.rm = TRUE)),
rms_y = sqrt(mean(CoM.acc.y^2, na.rm = TRUE)),
rms_z = sqrt(mean(CoM.acc.z^2, na.rm = TRUE)),
.groups = "drop"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = gsub("rms_", "", Axis),
Step = as.numeric(Step),
subject = factor(subject),
Block = factor(Block)
) %>%
tag_performance_group()
}
# --- Run LMM to Compare Top vs Bottom ---
run_group_comparison_model <- function(df, label) {
axes <- c("x", "y", "z")
results <- list()
for (axis in axes) {
model <- lmer(
RMS ~ Step * Block * PerformanceGroup + (1 | subject),
data = filter(df, Axis == axis)
)
key <- paste0(label, "_", toupper(axis))
results[[key]] <- list(
ANOVA = anova(model),
Emmeans = emmeans(model, ~ Step * Block * PerformanceGroup),
FixedEffects = fixef(model),
RandomEffects = ranef(model),
ScaledResiduals = resid(model, scaled = TRUE),
Model = model
)
}
return(results)
}
# --- Compute and Run ---
step_rms_grouped <- compute_step_rms_grouped(tagged_data)
top_bottom_results <- run_group_comparison_model(step_rms_grouped, "StepRMS_PerfGroup")
# --- Inspect X-Axis Results ---
cat("\n=== TOP vs BOTTOM PERFORMER ANALYSIS: Axis X ===\n")
=== TOP vs BOTTOM PERFORMER ANALYSIS: Axis X ===print(top_bottom_results$StepRMS_PerfGroup_X$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 0.23877 0.23877 1 1260.00 7.0907 0.007847 **
Block 2.41418 0.60355 4 1260.00 17.9236 2.521e-14 ***
PerformanceGroup 0.04629 0.04629 1 16.82 1.3745 0.257371
Step:Block 0.31355 0.07839 4 1260.00 2.3279 0.054362 .
Step:PerformanceGroup 0.00013 0.00013 1 1260.00 0.0040 0.949598
Block:PerformanceGroup 0.20777 0.05194 4 1260.00 1.5426 0.187512
Step:Block:PerformanceGroup 0.03661 0.00915 4 1260.00 0.2718 0.896234
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(top_bottom_results$StepRMS_PerfGroup_X$Emmeans)) Step Block PerformanceGroup emmean SE df lower.CL upper.CL
8.5 1 Top 0.993 0.1250 41.6 0.742 1.245
8.5 2 Top 0.847 0.0998 17.2 0.636 1.057
8.5 3 Top 0.713 0.0988 16.6 0.504 0.921
8.5 4 Top 0.898 0.0988 16.6 0.689 1.107
8.5 5 Top 0.741 0.0988 16.6 0.532 0.950
8.5 1 Bottom 0.858 0.1250 41.6 0.607 1.109
8.5 2 Bottom 0.713 0.0998 17.2 0.503 0.924
8.5 3 Bottom 0.598 0.0988 16.6 0.389 0.807
8.5 4 Bottom 0.620 0.0988 16.6 0.412 0.829
8.5 5 Bottom 0.567 0.0988 16.6 0.359 0.776
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(top_bottom_results$StepRMS_PerfGroup_X$FixedEffects) (Intercept) Step
1.0232570767 -0.0035240667
Block2 Block3
-0.0573792029 -0.2653315214
Block4 Block5
-0.0915947287 -0.2567999848
PerformanceGroupBottom Step:Block2
-0.2162754294 -0.0105202952
Step:Block3 Step:Block4
-0.0018176046 -0.0003923818
Step:Block5 Step:PerformanceGroupBottom
0.0005326844 0.0095235474
Block2:PerformanceGroupBottom Block3:PerformanceGroupBottom
0.1127140176 0.1095931338
Block4:PerformanceGroupBottom Block5:PerformanceGroupBottom
-0.0172128406 0.0537323855
Step:Block2:PerformanceGroupBottom Step:Block3:PerformanceGroupBottom
-0.0130146071 -0.0104805832
Step:Block4:PerformanceGroupBottom Step:Block5:PerformanceGroupBottom
-0.0147540559 -0.0108188871 print(top_bottom_results$StepRMS_PerfGroup_X$RandomEffects)$subject
(Intercept)
2 0.02063865
3 0.12662081
4 -0.15993058
5 -0.25401347
7 -0.24936885
8 0.22457842
10 0.72589981
11 0.61103956
13 -0.27790222
14 -0.05475486
15 0.01966492
16 -0.15050185
17 -0.27500607
18 0.05901077
19 -0.19351392
20 -0.15570200
22 -0.05317609
23 0.03641696
with conditional variances for "subject" print(head(top_bottom_results$StepRMS_PerfGroup_X$ScaledResiduals)) 1 2 3 4 5 6
-2.514963 -2.447129 -2.415125 -2.395921 -2.202891 -2.255295 cat("\n=== TOP vs BOTTOM PERFORMER ANALYSIS: Axis Y ===\n")
=== TOP vs BOTTOM PERFORMER ANALYSIS: Axis Y ===print(top_bottom_results$StepRMS_PerfGroup_Y$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 0.44036 0.44036 1 1260.00 6.4295 0.0113442 *
Block 2.38367 0.59592 4 1260.00 8.7008 6.291e-07 ***
PerformanceGroup 0.31771 0.31771 1 17.48 4.6388 0.0454853 *
Step:Block 0.36458 0.09115 4 1260.00 1.3308 0.2563668
Step:PerformanceGroup 0.07288 0.07288 1 1260.00 1.0641 0.3024866
Block:PerformanceGroup 1.34674 0.33669 4 1260.00 4.9158 0.0006191 ***
Step:Block:PerformanceGroup 0.30489 0.07622 4 1260.00 1.1129 0.3488521
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(top_bottom_results$StepRMS_PerfGroup_Y$Emmeans)) Step Block PerformanceGroup emmean SE df lower.CL upper.CL
8.5 1 Top 1.081 0.152 69.5 0.779 1.384
8.5 2 Top 0.936 0.108 18.2 0.709 1.163
8.5 3 Top 0.754 0.106 17.0 0.530 0.978
8.5 4 Top 0.940 0.106 17.0 0.716 1.164
8.5 5 Top 0.969 0.106 17.0 0.745 1.193
8.5 1 Bottom 0.759 0.152 69.5 0.456 1.061
8.5 2 Bottom 0.768 0.108 18.2 0.541 0.995
8.5 3 Bottom 0.597 0.106 17.0 0.373 0.821
8.5 4 Bottom 0.685 0.106 17.0 0.460 0.909
8.5 5 Bottom 0.531 0.106 17.0 0.307 0.755
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(top_bottom_results$StepRMS_PerfGroup_Y$FixedEffects) (Intercept) Step
1.106875568 -0.003007596
Block2 Block3
0.010226216 -0.310633623
Block4 Block5
-0.050906970 -0.006449728
PerformanceGroupBottom Step:Block2
-0.342310751 -0.018299576
Step:Block3 Step:Block4
-0.001928342 -0.010668633
Step:Block5 Step:PerformanceGroupBottom
-0.012418844 0.002296496
Block2:PerformanceGroupBottom Block3:PerformanceGroupBottom
0.002381548 0.181628879
Block4:PerformanceGroupBottom Block5:PerformanceGroupBottom
0.083415750 -0.184748314
Step:Block2:PerformanceGroupBottom Step:Block3:PerformanceGroupBottom
0.017889324 -0.001903484
Step:Block4:PerformanceGroupBottom Step:Block5:PerformanceGroupBottom
-0.001861503 0.008141814 print(top_bottom_results$StepRMS_PerfGroup_Y$RandomEffects)$subject
(Intercept)
2 0.080783799
3 0.316720216
4 -0.159994548
5 -0.215888243
7 -0.334291290
8 0.268888618
10 0.631831875
11 0.562812662
13 -0.323033692
14 -0.129143313
15 0.004970042
16 -0.213143582
17 -0.295401150
18 0.059210951
19 -0.211773333
20 -0.139635931
22 -0.216421817
23 0.313508736
with conditional variances for "subject" print(head(top_bottom_results$StepRMS_PerfGroup_Y$ScaledResiduals)) 1 2 3 4 5 6
-2.195117 -2.331548 -2.293657 -2.282164 -2.182169 -2.058850 cat("\n=== TOP vs BOTTOM PERFORMER ANALYSIS: Axis Z ===\n")
=== TOP vs BOTTOM PERFORMER ANALYSIS: Axis Z ===print(top_bottom_results$StepRMS_PerfGroup_Z$ANOVA)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Step 0.5721 0.57209 1 1260.00 3.7189 0.05403 .
Block 9.0610 2.26524 4 1260.00 14.7251 9.211e-12 ***
PerformanceGroup 0.2571 0.25706 1 16.71 1.6710 0.21371
Step:Block 1.4199 0.35499 4 1260.00 2.3076 0.05620 .
Step:PerformanceGroup 0.0209 0.02091 1 1260.00 0.1359 0.71242
Block:PerformanceGroup 1.5166 0.37915 4 1260.00 2.4646 0.04343 *
Step:Block:PerformanceGroup 0.8110 0.20275 4 1260.00 1.3180 0.26117
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1print(summary(top_bottom_results$StepRMS_PerfGroup_Z$Emmeans)) Step Block PerformanceGroup emmean SE df lower.CL upper.CL
8.5 1 Top 2.04 0.278 37.6 1.477 2.60
8.5 2 Top 1.80 0.228 17.1 1.320 2.28
8.5 3 Top 1.66 0.226 16.5 1.178 2.13
8.5 4 Top 1.92 0.226 16.5 1.437 2.39
8.5 5 Top 1.73 0.226 16.5 1.252 2.21
8.5 1 Bottom 1.85 0.278 37.6 1.284 2.41
8.5 2 Bottom 1.63 0.228 17.1 1.151 2.11
8.5 3 Bottom 1.22 0.226 16.5 0.741 1.70
8.5 4 Bottom 1.39 0.226 16.5 0.907 1.86
8.5 5 Bottom 1.14 0.226 16.5 0.660 1.62
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(top_bottom_results$StepRMS_PerfGroup_Z$FixedEffects) (Intercept) Step
1.9798197650 0.0070763960
Block2 Block3
0.0622920952 -0.3092209024
Block4 Block5
0.0929333079 -0.1240243787
PerformanceGroupBottom Step:Block2
-0.1451560468 -0.0353677728
Step:Block3 Step:Block4
-0.0087974049 -0.0256342517
Step:Block5 Step:PerformanceGroupBottom
-0.0218997384 -0.0055526106
Block2:PerformanceGroupBottom Block3:PerformanceGroupBottom
-0.2502326036 -0.2393959275
Block4:PerformanceGroupBottom Block5:PerformanceGroupBottom
-0.3308038330 -0.5278751442
Step:Block2:PerformanceGroupBottom Step:Block3:PerformanceGroupBottom
0.0320788302 -0.0005753052
Step:Block4:PerformanceGroupBottom Step:Block5:PerformanceGroupBottom
-0.0008040793 0.0151415792 print(top_bottom_results$StepRMS_PerfGroup_Z$RandomEffects)$subject
(Intercept)
2 -0.31728819
3 0.51870711
4 -0.43996627
5 -0.59511638
7 -0.08056039
8 0.33987120
10 1.74233164
11 1.05116300
13 -0.35881823
14 -0.48942616
15 0.17014432
16 -0.06426532
17 -0.85998160
18 0.55890285
19 -0.51225727
20 -0.37537986
22 -0.37619749
23 0.08813705
with conditional variances for "subject" print(head(top_bottom_results$StepRMS_PerfGroup_Z$ScaledResiduals)) 1 2 3 4 5 6
-1.368196 -1.521605 -1.692512 -1.710554 -1.966153 -1.902318 #5.1 step lvl rms
# --- Summarize Data for Plotting by Block ---
plot_summary_by_block <- step_rms_grouped %>%
group_by(Block, Step, Axis, PerformanceGroup) %>%
summarise(
MeanRMS = mean(RMS, na.rm = TRUE),
SERMS = sd(RMS, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
# --- Plot Function per Block and Axis ---
plot_rms_by_step_group_blocked <- function(summary_data, axis_label) {
blocks <- unique(summary_data$Block)
plots <- lapply(blocks, function(blk) {
df_blk <- filter(summary_data, Block == blk, Axis == axis_label)
ggplot(df_blk, aes(x = Step, y = MeanRMS, color = PerformanceGroup)) +
geom_line(size = 1.2) +
geom_point(size = 2) +
geom_errorbar(aes(ymin = MeanRMS - SERMS, ymax = MeanRMS + SERMS), width = 0.2) +
labs(
title = paste("Block", blk, "- RMS (Axis", toupper(axis_label), ") by Step & Performance Group"),
x = "Step",
y = "Mean RMS Acceleration",
color = "Group"
) +
theme_minimal(base_size = 13)
})
return(plots)
}
# --- Plot for Axis X (returns list of ggplot objects, one per block) ---
plots_x <- plot_rms_by_step_group_blocked(plot_summary_by_block, "x")Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.plots_y <- plot_rms_by_step_group_blocked(plot_summary_by_block, "y")
plots_z <- plot_rms_by_step_group_blocked(plot_summary_by_block, "z")
# --- Display (e.g., first plot) ---
print(plots_x[[1]]) # Change index to view Block 2, 3, etc.
print(plots_x[[2]])
print(plots_x[[3]])
print(plots_x[[4]])
print(plots_x[[5]])
print(plots_y[[1]]) # Change index to view Block 2, 3, etc.
print(plots_y[[2]])
print(plots_y[[3]])
print(plots_y[[4]])
print(plots_y[[5]])
print(plots_z[[1]]) # Change index to view Block 2, 3, etc.
print(plots_z[[2]])
print(plots_z[[3]])
print(plots_z[[4]])
print(plots_z[[5]])
#5.2 Block avg RMS
# --- Assign Top/Bottom Groups ---
top_subjects <- c(17, 7, 23, 16, 10, 14, 13, 2, 8)
tagged_data <- tagged_data %>%
mutate(
PerformanceGroup = ifelse(subject %in% top_subjects, "Top", "Bottom"),
subject = factor(subject),
Block = factor(Block),
phase = factor(phase)
)
# --- Compute RMS per Block, Phase, and Subject ---
block_rms_summary <- tagged_data %>%
group_by(subject, Block, phase, PerformanceGroup) %>%
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"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(Axis = gsub("rms_", "", Axis))
# --- LMM: Does Performance Group Affect RMS? ---
library(lmerTest)
run_group_block_rms_model <- function(axis) {
lmer(RMS ~ PerformanceGroup * Block * phase + (1 | subject),
data = filter(block_rms_summary, Axis == axis))
}
model_x <- run_group_block_rms_model("x")
model_y <- run_group_block_rms_model("y")
model_z <- run_group_block_rms_model("z")
anova(model_x)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
PerformanceGroup 0.0665 0.0665 1 16 2.7902 0.11429
Block 0.2654 0.0664 4 144 2.7851 0.02885 *
phase 11.7375 11.7375 1 144 492.6213 < 2.2e-16 ***
PerformanceGroup:Block 0.0634 0.0159 4 144 0.6654 0.61703
PerformanceGroup:phase 0.1430 0.1430 1 144 5.9999 0.01551 *
Block:phase 1.0974 0.2744 4 144 11.5147 3.874e-08 ***
PerformanceGroup:Block:phase 0.0123 0.0031 4 144 0.1290 0.97165
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(model_y)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
PerformanceGroup 0.1166 0.1166 1 16 2.7670 0.115689
Block 0.3670 0.0918 4 144 2.1781 0.074368 .
phase 13.3322 13.3322 1 144 316.4564 < 2.2e-16 ***
PerformanceGroup:Block 0.0473 0.0118 4 144 0.2804 0.890276
PerformanceGroup:phase 0.4641 0.4641 1 144 11.0158 0.001144 **
Block:phase 1.2060 0.3015 4 144 7.1564 2.77e-05 ***
PerformanceGroup:Block:phase 0.0937 0.0234 4 144 0.5563 0.694730
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(model_z)Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
PerformanceGroup 0.247 0.247 1 16 2.7790 0.114960
Block 1.034 0.258 4 144 2.9125 0.023595 *
phase 36.376 36.376 1 144 409.9435 < 2.2e-16 ***
PerformanceGroup:Block 0.300 0.075 4 144 0.8446 0.499088
PerformanceGroup:phase 0.717 0.717 1 144 8.0850 0.005113 **
Block:phase 2.847 0.712 4 144 8.0198 7.259e-06 ***
PerformanceGroup:Block:phase 0.070 0.018 4 144 0.1985 0.938791
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# --- Plot Group Differences by Axis, Phase, and Block ---
plot_block_summary <- block_rms_summary %>%
group_by(PerformanceGroup, Block, phase, Axis) %>%
summarise(
MeanRMS = mean(RMS, na.rm = TRUE),
SERMS = sd(RMS, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
ggplot(plot_block_summary, aes(x = Block, y = MeanRMS, fill = PerformanceGroup)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.8)) +
geom_errorbar(aes(ymin = MeanRMS - SERMS, ymax = MeanRMS + SERMS),
width = 0.2, position = position_dodge(0.8)) +
facet_grid(phase ~ Axis) +
labs(
title = "Block-Level RMS by Group, Phase, and Axis",
x = "Block", y = "Mean RMS"
) +
theme_minimal(base_size = 14)
#6. Difficulty comparison #6.1 6 steps Block 1,4 & 5
# -------- Step-Wise RMS ± SD: Blocks 1, 4, 5 — First 6 Steps --------
plot_stepwise_rms_blocks_145_first6 <- function(tagged_data2) {
step_markers <- c(14, 15, 16, 17)
buffer <- 3
step_data <- tagged_data2 %>%
filter(phase == "Execution", Marker.Text %in% step_markers) %>%
assign_steps_by_block() %>%
arrange(subject, Block, trial, ms) %>%
group_by(subject, Block, trial) %>%
mutate(row_id = row_number()) %>%
ungroup()
step_indices <- step_data %>%
select(subject, Block, trial, row_id, Step)
window_data <- map_dfr(1:nrow(step_indices), function(i) {
step <- step_indices[i, ]
rows <- (step$row_id - buffer):(step$row_id + buffer)
step_data %>%
filter(subject == step$subject,
Block == step$Block,
trial == step$trial,
row_id %in% rows) %>%
mutate(Step = step$Step)
})
step_summary <- window_data %>%
group_by(subject, Block, Step) %>%
summarise(
rms_x = sqrt(mean(CoM.acc.x^2, na.rm = TRUE)),
rms_y = sqrt(mean(CoM.acc.y^2, na.rm = TRUE)),
rms_z = sqrt(mean(CoM.acc.z^2, na.rm = TRUE)),
.groups = "drop"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = toupper(gsub("rms_", "", Axis)),
Step = factor(Step),
Block = factor(Block),
subject = factor(subject)
) %>%
filter(Block %in% c("1", "4", "5"), Step %in% 1:6)
plot_data <- step_summary %>%
group_by(Block, Step, Axis) %>%
summarise(
mean_rms = mean(RMS, na.rm = TRUE),
sd_rms = sd(RMS, na.rm = TRUE),
.groups = "drop"
)
axis_labels <- unique(plot_data$Axis)
plots <- map(axis_labels, function(ax) {
axis_data <- filter(plot_data, Axis == ax)
ggplot(axis_data, aes(x = factor(Step), y = mean_rms, fill = Block)) +
geom_col(position = position_dodge(width = 0.8), width = 0.7) +
geom_errorbar(
aes(ymin = mean_rms - sd_rms, ymax = mean_rms + sd_rms),
position = position_dodge(width = 0.8),
width = 0.3
) +
ylim(0, 3.25) +
labs(
title = paste("Step-wise CoM RMS Acceleration ± SD — Axis", ax),
x = "Step Number (1–6)",
y = "RMS Acceleration (m/s²)",
fill = "Block"
) +
theme_minimal() +
theme(
text = element_text(size = 12),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 0, vjust = 0.5)
)
})
names(plots) <- axis_labels
return(list(
plots = plots,
step_summary = step_summary,
plot_data = plot_data,
window_data = window_data
))
}
# -------- Run the Analysis Pipeline --------
result <- plot_stepwise_rms_blocks_145_first6(tagged_data2)
stepwise_block145_plots <- result$plots
step_summary <- result$step_summary
plot_data <- result$plot_data
window_data <- result$window_data
# -------- Print Plots --------
for (plot_name in names(stepwise_block145_plots)) {
cat("\n\n==== Axis:", plot_name, "====\n\n")
print(stepwise_block145_plots[[plot_name]])
}
==== Axis: X ====
==== Axis: Y ====
==== Axis: Z ====
# -------- Compute SD Per Subject from Raw Window Data --------
sd_subject_data <- window_data %>%
group_by(subject, Block, Step) %>%
summarise(
sd_x = sd(CoM.acc.x, na.rm = TRUE),
sd_y = sd(CoM.acc.y, na.rm = TRUE),
sd_z = sd(CoM.acc.z, na.rm = TRUE),
.groups = "drop"
) %>%
pivot_longer(cols = starts_with("sd_"), names_to = "Axis", values_to = "sd_rms") %>%
mutate(
Axis = toupper(gsub("sd_", "", Axis)),
Step = factor(Step),
Block = factor(Block),
subject = factor(subject)
) %>%
filter(Step %in% 1:6, Block %in% c("1", "4", "5"))
# -------- Run LMM on sd_rms --------
cat("\n\n==== LMM: SD ~ Block + Step + Axis + (1|subject) ====\n\n")
==== LMM: SD ~ Block + Step + Axis + (1|subject) ====sd_model <- lmer(sd_rms ~ Block + Step + Axis + (1 | subject), data = sd_subject_data)
summary(sd_model)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: sd_rms ~ Block + Step + Axis + (1 | subject)
Data: sd_subject_data
REML criterion at convergence: 877.4
Scaled residuals:
Min 1Q Median 3Q Max
-3.0461 -0.5436 -0.0296 0.4436 6.7357
Random effects:
Groups Name Variance Std.Dev.
