top50_bottom50_analysis_v3
2024-10-11
##Packages
library(tidyverse)## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(readxl)
library(dplyr)
library(lme4)## Loading required package: Matrix
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## Attaching package: 'Matrix'
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## The following objects are masked from 'package:tidyr':
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## expand, pack, unpacklibrary(sjPlot)## Learn more about sjPlot with 'browseVignettes("sjPlot")'.library(lmerTest)##
## Attaching package: 'lmerTest'
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## The following object is masked from 'package:lme4':
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## lmer
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## The following object is masked from 'package:stats':
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## steplibrary(emmeans)
library(ggsignif)
library(emmeans)
library(effects)## Loading required package: carData
## lattice theme set by effectsTheme()
## See ?effectsTheme for details.library(car)##
## Attaching package: 'car'
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## The following object is masked from 'package:dplyr':
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## recode
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## somelibrary(ggtext)
library(ggplot2)#Load datasets
setwd("C:/Users/Marcel/Desktop/R Scripts Thesis/Sequence Execution Period")
# Load the trial_data
execution_data <- read_excel("trial_data_execution.xlsx")
execution_data$Block <- factor(execution_data$Block, ordered = TRUE, levels = c('1','2','3','4','5','6','7','8','9','10'))
execution_data$Trial <- as.numeric(execution_data$Trial)
# Create subset with only accurate trials
execution_data_acc <- execution_data %>% filter(trial_acc == 1)#Load datasets
setwd("C:/Users/Marcel/Desktop/R Scripts Thesis/Movement Preparation Period")
# Load the trial_data
preparation_data <- read_excel("trial_data_preparation.xlsx")
preparation_data$Block <- factor(preparation_data$Block, ordered = TRUE, levels = c('1','2','3','4','5','6','7','8','9','10'))
preparation_data$Trial <- as.numeric(preparation_data$Trial)
# Create subset with only include accurate trials
preparation_data_acc <- preparation_data %>% filter(trial_acc == 1)Grouped by Speed and Accuracy - Preparation Phase Stats
block_10_accurate_and_fast <- execution_data_acc %>% filter(Block == 10)
# Calculate average response time per participant and rank them
top_50_rt_accurate_and_fast <- block_10_accurate_and_fast %>%
group_by(Subject) %>%
summarise(avg_rt = mean(trial_total_rt)) %>%
mutate(rank = rank(avg_rt, ties.method = "first")) %>% # Lower avg_rt gets lower rank (better)
mutate(Group = ifelse(rank <= n() * 0.5, "Top 50%", "Bottom 50%"))
# Extract list of top 50% subjects
top_50_speed_acc_subjects <- top_50_rt_accurate_and_fast %>%
filter(Group == "Top 50%") %>%
pull(Subject)
# Apply speed/accuracy labels to execution_data_grouped_by_speed_and_accuracy
execution_data_grouped_by_speed_and_accuracy <- execution_data %>%
mutate(Group = ifelse(Subject %in% top_50_speed_acc_subjects, "Top 50%", "Bottom 50%"))
# Apply speed/accuracy labels to preparation_data_grouped_by_speed_and_accuracy
preparation_data_grouped_by_speed_and_accuracy <- preparation_data %>%
mutate(Group = ifelse(Subject %in% top_50_speed_acc_subjects, "Top 50%", "Bottom 50%"))
preparation_data_grouped_by_speed_and_accuracy$Block <- factor(preparation_data_grouped_by_speed_and_accuracy$Block,
levels = c("1", "9", "10"),
ordered = FALSE)
model_x <- lmer(hurst_acceleration_x ~ Group * Block + (1 | Subject),
data = preparation_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