subject (Intercept) 0.2358 0.4856
Residual 0.1273 0.3568
Number of obs: 972, groups: subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
(Intercept) 8.292e-01 1.201e-01 2.016e+01 6.907 9.97e-07 ***
Block4 -2.576e-02 2.804e-02 9.450e+02 -0.919 0.3585
Block5 -1.826e-01 2.804e-02 9.450e+02 -6.512 1.20e-10 ***
Step2 -1.458e-04 3.965e-02 9.450e+02 -0.004 0.9971
Step3 5.839e-03 3.965e-02 9.450e+02 0.147 0.8830
Step4 -1.036e-02 3.965e-02 9.450e+02 -0.261 0.7939
Step5 6.022e-03 3.965e-02 9.450e+02 0.152 0.8793
Step6 -3.091e-03 3.965e-02 9.450e+02 -0.078 0.9379
AxisY 7.130e-02 2.804e-02 9.450e+02 2.543 0.0111 *
AxisZ 6.758e-01 2.804e-02 9.450e+02 24.105 < 2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
(Intr) Block4 Block5 Step2 Step3 Step4 Step5 Step6 AxisY
Block4 -0.117
Block5 -0.117 0.500
Step2 -0.165 0.000 0.000
Step3 -0.165 0.000 0.000 0.500
Step4 -0.165 0.000 0.000 0.500 0.500
Step5 -0.165 0.000 0.000 0.500 0.500 0.500
Step6 -0.165 0.000 0.000 0.500 0.500 0.500 0.500
AxisY -0.117 0.000 0.000 0.000 0.000 0.000 0.000 0.000
AxisZ -0.117 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.500# Type III ANOVA
cat("\n\n---- Type III ANOVA ----\n")
---- Type III ANOVA ----print(anova(sd_model, type = 3))Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 6.327 3.164 2 945 24.8436 3.052e-11 ***
Step 0.030 0.006 5 945 0.0475 0.9986
Axis 89.346 44.673 2 945 350.8175 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Scaled Residuals
cat("\n\n---- Scaled Residuals ----\n")
---- Scaled Residuals ----print(resid(sd_model, scaled = TRUE)) 1 2 3 4 5
-0.5863259519 -0.7007378343 -1.2086572113 -0.5793124813 -0.8030092027
6 7 8 9 10
-1.3795749040 -0.5642909384 -0.7972276328 -1.5097775523 -0.5188865821
11 12 13 14 15
-0.7518232766 -1.4643731961 -0.4773458393 -0.7270903856 -1.7180274687
16 17 18 19 20
-0.4797327628 -0.6200311594 -1.5619792361 0.5965443569 0.2240465762
21 22 23 24 25
-0.1041884950 0.4935230838 0.2849053764 0.3269353913 0.7232950235
26 27 28 29 30
0.0812839377 -0.1076737406 0.4330325404 0.3124158800 -0.1158471402
31 32 33 34 35
0.2423260354 0.4365196440 -0.1533308077 0.0495486396 0.3345679354
36 37 38 39 40
-0.9408527188 0.5285054791 1.4510901798 0.5647242027 0.5938527102
41 42 43 44 45
1.4085432034 0.4407339743 0.3908508592 1.1161049350 -0.0179039373
46 47 48 49 50
0.5023573099 1.1125918268 0.8153800222 0.4763878325 1.0666607830
51 52 53 54 55
1.0252047291 0.2863771267 0.5986013700 0.9047974079 -0.1032479088
56 57 58 59 60
-0.2903247186 -0.6045009879 0.1401398749 -0.2181407500 0.1483590233
61 62 63 64 65
0.2267297142 -0.2982558968 0.3327156530 0.2721340705 -0.2528515405
66 67 68 69 70
0.3781200093 0.4288447155 -0.1920864298 0.6298752277 0.4087846966
71 72 73 74 75
-0.3221509231 0.7193257448 -0.6172909142 0.5050578416 1.0981819198
76 77 78 79 80
-0.5548924119 0.4174715309 1.3788068572 -0.6028932787 0.2624629099
81 82 83 84 85
1.0470657626 -0.6137127051 0.4073641268 1.0902499336 -0.6127318867
86 87 88 89 90
0.0706576327 0.7746613070 -0.5438160720 -0.2673819910 0.3821851792
91 92 93 94 95
-0.3648802816 -0.0194167954 -0.1896099389 -0.2453329262 0.0671281858
96 97 98 99 100
-0.1721482879 -0.3012763172 -0.1914166795 0.0051073040 -0.4007568941
101 102 103 104 105
-0.0166606899 -0.1619924757 -0.5121946636 -0.0709626344 -0.6084514014
106 107 108 109 110
-0.6622673237 -0.1749520347 -0.8750094571 -0.1334369958 -0.7331683740
111 112 113 114 115
-1.1203278730 0.0710694053 -0.4590445076 -0.8673502596 -0.0090500073
116 117 118 119 120
-0.4763396374 -0.9885034387 0.0363543490 -0.4309352812 -0.9430990824
121 122 123 124 125
0.0178321772 -0.4266145749 -1.0072895420 0.0975848675 -0.3581054373
126 127 128 129 130
-1.2172769758 -0.1472519421 -0.1630053331 -0.4817786524 -0.0462100710
131 132 133 134 135
-0.1443173908 -0.5123853825 0.0523225772 0.0794256162 -0.2259387409
136 137 138 139 140
-0.1034920773 -0.0165570922 -0.5167167428 -0.0563264946 0.1658836867
141 142 143 144 145
-0.3857662222 0.2013307441 0.4242236333 -0.0158139304 0.7166625414
146 147 148 149 150
0.7400515019 0.1678926576 0.9567250281 0.8041485410 0.5273792496
151 152 153 154 155
1.0276880248 0.8528194814 0.2768529149 0.8045203307 0.6200499946
156 157 158 159 160
0.1547337248 0.7763738173 0.6113248739 0.1995309415 0.8129094630
161 162 163 164 165
0.3562996181 -0.0765978302 0.2302108911 0.2069936428 -0.6676969201
166 167 168 169 170
0.4198542482 0.2318035535 -0.5935183467 0.3978688646 0.1719287117
171 172 173 174 175
-0.6702383525 0.4636663951 0.2227468191 -0.5909857388 0.4143418665
176 177 178 179 180
0.0933062359 -0.5522435812 0.3626191362 0.1540512223 -0.8521109123
181 182 183 184 185
0.1319592497 0.4222972856 -0.6661686439 0.0388757986 0.4020805975
186 187 188 189 190
-0.6222615848 0.0595112669 0.4800392691 -0.3305667840 0.0976558104
191 192 193 194 195
0.3242111739 -0.7618004381 -0.0575591694 0.2796851615 -0.7865178157
196 197 198 199 200
0.0330021843 0.0216426201 -0.9944423978 0.4909418498 0.3531680377
201 202 203 204 205
-0.4244194179 0.5440915388 0.3596757499 -0.4886623271 0.5100380485
206 207 208 209 210
0.4239828992 -0.5639568624 0.6099645616 0.3937761667 -0.4586320442
211 212 213 214 215
0.4822925366 0.3384739990 -0.6290293230 0.4861356571 0.2949507309
216 217 218 219 220
-1.0180992060 -0.3793567257 -0.7260850868 -0.3193020423 -0.3257021301
221 222 223 224 225
-0.6765926588 -0.1166030983 -0.2807817697 -0.6110238277 0.1223498589
226 227 228 229 230
-0.2353774134 -0.5656194714 0.1677542151 -0.1686584849 -0.5350457007
231 232 233 234 235
0.3956358697 -0.0621352114 -0.4753337159 0.4417847361 0.1064500993
236 237 238 239 240
-0.4752901839 1.4192331775 0.0634274148 -0.4757263060 1.5419619396
241 242 243 244 245
0.2131169684 -0.4740673870 1.9854996575 0.1705432600 -0.4965576818
246 247 248 249 250
1.5126541542 0.1437853587 -0.5181075084 1.6753572476 0.0656248302
251 252 253 254 255
-0.5542054654 1.7858089132 -0.2638392934 0.1215818347 0.0503512516
256 257 258 259 260
-0.3187989532 0.1547728386 -0.1714326202 -0.4451837173 -0.2822884655
261 262 263 264 265
-0.5434636742 -0.2656688869 0.1265506163 -0.0072514812 -0.2922876660
266 267 268 269 270
0.1224593499 -0.1216776416 -0.4723362049 -0.3566549494 -0.5544444159
271 272 273 274 275
-0.2204500361 -0.5365617516 1.9041574648 -0.2617880859 -0.4264536200
276 277 278 279 280
1.8266787936 -0.3669315502 -0.3864335293 1.8728155493 -0.4433149278
281 282 283 284 285
-0.2720842144 2.0381370127 -0.3583592102 -0.7975329011 1.4398523371
286 287 288 289 290
-0.3403424041 -0.6706439956 1.2816298497 -0.8033170021 -0.9597548714
291 292 293 294 295
1.3980148585 -1.0184437121 -0.8501831262 1.1311657202 -0.5678141904
296 297 298 299 300
-0.5633051640 2.2904236780 -0.6653344799 -0.8380784469 1.6249648372
301 302 303 304 305
-0.8540355801 -0.8898801909 1.2794439133 -0.2653451345 -0.6641959655
306 307 308 309 310
1.7970545597 -0.0338159572 -0.2590424023 0.5734752967 -0.1933745272
311 312 313 314 315
-0.5996808468 0.4535668049 0.1731876040 -0.4234574689 0.4354429393
316 317 318 319 320
-0.3006745678 -0.7391925364 -0.0718269290 -0.3788483698 -1.0152843679
321 322 323 324 325
-0.2665044884 -0.7409869571 -0.8651397543 -0.6407132756 -0.1793594936
326 327 328 329 330
1.0638068342 -0.3808148664 -0.2908936555 1.1372051869 1.5553557953
331 332 333 334 335
-0.6720529607 1.0475954107 0.9800195428 -0.6266486045 1.0929997670
336 337 338 339 340
1.0254238990 -0.4546781599 1.5763428177 1.4573364505 -0.2400055055
341 342 343 344 345
1.3907293207 1.8523801624 -0.6473750869 0.7320762057 0.1533069373
346 347 348 349 350
-0.7432481842 0.6616282973 0.5897432686 -0.4701396597 0.7397293163
351 352 353 354 355
0.4997813460 -0.6542949172 0.2565015554 0.4445404428 -0.3638801734
356 357 358 359 360
0.4513977212 1.4589585688 -0.0876728109 0.1005861051 1.5318633958
361 362 363 364 365
-1.7739751425 -1.8460142128 1.5690069672 -1.8860400470 -1.8116062415
366 367 368 369 370
1.6629516389 -2.0822675627 -1.9389304771 0.9874025933 -1.9044502364
371 372 373 374 375
-1.8768448562 0.8915567932 -1.9925068581 -1.7990445469 1.3293602660
376 377 378 379 380
-1.2777959107 -1.9604284204 1.3985274628 1.8400199255 2.4327297098
381 382 383 384 385
4.3417166612 1.4387125924 2.0532944957 4.0921911155 1.1842586858
386 387 388 389 390
1.5775903959 4.3165642618 1.2296630421 1.6229947522 4.3619686181
391 392 393 394 395
1.6820887210 1.8859717344 5.1351079119 2.0936357487 2.2419763828
396 397 398 399 400
5.7803415896 -0.3571624215 0.3266712504 1.1011971677 -0.4382540606
401 402 403 404 405
0.2926726547 0.7609574946 -0.7745488089 -1.2447997625 -0.6390544579
406 407 408 409 410
-0.4753425995 0.1482960400 0.9883546686 -0.5900398087 0.2391177989
411 412 413 414 415
0.9485920846 -0.6873775684 -2.0281079551 -0.6516698914 -2.3652280797
416 417 418 419 420
-2.6166053177 -3.0460523353 -2.3004824477 -2.7045899316 -2.8141886875
421 422 423 424 425
-1.8216499990 -2.5966396430 -2.2879740767 -2.0493336338 -2.6407358059
426 427 428 429 430
-2.9240629147 -2.1227457908 -2.8200860173 -2.7880006627 -1.9352699384
431 432 433 434 435
-2.5948970950 -2.5589073763 -0.4234643541 0.2395995303 0.9502831449
436 437 438 439 440
-0.4658632078 0.1159525270 0.7361812280 -0.4543611175 0.1229069728
441 442 443 444 445
0.5955651689 -0.4089567612 0.1683113291 0.6409695252 -0.3960364994
446 447 448 449 450
0.1868621971 0.3776780519 -0.4168188910 0.3694379803 0.5543026419
451 452 453 454 455
-0.3750857498 -0.6633498571 -0.2617928009 -0.2761881550 -0.5697726112
456 457 458 459 460
-0.2883838576 -0.1226726637 -0.5637428439 -0.2166684124 -0.4107846058
461 462 463 464 465
-0.6308704915 -0.5184700817 -0.3644504589 -0.7104695224 -0.5806615546
466 467 468 469 470
-0.3463393265 -0.6670842561 -0.9099235879 -0.1009739583 -0.3102768001
471 472 473 474 475
1.1997908998 0.0722436569 -0.2295501839 1.3744824143 0.2523872378
476 477 478 479 480
-0.2940516485 1.4284116529 0.0356572420 -0.0780334609 1.1852811044
481 482 483 484 485
-0.0237944484 -0.0203740066 1.2613040507 0.0004866387 -0.3042331978
486 487 488 489 490
0.3720006355 1.3949883107 1.3997071203 -2.4214161852 1.2478771585
491 492 493 494 495
1.2738734690 -1.8215177491 1.1601108603 1.1613437082 -1.8512955749
496 497 498 499 500
1.2055152166 1.2067480645 -1.8058912186 1.2817484472 1.3158059918
501 502 503 504 505
-1.7888128318 0.3898413643 1.6761012980 -1.6433309343 -0.1269114611
506 507 508 509 510
-0.1111524362 0.6811659661 -0.0191526281 -0.1102873475 0.5333579183
511 512 513 514 515
-0.1520009181 -0.0637736957 0.7876752631 -0.0619476688 -0.2176496241
516 517 518 519 520
0.3877718096 -0.0290506170 -0.1439403642 0.2982611335 -0.4040816564
521 522 523 524 525
-0.5492627060 -0.2155808498 0.3148276162 0.0277308857 -1.0628880232
526 527 528 529 530
0.1100094185 0.0842629952 -1.1056914806 0.3897069136 0.0918896181
531 532 533 534 535
-0.9891772770 0.2995063784 -0.0241322046 -1.0481115260 0.1797405901
536 537 538 539 540
-0.0071589512 -0.8792624610 0.5999089166 -0.0427240601 -0.9152492606
541 542 543 544 545
-0.1882698857 -0.7235809652 -0.2300762989 -0.1714814560 -0.7559246013
546 547 548 549 550
-0.3980949590 -0.2688671281 -0.8113708452 -0.4368807270 -0.2234627718
551 552 553 554 555
-0.7659664889 -0.3914763708 -0.1721307660 -0.7238244951 -0.2479090579
556 557 558 559 560
-0.0755640936 -0.7733675732 -0.0558916283 -0.4016040440 -0.7246079030
561 562 563 564 565
-0.8127565405 -0.5693140956 -0.7269202997 -1.0013397263 -0.5780407427
566 567 568 569 570
-0.6392829452 -0.7441166849 -0.4162370427 -0.6739620195 -0.8368053041
571 572 573 574 575
-0.4730619672 -0.6203900942 -0.8928098846 -0.6172448877 -0.6663533777
576 577 578 579 580
-0.7851972646 0.4522822618 0.4693245124 1.4744023886 0.4433078263
581 582 583 584 585
0.6968949718 1.5335519781 1.1624085434 0.6891368678 2.2615205642
586 587 588 589 590
0.8001081948 0.3744831224 1.5060559507 0.8807559799 0.3697794753
591 592 593 594 595
1.8640531852 1.2472254582 0.4825874792 2.7564159507 -0.2733303670
596 597 598 599 600
-0.7083082149 -1.0815642479 -0.3194852348 -0.7465549603 -1.1168100235
601 602 603 604 605
-0.1936435433 -0.7728998989 -0.8793869678 -0.1482391870 -0.7274955426
606 607 608 609 610
-0.8339826115 -0.1915446765 -0.8004954469 -0.8503490724 -0.1102469856
611 612 613 614 615
-0.8370873438 -0.6870111117 1.0159230428 0.5020341201 0.3970440565
616 617 618 619 620
0.4944573086 0.2574300872 -0.2585075228 0.7787513744 0.3592203532
621 622 623 624 625
-0.2868624929 0.9616025986 0.5945411618 0.4360061368 0.7593446518
626 627 628 629 630
0.3855402156 0.1656011529 0.8665980418 0.4191459504 -0.2021261145
631 632 633 634 635
0.1443631296 0.5547652268 1.1019204723 0.0376814854 0.3162944460
636 637 638 639 640
0.1246235072 -0.1724028373 0.2985334876 -1.1827696383 0.0938139177
641 642 643 644 645
0.4832598167 1.1584919380 0.0314025298 -0.0189124663 0.4702066757
646 647 648 649 650
0.0166607074 -0.2177802167 0.1556259897 -0.2681510053 -0.6202256551
651 652 653 654 655
-2.1819533823 -0.2243521002 -0.5264612457 -2.1484963552 -0.1982554100
656 657 658 659 660
-0.3350335791 -2.1351115375 -0.1528510537 -0.2896292228 -2.0897071812
661 662 663 664 665
-0.1933816080 -0.2863530965 -2.1185991710 -0.1475484514 -0.1921465009
666 667 668 669 670
-2.1158885675 0.2851980376 0.3462671330 -0.3342125986 0.3527623754
671 672 673 674 675
0.6837474528 -0.0399813939 0.4427364748 0.5531308638 -0.0653481976
676 677 678 679 680
0.4104341590 0.6657894752 -0.1163733497 0.5666508784 0.7717698438
681 682 683 684 685
-0.0318337264 0.7098997844 0.7548341309 0.0002850116 0.9238918246
686 687 688 689 690
0.9330109353 -0.2277595644 1.1054657682 1.0120401435 -0.2952446350
691 692 693 694 695
1.1400712150 1.0556109073 0.1091880583 0.9458259001 0.8124593155
696 697 698 699 700
-0.2776566897 0.9676194833 0.8731792548 -0.3828070643 0.8480968703
701 702 703 704 705
0.5347600810 -0.4398577829 -0.6814500652 -0.7118129207 -1.0584189193
706 707 708 709 710
-0.6757475749 -0.6751007466 -1.1394029823 -0.7026801904 -0.5524260963
711 712 713 714 715
-1.2011326730 -0.6572758341 -0.5070217400 -1.1557283167 -0.6589674473
716 717 718 719 720
-0.4830222988 -1.1788887374 -0.6218123483 -0.3285021580 -1.1558335885
721 722 723 724 725
0.3943424946 0.2786723801 1.7469126987 0.4343417854 0.1425927882
726 727 728 729 730
0.4101493146 0.9341073059 0.2236139723 1.5806842584 0.5110467738
731 732 733 734 735
0.2718119920 1.6205447895 0.5562147021 0.2661115145 1.3404123026
736 737 738 739 740
0.6504227686 0.1500575168 1.4027537673 0.3759990129 -0.0363692432
741 742 743 744 745
-0.0858206412 0.3525345662 -0.0331507272 0.2386771221 0.1380326450
746 747 748 749 750
-0.0882674253 -0.0301417860 0.3021923608 -0.0019990444 -0.0625775661
751 752 753 754 755
0.1779932081 -0.0839694238 0.1310297244 0.0627916473 -0.2471657555
756 757 758 759 760
-0.0780709794 -0.1349255315 -0.2251034696 -0.5482561651 -0.1061196082
761 762 763 764 765
-0.2096332966 -0.6726240076 -0.0989213223 -0.2703280454 -0.7415670354
766 767 768 769 770
-0.0535169660 -0.2249236891 -0.6961626791 -0.0990727666 -0.2746239785
771 772 773 774 775
-0.6994428780 -0.0332885165 -0.2070456662 -0.5147404469 0.6308772860
776 777 778 779 780
0.1530787498 -0.2901021601 0.5839097817 0.0997985976 -0.4334616342
781 782 783 784 785
0.5638002367 0.2401495514 -0.3256453528 0.6554870252 0.2110648469
786 787 788 789 790
-0.1408803270 0.4449672304 0.1736710445 -0.3445625343 0.3638248369
791 792 793 794 795
0.2870272415 -0.4426780383 0.6436866470 0.4393005663 -0.2688373676
796 797 798 799 800
0.5700970137 0.2955404957 -0.3764364733 0.5859996733 0.2734930440
801 802 803 804 805
-0.2017711013 0.5658485752 0.3747744557 -0.4364240125 0.5173801270
806 807 808 809 810
0.2260243367 -0.7100561734 0.5316276923 0.2238084223 -0.6139448452
811 812 813 814 815
0.3681482841 0.0899184868 -0.7052190523 0.2974490335 0.1636710764
816 817 818 819 820
-0.8228034191 0.2336926538 0.1221940885 -0.9445362327 0.2790970101
821 822 823 824 825
0.1675984448 -0.8991318764 0.2237404366 0.1524846638 -0.8473306690
826 827 828 829 830
0.1751167429 0.1775236442 -0.8293575464 0.1558982214 0.2144486577
831 832 833 834 835
-0.0246265665 0.3061299272 0.3257835335 0.2120417156 0.1344662449
836 837 838 839 840
0.2878326815 0.0738096333 0.1702833270 0.2811648961 -0.1726355109
841 842 843 844 845
0.1396733365 0.2882271791 0.0513670089 0.0323121190 0.2177156473
846 847 848 849 850
0.0813365877 0.4539859910 -0.0313842800 -0.6825113311 0.5269939767
851 852 853 854 855
0.0166358023 -0.7487215573 0.5311379043 0.0402367015 -0.5030553130
856 857 858 859 860
0.5152908449 0.0365808159 -0.5721445002 0.3982766198 -0.0172388350
861 862 863 864 865
-0.6724849405 0.4793882994 0.0148965792 -0.5306441094 0.3515331669
866 867 868 869 870
-0.4046290332 -1.2297498384 0.3344018011 -0.3933970640 -1.2469741939
871 872 873 874 875
0.3238462612 -0.3573276005 -1.1972479334 0.3692506174 -0.3119232443
876 877 878 879 880
-1.1518435771 0.2704575139 -0.3789530118 -1.3392202275 0.3528466171
881 882 883 884 885
-0.3708585808 -1.3025136371 0.5092053732 0.3472364671 -0.5484655465
886 887 888 889 890
0.4882511500 0.3411025357 -0.6073236524 0.3409990747 0.2021539218
891 892 893 894 895
-0.6133365610 0.5467102138 0.2052494685 -0.5415754253 0.5594779495
896 897 898 899 900
0.1887233013 -0.6610906795 0.4483074532 0.2273642545 -0.5261198011
901 902 903 904 905
1.0336893629 0.3315975689 -0.3635618793 0.7913749325 0.2883213423
906 907 908 909 910
-0.4715555950 0.8130854008 0.4619589894 -0.1388560764 0.9761197224
911 912 913 914 915
0.4226981934 -0.3167366238 0.8471691500 0.4400387955 -0.3210937325
916 917 918 919 920
1.0335117786 0.4857151051 -0.2270489755 1.5788605014 0.9640660389
921 922 923 924 925
2.0197217673 1.1118463733 0.5712695539 2.8091024130 0.8065025366
926 927 928 929 930
0.2321548879 2.2154960095 0.8519068929 0.2775592442 2.2609003657
931 932 933 934 935
0.9364245069 0.0407549992 2.4156316468 0.8662786759 -0.2629515555
936 937 938 939 940
2.5548038183 -2.1862830483 -1.5297214750 -0.0693366475 -2.2171467117
941 942 943 944 945
-1.4948094037 -0.5707053962 -1.9862581955 -1.5416572329 -0.4592331858
946 947 948 949 950
-2.1914961557 -1.6606184058 -0.7428236184 -2.1081566600 -1.7422702598
951 952 953 954 955
-0.7780034405 -1.9074257908 -1.8864053567 -0.1447109661 -1.4879843673
956 957 958 959 960
1.3509556002 -1.3486604477 -1.2825637973 1.9390445283 -1.2566508242
961 962 963 964 965
-1.0206457992 3.5061639506 -2.1386188796 -1.2441781928 0.6889522944
966 967 968 969 970
-0.6268668596 -1.1691390675 2.4726969642 -0.4004689663 -0.9374701168
971 972
6.7356983514 0.1715341641 # Random Effects
cat("\n\n---- Random Effects ----\n")
---- Random Effects ----print(ranef(sd_model))$subject
(Intercept)
2 -0.04381153
3 0.08579759
4 -0.33752134
5 -0.51619590
7 -0.11908924
8 0.64164441
10 1.10840251
11 0.82140135
13 -0.10799117
14 -0.07400293
15 -0.08584267
16 -0.15657266
17 -0.41668536
18 -0.13132725
19 -0.48896244
20 -0.37490556
22 -0.45535399
23 0.65101616
with conditional variances for "subject" # Fixed Effects
cat("\n\n---- Fixed Effects ----\n")
---- Fixed Effects ----print(fixef(sd_model)) (Intercept) Block4 Block5 Step2 Step3
0.8292311235 -0.0257559203 -0.1825684201 -0.0001458282 0.0058387659
Step4 Step5 Step6 AxisY AxisZ
-0.0103636459 0.0060215586 -0.0030908040 0.0712998953 0.6758268839 # Pairwise Comparisons with emmeans
cat("\n\n---- Pairwise Comparisons (emmeans) ----\n")
---- Pairwise Comparisons (emmeans) ----emmeans_sd <- emmeans(sd_model, pairwise ~ Block)
print(emmeans_sd$emmeans) Block emmean SE df lower.CL upper.CL
1 1.078 0.116 17.7 0.834 1.32
4 1.052 0.116 17.7 0.808 1.30
5 0.895 0.116 17.7 0.651 1.14
Results are averaged over the levels of: Step, Axis
Degrees-of-freedom method: kenward-roger
Confidence level used: 0.95 print(emmeans_sd$contrasts) contrast estimate SE df t.ratio p.value
Block1 - Block4 0.0258 0.028 945 0.919 0.6286
Block1 - Block5 0.1826 0.028 945 6.512 <.0001
Block4 - Block5 0.1568 0.028 945 5.593 <.0001
Results are averaged over the levels of: Step, Axis
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates print_stepwise_lmm_diagnostics <- function(results_list, dataset_name = "Mixed") {
cat(glue::glue("\n=========== STEPWISE LMM DIAGNOSTICS: {dataset_name} ===========\n"))
for (key in names(results_list)) {
cat("\n---", key, "---\n")
cat("ANOVA:\n")
print(results_list[[key]]$ANOVA)
cat("\nPairwise Comparisons:\n")
print(results_list[[key]]$Pairwise)
cat("\nFixed Effects:\n")
print(results_list[[key]]$FixedEffects)
cat("\nRandom Effects:\n")
print(results_list[[key]]$RandomEffects)
cat("\nSample Scaled Residuals:\n")
print(head(results_list[[key]]$ScaledResiduals))
cat("\n=============================================================\n")
}
}
# Run per-axis models for 6-step analysis: Blocks 1, 4, 5
rms_lmm_results_6step <- list()
sd_lmm_results_6step <- list()
axes <- c("X", "Y", "Z")
for (ax in axes) {
cat(glue("\n\n========== Running models for 6-step Axis: {ax} ==========\n\n"))
# ----- RMS Model -----
df_rms <- step_summary %>% filter(Axis == ax)
rms_model <- lmer(RMS ~ Block + Step + (1 | subject), data = df_rms)
rms_lmm_results_6step[[paste0("RMS_", ax)]] <- list(
Model = rms_model,
ANOVA = anova(rms_model, type = 3),
Pairwise = emmeans(rms_model, pairwise ~ Block)$contrasts,
FixedEffects = fixef(rms_model),
RandomEffects = ranef(rms_model),
ScaledResiduals = resid(rms_model, scaled = TRUE)
)
# ----- SD Model -----
df_sd <- sd_subject_data %>% filter(Axis == ax)
sd_model <- lmer(sd_rms ~ Block + Step + (1 | subject), data = df_sd)
sd_lmm_results_6step[[paste0("SD_", ax)]] <- list(
Model = sd_model,
ANOVA = anova(sd_model, type = 3),
Pairwise = emmeans(sd_model, pairwise ~ Block)$contrasts,
FixedEffects = fixef(sd_model),
RandomEffects = ranef(sd_model),
ScaledResiduals = resid(sd_model, scaled = TRUE)
)
}
========== Running models for 6-step Axis: X ==========
========== Running models for 6-step Axis: Y ==========
========== Running models for 6-step Axis: Z ==========print_stepwise_lmm_diagnostics(rms_lmm_results_6step, dataset_name = "6-Step RMS Acceleration")=========== STEPWISE LMM DIAGNOSTICS: 6-Step RMS Acceleration ===========
--- RMS_X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 3.1819 1.59093 2 299 28.8653 3.451e-12 ***
Step 0.0120 0.00241 5 299 0.0437 0.9989
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block1 - Block4 0.134 0.0319 299 4.179 0.0001
Block1 - Block5 0.242 0.0319 299 7.585 <.0001
Block4 - Block5 0.109 0.0319 299 3.405 0.0022
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3
9.219620e-01 -1.335255e-01 -2.423214e-01 -6.770361e-03 7.181157e-03
Step4 Step5 Step6
-1.242737e-02 -2.986672e-03 -6.056655e-05
Random Effects:
$subject
(Intercept)
2 -0.03329716
3 0.08668532
4 -0.23981010
5 -0.39752483
7 -0.18724692
8 0.46667437
10 0.77455961
11 0.66234450
13 -0.19133789
14 0.05513399
15 -0.02003981
16 -0.11499726
17 -0.28275395
18 -0.09507468
19 -0.35656446
20 -0.28989425
22 -0.20532253
23 0.36846603
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-1.319584 -1.252735 -1.302158 -1.218634 -1.122979 -1.191415
=============================================================
--- RMS_Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 1.01322 0.50661 2 299 4.6572 0.01019 *
Step 0.05595 0.01119 5 299 0.1029 0.99151
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block1 - Block4 0.0498 0.0449 299 1.111 0.5083
Block1 - Block5 0.1354 0.0449 299 3.017 0.0078
Block4 - Block5 0.0856 0.0449 299 1.907 0.1386
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
0.944456997 -0.049844127 -0.135417037 -0.001657183 -0.006629423 -0.031823331
Step5 Step6
-0.020561637 -0.030795551
Random Effects:
$subject
(Intercept)
2 0.15266341
3 0.09118758
4 -0.33223110
5 -0.42540054
7 -0.27838104
8 0.40093672
10 1.02463149
11 0.68773823
13 -0.18641948
14 0.03947274
15 -0.21805074
16 -0.15413790
17 -0.28614944
18 -0.12125429
19 -0.43943192
20 -0.34642676
22 -0.42062532
23 0.81187836
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-1.476399 -1.588750 -1.552726 -1.476339 -1.440259 -1.320497
=============================================================
--- RMS_Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 10.1069 5.0535 2 299 22.1571 1.064e-09 ***
Step 0.1189 0.0238 5 299 0.1042 0.9912
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block1 - Block4 0.176 0.065 299 2.701 0.0199
Block1 - Block5 0.430 0.065 299 6.620 <.0001
Block4 - Block5 0.255 0.065 299 3.918 0.0003
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3
1.9327062573 -0.1755507644 -0.4302084489 0.0140812984 -0.0005561811
Step4 Step5 Step6
-0.0155314268 -0.0131648598 -0.0473140198
Random Effects:
$subject
(Intercept)
2 -0.25936063
3 0.42090751
4 -0.60016627
5 -0.92240086
7 0.24758892
8 0.78908937
10 1.67458863
11 1.11922982
13 0.16825428
14 -0.50934851
15 -0.01206954
16 -0.19401092
17 -0.84071444
18 0.26909682
19 -0.84057282
20 -0.64937445
22 -0.68150651
23 0.82076960
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-1.131495 -1.272155 -1.367050 -1.335693 -1.535748 -1.396999
=============================================================print_stepwise_lmm_diagnostics(sd_lmm_results_6step, dataset_name = "6-Step SD Acceleration")=========== STEPWISE LMM DIAGNOSTICS: 6-Step SD Acceleration ===========
--- SD_X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 2.45734 1.22867 2 299 23.5123 3.284e-10 ***
Step 0.01737 0.00347 5 299 0.0665 0.997
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block1 - Block4 0.0936 0.0311 299 3.010 0.0080
Block1 - Block5 0.2128 0.0311 299 6.841 <.0001
Block4 - Block5 0.1192 0.0311 299 3.831 0.0005
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3
0.8622323418 -0.0936387821 -0.2128120061 -0.0077653666 0.0107211719
Step4 Step5 Step6
-0.0113872288 0.0006561473 0.0042809082
Random Effects:
$subject
(Intercept)
2 -0.001921724
3 -0.006408225
4 -0.231431386
5 -0.393590191
7 -0.169546559
8 0.478870931
10 0.766220508
11 0.677544824
13 -0.187326369
14 0.078395064
15 -0.067771117
16 -0.079524174
17 -0.261541135
18 -0.111019281
19 -0.358924693
20 -0.261261956
22 -0.243614530
23 0.372850013
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-1.242885 -1.198605 -1.229846 -1.133132 -1.049293 -1.108737
=============================================================
--- SD_Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 1.13378 0.56689 2 299 5.6333 0.003967 **