summary(model_x)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hurst_acceleration_x ~ Group * Block + (1 | Subject)
## Data: preparation_data_grouped_by_speed_and_accuracy %>% filter(Block %in%
## c(1, 9, 10))
##
## REML criterion at convergence: -4578.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8732 -0.5402 0.2554 0.6752 2.3404
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.003584 0.05987
## Residual 0.013286 0.11526
## Number of obs: 3168, groups: Subject, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.461e-01 1.873e-02 2.206e+01 45.162 < 2e-16 ***
## GroupTop 50% 4.203e-02 2.649e-02 2.206e+01 1.586 0.12691
## Block9 8.661e-03 7.094e-03 3.142e+03 1.221 0.22221
## Block10 1.000e-02 7.094e-03 3.142e+03 1.410 0.15874
## GroupTop 50%:Block9 2.321e-02 1.003e-02 3.142e+03 2.314 0.02075 *
## GroupTop 50%:Block10 2.858e-02 1.003e-02 3.142e+03 2.849 0.00441 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrT50% Block9 Blck10 GT50%:B9
## GroupTop50% -0.707
## Block9 -0.189 0.134
## Block10 -0.189 0.134 0.500
## GrpT50%:Bl9 0.134 -0.189 -0.707 -0.354
## GrpT50%:B10 0.134 -0.189 -0.354 -0.707 0.500anova(model_x)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.06988 0.069880 1 20 5.2598 0.03279 *
## Block 0.35801 0.179006 2 3142 13.4737 1.491e-06 ***
## Group:Block 0.12186 0.060929 2 3142 4.5861 0.01026 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans_obj <- emmeans(model_x, ~ Group | Block)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.group_block_comparisons <- contrast(emmeans_obj, method = "pairwise")
summary(group_block_comparisons)## Block = 1:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% -0.0420 0.0265 Inf -1.586 0.1127
##
## Block = 9:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% -0.0652 0.0265 Inf -2.462 0.0138
##
## Block = 10:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% -0.0706 0.0265 Inf -2.665 0.0077
##
## Degrees-of-freedom method: asymptoticmodel_y <- lmer(hurst_acceleration_y ~ Group * Block + (1 | Subject),
data = preparation_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
summary(model_y)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hurst_acceleration_y ~ Group * Block + (1 | Subject)
## Data: preparation_data_grouped_by_speed_and_accuracy %>% filter(Block %in%
## c(1, 9, 10))
##
## REML criterion at convergence: -3797.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.2622 -0.6029 0.2574 0.6848 2.4662
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.004746 0.06889
## Residual 0.017003 0.13039
## Number of obs: 3168, groups: Subject, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.191e-01 2.153e-02 2.199e+01 38.042 < 2e-16 ***
## GroupTop 50% 4.561e-02 3.045e-02 2.199e+01 1.498 0.14842
## Block9 -1.860e-03 8.025e-03 3.142e+03 -0.232 0.81677
## Block10 7.442e-03 8.025e-03 3.142e+03 0.927 0.35381
## GroupTop 50%:Block9 3.117e-02 1.135e-02 3.142e+03 2.746 0.00606 **
## GroupTop 50%:Block10 2.369e-02 1.135e-02 3.142e+03 2.088 0.03692 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrT50% Block9 Blck10 GT50%:B9
## GroupTop50% -0.707
## Block9 -0.186 0.132
## Block10 -0.186 0.132 0.500
## GrpT50%:Bl9 0.132 -0.186 -0.707 -0.354
## GrpT50%:B10 0.132 -0.186 -0.354 -0.707 0.500anova(model_y)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.078494 0.078494 1 20 4.6166 0.044091 *
## Block 0.208160 0.104080 2 3142 6.1214 0.002222 **
## Group:Block 0.139806 0.069903 2 3142 4.1113 0.016475 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans_obj <- emmeans(model_y, ~ Group | Block)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.group_block_comparisons <- contrast(emmeans_obj, method = "pairwise")
summary(group_block_comparisons)## Block = 1:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% -0.0456 0.0305 Inf -1.498 0.1342
##
## Block = 9:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% -0.0768 0.0305 Inf -2.521 0.0117
##
## Block = 10:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% -0.0693 0.0305 Inf -2.276 0.0229
##
## Degrees-of-freedom method: asymptoticmodel_z <- lmer(hurst_acceleration_z ~ Group * Block + (1 | Subject),
data = preparation_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
summary(model_z)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hurst_acceleration_z ~ Group * Block + (1 | Subject)
## Data: preparation_data_grouped_by_speed_and_accuracy %>% filter(Block %in%
## c(1, 9, 10))
##
## REML criterion at convergence: -3026.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6613 -0.5348 0.2198 0.5697 2.0304
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.006773 0.0823
## Residual 0.021687 0.1473
## Number of obs: 3168, groups: Subject, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.022e-01 2.563e-02 2.178e+01 31.301 < 2e-16 ***
## GroupTop 50% 7.597e-02 3.624e-02 2.178e+01 2.096 0.04791 *
## Block9 -9.044e-03 9.064e-03 3.142e+03 -0.998 0.31842
## Block10 3.550e-03 9.064e-03 3.142e+03 0.392 0.69529
## GroupTop 50%:Block9 3.349e-02 1.282e-02 3.142e+03 2.613 0.00903 **
## GroupTop 50%:Block10 2.609e-02 1.282e-02 3.142e+03 2.035 0.04191 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrT50% Block9 Blck10 GT50%:B9
## GroupTop50% -0.707
## Block9 -0.177 0.125
## Block10 -0.177 0.125 0.500
## GrpT50%:Bl9 0.125 -0.177 -0.707 -0.354
## GrpT50%:B10 0.125 -0.177 -0.354 -0.707 0.500anova(model_z)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.15822 0.158220 1 20 7.2956 0.01375 *
## Block 0.14565 0.072823 2 3142 3.3579 0.03493 *
## Group:Block 0.16339 0.081693 2 3142 3.7669 0.02323 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans_obj <- emmeans(model_z, ~ Group | Block)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.group_block_comparisons <- contrast(emmeans_obj, method = "pairwise")
summary(group_block_comparisons)## Block = 1:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% -0.076 0.0362 Inf -2.096 0.0361
##
## Block = 9:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% -0.109 0.0362 Inf -3.020 0.0025
##
## Block = 10:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% -0.102 0.0362 Inf -2.816 0.0049
##
## Degrees-of-freedom method: asymptoticmodel_x <- lmer(hurst_acceleration_x ~ Group * Block + (1 | Subject),
data = preparation_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
model_y <- lmer(hurst_acceleration_y ~ Group * Block + (1 | Subject),
data = preparation_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
model_z <- lmer(hurst_acceleration_z ~ Group * Block + (1 | Subject),
data = preparation_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
get_model_effects_df <- function(model) {
model_effects <- allEffects(model)
model_effects_df <- as.data.frame(model_effects[[1]])
# Change conf interval to 83% to match p = .05
model_effects_df$l83 <- model_effects_df$fit - 1.3722 * model_effects_df$se
model_effects_df$u83 <- model_effects_df$fit + 1.3722 * model_effects_df$se
return(model_effects_df)
}
# Apply the function to the three models (X, Y, Z)
model_x_effects_df <- get_model_effects_df(model_x)
model_y_effects_df <- get_model_effects_df(model_y)
model_z_effects_df <- get_model_effects_df(model_z)
# Combine the model effect data frames into one for plotting
model_x_effects_df$Axis <- "Acceleration X"
model_y_effects_df$Axis <- "Acceleration Y"
model_z_effects_df$Axis <- "Acceleration Z"
combined_effects_df <- rbind(model_x_effects_df, model_y_effects_df, model_z_effects_df)
# Rename the X Axis Labels for plotting
combined_effects_df <- combined_effects_df %>%