Step 0.03017 0.00603 5 299 0.0600 0.997621
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block1 - Block4 0.0490 0.0432 299 1.136 0.4928
Block1 - Block5 0.1426 0.0432 299 3.303 0.0031
Block4 - Block5 0.0936 0.0432 299 2.167 0.0785
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3
0.9022021398 -0.0490339401 -0.1426004088 0.0009374256 -0.0002039485
Step4 Step5 Step6
-0.0250563364 -0.0052720965 -0.0155517076
Random Effects:
$subject
(Intercept)
2 0.03506852
3 0.07222790
4 -0.30059943
5 -0.40255462
7 -0.24513116
8 0.39764892
10 1.05983331
11 0.67547976
13 -0.17917461
14 0.06241090
15 -0.19012261
16 -0.16510012
17 -0.27537227
18 -0.17443423
19 -0.44529403
20 -0.31571863
22 -0.40990160
23 0.80073401
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-1.0421879 -1.1606481 -1.1316809 -1.0533377 -1.0362305 -0.9121195
=============================================================
--- SD_Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 3.8759 1.93793 2 299 12.1960 8.097e-06 ***
Step 0.0174 0.00348 5 299 0.0219 0.9998
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block1 - Block4 -0.0654 0.0542 299 -1.206 0.4507
Block1 - Block5 0.1923 0.0542 299 3.545 0.0013
Block4 - Block5 0.2577 0.0542 299 4.751 <.0001
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
1.470385668 0.065404961 -0.192292845 0.006390456 0.006999074 0.005352627
Step5 Step6
0.022680625 0.001998387
Random Effects:
$subject
(Intercept)
2 -0.163641417
3 0.188658864
4 -0.467870778
5 -0.733365115
7 0.063344655
8 1.025555930
10 1.456969613
11 1.080332180
13 0.047707373
14 -0.361591841
15 0.004523366
16 -0.219132359
17 -0.698368965
18 -0.103059010
19 -0.644148552
20 -0.533797637
22 -0.695713745
23 0.753597438
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.6944028 -0.8638062 -0.9668776 -0.9627473 -1.1921847 -1.0234650
=============================================================#6.2 12 steps Block 2,4 & 5
# --- Updated function for Blocks 2, 4, 5 (12 steps only) ---
plot_stepwise_rms_blocks_245_12steps <- function(tagged_data2) {
step_markers <- c(14, 15, 16, 17)
buffer <- 3
# Filter and tag steps
step_data <- tagged_data2 %>%
filter(phase == "Execution", Marker.Text %in% step_markers) %>%
assign_steps_by_block() %>%
filter(Block %in% c(2, 4, 5)) %>%
mutate(Step = as.numeric(Step)) %>%
group_by(subject, Block, trial) %>%
mutate(step_count = max(Step, na.rm = TRUE)) %>%
ungroup() %>%
filter(step_count %in% c(12, 18)) %>%
arrange(subject, Block, trial, ms) %>%
group_by(subject, Block, trial) %>%
mutate(row_id = row_number()) %>%
ungroup() %>%
filter(Step <= 12)
# Extract indices for step windows
step_indices <- step_data %>%
select(subject, Block, trial, row_id, Step)
window_data <- map_dfr(1:nrow(step_indices), function(i) {
step <- step_indices[i, ]
rows <- (step$row_id - buffer):(step$row_id + buffer)
step_data %>%
filter(subject == step$subject,
Block == step$Block,
trial == step$trial,
row_id %in% rows) %>%
mutate(Step = step$Step)
})
# Compute per-step RMS
step_summary <- window_data %>%
group_by(subject, Block, Step) %>%
summarise(
rms_x = sqrt(mean(CoM.acc.x^2, na.rm = TRUE)),
rms_y = sqrt(mean(CoM.acc.y^2, na.rm = TRUE)),
rms_z = sqrt(mean(CoM.acc.z^2, na.rm = TRUE)),
.groups = "drop"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = toupper(gsub("rms_", "", Axis)),
Step = factor(Step),
Block = factor(Block),
subject = factor(subject)
) %>%
filter(Step %in% 1:12)
# Aggregate for plotting
plot_data <- step_summary %>%
group_by(Block, Step, Axis) %>%
summarise(
mean_rms = mean(RMS, na.rm = TRUE),
sd_rms = sd(RMS, na.rm = TRUE),
.groups = "drop"
)
# Create bar plots
axis_labels <- unique(plot_data$Axis)
plots <- map(axis_labels, function(ax) {
axis_data <- filter(plot_data, Axis == ax)
ggplot(axis_data, aes(x = Step, y = mean_rms, fill = Block)) +
geom_col(position = position_dodge(width = 0.8), width = 0.7) +
geom_errorbar(
aes(ymin = mean_rms - sd_rms, ymax = mean_rms + sd_rms),
position = position_dodge(width = 0.8),
width = 0.3
) +
ylim(0, 3.25) +
labs(
title = paste("Step-wise CoM RMS Acceleration ± SD — Axis", ax),
x = "Step Number (1–12)",
y = "RMS Acceleration (m/s²)",
fill = "Block"
) +
theme_minimal() +
theme(
text = element_text(size = 12),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 0, vjust = 0.5)
)
})
names(plots) <- axis_labels
return(list(
plots = plots,
step_summary = step_summary,
plot_data = plot_data,
window_data = window_data
))
}
# --- Run function and extract results ---
result <- plot_stepwise_rms_blocks_245_12steps(tagged_data2)
stepwise_block245_plots <- result$plots
step_summary <- result$step_summary
plot_data <- result$plot_data
window_data <- result$window_data
# --- Print plots ---
for (plot_name in names(stepwise_block245_plots)) {
cat("\n\n==== Axis:", plot_name, "====\n\n")
print(stepwise_block245_plots[[plot_name]])
}
==== Axis: X ====
==== Axis: Y ====
==== Axis: Z ====
# --- Compute SD from raw data (windowed) ---
sd_subject_data <- window_data %>%
group_by(subject, Block, Step) %>%
summarise(
sd_x = sd(CoM.acc.x, na.rm = TRUE),
sd_y = sd(CoM.acc.y, na.rm = TRUE),
sd_z = sd(CoM.acc.z, na.rm = TRUE),
.groups = "drop"
) %>%
pivot_longer(cols = starts_with("sd_"), names_to = "Axis", values_to = "sd_rms") %>%
mutate(
Axis = toupper(gsub("sd_", "", Axis)),
Step = factor(Step),
Block = factor(Block),
subject = factor(subject)
) %>%
filter(Step %in% 1:12, Block %in% c("2", "4", "5"))print_stepwise_lmm_diagnostics <- function(results_list, dataset_name = "Mixed") {
cat(glue::glue("\n=========== STEPWISE LMM DIAGNOSTICS: {dataset_name} ===========\n"))
for (key in names(results_list)) {
cat("\n---", key, "---\n")
cat("ANOVA:\n")
print(results_list[[key]]$ANOVA)
cat("\nPairwise Comparisons:\n")
print(results_list[[key]]$Pairwise)
cat("\nFixed Effects:\n")
print(results_list[[key]]$FixedEffects)
cat("\nRandom Effects:\n")
print(results_list[[key]]$RandomEffects)
cat("\nSample Scaled Residuals:\n")
print(head(results_list[[key]]$ScaledResiduals))
cat("\n=============================================================\n")
}
}
# Initialize result containers
rms_lmm_results <- list()
sd_lmm_results <- list()
axes <- c("X", "Y", "Z")
for (ax in axes) {
cat(glue("\n\n========== Running models for Axis: {ax} ==========\n\n"))
# ----- RMS Model -----
df_rms <- step_summary %>% filter(Axis == ax)
rms_model <- lmer(RMS ~ Block + Step + (1 | subject), data = df_rms)
rms_lmm_results[[paste0("RMS_", ax)]] <- list(
Model = rms_model,
ANOVA = anova(rms_model, type = 3),
Pairwise = emmeans(rms_model, pairwise ~ Block)$contrasts,
FixedEffects = fixef(rms_model),
RandomEffects = ranef(rms_model),
ScaledResiduals = resid(rms_model, scaled = TRUE)
)
# ----- SD Model -----
df_sd <- sd_subject_data %>% filter(Axis == ax)
sd_model <- lmer(sd_rms ~ Block + Step + (1 | subject), data = df_sd)
sd_lmm_results[[paste0("SD_", ax)]] <- list(
Model = sd_model,
ANOVA = anova(sd_model, type = 3),
Pairwise = emmeans(sd_model, pairwise ~ Block)$contrasts,
FixedEffects = fixef(sd_model),
RandomEffects = ranef(sd_model),
ScaledResiduals = resid(sd_model, scaled = TRUE)
)
}
========== Running models for Axis: X ==========
========== Running models for Axis: Y ==========
========== Running models for Axis: Z ==========print_stepwise_lmm_diagnostics(rms_lmm_results, dataset_name = "RMS Acceleration")=========== STEPWISE LMM DIAGNOSTICS: RMS Acceleration ===========
--- RMS_X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 2.73566 1.36783 2 617 47.7813 <2e-16 ***
Step 0.38686 0.03517 11 617 1.2285 0.264
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block2 - Block4 0.0246 0.0163 617 1.511 0.2866
Block2 - Block5 0.1485 0.0163 617 9.120 <.0001
Block4 - Block5 0.1239 0.0163 617 7.609 <.0001
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
0.842993606 -0.024593926 -0.148473177 -0.007393845 0.007656765 -0.014015367
Step5 Step6 Step7 Step8 Step9 Step10
-0.016791111 -0.023318483 -0.043879190 -0.042966029 -0.056834256 -0.066463355
Step11 Step12
-0.057617109 -0.056746826
Random Effects:
$subject
(Intercept)
2 0.15848406
3 0.01317072
4 -0.25170095
5 -0.37304767
7 -0.16276342
8 0.37708038
10 0.86210464
11 0.55087302
13 -0.19837272
14 0.04684232
15 -0.05856620
16 -0.05353192
17 -0.20818606
18 -0.06107446
19 -0.28530476
20 -0.27975006
22 -0.13125727
23 0.05500034
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
0.4556295 0.8890487 0.6382654 0.4688176 0.4858263 0.5978940
=============================================================
--- RMS_Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 0.87068 0.43534 2 617 4.7495 0.008975 **
Step 0.55087 0.05008 11 617 0.5464 0.871733
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block2 - Block4 0.0283 0.0291 617 0.971 0.5957
Block2 - Block5 0.0879 0.0291 617 3.019 0.0074
Block4 - Block5 0.0597 0.0291 617 2.048 0.1018
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3
0.9139496619 -0.0282768480 -0.0879402579 0.0028714077 -0.0005287429
Step4 Step5 Step6 Step7 Step8
-0.0231990301 -0.0219622003 -0.0407700454 -0.0506262624 -0.0533103869
Step9 Step10 Step11 Step12
-0.0726737185 -0.0737777137 -0.0626563873 -0.0798290792
Random Effects:
$subject
(Intercept)
2 0.32010117
3 0.17789913
4 -0.31755846
5 -0.42182350
7 -0.22339555
8 0.46725710
10 0.77354998
11 0.49820582
13 -0.24676053
14 -0.05057119
15 -0.19278986
16 -0.06002448
17 -0.18638106
18 -0.08170518
19 -0.38547812
20 -0.31701438
22 -0.39563383
23 0.64212294
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.9661683 -0.8783427 -0.6806564 -0.5966132 -0.3347132 -0.2240945
=============================================================
--- RMS_Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 10.6881 5.3441 2 617 36.9795 6.791e-16 ***
Step 1.4505 0.1319 11 617 0.9124 0.5278
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block2 - Block4 0.0344 0.0366 617 0.942 0.6141
Block2 - Block5 0.2880 0.0366 617 7.874 <.0001
Block4 - Block5 0.2536 0.0366 617 6.932 <.0001
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
1.81885763 -0.03444481 -0.28802335 -0.00738757 -0.02123234 -0.03290710
Step5 Step6 Step7 Step8 Step9 Step10
-0.04693632 -0.08607941 -0.08972365 -0.08232825 -0.13001174 -0.13327835
Step11 Step12
-0.09739196 -0.13820924
Random Effects:
$subject
(Intercept)
2 -0.05133255
3 0.18879046
4 -0.65680395
5 -0.87293904
7 0.17360114
8 0.73664753
10 1.88431290
11 0.99897785
13 -0.13716572
14 -0.38876009
15 -0.04815916
16 0.21177573
17 -0.69080677
18 0.32924469
19 -0.76524197
20 -0.59590204
22 -0.61509897
23 0.29885996
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.15697426 -0.02940094 -0.13727066 -0.19152371 -0.20090476 -0.20340640
=============================================================print_stepwise_lmm_diagnostics(sd_lmm_results, dataset_name = "SD Acceleration")=========== STEPWISE LMM DIAGNOSTICS: SD Acceleration ===========
--- SD_X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 3.13277 1.56639 2 617 54.9112 <2e-16 ***
Step 0.33935 0.03085 11 617 1.0815 0.3736
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block2 - Block4 0.0235 0.0163 617 1.445 0.3187
Block2 - Block5 0.1578 0.0163 617 9.711 <.0001
Block4 - Block5 0.1344 0.0163 617 8.267 <.0001
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
0.820007226 -0.023476605 -0.157827323 -0.006692571 0.013830892 -0.008683006
Step5 Step6 Step7 Step8 Step9 Step10
-0.009609194 -0.014823923 -0.036898338 -0.034873439 -0.048375019 -0.058928452
Step11 Step12
-0.051007297 -0.049479024
Random Effects:
$subject
(Intercept)
2 0.174279479
3 -0.009755408
4 -0.247121976
5 -0.357605215
7 -0.153379808
8 0.367189453
10 0.845347050
11 0.551005897
13 -0.187624421
14 0.059879944
15 -0.069572910
16 -0.035470410
17 -0.196336208
18 -0.077042210
19 -0.293555212
20 -0.264354937
22 -0.176610423
23 0.070727316
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
0.5013051 0.9445774 0.6544064 0.4738771 0.4774416 0.5605867
=============================================================
--- SD_Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 1.22549 0.61275 2 617 6.5770 0.001492 **
Step 0.46254 0.04205 11 617 0.4513 0.932093
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block2 - Block4 0.0356 0.0294 617 1.213 0.4457
Block2 - Block5 0.1048 0.0294 617 3.567 0.0011
Block4 - Block5 0.0691 0.0294 617 2.353 0.0495
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
0.885388031 -0.035635153 -0.104754046 0.004624095 0.004968428 -0.017165126
Step5 Step6 Step7 Step8 Step9 Step10
-0.010661964 -0.030588056 -0.040429105 -0.047572819 -0.060673076 -0.063621829
Step11 Step12
-0.058763709 -0.068132108
Random Effects:
$subject
(Intercept)
2 0.21230605
3 0.14448454
4 -0.29580232
5 -0.40834224
7 -0.19592017
8 0.45879206
10 0.79208586
11 0.51998579
13 -0.22914933
14 -0.03523675
15 -0.19065391
16 -0.07164817
17 -0.18654560
18 -0.13035689
19 -0.38143035
20 -0.29246169
22 -0.37027103
23 0.66016415
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.56127428 -0.47007162 -0.27687601 -0.19786186 0.01426335 0.12584559
=============================================================
--- SD_Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 10.5854 5.2927 2 617 43.7683 <2e-16 ***
Step 0.5092 0.0463 11 617 0.3828 0.9628
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block2 - Block4 0.0444 0.0335 617 1.326 0.3813
Block2 - Block5 0.2906 0.0335 617 8.684 <.0001
Block4 - Block5 0.2462 0.0335 617 7.358 <.0001
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
1.60945970 -0.04436577 -0.29057288 -0.01618399 -0.02059153 -0.01725852
Step5 Step6 Step7 Step8 Step9 Step10
-0.01342257 -0.04860412 -0.04679831 -0.04415617 -0.07470931 -0.08362982
Step11 Step12
-0.06059107 -0.08556150
Random Effects:
$subject
(Intercept)
2 -0.04767230
3 0.16718136
4 -0.51314417
5 -0.75114593
7 0.08325545
8 0.86632313
10 1.55430049
11 0.99992179
13 -0.15347611
14 -0.28020757
15 -0.01866363
16 0.10302935
17 -0.60287502
18 0.10000760
19 -0.65319437
20 -0.61075192
22 -0.62910178
23 0.38621362
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.26483192 0.01196484 0.07890902 -0.09647006 -0.15243761 -0.17348686
=============================================================#6.3 18 steps Block 3,4 & 5
# -------- Function: Plot + Extract Stepwise RMS ± SD --------
plot_stepwise_rms_blocks_345_18steps <- function(tagged_data2) {
step_markers <- c(14, 15, 16, 17)
buffer <- 3
step_data <- tagged_data2 %>%
filter(phase == "Execution", Marker.Text %in% step_markers) %>%
assign_steps_by_block() %>%
filter(Block %in% c(3, 4, 5)) %>%
mutate(Step = as.numeric(Step)) %>%
group_by(subject, Block, trial) %>%
mutate(step_count = max(Step, na.rm = TRUE)) %>%
ungroup() %>%
filter(step_count == 18) %>%
arrange(subject, Block, trial, ms) %>%
group_by(subject, Block, trial) %>%
mutate(row_id = row_number()) %>%
ungroup() %>%
filter(Step <= 18)
step_indices <- step_data %>%
select(subject, Block, trial, row_id, Step)
window_data <- map_dfr(1:nrow(step_indices), function(i) {
step <- step_indices[i, ]
rows <- (step$row_id - buffer):(step$row_id + buffer)
step_data %>%
filter(subject == step$subject,
Block == step$Block,
trial == step$trial,
row_id %in% rows) %>%
mutate(Step = step$Step)
})
step_summary <- window_data %>%
group_by(subject, Block, Step) %>%
summarise(
rms_x = sqrt(mean(CoM.acc.x^2, na.rm = TRUE)),
rms_y = sqrt(mean(CoM.acc.y^2, na.rm = TRUE)),
rms_z = sqrt(mean(CoM.acc.z^2, na.rm = TRUE)),
.groups = "drop"
) %>%
pivot_longer(cols = starts_with("rms_"), names_to = "Axis", values_to = "RMS") %>%
mutate(
Axis = toupper(gsub("rms_", "", Axis)),
Step = factor(Step),
Block = factor(Block),
subject = factor(subject)
)
plot_data <- step_summary %>%
group_by(Block, Step, Axis) %>%
summarise(
mean_rms = mean(RMS, na.rm = TRUE),
sd_rms = sd(RMS, na.rm = TRUE),
.groups = "drop"
)
axis_labels <- unique(plot_data$Axis)
plots <- map(axis_labels, function(ax) {
axis_data <- filter(plot_data, Axis == ax)
ggplot(axis_data, aes(x = Step, y = mean_rms, fill = Block)) +
geom_col(position = position_dodge(width = 0.8), width = 0.7) +
geom_errorbar(
aes(ymin = mean_rms - sd_rms, ymax = mean_rms + sd_rms),
position = position_dodge(width = 0.8),
width = 0.3
) +
ylim(0, 3.25) +
labs(
title = paste("Step-wise CoM RMS Acceleration ± SD — Axis", ax),
x = "Step Number (1–18)",
y = "RMS Acceleration (m/s²)",
fill = "Block"
) +
theme_minimal() +
theme(
text = element_text(size = 12),
plot.title = element_text(face = "bold"),
axis.text.x = element_text(angle = 0)
)
})
names(plots) <- axis_labels
return(list(
plots = plots,
step_summary = step_summary,
plot_data = plot_data,
window_data = window_data
))
}
# -------- Run analysis function for Blocks 3, 4, 5 --------
result <- plot_stepwise_rms_blocks_345_18steps(tagged_data2)
stepwise_block345_plots <- result$plots
step_summary <- result$step_summary
plot_data <- result$plot_data
window_data <- result$window_data
# -------- Plot (optional) --------
for (plot_name in names(stepwise_block345_plots)) {
cat("\n\n==== Axis:", plot_name, "====\n\n")
print(stepwise_block345_plots[[plot_name]])
}
==== Axis: X ====
==== Axis: Y ====
==== Axis: Z ====
# -------- Compute SD from raw data --------
sd_subject_data <- window_data %>%
group_by(subject, Block, Step) %>%
summarise(
sd_x = sd(CoM.acc.x, na.rm = TRUE),
sd_y = sd(CoM.acc.y, na.rm = TRUE),
sd_z = sd(CoM.acc.z, na.rm = TRUE),
.groups = "drop"
) %>%
pivot_longer(cols = starts_with("sd_"), names_to = "Axis", values_to = "sd_rms") %>%
mutate(
Axis = toupper(gsub("sd_", "", Axis)),
Step = factor(Step),
Block = factor(Block),
subject = factor(subject)
)
# -------- Print Function (if not already defined) --------
print_stepwise_lmm_diagnostics <- function(results_list, dataset_name = "Mixed") {
cat(glue::glue("\n=========== STEPWISE LMM DIAGNOSTICS: {dataset_name} ===========\n"))
for (key in names(results_list)) {
cat("\n---", key, "---\n")
cat("ANOVA:\n")
print(results_list[[key]]$ANOVA)
cat("\nPairwise Comparisons:\n")
print(results_list[[key]]$Pairwise)
cat("\nFixed Effects:\n")
print(results_list[[key]]$FixedEffects)
cat("\nRandom Effects:\n")
print(results_list[[key]]$RandomEffects)
cat("\nSample Scaled Residuals:\n")
print(head(results_list[[key]]$ScaledResiduals))
cat("\n=============================================================\n")
}
}
# -------- Run axis-wise LMMs for RMS and SD --------
rms_lmm_results_18step <- list()
sd_lmm_results_18step <- list()
axes <- c("X", "Y", "Z")
for (ax in axes) {
cat(glue("\n\n========== Running models for 18-step Axis: {ax} ==========\n\n"))
# ---- RMS Model ----
df_rms <- step_summary %>% filter(Axis == ax)
rms_model <- lmer(RMS ~ Block + Step + (1 | subject), data = df_rms)
rms_lmm_results_18step[[paste0("RMS_", ax)]] <- list(
Model = rms_model,
ANOVA = anova(rms_model, type = 3),
Pairwise = emmeans(rms_model, pairwise ~ Block)$contrasts,
FixedEffects = fixef(rms_model),
RandomEffects = ranef(rms_model),
ScaledResiduals = resid(rms_model, scaled = TRUE)
)
# ---- SD Model ----
df_sd <- sd_subject_data %>% filter(Axis == ax)
sd_model <- lmer(sd_rms ~ Block + Step + (1 | subject), data = df_sd)
sd_lmm_results_18step[[paste0("SD_", ax)]] <- list(
Model = sd_model,
ANOVA = anova(sd_model, type = 3),
Pairwise = emmeans(sd_model, pairwise ~ Block)$contrasts,
FixedEffects = fixef(sd_model),
RandomEffects = ranef(sd_model),
ScaledResiduals = resid(sd_model, scaled = TRUE)
)
}
========== Running models for 18-step Axis: X ==========
========== Running models for 18-step Axis: Y ==========
========== Running models for 18-step Axis: Z ==========# -------- Print nicely formatted output --------
print_stepwise_lmm_diagnostics(rms_lmm_results_18step, dataset_name = "18-Step RMS Acceleration")=========== STEPWISE LMM DIAGNOSTICS: 18-Step RMS Acceleration ===========
--- RMS_X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 2.28798 1.14399 2 935 58.6656 < 2.2e-16 ***
Step 0.79508 0.04677 17 935 2.3984 0.001202 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block3 - Block4 -0.10357 0.011 935 -9.440 <.0001
Block3 - Block5 -0.00132 0.011 935 -0.120 0.9920
Block4 - Block5 0.10225 0.011 935 9.320 <.0001
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
0.685390730 0.103574426 0.001321964 -0.008549400 0.012078397 -0.008074561
Step5 Step6 Step7 Step8 Step9 Step10
-0.011227122 -0.011425203 -0.016229456 -0.026155269 -0.028195704 -0.044890004
Step11 Step12 Step13 Step14 Step15 Step16
-0.042937031 -0.041654826 -0.054952255 -0.065178826 -0.067303345 -0.085352878
Step17 Step18
-0.077222807 -0.072495850
Random Effects:
$subject
(Intercept)
2 0.116418461
3 -0.039675170
4 -0.212745971
5 -0.351999215
7 -0.142466723
8 0.334466843
10 0.803169849
11 0.327416480
13 -0.157676835
14 -0.026028783
15 -0.049004873
16 -0.005954662
17 -0.115201918
18 0.034041517
19 -0.231769138
20 -0.219236724
22 -0.113469088
23 0.049715952
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
0.4553314 0.3734032 0.2403991 0.7324080 0.2523302 0.5573982
=============================================================
--- RMS_Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 2.6511 1.32556 2 935 24.8842 2.956e-11 ***
Step 2.5040 0.14729 17 935 2.7651 0.0001594 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block3 - Block4 -0.1278 0.0181 935 -7.047 <.0001
Block3 - Block5 -0.0691 0.0181 935 -3.808 0.0004
Block4 - Block5 0.0587 0.0181 935 3.239 0.0036
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
0.731609270 0.127786732 0.069050690 0.001280471 0.004547396 -0.021933270
Step5 Step6 Step7 Step8 Step9 Step10
-0.010545659 -0.027221312 -0.030146465 -0.034423821 -0.046119991 -0.057639370
Step11 Step12 Step13 Step14 Step15 Step16
-0.047573690 -0.078040381 -0.086734489 -0.107826316 -0.121343771 -0.144502533
Step17 Step18
-0.151214555 -0.133753710
Random Effects:
$subject
(Intercept)
2 0.28423585
3 0.16068140
4 -0.23276914
5 -0.31869462
7 -0.18242332
8 0.33874983
10 0.67779750
11 0.13990538
13 -0.18898201
14 -0.05763280
15 -0.07167369
16 -0.02814295
17 -0.08962412
18 -0.02179947
19 -0.32783051
20 -0.27306156
22 -0.29998782
23 0.49125206
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-1.114306 -1.175336 -1.160312 -0.959026 -1.157868 -0.779443
=============================================================
--- RMS_Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 8.6392 4.3196 2 935 49.4093 < 2.2e-16 ***
Step 4.0556 0.2386 17 935 2.7288 0.0001957 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block3 - Block4 -0.19541 0.0232 935 -8.412 <.0001
Block3 - Block5 0.00886 0.0232 935 0.381 0.9230
Block4 - Block5 0.20427 0.0232 935 8.793 <.0001
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
1.527644728 0.195413536 -0.008860052 -0.009813911 -0.006933563 -0.025309247
Step5 Step6 Step7 Step8 Step9 Step10
-0.024285362 -0.068148694 -0.062056577 -0.073709087 -0.099765061 -0.112348529
Step11 Step12 Step13 Step14 Step15 Step16
-0.088214312 -0.126271606 -0.116744474 -0.158440962 -0.152759518 -0.184948014
Step17 Step18
-0.212336462 -0.183937294
Random Effects:
$subject
(Intercept)
2 0.001619598
3 0.169135755
4 -0.614465634
5 -0.796696828
7 0.146006858
8 0.457705306
10 2.126216988
11 0.326658731
13 -0.122259417
14 -0.134923482
15 0.008717373
16 0.180610272
17 -0.506454910
18 0.360735138
19 -0.702406710
20 -0.534688810
22 -0.549999194
23 0.184488965
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
0.4752407 0.6156987 0.3100476 0.5455948 0.2487787 0.1835137
=============================================================print_stepwise_lmm_diagnostics(sd_lmm_results_18step, dataset_name = "18-Step SD Acceleration")=========== STEPWISE LMM DIAGNOSTICS: 18-Step SD Acceleration ===========
--- SD_X ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 2.43077 1.21539 2 935 62.1475 < 2.2e-16 ***
Step 0.75458 0.04439 17 935 2.2697 0.002372 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block3 - Block4 -0.10330 0.011 935 -9.402 <.0001
Block3 - Block5 0.00537 0.011 935 0.489 0.8767
Block4 - Block5 0.10867 0.011 935 9.890 <.0001
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
0.664669620 0.103296700 -0.005368431 -0.009744697 0.015497426 -0.006890933
Step5 Step6 Step7 Step8 Step9 Step10
-0.006819232 -0.005023495 -0.012219742 -0.022284789 -0.022527925 -0.039758993
Step11 Step12 Step13 Step14 Step15 Step16
-0.037185786 -0.035730845 -0.046941225 -0.059584885 -0.062958155 -0.082831722
Step17 Step18
-0.073714739 -0.067541733
Random Effects:
$subject
(Intercept)
2 0.133649080
3 -0.042217886
4 -0.213041455
5 -0.333690610
7 -0.133066813
8 0.319093276
10 0.785735565
11 0.330056906
13 -0.145178452
14 -0.011124509
15 -0.046464808
16 0.006818412
17 -0.107690998
18 0.007442294
19 -0.246196429
20 -0.204530033
22 -0.166071420
23 0.066477880
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
0.4993757 0.4239849 0.2562167 0.7565987 0.2561125 0.5483282
=============================================================
--- SD_Y ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 2.2589 1.12944 2 935 21.0905 1.099e-09 ***
Step 2.1150 0.12441 17 935 2.3232 0.001792 **
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block3 - Block4 -0.1180 0.0182 935 -6.488 <.0001
Block3 - Block5 -0.0543 0.0182 935 -2.985 0.0082
Block4 - Block5 0.0637 0.0182 935 3.503 0.0014
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
0.701915882 0.117957776 0.054266770 0.002768840 0.010375572 -0.015669879
Step5 Step6 Step7 Step8 Step9 Step10
0.001553698 -0.016756482 -0.018147540 -0.024890025 -0.030008291 -0.042296670
Step11 Step12 Step13 Step14 Step15 Step16
-0.035119034 -0.060632363 -0.066139468 -0.087261088 -0.103472604 -0.127958683
Step17 Step18
-0.135768747 -0.120468492
Random Effects:
$subject
(Intercept)
2 0.19623149
3 0.13515423
4 -0.21560684
5 -0.30834798
7 -0.15932454
8 0.32433378
10 0.69743859
11 0.14812475
13 -0.17154155
14 -0.04787943
15 -0.06015274
16 -0.03522383
17 -0.08812562
18 -0.07764270
19 -0.31936312
20 -0.25389406
22 -0.27670284
23 0.51252239
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
-0.5977339 -0.6807506 -0.7015031 -0.5038350 -0.6983478 -0.3094323
=============================================================
--- SD_Z ---
ANOVA:
Type III Analysis of Variance Table with Satterthwaite's method
Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
Block 6.6300 3.3150 2 935 46.1539 < 2e-16 ***
Step 1.8642 0.1097 17 935 1.5268 0.07807 .
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Pairwise Comparisons:
contrast estimate SE df t.ratio p.value
Block3 - Block4 -0.1432 0.0211 935 -6.799 <.0001
Block3 - Block5 0.0522 0.0211 935 2.480 0.0355
Block4 - Block5 0.1954 0.0211 935 9.279 <.0001
Results are averaged over the levels of: Step
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 3 estimates
Fixed Effects:
(Intercept) Block4 Block5 Step2 Step3 Step4
1.362910880 0.143152648 -0.052216714 -0.014907745 -0.008575322 -0.009667555
Step5 Step6 Step7 Step8 Step9 Step10
0.008934875 -0.033549943 -0.021087484 -0.030968462 -0.042874348 -0.056343361
Step11 Step12 Step13 Step14 Step15 Step16
-0.035483728 -0.065232552 -0.053591531 -0.085352318 -0.087043858 -0.122206725
Step17 Step18
-0.143614922 -0.123449973
Random Effects:
$subject
(Intercept)
2 0.0001078391
3 0.2033690975
4 -0.4914968314
5 -0.6855571731
7 0.0825640490
8 0.5528861374
10 1.7836696302
11 0.3283699393
13 -0.1020702155
14 -0.0712725465
15 0.0410698488
16 0.0682422986
17 -0.4210071801
18 0.1810042955
19 -0.6148012777
20 -0.5356898469
22 -0.5726784251
23 0.2532903609
with conditional variances for "subject"
Sample Scaled Residuals:
1 2 3 4 5 6
0.3169071 0.6984461 0.4729325 0.7633309 0.3118129 0.2998259
=============================================================#7 Reaction Times #7.1 RT stepwise per block
RT <- read.csv("/Users/can/Documents/Uni/Thesis/Data/E-Prime/all_excluded2.csv", sep = ";")
# Filter only response procedure entries, remove subject 12, convert to numeric
RTR <- RT %>%
filter(procedure == "responsprocedure") %>%
mutate(
feedback.ACC = as.numeric(feedback.ACC),
feedback.RT = as.numeric(feedback.RT)
) %>%
filter(subject != 12)
# -------- Assign trial numbers dynamically --------
RTR <- RTR %>%
group_by(subject, session) %>%
mutate(trial = cumsum(sub.trial.number == 1)) %>%
ungroup()
# -------- Compute trial-level accuracy and mean RT --------
df <- RTR %>%
group_by(subject, session, trial) %>%
mutate(
trial.acc = sum(feedback.ACC, na.rm = TRUE) / n(),
trial.RT = mean(feedback.RT, na.rm = TRUE)
) %>%
ungroup()
# -------- Filter only trials with 80% accuracy --------
df_acc <- df %>%
filter(trial.acc >= 0.8) %>%
mutate(
subject = as.factor(subject),
sub.trial.number = as.factor(sub.trial.number),
session = as.factor(session)
)
# -------- Add corr_trials per subject --------
df_acc5 <- df_acc %>%
distinct(subject, trial, session) %>%
count(subject, name = "corr_trials")
df_acc <- left_join(df_acc, df_acc5, by = "subject")
df_acc <- df_acc %>% select(-feedback.CRESP, -feedback.RESP, -cue.OnsetDelay, -cue.OnsetTime)# Full model across all blocks
#M2 <- lmer(feedback.RT ~ 0 + sub.trial.number +
# (1 | subject) + (1 | session) + (1 | trial) + (1 | corr_trials),
# data = df_acc)
# Full model stats
#Anova(M2)
#ae.m.M2 <- allEffects(M2)
#ae.m.M2.df <- as.data.frame(ae.m.M2[1])
#plot(ae.m.M2)
#summary(M2)
# Post hoc for full model
#posthoc <- emmeans(M2, ~ factor(sub.trial.number))
#pairwise_comparisons <- pairs(posthoc)
#summary(pairwise_comparisons)
# Subset by block (sessions 1 to 5)
df_B1 <- df_acc %>% filter(session == 1)
df_B2 <- df_acc %>% filter(session == 2)
df_B3 <- df_acc %>% filter(session == 3)
df_B4 <- df_acc %>% filter(session == 4)
df_B5 <- df_acc %>% filter(session == 5)
### BLOCK 1 ANALYSIS
M_B1 <- lmer(feedback.RT ~ 0 + sub.trial.number +
(1 | subject) + (1 | trial),
data = df_B1)
Anova(M_B1)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: feedback.RT
Chisq Df Pr(>Chisq)
sub.trial.number 602.58 6 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ae.m.M_B1 <- allEffects(M_B1)
ae.m.M_B1.df <- as.data.frame(ae.m.M_B1[1])
plot(ae.m.M_B1)
summary(M_B1)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: feedback.RT ~ 0 + sub.trial.number + (1 | subject) + (1 | trial)
Data: df_B1
REML criterion at convergence: 63545
Scaled residuals:
Min 1Q Median 3Q Max
-4.2607 -0.3704 -0.0988 0.2392 29.5066
Random effects:
Groups Name Variance Std.Dev.
trial (Intercept) 13795 117.5
subject (Intercept) 68992 262.7
Residual 102211 319.7
Number of obs: 4410, groups: trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
sub.trial.number1 760.52 65.27 20.68 11.652 1.52e-10 ***
sub.trial.number2 464.27 65.27 20.68 7.113 5.62e-07 ***
sub.trial.number3 442.12 65.27 20.68 6.774 1.15e-06 ***
sub.trial.number4 453.28 65.27 20.68 6.945 8.02e-07 ***
sub.trial.number5 472.08 65.27 20.68 7.233 4.38e-07 ***
sub.trial.number6 479.82 65.27 20.68 7.351 3.42e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
sb.t.1 sb.t.2 sb.t.3 sb.t.4 sb.t.5
sb.trl.nmb2 0.967
sb.trl.nmb3 0.967 0.967
sb.trl.nmb4 0.967 0.967 0.967
sb.trl.nmb5 0.967 0.967 0.967 0.967
sb.trl.nmb6 0.967 0.967 0.967 0.967 0.967 posthocM_B1 <- emmeans(M_B1, ~ factor(sub.trial.number))
pairwise_comparisonsM_B1 <- pairs(posthocM_B1)
summary(pairwise_comparisonsM_B1) contrast estimate SE df t.ratio p.value
sub.trial.number1 - sub.trial.number2 296.25 16.7 4340 17.764 <.0001
sub.trial.number1 - sub.trial.number3 318.39 16.7 4340 19.092 <.0001
sub.trial.number1 - sub.trial.number4 307.24 16.7 4340 18.423 <.0001
sub.trial.number1 - sub.trial.number5 288.44 16.7 4340 17.296 <.0001
sub.trial.number1 - sub.trial.number6 280.70 16.7 4340 16.832 <.0001
sub.trial.number2 - sub.trial.number3 22.14 16.7 4340 1.328 0.7696
sub.trial.number2 - sub.trial.number4 10.99 16.7 4340 0.659 0.9863
sub.trial.number2 - sub.trial.number5 -7.81 16.7 4340 -0.468 0.9972
sub.trial.number2 - sub.trial.number6 -15.55 16.7 4340 -0.932 0.9382
sub.trial.number3 - sub.trial.number4 -11.15 16.7 4340 -0.669 0.9853
sub.trial.number3 - sub.trial.number5 -29.95 16.7 4340 -1.796 0.4684
sub.trial.number3 - sub.trial.number6 -37.69 16.7 4340 -2.260 0.2109
sub.trial.number4 - sub.trial.number5 -18.80 16.7 4340 -1.127 0.8701
sub.trial.number4 - sub.trial.number6 -26.54 16.7 4340 -1.591 0.6043
sub.trial.number5 - sub.trial.number6 -7.74 16.7 4340 -0.464 0.9973
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 6 estimates ### BLOCK 2 ANALYSIS
M_B2 <- lmer(feedback.RT ~ 0 + sub.trial.number +
(1 | subject) + (1 | trial),
data = df_B2)
Anova(M_B2)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: feedback.RT
Chisq Df Pr(>Chisq)
sub.trial.number 726.83 12 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ae.m.M_B2 <- allEffects(M_B2)
ae.m.M_B2.df <- as.data.frame(ae.m.M_B2[1])
plot(ae.m.M_B2)
summary(M_B2)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: feedback.RT ~ 0 + sub.trial.number + (1 | subject) + (1 | trial)
Data: df_B2
REML criterion at convergence: 107563.7
Scaled residuals:
Min 1Q Median 3Q Max
-3.4650 -0.4593 -0.1806 0.1814 12.4992
Random effects:
Groups Name Variance Std.Dev.
trial (Intercept) 3901 62.46
subject (Intercept) 60187 245.33
Residual 81544 285.56
Number of obs: 7596, groups: trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
sub.trial.number1 766.95 59.62 19.02 12.863 7.82e-11 ***
sub.trial.number2 478.14 59.62 19.02 8.019 1.60e-07 ***
sub.trial.number3 448.41 59.62 19.02 7.521 4.11e-07 ***
sub.trial.number4 417.93 59.62 19.02 7.010 1.12e-06 ***
sub.trial.number5 528.07 59.62 19.02 8.857 3.55e-08 ***
sub.trial.number6 490.32 59.62 19.02 8.224 1.10e-07 ***
sub.trial.number7 536.07 59.62 19.02 8.991 2.81e-08 ***
sub.trial.number8 504.41 59.62 19.02 8.460 7.17e-08 ***
sub.trial.number9 528.69 59.62 19.02 8.867 3.48e-08 ***
sub.trial.number10 501.77 59.62 19.02 8.416 7.76e-08 ***
sub.trial.number11 464.25 59.62 19.02 7.786 2.48e-07 ***
sub.trial.number12 485.15 59.62 19.02 8.137 1.29e-07 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation of Fixed Effects:
sb.t.1 sb.t.2 sb.t.3 sb.t.4 sb.t.5 sb.t.6 sb.t.7 sb.t.8 sb.t.9
sb.trl.nmb2 0.964
sb.trl.nmb3 0.964 0.964
sb.trl.nmb4 0.964 0.964 0.964
sb.trl.nmb5 0.964 0.964 0.964 0.964
sb.trl.nmb6 0.964 0.964 0.964 0.964 0.964
sb.trl.nmb7 0.964 0.964 0.964 0.964 0.964 0.964
sb.trl.nmb8 0.964 0.964 0.964 0.964 0.964 0.964 0.964
sb.trl.nmb9 0.964 0.964 0.964 0.964 0.964 0.964 0.964 0.964
sb.trl.nm10 0.964 0.964 0.964 0.964 0.964 0.964 0.964 0.964 0.964
sb.trl.nm11 0.964 0.964 0.964 0.964 0.964 0.964 0.964 0.964 0.964
sb.trl.nm12 0.964 0.964 0.964 0.964 0.964 0.964 0.964 0.964 0.964
sb..10 sb..11
sb.trl.nmb2
sb.trl.nmb3
sb.trl.nmb4
sb.trl.nmb5
sb.trl.nmb6
sb.trl.nmb7
sb.trl.nmb8
sb.trl.nmb9
sb.trl.nm10
sb.trl.nm11 0.964
sb.trl.nm12 0.964 0.964 posthocM_B2 <- emmeans(M_B2, ~ factor(sub.trial.number))
pairwise_comparisonsM_B2 <- pairs(posthocM_B2)
summary(pairwise_comparisonsM_B2) contrast estimate SE df t.ratio p.value
sub.trial.number1 - sub.trial.number2 288.807 16.1 7520 17.993 <.0001
sub.trial.number1 - sub.trial.number3 318.537 16.1 7520 19.845 <.0001
sub.trial.number1 - sub.trial.number4 349.021 16.1 7520 21.744 <.0001
sub.trial.number1 - sub.trial.number5 238.880 16.1 7520 14.882 <.0001
sub.trial.number1 - sub.trial.number6 276.632 16.1 7520 17.234 <.0001
sub.trial.number1 - sub.trial.number7 230.885 16.1 7520 14.384 <.0001
sub.trial.number1 - sub.trial.number8 262.545 16.1 7520 16.357 <.0001
sub.trial.number1 - sub.trial.number9 238.256 16.1 7520 14.843 <.0001
sub.trial.number1 - sub.trial.number10 265.180 16.1 7520 16.521 <.0001
sub.trial.number1 - sub.trial.number11 302.703 16.1 7520 18.859 <.0001
sub.trial.number1 - sub.trial.number12 281.799 16.1 7520 17.556 <.0001
sub.trial.number2 - sub.trial.number3 29.730 16.1 7520 1.852 0.7887
sub.trial.number2 - sub.trial.number4 60.213 16.1 7520 3.751 0.0097
sub.trial.number2 - sub.trial.number5 -49.927 16.1 7520 -3.110 0.0800
sub.trial.number2 - sub.trial.number6 -12.175 16.1 7520 -0.759 0.9998
sub.trial.number2 - sub.trial.number7 -57.923 16.1 7520 -3.609 0.0162
sub.trial.number2 - sub.trial.number8 -26.262 16.1 7520 -1.636 0.8958
sub.trial.number2 - sub.trial.number9 -50.551 16.1 7520 -3.149 0.0715
sub.trial.number2 - sub.trial.number10 -23.627 16.1 7520 -1.472 0.9482
sub.trial.number2 - sub.trial.number11 13.896 16.1 7520 0.866 0.9994
sub.trial.number2 - sub.trial.number12 -7.008 16.1 7520 -0.437 1.0000
sub.trial.number3 - sub.trial.number4 30.483 16.1 7520 1.899 0.7601
sub.trial.number3 - sub.trial.number5 -79.657 16.1 7520 -4.963 <.0001
sub.trial.number3 - sub.trial.number6 -41.905 16.1 7520 -2.611 0.2736
sub.trial.number3 - sub.trial.number7 -87.652 16.1 7520 -5.461 <.0001
sub.trial.number3 - sub.trial.number8 -55.992 16.1 7520 -3.488 0.0246
sub.trial.number3 - sub.trial.number9 -80.281 16.1 7520 -5.002 <.0001
sub.trial.number3 - sub.trial.number10 -53.357 16.1 7520 -3.324 0.0421
sub.trial.number3 - sub.trial.number11 -15.834 16.1 7520 -0.986 0.9980
sub.trial.number3 - sub.trial.number12 -36.738 16.1 7520 -2.289 0.4847
sub.trial.number4 - sub.trial.number5 -110.141 16.1 7520 -6.862 <.0001
sub.trial.number4 - sub.trial.number6 -72.389 16.1 7520 -4.510 0.0004
sub.trial.number4 - sub.trial.number7 -118.136 16.1 7520 -7.360 <.0001
sub.trial.number4 - sub.trial.number8 -86.475 16.1 7520 -5.387 <.0001
sub.trial.number4 - sub.trial.number9 -110.765 16.1 7520 -6.901 <.0001
sub.trial.number4 - sub.trial.number10 -83.840 16.1 7520 -5.223 <.0001
sub.trial.number4 - sub.trial.number11 -46.318 16.1 7520 -2.886 0.1461
sub.trial.number4 - sub.trial.number12 -67.221 16.1 7520 -4.188 0.0017
sub.trial.number5 - sub.trial.number6 37.752 16.1 7520 2.352 0.4394
sub.trial.number5 - sub.trial.number7 -7.995 16.1 7520 -0.498 1.0000
sub.trial.number5 - sub.trial.number8 23.665 16.1 7520 1.474 0.9476
sub.trial.number5 - sub.trial.number9 -0.624 16.1 7520 -0.039 1.0000
sub.trial.number5 - sub.trial.number10 26.300 16.1 7520 1.639 0.8949
sub.trial.number5 - sub.trial.number11 63.823 16.1 7520 3.976 0.0041
sub.trial.number5 - sub.trial.number12 42.919 16.1 7520 2.674 0.2395
sub.trial.number6 - sub.trial.number7 -45.747 16.1 7520 -2.850 0.1595
sub.trial.number6 - sub.trial.number8 -14.087 16.1 7520 -0.878 0.9993
sub.trial.number6 - sub.trial.number9 -38.376 16.1 7520 -2.391 0.4123
sub.trial.number6 - sub.trial.number10 -11.452 16.1 7520 -0.713 0.9999
sub.trial.number6 - sub.trial.number11 26.071 16.1 7520 1.624 0.9005
sub.trial.number6 - sub.trial.number12 5.168 16.1 7520 0.322 1.0000
sub.trial.number7 - sub.trial.number8 31.660 16.1 7520 1.972 0.7124
sub.trial.number7 - sub.trial.number9 7.371 16.1 7520 0.459 1.0000
sub.trial.number7 - sub.trial.number10 34.295 16.1 7520 2.137 0.5962
sub.trial.number7 - sub.trial.number11 71.818 16.1 7520 4.474 0.0005
sub.trial.number7 - sub.trial.number12 50.915 16.1 7520 3.172 0.0670
sub.trial.number8 - sub.trial.number9 -24.289 16.1 7520 -1.513 0.9373
sub.trial.number8 - sub.trial.number10 2.635 16.1 7520 0.164 1.0000
sub.trial.number8 - sub.trial.number11 40.158 16.1 7520 2.502 0.3387
sub.trial.number8 - sub.trial.number12 19.254 16.1 7520 1.200 0.9891
sub.trial.number9 - sub.trial.number10 26.924 16.1 7520 1.677 0.8787
sub.trial.number9 - sub.trial.number11 64.447 16.1 7520 4.015 0.0035
sub.trial.number9 - sub.trial.number12 43.543 16.1 7520 2.713 0.2199
sub.trial.number10 - sub.trial.number11 37.523 16.1 7520 2.338 0.4496
sub.trial.number10 - sub.trial.number12 16.619 16.1 7520 1.035 0.9969
sub.trial.number11 - sub.trial.number12 -20.904 16.1 7520 -1.302 0.9790
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 12 estimates ### BLOCK 3 ANALYSIS
M_B3 <- lmer(feedback.RT ~ 0 + sub.trial.number +
(1 | subject) + (1 | trial),
data = df_B3)
Anova(M_B3)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: feedback.RT
Chisq Df Pr(>Chisq)
sub.trial.number 816.07 18 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ae.m.M_B3 <- allEffects(M_B3)
ae.m.M_B3.df <- as.data.frame(ae.m.M_B3[1])
plot(ae.m.M_B3)
summary(M_B3)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: feedback.RT ~ 0 + sub.trial.number + (1 | subject) + (1 | trial)
Data: df_B3
REML criterion at convergence: 148465.6
Scaled residuals:
Min 1Q Median 3Q Max
-2.5209 -0.4299 -0.1722 0.1637 29.6780
Random effects:
Groups Name Variance Std.Dev.
trial (Intercept) 1366 36.96
subject (Intercept) 42285 205.63
Residual 115584 339.98
Number of obs: 10242, groups: trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
sub.trial.number1 821.31 50.82 20.28 16.160 4.75e-13 ***
sub.trial.number2 495.98 50.82 20.28 9.759 4.17e-09 ***
sub.trial.number3 468.55 50.82 20.28 9.219 1.08e-08 ***
sub.trial.number4 449.90 50.82 20.28 8.852 2.10e-08 ***
sub.trial.number5 555.18 50.82 20.28 10.924 6.02e-10 ***
sub.trial.number6 533.84 50.82 20.28 10.504 1.19e-09 ***
sub.trial.number7 528.27 50.82 20.28 10.394 1.42e-09 ***
sub.trial.number8 513.04 50.82 20.28 10.095 2.35e-09 ***
sub.trial.number9 561.74 50.82 20.28 11.053 4.90e-10 ***
sub.trial.number10 574.64 50.82 20.28 11.307 3.29e-10 ***
sub.trial.number11 542.80 50.82 20.28 10.680 8.91e-10 ***
sub.trial.number12 481.91 50.82 20.28 9.482 6.76e-09 ***
sub.trial.number13 728.99 50.82 20.28 14.344 4.42e-12 ***
sub.trial.number14 586.87 50.82 20.28 11.547 2.27e-10 ***
sub.trial.number15 536.93 50.82 20.28 10.565 1.08e-09 ***
sub.trial.number16 481.77 50.82 20.28 9.479 6.80e-09 ***
sub.trial.number17 521.32 50.82 20.28 10.258 1.79e-09 ***
sub.trial.number18 535.39 50.82 20.28 10.534 1.13e-09 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 18 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itposthocM_B3 <- emmeans(M_B3, ~ factor(sub.trial.number))
pairwise_comparisonsM_B3 <- pairs(posthocM_B3)
summary(pairwise_comparisonsM_B3) contrast estimate SE df t.ratio p.value
sub.trial.number1 - sub.trial.number2 325.330 20.2 10160 16.141 <.0001
sub.trial.number1 - sub.trial.number3 352.761 20.2 10160 17.501 <.0001
sub.trial.number1 - sub.trial.number4 371.413 20.2 10160 18.427 <.0001
sub.trial.number1 - sub.trial.number5 266.135 20.2 10160 13.204 <.0001
sub.trial.number1 - sub.trial.number6 287.473 20.2 10160 14.262 <.0001
sub.trial.number1 - sub.trial.number7 293.037 20.2 10160 14.538 <.0001
sub.trial.number1 - sub.trial.number8 308.276 20.2 10160 15.294 <.0001
sub.trial.number1 - sub.trial.number9 259.576 20.2 10160 12.878 <.0001
sub.trial.number1 - sub.trial.number10 246.677 20.2 10160 12.238 <.0001
sub.trial.number1 - sub.trial.number11 278.510 20.2 10160 13.818 <.0001
sub.trial.number1 - sub.trial.number12 339.397 20.2 10160 16.838 <.0001
sub.trial.number1 - sub.trial.number13 92.325 20.2 10160 4.580 0.0007
sub.trial.number1 - sub.trial.number14 234.438 20.2 10160 11.631 <.0001
sub.trial.number1 - sub.trial.number15 284.378 20.2 10160 14.109 <.0001
sub.trial.number1 - sub.trial.number16 339.539 20.2 10160 16.845 <.0001
sub.trial.number1 - sub.trial.number17 299.988 20.2 10160 14.883 <.0001
sub.trial.number1 - sub.trial.number18 285.919 20.2 10160 14.185 <.0001
sub.trial.number2 - sub.trial.number3 27.431 20.2 10160 1.361 0.9969
sub.trial.number2 - sub.trial.number4 46.083 20.2 10160 2.286 0.6936
sub.trial.number2 - sub.trial.number5 -59.195 20.2 10160 -2.937 0.2322
sub.trial.number2 - sub.trial.number6 -37.858 20.2 10160 -1.878 0.9201
sub.trial.number2 - sub.trial.number7 -32.294 20.2 10160 -1.602 0.9815
sub.trial.number2 - sub.trial.number8 -17.055 20.2 10160 -0.846 1.0000
sub.trial.number2 - sub.trial.number9 -65.754 20.2 10160 -3.262 0.1000
sub.trial.number2 - sub.trial.number10 -78.654 20.2 10160 -3.902 0.0117
sub.trial.number2 - sub.trial.number11 -46.821 20.2 10160 -2.323 0.6666
sub.trial.number2 - sub.trial.number12 14.067 20.2 10160 0.698 1.0000
sub.trial.number2 - sub.trial.number13 -233.005 20.2 10160 -11.560 <.0001
sub.trial.number2 - sub.trial.number14 -90.893 20.2 10160 -4.509 0.0009
sub.trial.number2 - sub.trial.number15 -40.953 20.2 10160 -2.032 0.8540
sub.trial.number2 - sub.trial.number16 14.209 20.2 10160 0.705 1.0000
sub.trial.number2 - sub.trial.number17 -25.343 20.2 10160 -1.257 0.9988
sub.trial.number2 - sub.trial.number18 -39.411 20.2 10160 -1.955 0.8901
sub.trial.number3 - sub.trial.number4 18.652 20.2 10160 0.925 1.0000
sub.trial.number3 - sub.trial.number5 -86.626 20.2 10160 -4.298 0.0023
sub.trial.number3 - sub.trial.number6 -65.288 20.2 10160 -3.239 0.1068
sub.trial.number3 - sub.trial.number7 -59.724 20.2 10160 -2.963 0.2185
sub.trial.number3 - sub.trial.number8 -44.485 20.2 10160 -2.207 0.7492
sub.trial.number3 - sub.trial.number9 -93.184 20.2 10160 -4.623 0.0005
sub.trial.number3 - sub.trial.number10 -106.084 20.2 10160 -5.263 <.0001
sub.trial.number3 - sub.trial.number11 -74.251 20.2 10160 -3.684 0.0260
sub.trial.number3 - sub.trial.number12 -13.364 20.2 10160 -0.663 1.0000
sub.trial.number3 - sub.trial.number13 -260.436 20.2 10160 -12.921 <.0001
sub.trial.number3 - sub.trial.number14 -118.323 20.2 10160 -5.870 <.0001
sub.trial.number3 - sub.trial.number15 -68.383 20.2 10160 -3.393 0.0679
sub.trial.number3 - sub.trial.number16 -13.221 20.2 10160 -0.656 1.0000
sub.trial.number3 - sub.trial.number17 -52.773 20.2 10160 -2.618 0.4398
sub.trial.number3 - sub.trial.number18 -66.842 20.2 10160 -3.316 0.0854
sub.trial.number4 - sub.trial.number5 -105.278 20.2 10160 -5.223 <.0001
sub.trial.number4 - sub.trial.number6 -83.940 20.2 10160 -4.164 0.0041
sub.trial.number4 - sub.trial.number7 -78.376 20.2 10160 -3.888 0.0123
sub.trial.number4 - sub.trial.number8 -63.137 20.2 10160 -3.132 0.1430
sub.trial.number4 - sub.trial.number9 -111.837 20.2 10160 -5.549 <.0001
sub.trial.number4 - sub.trial.number10 -124.736 20.2 10160 -6.189 <.0001
sub.trial.number4 - sub.trial.number11 -92.903 20.2 10160 -4.609 0.0006
sub.trial.number4 - sub.trial.number12 -32.016 20.2 10160 -1.588 0.9831
sub.trial.number4 - sub.trial.number13 -279.088 20.2 10160 -13.846 <.0001
sub.trial.number4 - sub.trial.number14 -136.975 20.2 10160 -6.796 <.0001
sub.trial.number4 - sub.trial.number15 -87.035 20.2 10160 -4.318 0.0021
sub.trial.number4 - sub.trial.number16 -31.873 20.2 10160 -1.581 0.9838
sub.trial.number4 - sub.trial.number17 -71.425 20.2 10160 -3.544 0.0419
sub.trial.number4 - sub.trial.number18 -85.494 20.2 10160 -4.242 0.0030
sub.trial.number5 - sub.trial.number6 21.337 20.2 10160 1.059 0.9999
sub.trial.number5 - sub.trial.number7 26.902 20.2 10160 1.335 0.9976
sub.trial.number5 - sub.trial.number8 42.141 20.2 10160 2.091 0.8220
sub.trial.number5 - sub.trial.number9 -6.559 20.2 10160 -0.325 1.0000
sub.trial.number5 - sub.trial.number10 -19.459 20.2 10160 -0.965 1.0000
sub.trial.number5 - sub.trial.number11 12.374 20.2 10160 0.614 1.0000
sub.trial.number5 - sub.trial.number12 73.262 20.2 10160 3.635 0.0308
sub.trial.number5 - sub.trial.number13 -173.810 20.2 10160 -8.623 <.0001
sub.trial.number5 - sub.trial.number14 -31.698 20.2 10160 -1.573 0.9847
sub.trial.number5 - sub.trial.number15 18.242 20.2 10160 0.905 1.0000
sub.trial.number5 - sub.trial.number16 73.404 20.2 10160 3.642 0.0301
sub.trial.number5 - sub.trial.number17 33.852 20.2 10160 1.680 0.9706
sub.trial.number5 - sub.trial.number18 19.784 20.2 10160 0.982 1.0000
sub.trial.number6 - sub.trial.number7 5.564 20.2 10160 0.276 1.0000
sub.trial.number6 - sub.trial.number8 20.803 20.2 10160 1.032 0.9999
sub.trial.number6 - sub.trial.number9 -27.896 20.2 10160 -1.384 0.9963
sub.trial.number6 - sub.trial.number10 -40.796 20.2 10160 -2.024 0.8580
sub.trial.number6 - sub.trial.number11 -8.963 20.2 10160 -0.445 1.0000
sub.trial.number6 - sub.trial.number12 51.924 20.2 10160 2.576 0.4716
sub.trial.number6 - sub.trial.number13 -195.148 20.2 10160 -9.682 <.0001
sub.trial.number6 - sub.trial.number14 -53.035 20.2 10160 -2.631 0.4302
sub.trial.number6 - sub.trial.number15 -3.095 20.2 10160 -0.154 1.0000
sub.trial.number6 - sub.trial.number16 52.067 20.2 10160 2.583 0.4662
sub.trial.number6 - sub.trial.number17 12.515 20.2 10160 0.621 1.0000
sub.trial.number6 - sub.trial.number18 -1.554 20.2 10160 -0.077 1.0000
sub.trial.number7 - sub.trial.number8 15.239 20.2 10160 0.756 1.0000
sub.trial.number7 - sub.trial.number9 -33.461 20.2 10160 -1.660 0.9737
sub.trial.number7 - sub.trial.number10 -46.360 20.2 10160 -2.300 0.6835
sub.trial.number7 - sub.trial.number11 -14.527 20.2 10160 -0.721 1.0000
sub.trial.number7 - sub.trial.number12 46.360 20.2 10160 2.300 0.6835
sub.trial.number7 - sub.trial.number13 -200.712 20.2 10160 -9.958 <.0001
sub.trial.number7 - sub.trial.number14 -58.599 20.2 10160 -2.907 0.2484
sub.trial.number7 - sub.trial.number15 -8.659 20.2 10160 -0.430 1.0000
sub.trial.number7 - sub.trial.number16 46.503 20.2 10160 2.307 0.6783
sub.trial.number7 - sub.trial.number17 6.951 20.2 10160 0.345 1.0000
sub.trial.number7 - sub.trial.number18 -7.118 20.2 10160 -0.353 1.0000
sub.trial.number8 - sub.trial.number9 -48.700 20.2 10160 -2.416 0.5955
sub.trial.number8 - sub.trial.number10 -61.599 20.2 10160 -3.056 0.1742
sub.trial.number8 - sub.trial.number11 -29.766 20.2 10160 -1.477 0.9922
sub.trial.number8 - sub.trial.number12 31.121 20.2 10160 1.544 0.9874
sub.trial.number8 - sub.trial.number13 -215.951 20.2 10160 -10.714 <.0001
sub.trial.number8 - sub.trial.number14 -73.838 20.2 10160 -3.663 0.0279
sub.trial.number8 - sub.trial.number15 -23.898 20.2 10160 -1.186 0.9994
sub.trial.number8 - sub.trial.number16 31.264 20.2 10160 1.551 0.9868
sub.trial.number8 - sub.trial.number17 -8.288 20.2 10160 -0.411 1.0000
sub.trial.number8 - sub.trial.number18 -22.357 20.2 10160 -1.109 0.9998
sub.trial.number9 - sub.trial.number10 -12.900 20.2 10160 -0.640 1.0000
sub.trial.number9 - sub.trial.number11 18.933 20.2 10160 0.939 1.0000
sub.trial.number9 - sub.trial.number12 79.821 20.2 10160 3.960 0.0094
sub.trial.number9 - sub.trial.number13 -167.251 20.2 10160 -8.298 <.0001
sub.trial.number9 - sub.trial.number14 -25.139 20.2 10160 -1.247 0.9989
sub.trial.number9 - sub.trial.number15 24.801 20.2 10160 1.230 0.9991
sub.trial.number9 - sub.trial.number16 79.963 20.2 10160 3.967 0.0091
sub.trial.number9 - sub.trial.number17 40.411 20.2 10160 2.005 0.8674
sub.trial.number9 - sub.trial.number18 26.343 20.2 10160 1.307 0.9981
sub.trial.number10 - sub.trial.number11 31.833 20.2 10160 1.579 0.9841
sub.trial.number10 - sub.trial.number12 92.721 20.2 10160 4.600 0.0006
sub.trial.number10 - sub.trial.number13 -154.351 20.2 10160 -7.658 <.0001
sub.trial.number10 - sub.trial.number14 -12.239 20.2 10160 -0.607 1.0000
sub.trial.number10 - sub.trial.number15 37.701 20.2 10160 1.870 0.9228
sub.trial.number10 - sub.trial.number16 92.863 20.2 10160 4.607 0.0006
sub.trial.number10 - sub.trial.number17 53.311 20.2 10160 2.645 0.4201
sub.trial.number10 - sub.trial.number18 39.242 20.2 10160 1.947 0.8936
sub.trial.number11 - sub.trial.number12 60.888 20.2 10160 3.021 0.1902
sub.trial.number11 - sub.trial.number13 -186.185 20.2 10160 -9.237 <.0001
sub.trial.number11 - sub.trial.number14 -44.072 20.2 10160 -2.187 0.7629
sub.trial.number11 - sub.trial.number15 5.868 20.2 10160 0.291 1.0000
sub.trial.number11 - sub.trial.number16 61.030 20.2 10160 3.028 0.1869
sub.trial.number11 - sub.trial.number17 21.478 20.2 10160 1.066 0.9999
sub.trial.number11 - sub.trial.number18 7.410 20.2 10160 0.368 1.0000
sub.trial.number12 - sub.trial.number13 -247.072 20.2 10160 -12.258 <.0001
sub.trial.number12 - sub.trial.number14 -104.960 20.2 10160 -5.207 <.0001
sub.trial.number12 - sub.trial.number15 -55.019 20.2 10160 -2.730 0.3596
sub.trial.number12 - sub.trial.number16 0.142 20.2 10160 0.007 1.0000
sub.trial.number12 - sub.trial.number17 -39.410 20.2 10160 -1.955 0.8901
sub.trial.number12 - sub.trial.number18 -53.478 20.2 10160 -2.653 0.4140
sub.trial.number13 - sub.trial.number14 142.113 20.2 10160 7.051 <.0001
sub.trial.number13 - sub.trial.number15 192.053 20.2 10160 9.528 <.0001
sub.trial.number13 - sub.trial.number16 247.214 20.2 10160 12.265 <.0001
sub.trial.number13 - sub.trial.number17 207.663 20.2 10160 10.303 <.0001
sub.trial.number13 - sub.trial.number18 193.594 20.2 10160 9.605 <.0001
sub.trial.number14 - sub.trial.number15 49.940 20.2 10160 2.478 0.5477
sub.trial.number14 - sub.trial.number16 105.102 20.2 10160 5.214 <.0001
sub.trial.number14 - sub.trial.number17 65.550 20.2 10160 3.252 0.1029
sub.trial.number14 - sub.trial.number18 51.481 20.2 10160 2.554 0.4884
sub.trial.number15 - sub.trial.number16 55.162 20.2 10160 2.737 0.3548
sub.trial.number15 - sub.trial.number17 15.610 20.2 10160 0.774 1.0000
sub.trial.number15 - sub.trial.number18 1.541 20.2 10160 0.076 1.0000
sub.trial.number16 - sub.trial.number17 -39.552 20.2 10160 -1.962 0.8870
sub.trial.number16 - sub.trial.number18 -53.620 20.2 10160 -2.660 0.4088
sub.trial.number17 - sub.trial.number18 -14.069 20.2 10160 -0.698 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates ### BLOCK 4 ANALYSIS
M_B4 <- lmer(feedback.RT ~ 0 + sub.trial.number +
(1 | subject) + (1 | trial),
data = df_B4)
Anova(M_B4)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: feedback.RT
Chisq Df Pr(>Chisq)
sub.trial.number 837.9 18 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ae.m.M_B4 <- allEffects(M_B4)
ae.m.M_B4.df <- as.data.frame(ae.m.M_B4[1])
plot(ae.m.M_B4)
summary(M_B4)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: feedback.RT ~ 0 + sub.trial.number + (1 | subject) + (1 | trial)
Data: df_B4
REML criterion at convergence: 120490.4
Scaled residuals:
Min 1Q Median 3Q Max
-2.1704 -0.3740 -0.1464 0.1338 26.0516
Random effects:
Groups Name Variance Std.Dev.