mutate(Block = case_when(
Block == "1" ~ "Block 1\n(Initial Exposure)",
Block == "9" ~ "Block 9\n(Extended Practice)",
Block == "10" ~ "Block 10\n(Unfamiliar Sequences)",
TRUE ~ Block
))
# Reorder the Group factor to have Top 50% on top in the legend
combined_effects_df$Group <- factor(combined_effects_df$Group, levels = c("Top 50%", "Bottom 50%"))
# Set Order
combined_effects_df <- combined_effects_df %>%
mutate(Block = factor(Block, levels = c(
"Block 1\n(Initial Exposure)",
"Block 9\n(Extended Practice)",
"Block 10\n(Unfamiliar Sequences)"
)))
# Adjust plot for Hurst Exponent X
plot_x <- ggplot(combined_effects_df %>% filter(Axis == "Acceleration X"), aes(x = Block, y = fit, color = Group, group = Group)) +
geom_vline(xintercept = 3, color = "darkorchid", size = 1.5, alpha = 0.25, linetype = "solid") +
geom_line(size = 2.5) +
geom_point(size = 5) +
geom_ribbon(aes(ymin = l83, ymax = u83, fill = Group), alpha = 0.5) +
labs(
title = " ",
subtitle = " Acceleration X",
x = NULL,
y = "Estimated Hurst Exponent"
) +
theme_minimal() +
theme(
text = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 20),
legend.position = "none",
plot.title = element_text(face = "bold", size = 24),
plot.subtitle = element_text(size = 26),
axis.title.y = element_text(size = 26),
axis.text.x = ggtext::element_markdown(size = 22, face = "bold", angle = 0, hjust = 0.5),
axis.text.y = element_text(size = 24),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(color = "gray80", size = 0.5),
panel.grid.minor.y = element_line(color = "gray80", size = 0.25)
) +
scale_color_manual(values = c("Top 50%" = "darkorchid3", "Bottom 50%" = "grey")) +
scale_fill_manual(values = c("Top 50%" = "darkorchid3", "Bottom 50%" = "grey")) +
ylim(0.7, 1.02) +
annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7, face = "bold",color = "darkorchid", hjust = 0.5, vjust = 0, alpha = 0.8)## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## Warning in annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7,
## : Ignoring unknown parameters: `face`# Customizing the x-axis labels with color to signify significance
plot_x <- plot_x +
scale_x_discrete(labels = c(
"Block 1<br><span style='color:#333333; font-size:18pt;'>Initial<br>Exposure</span>",
"<span style='color:red;'>Block 9</span><span style='color:#333333; font-size:18pt;'><br>Extended<br>Practice</span>",
"<span style='color:red;'>Block 10"
))
print(plot_x)
# Plot for Hurst Exponent Y
plot_y <- ggplot(combined_effects_df %>% filter(Axis == "Acceleration Y"), aes(x = Block, y = fit, color = Group, group = Group)) +
geom_vline(xintercept = 3, color = "darkorchid", size = 1.5, alpha = 0.25, linetype = "solid") +
geom_line(size = 2.5) +
geom_point(size = 5) +
geom_ribbon(aes(ymin = l83, ymax = u83, fill = Group), alpha = 0.5) +
labs(
title = " ",
subtitle = " Acceleration Y",
x = NULL,
y = "Estimated Hurst Exponent"
) +
theme_minimal() +
theme(
text = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 20),
legend.position = "none",
plot.title = element_text(face = "bold", size = 24),
plot.subtitle = element_text(size = 26),
axis.title.y = element_text(size = 26),
axis.text.x = ggtext::element_markdown(size = 22, face = "bold", angle = 0, hjust = 0.5),
axis.text.y = element_text(size = 24),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(color = "gray80", size = 0.5),
panel.grid.minor.y = element_line(color = "gray80", size = 0.25)
) +
scale_color_manual(values = c("Top 50%" = "darkorchid3", "Bottom 50%" = "grey")) +
scale_fill_manual(values = c("Top 50%" = "darkorchid3", "Bottom 50%" = "grey")) +
ylim(0.7, 1.02) +
annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7, face = "bold",color = "darkorchid", hjust = 0.5, vjust = 0, alpha = 0.8)## Warning in annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7,
## : Ignoring unknown parameters: `face`# Customizing the x-axis labels with color to signify significance
plot_y <- plot_y +
scale_x_discrete(labels = c(
"Block 1<br><span style='color:#333333; font-size:18pt;'>Initial<br>Exposure</span>",
"<span style='color:red;'>Block 9</span><span style='color:#333333; font-size:18pt;'><br>Extended<br>Practice</span>",
"<span style='color:red;'>Block 10"
))
print(plot_y)
# Plot for Hurst Exponent Z
plot_z <- ggplot(combined_effects_df %>% filter(Axis == "Acceleration Z"), aes(x = Block, y = fit, color = Group, group = Group)) +
geom_vline(xintercept = 3, color = "darkorchid", size = 1.5, alpha = 0.25, linetype = "solid") +
geom_line(size = 2.5) +
geom_point(size = 5) +
geom_ribbon(aes(ymin = l83, ymax = u83, fill = Group), alpha = 0.5) +
labs(
title = " ",
subtitle = " Acceleration Z",
x = NULL,
y = "Estimated Hurst Exponent"
) +
theme_minimal() +
theme(
text = element_text(size = 20),
legend.title = element_text(size = 20, face = "bold"),
legend.text = element_text(size = 20, face = "bold"),
legend.position = "right",
plot.title = element_text(face = "bold", size = 24),
plot.subtitle = element_text(size = 26),
axis.title.y = element_text(size = 26),
axis.text.x = ggtext::element_markdown(size = 22, face = "bold", angle = 0, hjust = 0.5),
axis.text.y = element_text(size = 24),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(color = "gray80", size = 0.5),
panel.grid.minor.y = element_line(color = "gray80", size = 0.25)
) +
scale_color_manual(values = c("Top 50%" = "darkorchid3", "Bottom 50%" = "grey")) +
scale_fill_manual(values = c("Top 50%" = "darkorchid3", "Bottom 50%" = "grey")) +
ylim(0.7, 1.02) +
annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7, face = "bold",color = "darkorchid", hjust = 0.5, vjust = 0, alpha = 0.8)## Warning in annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7,
## : Ignoring unknown parameters: `face`# Customizing the x-axis labels with color to signify significance
plot_z <- plot_z +
scale_x_discrete(labels = c(
"<span style='color:red;'>Block 1<br><span style='color:#333333; font-size:18pt;'>Initial<br>Exposure</span>",
"<span style='color:red;'>Block 9</span><span style='color:#333333; font-size:18pt;'><br>Extended<br>Practice</span>",
"<span style='color:red;'>Block 10"
))
print(plot_z)
# Save the plot for Hurst Exponent X
ggsave("Preparation_Hurst_Exponent_X.png", plot = plot_x, width = 8.2, height = 7, dpi = 300)
# Save the plot for Hurst Exponent Y
ggsave("Preparation_Hurst_Exponent_Y.png", plot = plot_y, width = 8.2, height = 7, dpi = 300)
# Save the plot for Hurst Exponent Z
ggsave("Preparation_Hurst_Exponent_Z.png", plot = plot_z, width = 10, height = 7, dpi = 300)Grouped by Speed and Accuracy - Execution Phase Stats
execution_data_grouped_by_speed_and_accuracy$Block <- factor(execution_data_grouped_by_speed_and_accuracy$Block,
levels = c("1", "9", "10"),
ordered = FALSE)
model_x <- lmer(hurst_acceleration_x ~ Group * Block + (1 | Subject),
data = execution_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
summary(model_x)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hurst_acceleration_x ~ Group * Block + (1 | Subject)
## Data: execution_data_grouped_by_speed_and_accuracy %>% filter(Block %in%
## c(1, 9, 10))
##
## REML criterion at convergence: -7412.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.0492 -0.0725 0.1367 0.3721 2.8406
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.002956 0.05437
## Residual 0.005398 0.07347
## Number of obs: 3168, groups: Subject, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.837e-01 1.670e-02 2.101e+01 58.896 < 2e-16 ***