trial (Intercept) 984.3 31.37
subject (Intercept) 38348.3 195.83
Residual 118868.8 344.77
Number of obs: 8298, groups: trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
sub.trial.number1 771.42 48.14 19.82 16.024 8.26e-13 ***
sub.trial.number2 429.67 48.14 19.82 8.925 2.23e-08 ***
sub.trial.number3 392.65 48.14 19.82 8.156 9.22e-08 ***
sub.trial.number4 385.34 48.14 19.82 8.004 1.23e-07 ***
sub.trial.number5 432.79 48.14 19.82 8.990 1.98e-08 ***
sub.trial.number6 444.94 48.14 19.82 9.242 1.27e-08 ***
sub.trial.number7 566.63 49.11 21.47 11.538 1.14e-10 ***
sub.trial.number8 464.89 49.11 21.47 9.466 4.08e-09 ***
sub.trial.number9 497.81 49.11 21.47 10.136 1.22e-09 ***
sub.trial.number10 471.20 49.11 21.47 9.595 3.22e-09 ***
sub.trial.number11 455.31 49.11 21.47 9.271 5.87e-09 ***
sub.trial.number12 445.95 49.11 21.47 9.080 8.40e-09 ***
sub.trial.number13 592.53 52.12 27.23 11.368 7.58e-12 ***
sub.trial.number14 542.87 52.12 27.23 10.416 5.34e-11 ***
sub.trial.number15 432.58 52.12 27.23 8.300 6.16e-09 ***
sub.trial.number16 442.77 52.12 27.23 8.495 3.87e-09 ***
sub.trial.number17 450.63 52.12 27.23 8.646 2.72e-09 ***
sub.trial.number18 550.28 52.12 27.23 10.558 3.96e-11 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 18 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itposthocM_B4 <- emmeans(M_B4, ~ factor(sub.trial.number))
pairwise_comparisonsM_B4 <- pairs(posthocM_B4)
summary(pairwise_comparisonsM_B4) contrast estimate SE df t.ratio p.value
sub.trial.number1 - sub.trial.number2 341.747 18.2 8216 18.742 <.0001
sub.trial.number1 - sub.trial.number3 378.772 18.2 8216 20.772 <.0001
sub.trial.number1 - sub.trial.number4 386.085 18.2 8216 21.173 <.0001
sub.trial.number1 - sub.trial.number5 338.631 18.2 8216 18.571 <.0001
sub.trial.number1 - sub.trial.number6 326.477 18.2 8216 17.904 <.0001
sub.trial.number1 - sub.trial.number7 204.786 20.7 8228 9.912 <.0001
sub.trial.number1 - sub.trial.number8 306.530 20.7 8228 14.837 <.0001
sub.trial.number1 - sub.trial.number9 273.607 20.7 8228 13.243 <.0001
sub.trial.number1 - sub.trial.number10 300.217 20.7 8228 14.531 <.0001
sub.trial.number1 - sub.trial.number11 316.112 20.7 8228 15.301 <.0001
sub.trial.number1 - sub.trial.number12 325.467 20.7 8228 15.753 <.0001
sub.trial.number1 - sub.trial.number13 178.887 27.0 8233 6.615 <.0001
sub.trial.number1 - sub.trial.number14 228.550 27.0 8233 8.452 <.0001
sub.trial.number1 - sub.trial.number15 338.840 27.0 8233 12.531 <.0001
sub.trial.number1 - sub.trial.number16 328.650 27.0 8233 12.154 <.0001
sub.trial.number1 - sub.trial.number17 320.792 27.0 8233 11.863 <.0001
sub.trial.number1 - sub.trial.number18 221.138 27.0 8233 8.178 <.0001
sub.trial.number2 - sub.trial.number3 37.025 18.2 8216 2.030 0.8546
sub.trial.number2 - sub.trial.number4 44.339 18.2 8216 2.432 0.5835
sub.trial.number2 - sub.trial.number5 -3.116 18.2 8216 -0.171 1.0000
sub.trial.number2 - sub.trial.number6 -15.270 18.2 8216 -0.837 1.0000
sub.trial.number2 - sub.trial.number7 -136.961 20.7 8228 -6.629 <.0001
sub.trial.number2 - sub.trial.number8 -35.217 20.7 8228 -1.705 0.9661
sub.trial.number2 - sub.trial.number9 -68.140 20.7 8228 -3.298 0.0901
sub.trial.number2 - sub.trial.number10 -41.530 20.7 8228 -2.010 0.8648
sub.trial.number2 - sub.trial.number11 -25.635 20.7 8228 -1.241 0.9990
sub.trial.number2 - sub.trial.number12 -16.280 20.7 8228 -0.788 1.0000
sub.trial.number2 - sub.trial.number13 -162.860 27.0 8233 -6.023 <.0001
sub.trial.number2 - sub.trial.number14 -113.196 27.0 8233 -4.186 0.0038
sub.trial.number2 - sub.trial.number15 -2.907 27.0 8233 -0.108 1.0000
sub.trial.number2 - sub.trial.number16 -13.097 27.0 8233 -0.484 1.0000
sub.trial.number2 - sub.trial.number17 -20.955 27.0 8233 -0.775 1.0000
sub.trial.number2 - sub.trial.number18 -120.609 27.0 8233 -4.460 0.0011
sub.trial.number3 - sub.trial.number4 7.313 18.2 8216 0.401 1.0000
sub.trial.number3 - sub.trial.number5 -40.141 18.2 8216 -2.201 0.7530
sub.trial.number3 - sub.trial.number6 -52.295 18.2 8216 -2.868 0.2711
sub.trial.number3 - sub.trial.number7 -173.986 20.7 8228 -8.421 <.0001
sub.trial.number3 - sub.trial.number8 -72.242 20.7 8228 -3.497 0.0489
sub.trial.number3 - sub.trial.number9 -105.165 20.7 8228 -5.090 0.0001
sub.trial.number3 - sub.trial.number10 -78.555 20.7 8228 -3.802 0.0170
sub.trial.number3 - sub.trial.number11 -62.660 20.7 8228 -3.033 0.1846
sub.trial.number3 - sub.trial.number12 -53.306 20.7 8228 -2.580 0.4686
sub.trial.number3 - sub.trial.number13 -199.885 27.0 8233 -7.392 <.0001
sub.trial.number3 - sub.trial.number14 -150.222 27.0 8233 -5.555 <.0001
sub.trial.number3 - sub.trial.number15 -39.932 27.0 8233 -1.477 0.9922
sub.trial.number3 - sub.trial.number16 -50.122 27.0 8233 -1.854 0.9284
sub.trial.number3 - sub.trial.number17 -57.980 27.0 8233 -2.144 0.7901
sub.trial.number3 - sub.trial.number18 -157.634 27.0 8233 -5.830 <.0001
sub.trial.number4 - sub.trial.number5 -47.455 18.2 8216 -2.602 0.4517
sub.trial.number4 - sub.trial.number6 -59.608 18.2 8216 -3.269 0.0981
sub.trial.number4 - sub.trial.number7 -181.299 20.7 8228 -8.775 <.0001
sub.trial.number4 - sub.trial.number8 -79.555 20.7 8228 -3.851 0.0142
sub.trial.number4 - sub.trial.number9 -112.479 20.7 8228 -5.444 <.0001
sub.trial.number4 - sub.trial.number10 -85.868 20.7 8228 -4.156 0.0043
sub.trial.number4 - sub.trial.number11 -69.973 20.7 8228 -3.387 0.0691
sub.trial.number4 - sub.trial.number12 -60.619 20.7 8228 -2.934 0.2338
sub.trial.number4 - sub.trial.number13 -207.198 27.0 8233 -7.662 <.0001
sub.trial.number4 - sub.trial.number14 -157.535 27.0 8233 -5.826 <.0001
sub.trial.number4 - sub.trial.number15 -47.246 27.0 8233 -1.747 0.9573
sub.trial.number4 - sub.trial.number16 -57.435 27.0 8233 -2.124 0.8024
sub.trial.number4 - sub.trial.number17 -65.293 27.0 8233 -2.415 0.5967
sub.trial.number4 - sub.trial.number18 -164.947 27.0 8233 -6.100 <.0001
sub.trial.number5 - sub.trial.number6 -12.154 18.2 8216 -0.667 1.0000
sub.trial.number5 - sub.trial.number7 -133.845 20.7 8228 -6.478 <.0001
sub.trial.number5 - sub.trial.number8 -32.101 20.7 8228 -1.554 0.9865
sub.trial.number5 - sub.trial.number9 -65.024 20.7 8228 -3.147 0.1375
sub.trial.number5 - sub.trial.number10 -38.414 20.7 8228 -1.859 0.9265
sub.trial.number5 - sub.trial.number11 -22.519 20.7 8228 -1.090 0.9998
sub.trial.number5 - sub.trial.number12 -13.164 20.7 8228 -0.637 1.0000
sub.trial.number5 - sub.trial.number13 -159.744 27.0 8233 -5.908 <.0001
sub.trial.number5 - sub.trial.number14 -110.080 27.0 8233 -4.071 0.0060
sub.trial.number5 - sub.trial.number15 0.209 27.0 8233 0.008 1.0000
sub.trial.number5 - sub.trial.number16 -9.981 27.0 8233 -0.369 1.0000
sub.trial.number5 - sub.trial.number17 -17.839 27.0 8233 -0.660 1.0000
sub.trial.number5 - sub.trial.number18 -117.493 27.0 8233 -4.345 0.0019
sub.trial.number6 - sub.trial.number7 -121.691 20.7 8228 -5.890 <.0001
sub.trial.number6 - sub.trial.number8 -19.947 20.7 8228 -0.965 1.0000
sub.trial.number6 - sub.trial.number9 -52.870 20.7 8228 -2.559 0.4847
sub.trial.number6 - sub.trial.number10 -26.260 20.7 8228 -1.271 0.9987
sub.trial.number6 - sub.trial.number11 -10.365 20.7 8228 -0.502 1.0000
sub.trial.number6 - sub.trial.number12 -1.010 20.7 8228 -0.049 1.0000
sub.trial.number6 - sub.trial.number13 -147.590 27.0 8233 -5.458 <.0001
sub.trial.number6 - sub.trial.number14 -97.926 27.0 8233 -3.621 0.0323
sub.trial.number6 - sub.trial.number15 12.363 27.0 8233 0.457 1.0000
sub.trial.number6 - sub.trial.number16 2.173 27.0 8233 0.080 1.0000
sub.trial.number6 - sub.trial.number17 -5.685 27.0 8233 -0.210 1.0000
sub.trial.number6 - sub.trial.number18 -105.339 27.0 8233 -3.896 0.0120
sub.trial.number7 - sub.trial.number8 101.744 22.8 8216 4.461 0.0011
sub.trial.number7 - sub.trial.number9 68.821 22.8 8216 3.017 0.1918
sub.trial.number7 - sub.trial.number10 95.431 22.8 8216 4.184 0.0038
sub.trial.number7 - sub.trial.number11 111.326 22.8 8216 4.881 0.0002
sub.trial.number7 - sub.trial.number12 120.680 22.8 8216 5.291 <.0001
sub.trial.number7 - sub.trial.number13 -25.899 28.7 8229 -0.902 1.0000
sub.trial.number7 - sub.trial.number14 23.764 28.7 8229 0.828 1.0000
sub.trial.number7 - sub.trial.number15 134.054 28.7 8229 4.668 0.0004
sub.trial.number7 - sub.trial.number16 123.864 28.7 8229 4.313 0.0022
sub.trial.number7 - sub.trial.number17 116.006 28.7 8229 4.040 0.0069
sub.trial.number7 - sub.trial.number18 16.352 28.7 8229 0.569 1.0000
sub.trial.number8 - sub.trial.number9 -32.923 22.8 8216 -1.443 0.9940
sub.trial.number8 - sub.trial.number10 -6.313 22.8 8216 -0.277 1.0000
sub.trial.number8 - sub.trial.number11 9.582 22.8 8216 0.420 1.0000
sub.trial.number8 - sub.trial.number12 18.936 22.8 8216 0.830 1.0000
sub.trial.number8 - sub.trial.number13 -127.643 28.7 8229 -4.445 0.0012
sub.trial.number8 - sub.trial.number14 -77.980 28.7 8229 -2.715 0.3696
sub.trial.number8 - sub.trial.number15 32.310 28.7 8229 1.125 0.9997
sub.trial.number8 - sub.trial.number16 22.120 28.7 8229 0.770 1.0000
sub.trial.number8 - sub.trial.number17 14.262 28.7 8229 0.497 1.0000
sub.trial.number8 - sub.trial.number18 -85.392 28.7 8229 -2.973 0.2132
sub.trial.number9 - sub.trial.number10 26.610 22.8 8216 1.167 0.9996
sub.trial.number9 - sub.trial.number11 42.505 22.8 8216 1.864 0.9251
sub.trial.number9 - sub.trial.number12 51.860 22.8 8216 2.274 0.7026
sub.trial.number9 - sub.trial.number13 -94.720 28.7 8229 -3.298 0.0901
sub.trial.number9 - sub.trial.number14 -45.056 28.7 8229 -1.569 0.9851
sub.trial.number9 - sub.trial.number15 65.233 28.7 8229 2.272 0.7042
sub.trial.number9 - sub.trial.number16 55.043 28.7 8229 1.917 0.9059
sub.trial.number9 - sub.trial.number17 47.186 28.7 8229 1.643 0.9762
sub.trial.number9 - sub.trial.number18 -52.468 28.7 8229 -1.827 0.9366
sub.trial.number10 - sub.trial.number11 15.895 22.8 8216 0.697 1.0000
sub.trial.number10 - sub.trial.number12 25.250 22.8 8216 1.107 0.9998
sub.trial.number10 - sub.trial.number13 -121.330 28.7 8229 -4.225 0.0032
sub.trial.number10 - sub.trial.number14 -71.667 28.7 8229 -2.496 0.5338
sub.trial.number10 - sub.trial.number15 38.623 28.7 8229 1.345 0.9973
sub.trial.number10 - sub.trial.number16 28.433 28.7 8229 0.990 1.0000
sub.trial.number10 - sub.trial.number17 20.575 28.7 8229 0.716 1.0000
sub.trial.number10 - sub.trial.number18 -79.079 28.7 8229 -2.754 0.3433
sub.trial.number11 - sub.trial.number12 9.354 22.8 8216 0.410 1.0000
sub.trial.number11 - sub.trial.number13 -137.225 28.7 8229 -4.778 0.0003
sub.trial.number11 - sub.trial.number14 -87.562 28.7 8229 -3.049 0.1774
sub.trial.number11 - sub.trial.number15 22.727 28.7 8229 0.791 1.0000
sub.trial.number11 - sub.trial.number16 12.538 28.7 8229 0.437 1.0000
sub.trial.number11 - sub.trial.number17 4.680 28.7 8229 0.163 1.0000
sub.trial.number11 - sub.trial.number18 -94.974 28.7 8229 -3.307 0.0878
sub.trial.number12 - sub.trial.number13 -146.580 28.7 8229 -5.104 0.0001
sub.trial.number12 - sub.trial.number14 -96.916 28.7 8229 -3.375 0.0717
sub.trial.number12 - sub.trial.number15 13.373 28.7 8229 0.466 1.0000
sub.trial.number12 - sub.trial.number16 3.183 28.7 8229 0.111 1.0000
sub.trial.number12 - sub.trial.number17 -4.674 28.7 8229 -0.163 1.0000
sub.trial.number12 - sub.trial.number18 -104.328 28.7 8229 -3.633 0.0311
sub.trial.number13 - sub.trial.number14 49.663 33.6 8216 1.480 0.9921
sub.trial.number13 - sub.trial.number15 159.953 33.6 8216 4.765 0.0003
sub.trial.number13 - sub.trial.number16 149.763 33.6 8216 4.462 0.0011
sub.trial.number13 - sub.trial.number17 141.905 33.6 8216 4.228 0.0032
sub.trial.number13 - sub.trial.number18 42.251 33.6 8216 1.259 0.9988
sub.trial.number14 - sub.trial.number15 110.289 33.6 8216 3.286 0.0935
sub.trial.number14 - sub.trial.number16 100.100 33.6 8216 2.982 0.2089
sub.trial.number14 - sub.trial.number17 92.242 33.6 8216 2.748 0.3471
sub.trial.number14 - sub.trial.number18 -7.412 33.6 8216 -0.221 1.0000
sub.trial.number15 - sub.trial.number16 -10.190 33.6 8216 -0.304 1.0000
sub.trial.number15 - sub.trial.number17 -18.047 33.6 8216 -0.538 1.0000
sub.trial.number15 - sub.trial.number18 -117.701 33.6 8216 -3.507 0.0474
sub.trial.number16 - sub.trial.number17 -7.858 33.6 8216 -0.234 1.0000
sub.trial.number16 - sub.trial.number18 -107.512 33.6 8216 -3.203 0.1182
sub.trial.number17 - sub.trial.number18 -99.654 33.6 8216 -2.969 0.2156
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates ### BLOCK 5 ANALYSIS
M_B5 <- lmer(feedback.RT ~ 0 + sub.trial.number +
(1 | subject) + (1 | trial),
data = df_B5)
Anova(M_B5)Analysis of Deviance Table (Type II Wald chisquare tests)
Response: feedback.RT
Chisq Df Pr(>Chisq)
sub.trial.number 766.03 18 < 2.2e-16 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1ae.m.M_B5 <- allEffects(M_B5)
ae.m.M_B5.df <- as.data.frame(ae.m.M_B5[1])
plot(ae.m.M_B5)
summary(M_B5)Linear mixed model fit by REML. t-tests use Satterthwaite's method [
lmerModLmerTest]
Formula: feedback.RT ~ 0 + sub.trial.number + (1 | subject) + (1 | trial)
Data: df_B5
REML criterion at convergence: 96471.2
Scaled residuals:
Min 1Q Median 3Q Max
-1.8446 -0.4090 -0.1616 0.1312 21.3481
Random effects:
Groups Name Variance Std.Dev.