## GroupTop 50% -1.648e-02 2.362e-02 2.101e+01 -0.698 0.4929
## Block9 -8.505e-03 4.522e-03 3.142e+03 -1.881 0.0601 .
## Block10 -2.099e-03 4.522e-03 3.142e+03 -0.464 0.6425
## GroupTop 50%:Block9 -5.202e-02 6.395e-03 3.142e+03 -8.134 5.91e-16 ***
## GroupTop 50%:Block10 -7.381e-02 6.395e-03 3.142e+03 -11.541 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrT50% Block9 Blck10 GT50%:B9
## GroupTop50% -0.707
## Block9 -0.135 0.096
## Block10 -0.135 0.096 0.500
## GrpT50%:Bl9 0.096 -0.135 -0.707 -0.354
## GrpT50%:B10 0.096 -0.135 -0.354 -0.707 0.500anova(model_x)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.03385 0.03385 1 20 6.2713 0.02104 *
## Block 0.96188 0.48094 2 3142 89.0909 < 2e-16 ***
## Group:Block 0.75929 0.37964 2 3142 70.3269 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans_obj <- emmeans(model_x, ~ Group | Block)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.group_block_comparisons <- contrast(emmeans_obj, method = "pairwise")
summary(group_block_comparisons)## Block = 1:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% 0.0165 0.0236 Inf 0.698 0.4853
##
## Block = 9:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% 0.0685 0.0236 Inf 2.900 0.0037
##
## Block = 10:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% 0.0903 0.0236 Inf 3.823 0.0001
##
## Degrees-of-freedom method: asymptoticmodel_y <- lmer(hurst_acceleration_y ~ Group * Block + (1 | Subject),
data = execution_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
summary(model_y)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hurst_acceleration_y ~ Group * Block + (1 | Subject)
## Data: execution_data_grouped_by_speed_and_accuracy %>% filter(Block %in%
## c(1, 9, 10))
##
## REML criterion at convergence: -7345
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.6751 -0.0381 0.0891 0.2834 3.1985
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 0.003487 0.05905
## Residual 0.005510 0.07423
## Number of obs: 3168, groups: Subject, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.858e-01 1.810e-02 2.088e+01 54.478 < 2e-16 ***
## GroupTop 50% -2.628e-02 2.559e-02 2.088e+01 -1.027 0.31624
## Block9 2.032e-03 4.568e-03 3.142e+03 0.445 0.65647
## Block10 -6.375e-03 4.568e-03 3.142e+03 -1.395 0.16297
## GroupTop 50%:Block9 -5.066e-02 6.461e-03 3.142e+03 -7.841 6.09e-15 ***
## GroupTop 50%:Block10 -1.988e-02 6.461e-03 3.142e+03 -3.077 0.00211 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrT50% Block9 Blck10 GT50%:B9
## GroupTop50% -0.707
## Block9 -0.126 0.089
## Block10 -0.126 0.089 0.500
## GrpT50%:Bl9 0.089 -0.126 -0.707 -0.354
## GrpT50%:B10 0.089 -0.126 -0.354 -0.707 0.500anova(model_y)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.02131 0.021308 1 20 3.8675 0.06326 .
## Block 0.30187 0.150937 2 3142 27.3954 1.603e-12 ***
## Group:Block 0.34394 0.171968 2 3142 31.2127 3.779e-14 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans_obj <- emmeans(model_y, ~ Group | Block)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.group_block_comparisons <- contrast(emmeans_obj, method = "pairwise")
summary(group_block_comparisons)## Block = 1:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% 0.0263 0.0256 Inf 1.027 0.3045
##
## Block = 9:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% 0.0769 0.0256 Inf 3.006 0.0026
##
## Block = 10:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% 0.0462 0.0256 Inf 1.804 0.0713
##
## Degrees-of-freedom method: asymptoticmodel_z <- lmer(hurst_acceleration_z ~ Group * Block + (1 | Subject),
data = execution_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
summary(model_z)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: hurst_acceleration_z ~ Group * Block + (1 | Subject)
## Data: execution_data_grouped_by_speed_and_accuracy %>% filter(Block %in%
## c(1, 9, 10))
##
## REML criterion at convergence: -12911.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -16.7370 -0.0333 0.1121 0.2361 0.8155