trial (Intercept) 4131 64.27
subject (Intercept) 35216 187.66
Residual 221693 470.84
Number of obs: 6372, groups: trial, 48; subject, 18
Fixed effects:
Estimate Std. Error df t value Pr(>|t|)
sub.trial.number1 962.44 49.35 24.94 19.503 < 2e-16 ***
sub.trial.number2 538.30 49.35 24.94 10.908 5.55e-11 ***
sub.trial.number3 525.39 49.35 24.94 10.647 9.17e-11 ***
sub.trial.number4 544.89 49.35 24.94 11.042 4.31e-11 ***
sub.trial.number5 571.99 49.35 24.94 11.591 1.55e-11 ***
sub.trial.number6 564.08 49.35 24.94 11.431 2.08e-11 ***
sub.trial.number7 708.50 52.02 30.78 13.621 1.43e-14 ***
sub.trial.number8 591.56 52.02 30.78 11.373 1.50e-12 ***
sub.trial.number9 794.72 52.02 30.78 15.279 6.47e-16 ***
sub.trial.number10 668.19 52.02 30.78 12.846 6.65e-14 ***
sub.trial.number11 507.50 52.02 30.78 9.757 6.20e-11 ***
sub.trial.number12 555.03 52.02 30.78 10.671 7.26e-12 ***
sub.trial.number13 997.24 59.53 52.72 16.753 < 2e-16 ***
sub.trial.number14 666.50 59.53 52.72 11.197 1.50e-15 ***
sub.trial.number15 508.67 59.53 52.72 8.545 1.59e-11 ***
sub.trial.number16 540.20 59.53 52.72 9.075 2.35e-12 ***
sub.trial.number17 570.91 59.53 52.72 9.591 3.75e-13 ***
sub.trial.number18 523.31 59.53 52.72 8.791 6.51e-12 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Correlation matrix not shown by default, as p = 18 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itposthocM_B5 <- emmeans(M_B5, ~ factor(sub.trial.number))
pairwise_comparisonsM_B5 <- pairs(posthocM_B5)
summary(pairwise_comparisonsM_B5) contrast estimate SE df t.ratio p.value
sub.trial.number1 - sub.trial.number2 424.14 27.8 6291 15.234 <.0001
sub.trial.number1 - sub.trial.number3 437.05 27.8 6291 15.698 <.0001
sub.trial.number1 - sub.trial.number4 417.55 27.8 6291 14.998 <.0001
sub.trial.number1 - sub.trial.number5 390.46 27.8 6291 14.024 <.0001
sub.trial.number1 - sub.trial.number6 398.36 27.8 6291 14.308 <.0001
sub.trial.number1 - sub.trial.number7 253.95 32.3 6303 7.865 <.0001
sub.trial.number1 - sub.trial.number8 370.88 32.3 6303 11.486 <.0001
sub.trial.number1 - sub.trial.number9 167.73 32.3 6303 5.195 <.0001
sub.trial.number1 - sub.trial.number10 294.25 32.3 6303 9.113 <.0001
sub.trial.number1 - sub.trial.number11 454.94 32.3 6303 14.090 <.0001
sub.trial.number1 - sub.trial.number12 407.41 32.3 6303 12.618 <.0001
sub.trial.number1 - sub.trial.number13 -34.80 43.3 6308 -0.803 1.0000
sub.trial.number1 - sub.trial.number14 295.94 43.3 6308 6.833 <.0001
sub.trial.number1 - sub.trial.number15 453.77 43.3 6308 10.478 <.0001
sub.trial.number1 - sub.trial.number16 422.24 43.3 6308 9.750 <.0001
sub.trial.number1 - sub.trial.number17 391.53 43.3 6308 9.041 <.0001
sub.trial.number1 - sub.trial.number18 439.13 43.3 6308 10.140 <.0001
sub.trial.number2 - sub.trial.number3 12.91 27.8 6291 0.464 1.0000
sub.trial.number2 - sub.trial.number4 -6.59 27.8 6291 -0.237 1.0000
sub.trial.number2 - sub.trial.number5 -33.68 27.8 6291 -1.210 0.9993
sub.trial.number2 - sub.trial.number6 -25.78 27.8 6291 -0.926 1.0000
sub.trial.number2 - sub.trial.number7 -170.19 32.3 6303 -5.271 <.0001
sub.trial.number2 - sub.trial.number8 -53.26 32.3 6303 -1.650 0.9753
sub.trial.number2 - sub.trial.number9 -256.41 32.3 6303 -7.941 <.0001
sub.trial.number2 - sub.trial.number10 -129.89 32.3 6303 -4.023 0.0073
sub.trial.number2 - sub.trial.number11 30.80 32.3 6303 0.954 1.0000
sub.trial.number2 - sub.trial.number12 -16.73 32.3 6303 -0.518 1.0000
sub.trial.number2 - sub.trial.number13 -458.94 43.3 6308 -10.597 <.0001
sub.trial.number2 - sub.trial.number14 -128.20 43.3 6308 -2.960 0.2202
sub.trial.number2 - sub.trial.number15 29.63 43.3 6308 0.684 1.0000
sub.trial.number2 - sub.trial.number16 -1.90 43.3 6308 -0.044 1.0000
sub.trial.number2 - sub.trial.number17 -32.61 43.3 6308 -0.753 1.0000
sub.trial.number2 - sub.trial.number18 14.99 43.3 6308 0.346 1.0000
sub.trial.number3 - sub.trial.number4 -19.50 27.8 6291 -0.700 1.0000
sub.trial.number3 - sub.trial.number5 -46.60 27.8 6291 -1.674 0.9715
sub.trial.number3 - sub.trial.number6 -38.69 27.8 6291 -1.390 0.9961
sub.trial.number3 - sub.trial.number7 -183.11 32.3 6303 -5.671 <.0001
sub.trial.number3 - sub.trial.number8 -66.18 32.3 6303 -2.049 0.8447
sub.trial.number3 - sub.trial.number9 -269.33 32.3 6303 -8.341 <.0001
sub.trial.number3 - sub.trial.number10 -142.80 32.3 6303 -4.423 0.0014
sub.trial.number3 - sub.trial.number11 17.89 32.3 6303 0.554 1.0000
sub.trial.number3 - sub.trial.number12 -29.64 32.3 6303 -0.918 1.0000
sub.trial.number3 - sub.trial.number13 -471.85 43.3 6308 -10.895 <.0001
sub.trial.number3 - sub.trial.number14 -141.11 43.3 6308 -3.258 0.1012
sub.trial.number3 - sub.trial.number15 16.72 43.3 6308 0.386 1.0000
sub.trial.number3 - sub.trial.number16 -14.81 43.3 6308 -0.342 1.0000
sub.trial.number3 - sub.trial.number17 -45.52 43.3 6308 -1.051 0.9999
sub.trial.number3 - sub.trial.number18 2.08 43.3 6308 0.048 1.0000
sub.trial.number4 - sub.trial.number5 -27.10 27.8 6291 -0.973 1.0000
sub.trial.number4 - sub.trial.number6 -19.19 27.8 6291 -0.689 1.0000
sub.trial.number4 - sub.trial.number7 -163.61 32.3 6303 -5.067 0.0001
sub.trial.number4 - sub.trial.number8 -46.68 32.3 6303 -1.446 0.9939
sub.trial.number4 - sub.trial.number9 -249.83 32.3 6303 -7.737 <.0001
sub.trial.number4 - sub.trial.number10 -123.30 32.3 6303 -3.819 0.0160
sub.trial.number4 - sub.trial.number11 37.39 32.3 6303 1.158 0.9996
sub.trial.number4 - sub.trial.number12 -10.14 32.3 6303 -0.314 1.0000
sub.trial.number4 - sub.trial.number13 -452.35 43.3 6308 -10.445 <.0001
sub.trial.number4 - sub.trial.number14 -121.61 43.3 6308 -2.808 0.3078
sub.trial.number4 - sub.trial.number15 36.22 43.3 6308 0.836 1.0000
sub.trial.number4 - sub.trial.number16 4.69 43.3 6308 0.108 1.0000
sub.trial.number4 - sub.trial.number17 -26.02 43.3 6308 -0.601 1.0000
sub.trial.number4 - sub.trial.number18 21.58 43.3 6308 0.498 1.0000
sub.trial.number5 - sub.trial.number6 7.90 27.8 6291 0.284 1.0000
sub.trial.number5 - sub.trial.number7 -136.51 32.3 6303 -4.228 0.0032
sub.trial.number5 - sub.trial.number8 -19.58 32.3 6303 -0.606 1.0000
sub.trial.number5 - sub.trial.number9 -222.73 32.3 6303 -6.898 <.0001
sub.trial.number5 - sub.trial.number10 -96.21 32.3 6303 -2.980 0.2102
sub.trial.number5 - sub.trial.number11 64.49 32.3 6303 1.997 0.8711
sub.trial.number5 - sub.trial.number12 16.95 32.3 6303 0.525 1.0000
sub.trial.number5 - sub.trial.number13 -425.25 43.3 6308 -9.819 <.0001
sub.trial.number5 - sub.trial.number14 -94.51 43.3 6308 -2.182 0.7656
sub.trial.number5 - sub.trial.number15 63.31 43.3 6308 1.462 0.9930
sub.trial.number5 - sub.trial.number16 31.79 43.3 6308 0.734 1.0000
sub.trial.number5 - sub.trial.number17 1.07 43.3 6308 0.025 1.0000
sub.trial.number5 - sub.trial.number18 48.67 43.3 6308 1.124 0.9997
sub.trial.number6 - sub.trial.number7 -144.41 32.3 6303 -4.473 0.0011
sub.trial.number6 - sub.trial.number8 -27.48 32.3 6303 -0.851 1.0000
sub.trial.number6 - sub.trial.number9 -230.63 32.3 6303 -7.143 <.0001
sub.trial.number6 - sub.trial.number10 -104.11 32.3 6303 -3.224 0.1114
sub.trial.number6 - sub.trial.number11 56.58 32.3 6303 1.752 0.9561
sub.trial.number6 - sub.trial.number12 9.05 32.3 6303 0.280 1.0000
sub.trial.number6 - sub.trial.number13 -433.16 43.3 6308 -10.002 <.0001
sub.trial.number6 - sub.trial.number14 -102.42 43.3 6308 -2.365 0.6350
sub.trial.number6 - sub.trial.number15 55.41 43.3 6308 1.279 0.9986
sub.trial.number6 - sub.trial.number16 23.88 43.3 6308 0.551 1.0000
sub.trial.number6 - sub.trial.number17 -6.83 43.3 6308 -0.158 1.0000
sub.trial.number6 - sub.trial.number18 40.77 43.3 6308 0.941 1.0000
sub.trial.number7 - sub.trial.number8 116.93 36.1 6291 3.238 0.1072
sub.trial.number7 - sub.trial.number9 -86.22 36.1 6291 -2.388 0.6175
sub.trial.number7 - sub.trial.number10 40.30 36.1 6291 1.116 0.9998
sub.trial.number7 - sub.trial.number11 201.00 36.1 6291 5.566 <.0001
sub.trial.number7 - sub.trial.number12 153.46 36.1 6291 4.250 0.0029
sub.trial.number7 - sub.trial.number13 -288.74 46.2 6298 -6.249 <.0001
sub.trial.number7 - sub.trial.number14 42.00 46.2 6298 0.909 1.0000
sub.trial.number7 - sub.trial.number15 199.82 46.2 6298 4.324 0.0021
sub.trial.number7 - sub.trial.number16 168.30 46.2 6298 3.642 0.0301
sub.trial.number7 - sub.trial.number17 137.58 46.2 6298 2.978 0.2113
sub.trial.number7 - sub.trial.number18 185.18 46.2 6298 4.008 0.0078
sub.trial.number8 - sub.trial.number9 -203.15 36.1 6291 -5.626 <.0001
sub.trial.number8 - sub.trial.number10 -76.63 36.1 6291 -2.122 0.8036
sub.trial.number8 - sub.trial.number11 84.06 36.1 6291 2.328 0.6628
sub.trial.number8 - sub.trial.number12 36.53 36.1 6291 1.012 0.9999
sub.trial.number8 - sub.trial.number13 -405.67 46.2 6298 -8.779 <.0001
sub.trial.number8 - sub.trial.number14 -74.93 46.2 6298 -1.622 0.9791
sub.trial.number8 - sub.trial.number15 82.89 46.2 6298 1.794 0.9459
sub.trial.number8 - sub.trial.number16 51.37 46.2 6298 1.112 0.9998
sub.trial.number8 - sub.trial.number17 20.65 46.2 6298 0.447 1.0000
sub.trial.number8 - sub.trial.number18 68.25 46.2 6298 1.477 0.9922
sub.trial.number9 - sub.trial.number10 126.52 36.1 6291 3.504 0.0479
sub.trial.number9 - sub.trial.number11 287.22 36.1 6291 7.954 <.0001
sub.trial.number9 - sub.trial.number12 239.69 36.1 6291 6.637 <.0001
sub.trial.number9 - sub.trial.number13 -202.52 46.2 6298 -4.383 0.0016
sub.trial.number9 - sub.trial.number14 128.22 46.2 6298 2.775 0.3293
sub.trial.number9 - sub.trial.number15 286.04 46.2 6298 6.190 <.0001
sub.trial.number9 - sub.trial.number16 254.52 46.2 6298 5.508 <.0001
sub.trial.number9 - sub.trial.number17 223.80 46.2 6298 4.843 0.0002
sub.trial.number9 - sub.trial.number18 271.40 46.2 6298 5.874 <.0001
sub.trial.number10 - sub.trial.number11 160.69 36.1 6291 4.450 0.0012
sub.trial.number10 - sub.trial.number12 113.16 36.1 6291 3.134 0.1427
sub.trial.number10 - sub.trial.number13 -329.05 46.2 6298 -7.121 <.0001
sub.trial.number10 - sub.trial.number14 1.69 46.2 6298 0.037 1.0000
sub.trial.number10 - sub.trial.number15 159.52 46.2 6298 3.452 0.0564
sub.trial.number10 - sub.trial.number16 127.99 46.2 6298 2.770 0.3325
sub.trial.number10 - sub.trial.number17 97.28 46.2 6298 2.105 0.8135
sub.trial.number10 - sub.trial.number18 144.88 46.2 6298 3.135 0.1420
sub.trial.number11 - sub.trial.number12 -47.53 36.1 6291 -1.316 0.9979
sub.trial.number11 - sub.trial.number13 -489.74 46.2 6298 -10.599 <.0001
sub.trial.number11 - sub.trial.number14 -159.00 46.2 6298 -3.441 0.0585
sub.trial.number11 - sub.trial.number15 -1.17 46.2 6298 -0.025 1.0000
sub.trial.number11 - sub.trial.number16 -32.70 46.2 6298 -0.708 1.0000
sub.trial.number11 - sub.trial.number17 -63.41 46.2 6298 -1.372 0.9966
sub.trial.number11 - sub.trial.number18 -15.81 46.2 6298 -0.342 1.0000
sub.trial.number12 - sub.trial.number13 -442.21 46.2 6298 -9.570 <.0001
sub.trial.number12 - sub.trial.number14 -111.47 46.2 6298 -2.412 0.5985
sub.trial.number12 - sub.trial.number15 46.36 46.2 6298 1.003 0.9999
sub.trial.number12 - sub.trial.number16 14.83 46.2 6298 0.321 1.0000
sub.trial.number12 - sub.trial.number17 -15.88 46.2 6298 -0.344 1.0000
sub.trial.number12 - sub.trial.number18 31.72 46.2 6298 0.686 1.0000
sub.trial.number13 - sub.trial.number14 330.74 54.4 6291 6.083 <.0001
sub.trial.number13 - sub.trial.number15 488.57 54.4 6291 8.986 <.0001
sub.trial.number13 - sub.trial.number16 457.04 54.4 6291 8.406 <.0001
sub.trial.number13 - sub.trial.number17 426.33 54.4 6291 7.841 <.0001
sub.trial.number13 - sub.trial.number18 473.93 54.4 6291 8.717 <.0001
sub.trial.number14 - sub.trial.number15 157.83 54.4 6291 2.903 0.2510
sub.trial.number14 - sub.trial.number16 126.30 54.4 6291 2.323 0.6664
sub.trial.number14 - sub.trial.number17 95.59 54.4 6291 1.758 0.9548
sub.trial.number14 - sub.trial.number18 143.19 54.4 6291 2.634 0.4284
sub.trial.number15 - sub.trial.number16 -31.53 54.4 6291 -0.580 1.0000
sub.trial.number15 - sub.trial.number17 -62.24 54.4 6291 -1.145 0.9997
sub.trial.number15 - sub.trial.number18 -14.64 54.4 6291 -0.269 1.0000
sub.trial.number16 - sub.trial.number17 -30.71 54.4 6291 -0.565 1.0000
sub.trial.number16 - sub.trial.number18 16.89 54.4 6291 0.311 1.0000
sub.trial.number17 - sub.trial.number18 47.60 54.4 6291 0.876 1.0000
Degrees-of-freedom method: kenward-roger
P value adjustment: tukey method for comparing a family of 18 estimates # -------- Helper to extract pairwise summary for one block --------
extract_pairwise_pvalues <- function(emmeans_obj, block_label) {
pairwise_df <- as.data.frame(summary(pairs(emmeans_obj)))
pairwise_df$Block <- block_label
pairwise_df %>%
select(Block, contrast, estimate, SE, df, t.ratio, p.value)
}
# -------- Apply to All 5 Blocks --------
pairwise_pvalues_summary <- bind_rows(
extract_pairwise_pvalues(emmeans(M_B1, ~ factor(sub.trial.number)), "Block 1"),
extract_pairwise_pvalues(emmeans(M_B2, ~ factor(sub.trial.number)), "Block 2"),
extract_pairwise_pvalues(emmeans(M_B3, ~ factor(sub.trial.number)), "Block 3"),
extract_pairwise_pvalues(emmeans(M_B4, ~ factor(sub.trial.number)), "Block 4"),
extract_pairwise_pvalues(emmeans(M_B5, ~ factor(sub.trial.number)), "Block 5")
)
# -------- View or Save --------
print(pairwise_pvalues_summary) Block contrast estimate SE
1 Block 1 sub.trial.number1 - sub.trial.number2 296.2503401 16.67711
2 Block 1 sub.trial.number1 - sub.trial.number3 318.3931973 16.67711
3 Block 1 sub.trial.number1 - sub.trial.number4 307.2421769 16.67711
4 Block 1 sub.trial.number1 - sub.trial.number5 288.4408163 16.67711
5 Block 1 sub.trial.number1 - sub.trial.number6 280.7020408 16.67711
6 Block 1 sub.trial.number2 - sub.trial.number3 22.1428571 16.67711
7 Block 1 sub.trial.number2 - sub.trial.number4 10.9918367 16.67711
8 Block 1 sub.trial.number2 - sub.trial.number5 -7.8095238 16.67711
9 Block 1 sub.trial.number2 - sub.trial.number6 -15.5482993 16.67711
10 Block 1 sub.trial.number3 - sub.trial.number4 -11.1510204 16.67711
11 Block 1 sub.trial.number3 - sub.trial.number5 -29.9523810 16.67711
12 Block 1 sub.trial.number3 - sub.trial.number6 -37.6911565 16.67711
13 Block 1 sub.trial.number4 - sub.trial.number5 -18.8013605 16.67711
14 Block 1 sub.trial.number4 - sub.trial.number6 -26.5401361 16.67711
15 Block 1 sub.trial.number5 - sub.trial.number6 -7.7387755 16.67711
16 Block 2 sub.trial.number1 - sub.trial.number2 288.8072670 16.05123
17 Block 2 sub.trial.number1 - sub.trial.number3 318.5371248 16.05123
18 Block 2 sub.trial.number1 - sub.trial.number4 349.0205371 16.05123
19 Block 2 sub.trial.number1 - sub.trial.number5 238.8799368 16.05123
20 Block 2 sub.trial.number1 - sub.trial.number6 276.6319115 16.05123
21 Block 2 sub.trial.number1 - sub.trial.number7 230.8846761 16.05123
22 Block 2 sub.trial.number1 - sub.trial.number8 262.5450237 16.05123
23 Block 2 sub.trial.number1 - sub.trial.number9 238.2559242 16.05123
24 Block 2 sub.trial.number1 - sub.trial.number10 265.1800948 16.05123
25 Block 2 sub.trial.number1 - sub.trial.number11 302.7030016 16.05123
26 Block 2 sub.trial.number1 - sub.trial.number12 281.7993681 16.05123
27 Block 2 sub.trial.number2 - sub.trial.number3 29.7298578 16.05123
28 Block 2 sub.trial.number2 - sub.trial.number4 60.2132701 16.05123
29 Block 2 sub.trial.number2 - sub.trial.number5 -49.9273302 16.05123
30 Block 2 sub.trial.number2 - sub.trial.number6 -12.1753555 16.05123
31 Block 2 sub.trial.number2 - sub.trial.number7 -57.9225908 16.05123
32 Block 2 sub.trial.number2 - sub.trial.number8 -26.2622433 16.05123
33 Block 2 sub.trial.number2 - sub.trial.number9 -50.5513428 16.05123
34 Block 2 sub.trial.number2 - sub.trial.number10 -23.6271722 16.05123
35 Block 2 sub.trial.number2 - sub.trial.number11 13.8957346 16.05123
36 Block 2 sub.trial.number2 - sub.trial.number12 -7.0078989 16.05123
37 Block 2 sub.trial.number3 - sub.trial.number4 30.4834123 16.05123
38 Block 2 sub.trial.number3 - sub.trial.number5 -79.6571880 16.05123
39 Block 2 sub.trial.number3 - sub.trial.number6 -41.9052133 16.05123
40 Block 2 sub.trial.number3 - sub.trial.number7 -87.6524487 16.05123
41 Block 2 sub.trial.number3 - sub.trial.number8 -55.9921011 16.05123
42 Block 2 sub.trial.number3 - sub.trial.number9 -80.2812006 16.05123
43 Block 2 sub.trial.number3 - sub.trial.number10 -53.3570300 16.05123
44 Block 2 sub.trial.number3 - sub.trial.number11 -15.8341232 16.05123
45 Block 2 sub.trial.number3 - sub.trial.number12 -36.7377567 16.05123
46 Block 2 sub.trial.number4 - sub.trial.number5 -110.1406003 16.05123
47 Block 2 sub.trial.number4 - sub.trial.number6 -72.3886256 16.05123
48 Block 2 sub.trial.number4 - sub.trial.number7 -118.1358610 16.05123
49 Block 2 sub.trial.number4 - sub.trial.number8 -86.4755134 16.05123
50 Block 2 sub.trial.number4 - sub.trial.number9 -110.7646130 16.05123
51 Block 2 sub.trial.number4 - sub.trial.number10 -83.8404423 16.05123
52 Block 2 sub.trial.number4 - sub.trial.number11 -46.3175355 16.05123
53 Block 2 sub.trial.number4 - sub.trial.number12 -67.2211690 16.05123
54 Block 2 sub.trial.number5 - sub.trial.number6 37.7519747 16.05123
55 Block 2 sub.trial.number5 - sub.trial.number7 -7.9952607 16.05123
56 Block 2 sub.trial.number5 - sub.trial.number8 23.6650869 16.05123
57 Block 2 sub.trial.number5 - sub.trial.number9 -0.6240126 16.05123
58 Block 2 sub.trial.number5 - sub.trial.number10 26.3001580 16.05123
59 Block 2 sub.trial.number5 - sub.trial.number11 63.8230648 16.05123
60 Block 2 sub.trial.number5 - sub.trial.number12 42.9194313 16.05123
61 Block 2 sub.trial.number6 - sub.trial.number7 -45.7472354 16.05123
62 Block 2 sub.trial.number6 - sub.trial.number8 -14.0868878 16.05123
63 Block 2 sub.trial.number6 - sub.trial.number9 -38.3759874 16.05123
64 Block 2 sub.trial.number6 - sub.trial.number10 -11.4518167 16.05123
65 Block 2 sub.trial.number6 - sub.trial.number11 26.0710900 16.05123
66 Block 2 sub.trial.number6 - sub.trial.number12 5.1674566 16.05123
67 Block 2 sub.trial.number7 - sub.trial.number8 31.6603476 16.05123
68 Block 2 sub.trial.number7 - sub.trial.number9 7.3712480 16.05123
69 Block 2 sub.trial.number7 - sub.trial.number10 34.2954186 16.05123
70 Block 2 sub.trial.number7 - sub.trial.number11 71.8183254 16.05123
71 Block 2 sub.trial.number7 - sub.trial.number12 50.9146919 16.05123
72 Block 2 sub.trial.number8 - sub.trial.number9 -24.2890995 16.05123
73 Block 2 sub.trial.number8 - sub.trial.number10 2.6350711 16.05123
74 Block 2 sub.trial.number8 - sub.trial.number11 40.1579779 16.05123
75 Block 2 sub.trial.number8 - sub.trial.number12 19.2543444 16.05123
76 Block 2 sub.trial.number9 - sub.trial.number10 26.9241706 16.05123
77 Block 2 sub.trial.number9 - sub.trial.number11 64.4470774 16.05123
78 Block 2 sub.trial.number9 - sub.trial.number12 43.5434439 16.05123
79 Block 2 sub.trial.number10 - sub.trial.number11 37.5229068 16.05123
80 Block 2 sub.trial.number10 - sub.trial.number12 16.6192733 16.05123
81 Block 2 sub.trial.number11 - sub.trial.number12 -20.9036335 16.05123
82 Block 3 sub.trial.number1 - sub.trial.number2 325.3304042 20.15615
83 Block 3 sub.trial.number1 - sub.trial.number3 352.7609842 20.15615
84 Block 3 sub.trial.number1 - sub.trial.number4 371.4130053 20.15615
85 Block 3 sub.trial.number1 - sub.trial.number5 266.1353251 20.15615
86 Block 3 sub.trial.number1 - sub.trial.number6 287.4727592 20.15615
87 Block 3 sub.trial.number1 - sub.trial.number7 293.0369069 20.15615
88 Block 3 sub.trial.number1 - sub.trial.number8 308.2759227 20.15615
89 Block 3 sub.trial.number1 - sub.trial.number9 259.5764499 20.15615
90 Block 3 sub.trial.number1 - sub.trial.number10 246.6766257 20.15615
91 Block 3 sub.trial.number1 - sub.trial.number11 278.5096661 20.15615
92 Block 3 sub.trial.number1 - sub.trial.number12 339.3971880 20.15615
93 Block 3 sub.trial.number1 - sub.trial.number13 92.3251318 20.15615
94 Block 3 sub.trial.number1 - sub.trial.number14 234.4376098 20.15615
95 Block 3 sub.trial.number1 - sub.trial.number15 284.3778559 20.15615
96 Block 3 sub.trial.number1 - sub.trial.number16 339.5395431 20.15615
97 Block 3 sub.trial.number1 - sub.trial.number17 299.9876977 20.15615
98 Block 3 sub.trial.number1 - sub.trial.number18 285.9191564 20.15615
99 Block 3 sub.trial.number2 - sub.trial.number3 27.4305800 20.15615
100 Block 3 sub.trial.number2 - sub.trial.number4 46.0826011 20.15615
101 Block 3 sub.trial.number2 - sub.trial.number5 -59.1950791 20.15615
102 Block 3 sub.trial.number2 - sub.trial.number6 -37.8576450 20.15615
103 Block 3 sub.trial.number2 - sub.trial.number7 -32.2934974 20.15615
104 Block 3 sub.trial.number2 - sub.trial.number8 -17.0544815 20.15615
105 Block 3 sub.trial.number2 - sub.trial.number9 -65.7539543 20.15615
106 Block 3 sub.trial.number2 - sub.trial.number10 -78.6537786 20.15615
107 Block 3 sub.trial.number2 - sub.trial.number11 -46.8207381 20.15615
108 Block 3 sub.trial.number2 - sub.trial.number12 14.0667838 20.15615
109 Block 3 sub.trial.number2 - sub.trial.number13 -233.0052724 20.15615
110 Block 3 sub.trial.number2 - sub.trial.number14 -90.8927944 20.15615
111 Block 3 sub.trial.number2 - sub.trial.number15 -40.9525483 20.15615
112 Block 3 sub.trial.number2 - sub.trial.number16 14.2091388 20.15615
113 Block 3 sub.trial.number2 - sub.trial.number17 -25.3427065 20.15615
114 Block 3 sub.trial.number2 - sub.trial.number18 -39.4112478 20.15615
115 Block 3 sub.trial.number3 - sub.trial.number4 18.6520211 20.15615
116 Block 3 sub.trial.number3 - sub.trial.number5 -86.6256591 20.15615
117 Block 3 sub.trial.number3 - sub.trial.number6 -65.2882250 20.15615
118 Block 3 sub.trial.number3 - sub.trial.number7 -59.7240773 20.15615
119 Block 3 sub.trial.number3 - sub.trial.number8 -44.4850615 20.15615
120 Block 3 sub.trial.number3 - sub.trial.number9 -93.1845343 20.15615
121 Block 3 sub.trial.number3 - sub.trial.number10 -106.0843585 20.15615
122 Block 3 sub.trial.number3 - sub.trial.number11 -74.2513181 20.15615
123 Block 3 sub.trial.number3 - sub.trial.number12 -13.3637961 20.15615
124 Block 3 sub.trial.number3 - sub.trial.number13 -260.4358524 20.15615
125 Block 3 sub.trial.number3 - sub.trial.number14 -118.3233743 20.15615
126 Block 3 sub.trial.number3 - sub.trial.number15 -68.3831283 20.15615
127 Block 3 sub.trial.number3 - sub.trial.number16 -13.2214411 20.15615
128 Block 3 sub.trial.number3 - sub.trial.number17 -52.7732865 20.15615
129 Block 3 sub.trial.number3 - sub.trial.number18 -66.8418278 20.15615
130 Block 3 sub.trial.number4 - sub.trial.number5 -105.2776801 20.15615
131 Block 3 sub.trial.number4 - sub.trial.number6 -83.9402460 20.15615
132 Block 3 sub.trial.number4 - sub.trial.number7 -78.3760984 20.15615
133 Block 3 sub.trial.number4 - sub.trial.number8 -63.1370826 20.15615
134 Block 3 sub.trial.number4 - sub.trial.number9 -111.8365554 20.15615