##
## Random effects:
## Groups Name Variance Std.Dev.
## Subject (Intercept) 4.836e-05 0.006954
## Residual 9.619e-04 0.031015
## Number of obs: 3168, groups: Subject, 22
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.861e-01 2.494e-03 3.088e+01 395.438 < 2e-16 ***
## GroupTop 50% 6.483e-03 3.527e-03 3.088e+01 1.838 0.07565 .
## Block9 7.539e-03 1.909e-03 3.142e+03 3.949 8.00e-05 ***
## Block10 6.160e-03 1.909e-03 3.142e+03 3.227 0.00126 **
## GroupTop 50%:Block9 -1.421e-02 2.699e-03 3.142e+03 -5.263 1.52e-07 ***
## GroupTop 50%:Block10 -1.199e-02 2.699e-03 3.142e+03 -4.442 9.22e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) GrT50% Block9 Blck10 GT50%:B9
## GroupTop50% -0.707
## Block9 -0.383 0.271
## Block10 -0.383 0.271 0.500
## GrpT50%:Bl9 0.271 -0.383 -0.707 -0.354
## GrpT50%:B10 0.271 -0.383 -0.354 -0.707 0.500anova(model_z)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Group 0.0004865 0.0004865 1 20 0.5057 0.4852
## Block 0.0001022 0.0000511 2 3142 0.0531 0.9483
## Group:Block 0.0308455 0.0154227 2 3142 16.0334 1.181e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1emmeans_obj <- emmeans(model_z, ~ Group | Block)## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'pbkrtest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(pbkrtest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.
## Note: D.f. calculations have been disabled because the number of observations exceeds 3000.
## To enable adjustments, add the argument 'lmerTest.limit = 3168' (or larger)
## [or, globally, 'set emm_options(lmerTest.limit = 3168)' or larger];
## but be warned that this may result in large computation time and memory use.group_block_comparisons <- contrast(emmeans_obj, method = "pairwise")
summary(group_block_comparisons)## Block = 1:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% -0.00648 0.00353 Inf -1.838 0.0660
##
## Block = 9:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% 0.00772 0.00353 Inf 2.190 0.0285
##
## Block = 10:
## contrast estimate SE df z.ratio p.value
## Bottom 50% - Top 50% 0.00551 0.00353 Inf 1.562 0.1183
##
## Degrees-of-freedom method: asymptoticmodel_x <- lmer(hurst_acceleration_x ~ Group * Block + (1 | Subject),
data = execution_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
model_y <- lmer(hurst_acceleration_y ~ Group * Block + (1 | Subject),
data = execution_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
model_z <- lmer(hurst_acceleration_z ~ Group * Block + (1 | Subject),
data = execution_data_grouped_by_speed_and_accuracy %>% filter(Block %in% c(1, 9, 10)))
get_model_effects_df <- function(model) {
model_effects <- allEffects(model)
model_effects_df <- as.data.frame(model_effects[[1]])
# Change conf interval to 83% to match p = .05
model_effects_df$l83 <- model_effects_df$fit - 1.3722 * model_effects_df$se
model_effects_df$u83 <- model_effects_df$fit + 1.3722 * model_effects_df$se
return(model_effects_df)
}
# Apply the function to the three models (X, Y, Z)
model_x_effects_df <- get_model_effects_df(model_x)
model_y_effects_df <- get_model_effects_df(model_y)
model_z_effects_df <- get_model_effects_df(model_z)
# Combine the model effect data frames into one for plotting
model_x_effects_df$Axis <- "Acceleration X"
model_y_effects_df$Axis <- "Acceleration Y"
model_z_effects_df$Axis <- "Acceleration Z"
combined_effects_df <- rbind(model_x_effects_df, model_y_effects_df, model_z_effects_df)
# Rename the X Axis Labels for plotting
combined_effects_df <- combined_effects_df %>%
mutate(Block = case_when(