135 Block 3 sub.trial.number4 - sub.trial.number10 -124.7363796 20.15615
136 Block 3 sub.trial.number4 - sub.trial.number11 -92.9033392 20.15615
137 Block 3 sub.trial.number4 - sub.trial.number12 -32.0158172 20.15615
138 Block 3 sub.trial.number4 - sub.trial.number13 -279.0878735 20.15615
139 Block 3 sub.trial.number4 - sub.trial.number14 -136.9753954 20.15615
140 Block 3 sub.trial.number4 - sub.trial.number15 -87.0351494 20.15615
141 Block 3 sub.trial.number4 - sub.trial.number16 -31.8734622 20.15615
142 Block 3 sub.trial.number4 - sub.trial.number17 -71.4253076 20.15615
143 Block 3 sub.trial.number4 - sub.trial.number18 -85.4938489 20.15615
144 Block 3 sub.trial.number5 - sub.trial.number6 21.3374341 20.15615
145 Block 3 sub.trial.number5 - sub.trial.number7 26.9015817 20.15615
146 Block 3 sub.trial.number5 - sub.trial.number8 42.1405975 20.15615
147 Block 3 sub.trial.number5 - sub.trial.number9 -6.5588752 20.15615
148 Block 3 sub.trial.number5 - sub.trial.number10 -19.4586995 20.15615
149 Block 3 sub.trial.number5 - sub.trial.number11 12.3743409 20.15615
150 Block 3 sub.trial.number5 - sub.trial.number12 73.2618629 20.15615
151 Block 3 sub.trial.number5 - sub.trial.number13 -173.8101933 20.15615
152 Block 3 sub.trial.number5 - sub.trial.number14 -31.6977153 20.15615
153 Block 3 sub.trial.number5 - sub.trial.number15 18.2425308 20.15615
154 Block 3 sub.trial.number5 - sub.trial.number16 73.4042179 20.15615
155 Block 3 sub.trial.number5 - sub.trial.number17 33.8523726 20.15615
156 Block 3 sub.trial.number5 - sub.trial.number18 19.7838313 20.15615
157 Block 3 sub.trial.number6 - sub.trial.number7 5.5641476 20.15615
158 Block 3 sub.trial.number6 - sub.trial.number8 20.8031634 20.15615
159 Block 3 sub.trial.number6 - sub.trial.number9 -27.8963093 20.15615
160 Block 3 sub.trial.number6 - sub.trial.number10 -40.7961336 20.15615
161 Block 3 sub.trial.number6 - sub.trial.number11 -8.9630931 20.15615
162 Block 3 sub.trial.number6 - sub.trial.number12 51.9244288 20.15615
163 Block 3 sub.trial.number6 - sub.trial.number13 -195.1476274 20.15615
164 Block 3 sub.trial.number6 - sub.trial.number14 -53.0351494 20.15615
165 Block 3 sub.trial.number6 - sub.trial.number15 -3.0949033 20.15615
166 Block 3 sub.trial.number6 - sub.trial.number16 52.0667838 20.15615
167 Block 3 sub.trial.number6 - sub.trial.number17 12.5149385 20.15615
168 Block 3 sub.trial.number6 - sub.trial.number18 -1.5536028 20.15615
169 Block 3 sub.trial.number7 - sub.trial.number8 15.2390158 20.15615
170 Block 3 sub.trial.number7 - sub.trial.number9 -33.4604569 20.15615
171 Block 3 sub.trial.number7 - sub.trial.number10 -46.3602812 20.15615
172 Block 3 sub.trial.number7 - sub.trial.number11 -14.5272408 20.15615
173 Block 3 sub.trial.number7 - sub.trial.number12 46.3602812 20.15615
174 Block 3 sub.trial.number7 - sub.trial.number13 -200.7117750 20.15615
175 Block 3 sub.trial.number7 - sub.trial.number14 -58.5992970 20.15615
176 Block 3 sub.trial.number7 - sub.trial.number15 -8.6590510 20.15615
177 Block 3 sub.trial.number7 - sub.trial.number16 46.5026362 20.15615
178 Block 3 sub.trial.number7 - sub.trial.number17 6.9507909 20.15615
179 Block 3 sub.trial.number7 - sub.trial.number18 -7.1177504 20.15615
180 Block 3 sub.trial.number8 - sub.trial.number9 -48.6994728 20.15615
181 Block 3 sub.trial.number8 - sub.trial.number10 -61.5992970 20.15615
182 Block 3 sub.trial.number8 - sub.trial.number11 -29.7662566 20.15615
183 Block 3 sub.trial.number8 - sub.trial.number12 31.1212654 20.15615
184 Block 3 sub.trial.number8 - sub.trial.number13 -215.9507909 20.15615
185 Block 3 sub.trial.number8 - sub.trial.number14 -73.8383128 20.15615
186 Block 3 sub.trial.number8 - sub.trial.number15 -23.8980668 20.15615
187 Block 3 sub.trial.number8 - sub.trial.number16 31.2636204 20.15615
188 Block 3 sub.trial.number8 - sub.trial.number17 -8.2882250 20.15615
189 Block 3 sub.trial.number8 - sub.trial.number18 -22.3567663 20.15615
190 Block 3 sub.trial.number9 - sub.trial.number10 -12.8998243 20.15615
191 Block 3 sub.trial.number9 - sub.trial.number11 18.9332162 20.15615
192 Block 3 sub.trial.number9 - sub.trial.number12 79.8207381 20.15615
193 Block 3 sub.trial.number9 - sub.trial.number13 -167.2513181 20.15615
194 Block 3 sub.trial.number9 - sub.trial.number14 -25.1388401 20.15615
195 Block 3 sub.trial.number9 - sub.trial.number15 24.8014060 20.15615
196 Block 3 sub.trial.number9 - sub.trial.number16 79.9630931 20.15615
197 Block 3 sub.trial.number9 - sub.trial.number17 40.4112478 20.15615
198 Block 3 sub.trial.number9 - sub.trial.number18 26.3427065 20.15615
199 Block 3 sub.trial.number10 - sub.trial.number11 31.8330404 20.15615
200 Block 3 sub.trial.number10 - sub.trial.number12 92.7205624 20.15615
201 Block 3 sub.trial.number10 - sub.trial.number13 -154.3514938 20.15615
202 Block 3 sub.trial.number10 - sub.trial.number14 -12.2390158 20.15615
203 Block 3 sub.trial.number10 - sub.trial.number15 37.7012302 20.15615
204 Block 3 sub.trial.number10 - sub.trial.number16 92.8629174 20.15615
205 Block 3 sub.trial.number10 - sub.trial.number17 53.3110721 20.15615
206 Block 3 sub.trial.number10 - sub.trial.number18 39.2425308 20.15615
207 Block 3 sub.trial.number11 - sub.trial.number12 60.8875220 20.15615
208 Block 3 sub.trial.number11 - sub.trial.number13 -186.1845343 20.15615
209 Block 3 sub.trial.number11 - sub.trial.number14 -44.0720562 20.15615
210 Block 3 sub.trial.number11 - sub.trial.number15 5.8681898 20.15615
211 Block 3 sub.trial.number11 - sub.trial.number16 61.0298770 20.15615
212 Block 3 sub.trial.number11 - sub.trial.number17 21.4780316 20.15615
213 Block 3 sub.trial.number11 - sub.trial.number18 7.4094903 20.15615
214 Block 3 sub.trial.number12 - sub.trial.number13 -247.0720562 20.15615
215 Block 3 sub.trial.number12 - sub.trial.number14 -104.9595782 20.15615
216 Block 3 sub.trial.number12 - sub.trial.number15 -55.0193322 20.15615
217 Block 3 sub.trial.number12 - sub.trial.number16 0.1423550 20.15615
218 Block 3 sub.trial.number12 - sub.trial.number17 -39.4094903 20.15615
219 Block 3 sub.trial.number12 - sub.trial.number18 -53.4780316 20.15615
220 Block 3 sub.trial.number13 - sub.trial.number14 142.1124780 20.15615
221 Block 3 sub.trial.number13 - sub.trial.number15 192.0527241 20.15615
222 Block 3 sub.trial.number13 - sub.trial.number16 247.2144112 20.15615
223 Block 3 sub.trial.number13 - sub.trial.number17 207.6625659 20.15615
224 Block 3 sub.trial.number13 - sub.trial.number18 193.5940246 20.15615
225 Block 3 sub.trial.number14 - sub.trial.number15 49.9402460 20.15615
226 Block 3 sub.trial.number14 - sub.trial.number16 105.1019332 20.15615
227 Block 3 sub.trial.number14 - sub.trial.number17 65.5500879 20.15615
228 Block 3 sub.trial.number14 - sub.trial.number18 51.4815466 20.15615
229 Block 3 sub.trial.number15 - sub.trial.number16 55.1616872 20.15615
230 Block 3 sub.trial.number15 - sub.trial.number17 15.6098418 20.15615
231 Block 3 sub.trial.number15 - sub.trial.number18 1.5413005 20.15615
232 Block 3 sub.trial.number16 - sub.trial.number17 -39.5518453 20.15615
233 Block 3 sub.trial.number16 - sub.trial.number18 -53.6203866 20.15615
234 Block 3 sub.trial.number17 - sub.trial.number18 -14.0685413 20.15615
235 Block 4 sub.trial.number1 - sub.trial.number2 341.7468531 18.23459
236 Block 4 sub.trial.number1 - sub.trial.number3 378.7720280 18.23459
237 Block 4 sub.trial.number1 - sub.trial.number4 386.0853147 18.23459
238 Block 4 sub.trial.number1 - sub.trial.number5 338.6307692 18.23459
239 Block 4 sub.trial.number1 - sub.trial.number6 326.4769231 18.23459
240 Block 4 sub.trial.number1 - sub.trial.number7 204.7860313 20.66022
241 Block 4 sub.trial.number1 - sub.trial.number8 306.5300138 20.66022
242 Block 4 sub.trial.number1 - sub.trial.number9 273.6066002 20.66022
243 Block 4 sub.trial.number1 - sub.trial.number10 300.2171035 20.66022
244 Block 4 sub.trial.number1 - sub.trial.number11 316.1120707 20.66022
245 Block 4 sub.trial.number1 - sub.trial.number12 325.4665565 20.66022
246 Block 4 sub.trial.number1 - sub.trial.number13 178.8869685 27.04069
247 Block 4 sub.trial.number1 - sub.trial.number14 228.5504756 27.04069
248 Block 4 sub.trial.number1 - sub.trial.number15 338.8395751 27.04069
249 Block 4 sub.trial.number1 - sub.trial.number16 328.6500016 27.04069
250 Block 4 sub.trial.number1 - sub.trial.number17 320.7921817 27.04069
251 Block 4 sub.trial.number1 - sub.trial.number18 221.1381533 27.04069
252 Block 4 sub.trial.number2 - sub.trial.number3 37.0251748 18.23459
253 Block 4 sub.trial.number2 - sub.trial.number4 44.3384615 18.23459
254 Block 4 sub.trial.number2 - sub.trial.number5 -3.1160839 18.23459
255 Block 4 sub.trial.number2 - sub.trial.number6 -15.2699301 18.23459
256 Block 4 sub.trial.number2 - sub.trial.number7 -136.9608219 20.66022
257 Block 4 sub.trial.number2 - sub.trial.number8 -35.2168394 20.66022
258 Block 4 sub.trial.number2 - sub.trial.number9 -68.1402529 20.66022
259 Block 4 sub.trial.number2 - sub.trial.number10 -41.5297497 20.66022
260 Block 4 sub.trial.number2 - sub.trial.number11 -25.6347825 20.66022
261 Block 4 sub.trial.number2 - sub.trial.number12 -16.2802967 20.66022
262 Block 4 sub.trial.number2 - sub.trial.number13 -162.8598847 27.04069
263 Block 4 sub.trial.number2 - sub.trial.number14 -113.1963776 27.04069
264 Block 4 sub.trial.number2 - sub.trial.number15 -2.9072780 27.04069
265 Block 4 sub.trial.number2 - sub.trial.number16 -13.0968515 27.04069
266 Block 4 sub.trial.number2 - sub.trial.number17 -20.9546714 27.04069
267 Block 4 sub.trial.number2 - sub.trial.number18 -120.6086998 27.04069
268 Block 4 sub.trial.number3 - sub.trial.number4 7.3132867 18.23459
269 Block 4 sub.trial.number3 - sub.trial.number5 -40.1412587 18.23459
270 Block 4 sub.trial.number3 - sub.trial.number6 -52.2951049 18.23459
271 Block 4 sub.trial.number3 - sub.trial.number7 -173.9859967 20.66022
272 Block 4 sub.trial.number3 - sub.trial.number8 -72.2420142 20.66022
273 Block 4 sub.trial.number3 - sub.trial.number9 -105.1654278 20.66022
274 Block 4 sub.trial.number3 - sub.trial.number10 -78.5549245 20.66022
275 Block 4 sub.trial.number3 - sub.trial.number11 -62.6599573 20.66022
276 Block 4 sub.trial.number3 - sub.trial.number12 -53.3054715 20.66022
277 Block 4 sub.trial.number3 - sub.trial.number13 -199.8850595 27.04069
278 Block 4 sub.trial.number3 - sub.trial.number14 -150.2215524 27.04069
279 Block 4 sub.trial.number3 - sub.trial.number15 -39.9324529 27.04069
280 Block 4 sub.trial.number3 - sub.trial.number16 -50.1220263 27.04069
281 Block 4 sub.trial.number3 - sub.trial.number17 -57.9798462 27.04069
282 Block 4 sub.trial.number3 - sub.trial.number18 -157.6338747 27.04069
283 Block 4 sub.trial.number4 - sub.trial.number5 -47.4545455 18.23459
284 Block 4 sub.trial.number4 - sub.trial.number6 -59.6083916 18.23459
285 Block 4 sub.trial.number4 - sub.trial.number7 -181.2992834 20.66022
286 Block 4 sub.trial.number4 - sub.trial.number8 -79.5553009 20.66022
287 Block 4 sub.trial.number4 - sub.trial.number9 -112.4787145 20.66022
288 Block 4 sub.trial.number4 - sub.trial.number10 -85.8682112 20.66022
289 Block 4 sub.trial.number4 - sub.trial.number11 -69.9732440 20.66022
290 Block 4 sub.trial.number4 - sub.trial.number12 -60.6187582 20.66022
291 Block 4 sub.trial.number4 - sub.trial.number13 -207.1983462 27.04069
292 Block 4 sub.trial.number4 - sub.trial.number14 -157.5348391 27.04069
293 Block 4 sub.trial.number4 - sub.trial.number15 -47.2457396 27.04069
294 Block 4 sub.trial.number4 - sub.trial.number16 -57.4353130 27.04069
295 Block 4 sub.trial.number4 - sub.trial.number17 -65.2931329 27.04069
296 Block 4 sub.trial.number4 - sub.trial.number18 -164.9471614 27.04069
297 Block 4 sub.trial.number5 - sub.trial.number6 -12.1538462 18.23459
298 Block 4 sub.trial.number5 - sub.trial.number7 -133.8447379 20.66022
299 Block 4 sub.trial.number5 - sub.trial.number8 -32.1007555 20.66022
300 Block 4 sub.trial.number5 - sub.trial.number9 -65.0241690 20.66022
301 Block 4 sub.trial.number5 - sub.trial.number10 -38.4136657 20.66022
302 Block 4 sub.trial.number5 - sub.trial.number11 -22.5186986 20.66022
303 Block 4 sub.trial.number5 - sub.trial.number12 -13.1642128 20.66022
304 Block 4 sub.trial.number5 - sub.trial.number13 -159.7438008 27.04069
305 Block 4 sub.trial.number5 - sub.trial.number14 -110.0802937 27.04069
306 Block 4 sub.trial.number5 - sub.trial.number15 0.2088059 27.04069
307 Block 4 sub.trial.number5 - sub.trial.number16 -9.9807676 27.04069
308 Block 4 sub.trial.number5 - sub.trial.number17 -17.8385875 27.04069
309 Block 4 sub.trial.number5 - sub.trial.number18 -117.4926159 27.04069
310 Block 4 sub.trial.number6 - sub.trial.number7 -121.6908918 20.66022
311 Block 4 sub.trial.number6 - sub.trial.number8 -19.9469093 20.66022
312 Block 4 sub.trial.number6 - sub.trial.number9 -52.8703229 20.66022
313 Block 4 sub.trial.number6 - sub.trial.number10 -26.2598196 20.66022
314 Block 4 sub.trial.number6 - sub.trial.number11 -10.3648524 20.66022
315 Block 4 sub.trial.number6 - sub.trial.number12 -1.0103666 20.66022
316 Block 4 sub.trial.number6 - sub.trial.number13 -147.5899546 27.04069
317 Block 4 sub.trial.number6 - sub.trial.number14 -97.9264475 27.04069
318 Block 4 sub.trial.number6 - sub.trial.number15 12.3626520 27.04069
319 Block 4 sub.trial.number6 - sub.trial.number16 2.1730786 27.04069
320 Block 4 sub.trial.number6 - sub.trial.number17 -5.6847413 27.04069
321 Block 4 sub.trial.number6 - sub.trial.number18 -105.3387698 27.04069
322 Block 4 sub.trial.number7 - sub.trial.number8 101.7439825 22.80819
323 Block 4 sub.trial.number7 - sub.trial.number9 68.8205689 22.80819
324 Block 4 sub.trial.number7 - sub.trial.number10 95.4310722 22.80819
325 Block 4 sub.trial.number7 - sub.trial.number11 111.3260394 22.80819
326 Block 4 sub.trial.number7 - sub.trial.number12 120.6805252 22.80819
327 Block 4 sub.trial.number7 - sub.trial.number13 -25.8990628 28.71792
328 Block 4 sub.trial.number7 - sub.trial.number14 23.7644443 28.71792
329 Block 4 sub.trial.number7 - sub.trial.number15 134.0535438 28.71792
330 Block 4 sub.trial.number7 - sub.trial.number16 123.8639704 28.71792
331 Block 4 sub.trial.number7 - sub.trial.number17 116.0061505 28.71792
332 Block 4 sub.trial.number7 - sub.trial.number18 16.3521220 28.71792
333 Block 4 sub.trial.number8 - sub.trial.number9 -32.9234136 22.80819
334 Block 4 sub.trial.number8 - sub.trial.number10 -6.3129103 22.80819
335 Block 4 sub.trial.number8 - sub.trial.number11 9.5820569 22.80819
336 Block 4 sub.trial.number8 - sub.trial.number12 18.9365427 22.80819
337 Block 4 sub.trial.number8 - sub.trial.number13 -127.6430453 28.71792
338 Block 4 sub.trial.number8 - sub.trial.number14 -77.9795382 28.71792
339 Block 4 sub.trial.number8 - sub.trial.number15 32.3095613 28.71792
340 Block 4 sub.trial.number8 - sub.trial.number16 22.1199879 28.71792
341 Block 4 sub.trial.number8 - sub.trial.number17 14.2621680 28.71792
342 Block 4 sub.trial.number8 - sub.trial.number18 -85.3918605 28.71792
343 Block 4 sub.trial.number9 - sub.trial.number10 26.6105033 22.80819
344 Block 4 sub.trial.number9 - sub.trial.number11 42.5054705 22.80819
345 Block 4 sub.trial.number9 - sub.trial.number12 51.8599562 22.80819
346 Block 4 sub.trial.number9 - sub.trial.number13 -94.7196317 28.71792
347 Block 4 sub.trial.number9 - sub.trial.number14 -45.0561246 28.71792
348 Block 4 sub.trial.number9 - sub.trial.number15 65.2329749 28.71792
349 Block 4 sub.trial.number9 - sub.trial.number16 55.0434014 28.71792
350 Block 4 sub.trial.number9 - sub.trial.number17 47.1855815 28.71792
351 Block 4 sub.trial.number9 - sub.trial.number18 -52.4684469 28.71792
352 Block 4 sub.trial.number10 - sub.trial.number11 15.8949672 22.80819
353 Block 4 sub.trial.number10 - sub.trial.number12 25.2494530 22.80819
354 Block 4 sub.trial.number10 - sub.trial.number13 -121.3301350 28.71792
355 Block 4 sub.trial.number10 - sub.trial.number14 -71.6666279 28.71792
356 Block 4 sub.trial.number10 - sub.trial.number15 38.6224716 28.71792
357 Block 4 sub.trial.number10 - sub.trial.number16 28.4328981 28.71792
358 Block 4 sub.trial.number10 - sub.trial.number17 20.5750782 28.71792
359 Block 4 sub.trial.number10 - sub.trial.number18 -79.0789502 28.71792
360 Block 4 sub.trial.number11 - sub.trial.number12 9.3544858 22.80819
361 Block 4 sub.trial.number11 - sub.trial.number13 -137.2251022 28.71792
362 Block 4 sub.trial.number11 - sub.trial.number14 -87.5615951 28.71792
363 Block 4 sub.trial.number11 - sub.trial.number15 22.7275044 28.71792
364 Block 4 sub.trial.number11 - sub.trial.number16 12.5379310 28.71792
365 Block 4 sub.trial.number11 - sub.trial.number17 4.6801111 28.71792
366 Block 4 sub.trial.number11 - sub.trial.number18 -94.9739174 28.71792
367 Block 4 sub.trial.number12 - sub.trial.number13 -146.5795880 28.71792
368 Block 4 sub.trial.number12 - sub.trial.number14 -96.9160809 28.71792
369 Block 4 sub.trial.number12 - sub.trial.number15 13.3730187 28.71792
370 Block 4 sub.trial.number12 - sub.trial.number16 3.1834452 28.71792
371 Block 4 sub.trial.number12 - sub.trial.number17 -4.6743747 28.71792
372 Block 4 sub.trial.number12 - sub.trial.number18 -104.3284031 28.71792
373 Block 4 sub.trial.number13 - sub.trial.number14 49.6635071 33.56663
374 Block 4 sub.trial.number13 - sub.trial.number15 159.9526066 33.56663
375 Block 4 sub.trial.number13 - sub.trial.number16 149.7630332 33.56663
376 Block 4 sub.trial.number13 - sub.trial.number17 141.9052133 33.56663
377 Block 4 sub.trial.number13 - sub.trial.number18 42.2511848 33.56663
378 Block 4 sub.trial.number14 - sub.trial.number15 110.2890995 33.56663
379 Block 4 sub.trial.number14 - sub.trial.number16 100.0995261 33.56663
380 Block 4 sub.trial.number14 - sub.trial.number17 92.2417062 33.56663
381 Block 4 sub.trial.number14 - sub.trial.number18 -7.4123223 33.56663
382 Block 4 sub.trial.number15 - sub.trial.number16 -10.1895735 33.56663
383 Block 4 sub.trial.number15 - sub.trial.number17 -18.0473934 33.56663
384 Block 4 sub.trial.number15 - sub.trial.number18 -117.7014218 33.56663
385 Block 4 sub.trial.number16 - sub.trial.number17 -7.8578199 33.56663
386 Block 4 sub.trial.number16 - sub.trial.number18 -107.5118483 33.56663
387 Block 4 sub.trial.number17 - sub.trial.number18 -99.6540284 33.56663
388 Block 5 sub.trial.number1 - sub.trial.number2 424.1398601 27.84154
389 Block 5 sub.trial.number1 - sub.trial.number3 437.0541958 27.84154
390 Block 5 sub.trial.number1 - sub.trial.number4 417.5541958 27.84154
391 Block 5 sub.trial.number1 - sub.trial.number5 390.4580420 27.84154
392 Block 5 sub.trial.number1 - sub.trial.number6 398.3601399 27.84154
393 Block 5 sub.trial.number1 - sub.trial.number7 253.9467010 32.28864
394 Block 5 sub.trial.number1 - sub.trial.number8 370.8790539 32.28864
395 Block 5 sub.trial.number1 - sub.trial.number9 167.7261127 32.28864
396 Block 5 sub.trial.number1 - sub.trial.number10 294.2496422 32.28864
397 Block 5 sub.trial.number1 - sub.trial.number11 454.9437598 32.28864
398 Block 5 sub.trial.number1 - sub.trial.number12 407.4114069 32.28864
399 Block 5 sub.trial.number1 - sub.trial.number13 -34.7957822 43.30840
400 Block 5 sub.trial.number1 - sub.trial.number14 295.9442178 43.30840
401 Block 5 sub.trial.number1 - sub.trial.number15 453.7708844 43.30840
402 Block 5 sub.trial.number1 - sub.trial.number16 422.2442178 43.30840
403 Block 5 sub.trial.number1 - sub.trial.number17 391.5308844 43.30840
404 Block 5 sub.trial.number1 - sub.trial.number18 439.1308844 43.30840
405 Block 5 sub.trial.number2 - sub.trial.number3 12.9143357 27.84154
406 Block 5 sub.trial.number2 - sub.trial.number4 -6.5856643 27.84154
407 Block 5 sub.trial.number2 - sub.trial.number5 -33.6818182 27.84154
408 Block 5 sub.trial.number2 - sub.trial.number6 -25.7797203 27.84154
409 Block 5 sub.trial.number2 - sub.trial.number7 -170.1931592 32.28864
410 Block 5 sub.trial.number2 - sub.trial.number8 -53.2608062 32.28864
411 Block 5 sub.trial.number2 - sub.trial.number9 -256.4137474 32.28864
412 Block 5 sub.trial.number2 - sub.trial.number10 -129.8902180 32.28864
413 Block 5 sub.trial.number2 - sub.trial.number11 30.8038997 32.28864
414 Block 5 sub.trial.number2 - sub.trial.number12 -16.7284533 32.28864
415 Block 5 sub.trial.number2 - sub.trial.number13 -458.9356424 43.30840
416 Block 5 sub.trial.number2 - sub.trial.number14 -128.1956424 43.30840
417 Block 5 sub.trial.number2 - sub.trial.number15 29.6310243 43.30840
418 Block 5 sub.trial.number2 - sub.trial.number16 -1.8956424 43.30840
419 Block 5 sub.trial.number2 - sub.trial.number17 -32.6089757 43.30840
420 Block 5 sub.trial.number2 - sub.trial.number18 14.9910243 43.30840
421 Block 5 sub.trial.number3 - sub.trial.number4 -19.5000000 27.84154
422 Block 5 sub.trial.number3 - sub.trial.number5 -46.5961538 27.84154
423 Block 5 sub.trial.number3 - sub.trial.number6 -38.6940559 27.84154
424 Block 5 sub.trial.number3 - sub.trial.number7 -183.1074948 32.28864
425 Block 5 sub.trial.number3 - sub.trial.number8 -66.1751419 32.28864
426 Block 5 sub.trial.number3 - sub.trial.number9 -269.3280831 32.28864
427 Block 5 sub.trial.number3 - sub.trial.number10 -142.8045536 32.28864
428 Block 5 sub.trial.number3 - sub.trial.number11 17.8895640 32.28864
429 Block 5 sub.trial.number3 - sub.trial.number12 -29.6427889 32.28864
430 Block 5 sub.trial.number3 - sub.trial.number13 -471.8499780 43.30840
431 Block 5 sub.trial.number3 - sub.trial.number14 -141.1099780 43.30840
432 Block 5 sub.trial.number3 - sub.trial.number15 16.7166886 43.30840
433 Block 5 sub.trial.number3 - sub.trial.number16 -14.8099780 43.30840
434 Block 5 sub.trial.number3 - sub.trial.number17 -45.5233114 43.30840
435 Block 5 sub.trial.number3 - sub.trial.number18 2.0766886 43.30840
436 Block 5 sub.trial.number4 - sub.trial.number5 -27.0961538 27.84154
437 Block 5 sub.trial.number4 - sub.trial.number6 -19.1940559 27.84154
438 Block 5 sub.trial.number4 - sub.trial.number7 -163.6074948 32.28864
439 Block 5 sub.trial.number4 - sub.trial.number8 -46.6751419 32.28864
440 Block 5 sub.trial.number4 - sub.trial.number9 -249.8280831 32.28864
441 Block 5 sub.trial.number4 - sub.trial.number10 -123.3045536 32.28864
442 Block 5 sub.trial.number4 - sub.trial.number11 37.3895640 32.28864
443 Block 5 sub.trial.number4 - sub.trial.number12 -10.1427889 32.28864
444 Block 5 sub.trial.number4 - sub.trial.number13 -452.3499780 43.30840
445 Block 5 sub.trial.number4 - sub.trial.number14 -121.6099780 43.30840
446 Block 5 sub.trial.number4 - sub.trial.number15 36.2166886 43.30840
447 Block 5 sub.trial.number4 - sub.trial.number16 4.6900220 43.30840
448 Block 5 sub.trial.number4 - sub.trial.number17 -26.0233114 43.30840
449 Block 5 sub.trial.number4 - sub.trial.number18 21.5766886 43.30840
450 Block 5 sub.trial.number5 - sub.trial.number6 7.9020979 27.84154
451 Block 5 sub.trial.number5 - sub.trial.number7 -136.5113410 32.28864
452 Block 5 sub.trial.number5 - sub.trial.number8 -19.5789880 32.28864
453 Block 5 sub.trial.number5 - sub.trial.number9 -222.7319292 32.28864
454 Block 5 sub.trial.number5 - sub.trial.number10 -96.2083998 32.28864
455 Block 5 sub.trial.number5 - sub.trial.number11 64.4857178 32.28864
456 Block 5 sub.trial.number5 - sub.trial.number12 16.9533649 32.28864
457 Block 5 sub.trial.number5 - sub.trial.number13 -425.2538242 43.30840
458 Block 5 sub.trial.number5 - sub.trial.number14 -94.5138242 43.30840
459 Block 5 sub.trial.number5 - sub.trial.number15 63.3128425 43.30840
460 Block 5 sub.trial.number5 - sub.trial.number16 31.7861758 43.30840
461 Block 5 sub.trial.number5 - sub.trial.number17 1.0728425 43.30840
462 Block 5 sub.trial.number5 - sub.trial.number18 48.6728425 43.30840
463 Block 5 sub.trial.number6 - sub.trial.number7 -144.4134389 32.28864
464 Block 5 sub.trial.number6 - sub.trial.number8 -27.4810859 32.28864
465 Block 5 sub.trial.number6 - sub.trial.number9 -230.6340271 32.28864
466 Block 5 sub.trial.number6 - sub.trial.number10 -104.1104977 32.28864
467 Block 5 sub.trial.number6 - sub.trial.number11 56.5836199 32.28864
468 Block 5 sub.trial.number6 - sub.trial.number12 9.0512670 32.28864
469 Block 5 sub.trial.number6 - sub.trial.number13 -433.1559221 43.30840
470 Block 5 sub.trial.number6 - sub.trial.number14 -102.4159221 43.30840
471 Block 5 sub.trial.number6 - sub.trial.number15 55.4107446 43.30840
472 Block 5 sub.trial.number6 - sub.trial.number16 23.8840779 43.30840
473 Block 5 sub.trial.number6 - sub.trial.number17 -6.8292554 43.30840
474 Block 5 sub.trial.number6 - sub.trial.number18 40.7707446 43.30840
475 Block 5 sub.trial.number7 - sub.trial.number8 116.9323529 36.11202
476 Block 5 sub.trial.number7 - sub.trial.number9 -86.2205882 36.11202
477 Block 5 sub.trial.number7 - sub.trial.number10 40.3029412 36.11202
478 Block 5 sub.trial.number7 - sub.trial.number11 200.9970588 36.11202
479 Block 5 sub.trial.number7 - sub.trial.number12 153.4647059 36.11202
480 Block 5 sub.trial.number7 - sub.trial.number13 -288.7424832 46.20778
481 Block 5 sub.trial.number7 - sub.trial.number14 41.9975168 46.20778
482 Block 5 sub.trial.number7 - sub.trial.number15 199.8241835 46.20778
483 Block 5 sub.trial.number7 - sub.trial.number16 168.2975168 46.20778
484 Block 5 sub.trial.number7 - sub.trial.number17 137.5841835 46.20778
485 Block 5 sub.trial.number7 - sub.trial.number18 185.1841835 46.20778
486 Block 5 sub.trial.number8 - sub.trial.number9 -203.1529412 36.11202
487 Block 5 sub.trial.number8 - sub.trial.number10 -76.6294118 36.11202
488 Block 5 sub.trial.number8 - sub.trial.number11 84.0647059 36.11202
489 Block 5 sub.trial.number8 - sub.trial.number12 36.5323529 36.11202
490 Block 5 sub.trial.number8 - sub.trial.number13 -405.6748361 46.20778
491 Block 5 sub.trial.number8 - sub.trial.number14 -74.9348361 46.20778
492 Block 5 sub.trial.number8 - sub.trial.number15 82.8918305 46.20778
493 Block 5 sub.trial.number8 - sub.trial.number16 51.3651639 46.20778
494 Block 5 sub.trial.number8 - sub.trial.number17 20.6518305 46.20778
495 Block 5 sub.trial.number8 - sub.trial.number18 68.2518305 46.20778
496 Block 5 sub.trial.number9 - sub.trial.number10 126.5235294 36.11202
497 Block 5 sub.trial.number9 - sub.trial.number11 287.2176471 36.11202
498 Block 5 sub.trial.number9 - sub.trial.number12 239.6852941 36.11202
499 Block 5 sub.trial.number9 - sub.trial.number13 -202.5218950 46.20778
500 Block 5 sub.trial.number9 - sub.trial.number14 128.2181050 46.20778
501 Block 5 sub.trial.number9 - sub.trial.number15 286.0447717 46.20778
502 Block 5 sub.trial.number9 - sub.trial.number16 254.5181050 46.20778
503 Block 5 sub.trial.number9 - sub.trial.number17 223.8047717 46.20778
504 Block 5 sub.trial.number9 - sub.trial.number18 271.4047717 46.20778
505 Block 5 sub.trial.number10 - sub.trial.number11 160.6941176 36.11202
506 Block 5 sub.trial.number10 - sub.trial.number12 113.1617647 36.11202
507 Block 5 sub.trial.number10 - sub.trial.number13 -329.0454244 46.20778
508 Block 5 sub.trial.number10 - sub.trial.number14 1.6945756 46.20778
509 Block 5 sub.trial.number10 - sub.trial.number15 159.5212423 46.20778
510 Block 5 sub.trial.number10 - sub.trial.number16 127.9945756 46.20778
511 Block 5 sub.trial.number10 - sub.trial.number17 97.2812423 46.20778
512 Block 5 sub.trial.number10 - sub.trial.number18 144.8812423 46.20778
513 Block 5 sub.trial.number11 - sub.trial.number12 -47.5323529 36.11202
514 Block 5 sub.trial.number11 - sub.trial.number13 -489.7395420 46.20778
515 Block 5 sub.trial.number11 - sub.trial.number14 -158.9995420 46.20778
516 Block 5 sub.trial.number11 - sub.trial.number15 -1.1728754 46.20778
517 Block 5 sub.trial.number11 - sub.trial.number16 -32.6995420 46.20778
518 Block 5 sub.trial.number11 - sub.trial.number17 -63.4128754 46.20778
519 Block 5 sub.trial.number11 - sub.trial.number18 -15.8128754 46.20778
520 Block 5 sub.trial.number12 - sub.trial.number13 -442.2071891 46.20778
521 Block 5 sub.trial.number12 - sub.trial.number14 -111.4671891 46.20778
522 Block 5 sub.trial.number12 - sub.trial.number15 46.3594776 46.20778
523 Block 5 sub.trial.number12 - sub.trial.number16 14.8328109 46.20778
524 Block 5 sub.trial.number12 - sub.trial.number17 -15.8805224 46.20778
525 Block 5 sub.trial.number12 - sub.trial.number18 31.7194776 46.20778
526 Block 5 sub.trial.number13 - sub.trial.number14 330.7400000 54.36828
527 Block 5 sub.trial.number13 - sub.trial.number15 488.5666667 54.36828
528 Block 5 sub.trial.number13 - sub.trial.number16 457.0400000 54.36828
529 Block 5 sub.trial.number13 - sub.trial.number17 426.3266667 54.36828
530 Block 5 sub.trial.number13 - sub.trial.number18 473.9266667 54.36828
531 Block 5 sub.trial.number14 - sub.trial.number15 157.8266667 54.36828
532 Block 5 sub.trial.number14 - sub.trial.number16 126.3000000 54.36828
533 Block 5 sub.trial.number14 - sub.trial.number17 95.5866667 54.36828
534 Block 5 sub.trial.number14 - sub.trial.number18 143.1866667 54.36828
535 Block 5 sub.trial.number15 - sub.trial.number16 -31.5266667 54.36828
536 Block 5 sub.trial.number15 - sub.trial.number17 -62.2400000 54.36828
537 Block 5 sub.trial.number15 - sub.trial.number18 -14.6400000 54.36828
538 Block 5 sub.trial.number16 - sub.trial.number17 -30.7133333 54.36828
539 Block 5 sub.trial.number16 - sub.trial.number18 16.8866667 54.36828
540 Block 5 sub.trial.number17 - sub.trial.number18 47.6000000 54.36828
df t.ratio p.value
1 4340.010 17.763887655 4.154063e-08
2 4340.010 19.091626980 4.154063e-08
3 4340.010 18.422984799 4.154063e-08
4 4340.010 17.295609700 4.154063e-08
5 4340.010 16.831573984 4.154063e-08
6 4340.010 1.327739325 7.696499e-01
7 4340.010 0.659097143 9.862602e-01
8 4340.010 -0.468277955 9.972040e-01
9 4340.010 -0.932313672 9.382289e-01
10 4340.010 -0.668642182 9.853366e-01
11 4340.010 -1.796017281 4.683787e-01
12 4340.010 -2.260052997 2.108759e-01
13 4340.010 -1.127375099 8.701355e-01
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410 6302.805 -1.649521331 9.752612e-01
411 6302.805 -7.941298227 0.000000e+00
412 6302.805 -4.022783366 7.341984e-03
413 6302.805 0.954016531 9.999716e-01
414 6302.805 -0.518090928 1.000000e+00
415 6307.936 -10.596919825 1.343592e-12
416 6307.936 -2.960064154 2.201508e-01
417 6307.936 0.684186539 9.999998e-01
418 6307.936 -0.043770778 1.000000e+00
419 6307.936 -0.752948059 9.999991e-01
420 6307.936 0.346145882 1.000000e+00
421 6291.156 -0.700392373 9.999997e-01
422 6291.156 -1.673620040 9.715060e-01
423 6291.156 -1.389795983 9.960866e-01
424 6302.805 -5.670956564 2.241659e-06
425 6302.805 -2.049486590 8.447256e-01
426 6302.805 -8.341263486 0.000000e+00
427 6302.805 -4.422748625 1.362806e-03
428 6302.805 0.554051272 1.000000e+00
429 6302.805 -0.918056187 9.999837e-01
430 6307.936 -10.895114533 1.324496e-12
431 6307.936 -3.258258862 1.012380e-01
432 6307.936 0.385991832 1.000000e+00
433 6307.936 -0.341965486 1.000000e+00
434 6307.936 -1.051142766 9.998894e-01
435 6307.936 0.047951174 1.000000e+00
436 6291.156 -0.973227666 9.999623e-01
437 6291.156 -0.689403610 9.999998e-01
438 6302.805 -5.067029057 6.127250e-05
439 6302.805 -1.445559083 9.938617e-01
440 6302.805 -7.737335978 1.203038e-12
441 6302.805 -3.818821117 1.602981e-02
442 6302.805 1.157978780 9.995936e-01
443 6302.805 -0.314128679 1.000000e+00
444 6307.936 -10.444855460 1.339928e-12
445 6307.936 -2.807999789 3.078286e-01
446 6307.936 0.836250904 9.999959e-01
447 6307.936 0.108293587 1.000000e+00
448 6307.936 -0.600883694 1.000000e+00
449 6307.936 0.498210247 1.000000e+00
450 6291.156 0.283824057 1.000000e+00
451 6302.805 -4.227843792 3.172892e-03
452 6302.805 -0.606373818 1.000000e+00
453 6302.805 -6.898150714 8.833267e-10
454 6302.805 -2.979635852 2.102065e-01
455 6302.805 1.997164045 8.710543e-01
456 6302.805 0.525056586 1.000000e+00
457 6307.936 -9.819199609 1.349587e-12
458 6307.936 -2.182343938 7.656327e-01
459 6307.936 1.461906755 9.930381e-01
460 6307.936 0.733949438 9.999994e-01
461 6307.936 0.024772157 1.000000e+00
462 6307.936 1.123866098 9.997263e-01
463 6302.805 -4.472576832 1.090111e-03
464 6302.805 -0.851106858 9.999946e-01
465 6302.805 -7.142883754 1.549019e-10
466 6302.805 -3.224368893 1.114276e-01
467 6302.805 1.752431004 9.561272e-01
468 6302.805 0.280323545 1.000000e+00
469 6307.936 -10.001660700 1.338929e-12
470 6307.936 -2.364805029 6.349509e-01
471 6307.936 1.279445664 9.985501e-01
472 6307.936 0.551488347 1.000000e+00
473 6307.936 -0.157688934 1.000000e+00
474 6307.936 0.941405007 9.999766e-01
475 6291.156 3.238045371 1.072227e-01
476 6291.156 -2.387587093 6.175206e-01
477 6291.156 1.116053418 9.997507e-01
478 6291.156 5.565932606 4.092925e-06
479 6291.156 4.249685122 2.892832e-03
480 6298.140 -6.248785047 6.717486e-08
481 6298.140 0.908884110 9.999859e-01
482 6298.140 4.324470565 2.098790e-03
483 6298.140 3.642190074 3.011556e-02
484 6298.140 2.977511237 2.112712e-01
485 6298.140 4.007640801 7.794834e-03
486 6291.156 -5.625632464 2.910825e-06
487 6291.156 -2.121991954 8.036177e-01
488 6291.156 2.327887235 6.628286e-01
489 6291.156 1.011639750 9.999350e-01
490 6298.140 -8.779362226 0.000000e+00
491 6298.140 -1.621693069 9.791059e-01
492 6298.140 1.793893387 9.459135e-01
493 6298.140 1.111612896 9.997636e-01
494 6298.140 0.446934058 1.000000e+00
495 6298.140 1.477063622 9.921946e-01
496 6291.156 3.503640511 4.786790e-02
497 6291.156 7.953519699 0.000000e+00
498 6291.156 6.637272215 5.287846e-09
499 6298.140 -4.382852758 1.626165e-03
500 6298.140 2.774816399 3.292960e-01
501 6298.140 6.190402854 9.718480e-08
502 6298.140 5.508122363 5.673641e-06
503 6298.140 4.843443525 1.893491e-04
504 6298.140 5.873573089 6.803057e-07
505 6291.156 4.449879189 1.207279e-03
506 6291.156 3.133631704 1.426968e-01
507 6298.140 -7.120996207 1.791504e-10
508 6298.140 0.036672950 1.000000e+00
509 6298.140 3.452259406 5.643161e-02
510 6298.140 2.769978915 3.324900e-01
511 6298.140 2.105300077 8.135383e-01
512 6298.140 3.135429641 1.420180e-01
513 6291.156 -1.316247485 9.979485e-01
514 6298.140 -10.598638251 0.000000e+00
515 6298.140 -3.440969094 5.847840e-02
516 6298.140 -0.025382638 1.000000e+00
517 6298.140 -0.707663129 9.999997e-01
518 6298.140 -1.372341967 9.966241e-01
519 6298.140 -0.342212403 1.000000e+00
520 6298.140 -9.569972663 0.000000e+00
521 6298.140 -2.412303505 5.984652e-01
522 6298.140 1.003282950 9.999421e-01
523 6298.140 0.321002459 1.000000e+00
524 6298.140 -0.343676379 1.000000e+00
525 6298.140 0.686453185 9.999998e-01
526 6291.156 6.083326610 1.896850e-07
527 6291.156 8.986244797 0.000000e+00
528 6291.156 8.406372358 0.000000e+00
529 6291.156 7.841459623 0.000000e+00
530 6291.156 8.716970135 0.000000e+00
531 6291.156 2.902918187 2.509624e-01
532 6291.156 2.323045748 6.664442e-01
533 6291.156 1.758133014 9.548141e-01
534 6291.156 2.633643525 4.284395e-01
535 6291.156 -0.579872438 1.000000e+00
536 6291.156 -1.144785173 9.996505e-01
537 6291.156 -0.269274662 1.000000e+00
538 6291.156 -0.564912735 1.000000e+00
539 6291.156 0.310597777 1.000000e+00
540 6291.156 0.875510512 9.999918e-01# Optional: Save to CSV
# write.csv(pairwise_pvalues_summary, "RT_pairwise_step_comparisons.csv", row.names = FALSE)d#7.2 rt and rms comparison
# --- Prepare RT data per step and block ---
rt_stepwise_data <- df_acc %>%
filter(trial.acc >= 0.8) %>%
mutate(
Step = as.numeric(as.character(sub.trial.number)),
Block = as.numeric(as.character(session))
) %>%
group_by(Block, Step) %>%
summarise(
mean_rt = mean(feedback.RT, na.rm = TRUE),
se_rt = sd(feedback.RT, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
# --- Prepare RMS stepwise summary (all axes) ---
rms_stepwise_summary <- step_rms_data %>%
group_by(Block, Step, Axis) %>%
summarise(
mean_rms = mean(RMS, na.rm = TRUE),
se_rms = sd(RMS, na.rm = TRUE) / sqrt(n()),
.groups = "drop"
)
# --- Ensure Block types match before joining ---
combined_data_all <- full_join(
rms_stepwise_summary %>% mutate(Block = as.integer(as.character(Block))),
rt_stepwise_data %>% mutate(Block = as.integer(Block)),
by = c("Block", "Step")
) %>%
mutate(
Block = factor(Block),
Step = as.numeric(Step),
Axis = toupper(Axis)
)
# --- Function to plot all 3 RMS axes with overlaid RT, per block ---
plot_dual_axis_all_axes <- function(block_num, y1_lim = c(0, 3), y2_lim = c(350, 950)) {
block_data <- combined_data_all %>% filter(Block == block_num)
ggplot(block_data, aes(x = Step)) +
geom_col(aes(y = mean_rms), fill = "steelblue", alpha = 0.7, width = 0.6) +
geom_errorbar(aes(ymin = mean_rms - se_rms, ymax = mean_rms + se_rms),
width = 0.3, color = "steelblue") +
geom_line(aes(y = rescale(mean_rt, to = y1_lim, from = y2_lim)),
color = "firebrick", size = 1.2, group = 1) +
geom_point(aes(y = rescale(mean_rt, to = y1_lim, from = y2_lim)),
color = "firebrick", size = 2) +
scale_y_continuous(
name = "RMS Acceleration (m/s²)",
limits = y1_lim,
sec.axis = sec_axis(~rescale(., from = y1_lim, to = y2_lim),
name = "Reaction Time (ms)")
) +
facet_wrap(~ Axis, nrow = 1, scales = "fixed") +
labs(
title = paste("Block", block_num, "- Stepwise RMS (X/Y/Z) + RT Overlay"),
x = "Step Number"
) +
theme_minimal(base_size = 13) +
theme(
axis.title.y.left = element_text(color = "steelblue"),
axis.title.y.right = element_text(color = "firebrick"),
strip.text = element_text(face = "bold")
)
}
# --- Generate and print plots for Blocks 1 to 5 ---
for (b in 1:5) {
print(plot_dual_axis_all_axes(b))
}
# -------- 1. Helper: Clean & Extract Significant Step Pairs --------
get_significant_pairs <- function(pw_df, p_thresh = 0.05) {
pw_df %>%
filter(p.value < p_thresh) %>%
transmute(
Step1_raw = gsub(".*[^0-9]", "", gsub(" - .*", "", contrast)),
Step2_raw = gsub(".*[^0-9]", "", gsub(".* - ", "", contrast))
) %>%
mutate(
Step1 = pmin(as.character(Step1_raw), as.character(Step2_raw)),
Step2 = pmax(as.character(Step1_raw), as.character(Step2_raw))
) %>%
select(Step1, Step2)
}
# -------- 2. Extract RT Significant Pairs (per block) --------
get_rt_significant_pairs <- function(models) {
list(
B1 = get_significant_pairs(summary(pairs(emmeans(models$B1, ~ factor(sub.trial.number))))),
B2 = get_significant_pairs(summary(pairs(emmeans(models$B2, ~ factor(sub.trial.number))))),
B3 = get_significant_pairs(summary(pairs(emmeans(models$B3, ~ factor(sub.trial.number))))),
B4 = get_significant_pairs(summary(pairs(emmeans(models$B4, ~ factor(sub.trial.number))))),
B5 = get_significant_pairs(summary(pairs(emmeans(models$B5, ~ factor(sub.trial.number)))))
)
}
# -------- 3. Extract RMS Significant Pairs (per block & axis) --------
get_rms_significant_pairs <- function(results_list, block_num, axis_label, p_thresh = 0.05) {
key <- paste0("Mixed - Block ", block_num, " - Axis ", axis_label)
if (!key %in% names(results_list)) return(NULL)
get_significant_pairs(results_list[[key]]$Pairwise, p_thresh) %>%
mutate(Block = block_num, Axis = axis_label)
}
# -------- 4. Gather All RMS Significant Pairs --------
rms_sig_pairs <- map_dfr(1:5, function(block) {
map_dfr(c("X", "Y", "Z"), function(axis) {
get_rms_significant_pairs(stepwise_lmm_diag_results, block, axis)
})
})
# -------- 5. Define RT Models --------
rt_models <- list(
B1 = M_B1,
B2 = M_B2,
B3 = M_B3,
B4 = M_B4,
B5 = M_B5
)
rt_sig_pairs <- get_rt_significant_pairs(rt_models)
# -------- 6. Compare: Match RT + RMS Step Pairs --------
compare_sig_pairs <- function(rt_df, rms_df, block) {
rt_block <- rt_df[[paste0("B", block)]]
rms_block <- rms_df %>% filter(Block == block)
if (nrow(rt_block) == 0 || nrow(rms_block) == 0) return(NULL)
inner_join(rt_block, rms_block, by = c("Step1", "Step2")) %>%
mutate(Block = block)
}
# -------- 7. Final Output: Matching Significant Step Pairs --------
matched_sig_pairs <- map_dfr(1:5, function(b) {
compare_sig_pairs(rt_sig_pairs, rms_sig_pairs, b)
})
# -------- 8. View Output --------
print(matched_sig_pairs) Step1 Step2 Block Axis
1 1 2 1 Z
2 1 8 2 X
3 1 9 2 X
4 1 9 2 Z
5 1 10 2 X
6 1 10 2 Y
7 1 10 2 Z
8 1 11 2 X
9 1 11 2 Y
10 1 11 2 Z
11 1 12 2 X
12 1 12 2 Y
13 1 12 2 Z
14 2 7 2 Z
15 3 8 2 X
16 3 9 2 X
17 3 9 2 Y
18 3 9 2 Z
19 10 3 2 X
20 10 3 2 Y
21 10 3 2 Z
22 4 8 2 X
23 4 9 2 X
24 4 9 2 Y
25 4 9 2 Z
26 10 4 2 X
27 10 4 2 Y
28 10 4 2 Z
29 12 4 2 X
30 12 4 2 Y
31 12 4 2 Z
32 11 5 2 X
33 11 5 2 Y
34 11 5 2 Z
35 11 7 2 X
36 11 7 2 Y
37 11 7 2 Z
38 1 16 3 Y
39 1 17 3 X
40 1 17 3 Y
41 1 18 3 X
42 1 18 3 Y
43 1 18 3 Z
44 14 3 3 X
45 14 4 3 X
46 15 4 3 X
47 15 4 3 Y
48 17 4 3 X
49 17 4 3 Y
50 17 4 3 Z
51 18 4 3 X
52 18 4 3 Y
53 18 4 3 Z
54 16 5 3 X
55 16 5 3 Y
56 16 5 3 Z
57 14 8 3 Y
58 16 9 3 X
59 16 9 3 Y
60 10 16 3 Y
61 13 18 3 Y
62 13 18 3 Z
63 1 8 4 Y
64 1 8 4 Z
65 1 9 4 Y
66 1 9 4 Z
67 1 10 4 Y
68 1 10 4 Z
69 1 11 4 Y
70 1 11 4 Z
71 1 12 4 Y
72 1 12 4 Z
73 1 13 4 Y
74 1 13 4 Z
75 1 14 4 Y
76 1 14 4 Z
77 1 15 4 Y
78 1 15 4 Z
79 1 16 4 X
80 1 16 4 Y
81 1 16 4 Z
82 1 17 4 X
83 1 17 4 Y
84 1 17 4 Z
85 1 18 4 X
86 1 18 4 Y
87 1 18 4 Z
88 13 2 4 Y
89 13 2 4 Z
90 14 2 4 X
91 14 2 4 Y
92 14 2 4 Z
93 18 2 4 X
94 18 2 4 Y
95 18 2 4 Z
96 3 8 4 Y
97 3 8 4 Z
98 3 9 4 Y
99 3 9 4 Z
100 10 3 4 Y
101 10 3 4 Z
102 13 3 4 X
103 13 3 4 Y
104 13 3 4 Z
105 14 3 4 X
106 14 3 4 Y
107 14 3 4 Z
108 18 3 4 X
109 18 3 4 Y
110 18 3 4 Z
111 4 8 4 Y
112 4 9 4 Y
113 10 4 4 Y
114 10 4 4 Z
115 13 4 4 Y
116 13 4 4 Z
117 14 4 4 Y
118 14 4 4 Z
119 18 4 4 X
120 18 4 4 Y
121 18 4 4 Z
122 13 5 4 Y
123 13 5 4 Z
124 14 5 4 X
125 14 5 4 Y
126 14 5 4 Z
127 18 5 4 X
128 18 5 4 Y
129 18 5 4 Z
130 14 6 4 X
131 14 6 4 Y
132 14 6 4 Z
133 18 6 4 X
134 18 6 4 Y
135 18 6 4 Z
136 12 7 4 Y
137 12 7 4 Z
138 15 7 4 X
139 15 7 4 Y
140 15 7 4 Z
141 16 7 4 X
142 16 7 4 Y
143 16 7 4 Z
144 17 7 4 X
145 17 7 4 Y
146 17 7 4 Z
147 12 18 4 X
148 12 18 4 Y
149 12 18 4 Z
150 13 16 4 Z
151 13 17 4 Z
152 15 18 4 X
153 15 18 4 Y
154 15 18 4 Z
155 1 8 5 Z
156 1 9 5 Y
157 1 9 5 Z
158 1 10 5 Y
159 1 10 5 Z
160 1 11 5 Z
161 1 12 5 Y
162 1 12 5 Z
163 1 14 5 Y
164 1 14 5 Z
165 1 15 5 X
166 1 15 5 Y
167 1 15 5 Z
168 1 16 5 Y
169 1 16 5 Z
170 1 17 5 X
171 1 17 5 Y
172 1 17 5 Z
173 1 18 5 X
174 1 18 5 Y
175 1 18 5 Z
176 2 9 5 Y
177 2 9 5 Z
178 10 2 5 Y
179 10 2 5 Z
180 13 2 5 Y
181 13 2 5 Z
182 3 9 5 Z
183 10 3 5 Z
184 13 3 5 Z
185 4 9 5 Z
186 10 4 5 Z
187 5 9 5 Z
188 13 5 5 Z
189 15 7 5 Z
190 16 7 5 Y
191 16 7 5 Z
192 18 7 5 X
193 18 7 5 Y
194 18 7 5 Z
195 17 9 5 Z
196 18 9 5 Z
197 13 18 5 Z# -------- 9. Count Matches, Show Blocks & Axes --------
matched_pair_counts <- matched_sig_pairs %>%
distinct(Step1, Step2, Block, Axis) %>% # keep axis info
group_by(Step1, Step2) %>%
summarise(
MatchingBlocks = n_distinct(Block),
BlocksMatched = paste(sort(unique(Block)), collapse = ", "),
AxesMatched = paste(sort(unique(Axis)), collapse = ", "),
.groups = "drop"
) %>%
arrange(desc(MatchingBlocks))
# -------- 10. View Summary --------
print(matched_pair_counts)# A tibble: 57 × 5
Step1 Step2 MatchingBlocks BlocksMatched AxesMatched
<chr> <chr> <int> <chr> <chr>
1 1 10 3 2, 4, 5 X, Y, Z
2 1 11 3 2, 4, 5 X, Y, Z
3 1 12 3 2, 4, 5 X, Y, Z
4 1 16 3 3, 4, 5 X, Y, Z
5 1 17 3 3, 4, 5 X, Y, Z
6 1 18 3 3, 4, 5 X, Y, Z
7 1 8 3 2, 4, 5 X, Y, Z
8 1 9 3 2, 4, 5 X, Y, Z
9 10 3 3 2, 4, 5 X, Y, Z
10 10 4 3 2, 4, 5 X, Y, Z
# ℹ 47 more rows# -------- Filter to Steps of Interest --------
steps_of_interest <- c("3", "4", "9", "10", "7", "14", "13")
step_axis_counts <- matched_sig_pairs %>%
filter(Step1 %in% steps_of_interest | Step2 %in% steps_of_interest) %>%
mutate(
Step = ifelse(Step1 %in% steps_of_interest, Step1, Step2)
) %>%
filter(Step %in% steps_of_interest) %>%
count(Step, Axis, name = "MatchCount") %>%
arrange(Step, Axis)
# -------- View Results --------
print(step_axis_counts) Step Axis MatchCount
1 10 X 3
2 10 Y 9
3 10 Z 10
4 13 X 1
5 13 Y 7
6 13 Z 12
7 14 X 6
8 14 Y 8
9 14 Z 7
10 3 X 3
11 3 Y 4
12 3 Z 5
13 4 X 7
14 4 Y 8
15 4 Z 6
16 7 X 5
17 7 Y 7
18 7 Z 9
19 9 X 2
20 9 Y 4
21 9 Z 7# -------- Count step occurrences in matched pairs per axis --------
step_axis_counts_all <- matched_sig_pairs %>%
# Create one row per step per pair
select(Step1, Step2, Axis) %>%
pivot_longer(cols = c(Step1, Step2), names_to = "Position", values_to = "Step") %>%
count(Step, Axis, name = "MatchCount") %>%
arrange(as.numeric(Step), Axis)
# -------- View Results --------
print(step_axis_counts_all)# A tibble: 54 × 3
Step Axis MatchCount
<chr> <chr> <int>
1 1 X 13
2 1 Y 25
3 1 Z 27
4 2 X 2
5 2 Y 6
6 2 Z 8
7 3 X 7
8 3 Y 8
9 3 Z 11
10 4 X 9
# ℹ 44 more rowsggplot(step_axis_counts_all, aes(x = Step, y = MatchCount, fill = Axis)) +
geom_col(position = "dodge") +
labs(title = "Stepwise RT–RMS Match Counts by Axis",
x = "Step", y = "Match Count") +
theme_minimal()
# -------- Count all step occurrences in matched pairs, by Block and Axis --------
step_block_axis_counts <- matched_sig_pairs %>%
# Create one row per step per match
pivot_longer(cols = c(Step1, Step2), names_to = "Position", values_to = "Step") %>%
count(Step, Block, Axis, name = "MatchCount") %>%
arrange(as.numeric(Step), Block, Axis)
# -------- View the results --------
print(step_block_axis_counts)# A tibble: 144 × 4
Step Block Axis MatchCount
<chr> <int> <chr> <int>
1 1 1 Z 1
2 1 2 X 5
3 1 2 Y 3
4 1 2 Z 4
5 1 3 X 2
6 1 3 Y 3
7 1 3 Z 1
8 1 4 X 3
9 1 4 Y 11
10 1 4 Z 11
# ℹ 134 more rows# -------- Mean RT per block --------
mean_rt_per_block <- df_acc %>%
group_by(session) %>%
summarise(
Mean_RT = mean(feedback.RT, na.rm = TRUE),
SD_RT = sd(feedback.RT, na.rm = TRUE),
N = n()
) %>%
rename(Block = session) %>%
arrange(Block)
# -------- View the results --------
print(mean_rt_per_block)# A tibble: 5 × 4
Block Mean_RT SD_RT N
<fct> <dbl> <dbl> <int>
1 1 504. 431. 4410
2 2 485. 365. 7596
3 3 509. 397. 10242
4 4 469. 405. 8298
5 5 587. 519. 6372