Block == "1" ~ "Block 1\n(Initial Exposure)",
Block == "9" ~ "Block 9\n(Extended Practice)",
Block == "10" ~ "Block 10\n(Unfamiliar Sequences)",
TRUE ~ Block
))
# Reorder the Group factor to have Top 50% on top in the legend
combined_effects_df$Group <- factor(combined_effects_df$Group, levels = c("Top 50%", "Bottom 50%"))
# Set Order
combined_effects_df <- combined_effects_df %>%
mutate(Block = factor(Block, levels = c(
"Block 1\n(Initial Exposure)",
"Block 9\n(Extended Practice)",
"Block 10\n(Unfamiliar Sequences)"
)))
# Adjust plot for Hurst Exponent X
plot_x <- ggplot(combined_effects_df %>% filter(Axis == "Acceleration X"), aes(x = Block, y = fit, color = Group, group = Group)) +
geom_vline(xintercept = 3, color = "darkseagreen", size = 1.5, alpha = 0.25, linetype = "solid") +
geom_line(size = 2.5) +
geom_point(size = 5) +
geom_ribbon(aes(ymin = l83, ymax = u83, fill = Group), alpha = 0.5) +
labs(
title = " ",
subtitle = " Acceleration X",
x = NULL,
y = "Estimated Hurst Exponent"
) +
theme_minimal() +
theme(
text = element_text(size = 20),
legend.title = element_text(size = 20),
legend.text = element_text(size = 20),
legend.position = "none",
plot.title = element_text(face = "bold", size = 24),
plot.subtitle = element_text(size = 26),
axis.title.y = element_text(size = 26),
axis.text.x = ggtext::element_markdown(size = 22, face = "bold", angle = 0, hjust = 0.5),
axis.text.y = element_text(size = 24),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(color = "gray80", size = 0.5),
panel.grid.minor.y = element_line(color = "gray80", size = 0.25)
) +
scale_color_manual(values = c("Top 50%" = "aquamarine4", "Bottom 50%" = "grey")) +
scale_fill_manual(values = c("Top 50%" = "aquamarine4", "Bottom 50%" = "grey")) +
ylim(0.7, 1.02) +
annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7, face = "bold",color = "darkseagreen4", hjust = 0.5, vjust = 0, alpha = 0.8)## Warning in annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7,
## : Ignoring unknown parameters: `face`# Customizing the x-axis labels with color to signify significance
plot_x <- plot_x +
scale_x_discrete(labels = c(
"Block 1<br><span style='color:#333333; font-size:18pt;'>Initial<br>Exposure</span>",
"<span style='color:red;'>Block 9</span><span style='color:#333333; font-size:18pt;'><br>Extended<br>Practice</span>",
"<span style='color:red;'>Block 10"
))
print(plot_x)
# Plot for Hurst Exponent Y
plot_y <- ggplot(combined_effects_df %>% filter(Axis == "Acceleration Y"), aes(x = Block, y = fit, color = Group, group = Group)) +
geom_vline(xintercept = 3, color = "darkseagreen", size = 1.5, alpha = 0.25, linetype = "solid") +
geom_line(size = 2.5) +
geom_point(size = 5) +
geom_ribbon(aes(ymin = l83, ymax = u83, fill = Group), alpha = 0.5) +
labs(
title = " ",
subtitle = " Acceleration Y",
x = NULL,
y = "Estimated Hurst Exponent"
) +
theme_minimal() +
theme(
text = element_text(size = 20),
legend.title = element_text(size = 120),
legend.text = element_text(size = 20),
legend.position = "none",
plot.title = element_text(face = "bold", size = 24),
plot.subtitle = element_text(size = 26),
axis.title.y = element_text(size = 26),
axis.text.x = ggtext::element_markdown(size = 22, face = "bold", angle = 0, hjust = 0.5),
axis.text.y = element_text(size = 24),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(color = "gray80", size = 0.5),
panel.grid.minor.y = element_line(color = "gray80", size = 0.25)
) +
scale_color_manual(values = c("Top 50%" = "aquamarine4", "Bottom 50%" = "grey")) +
scale_fill_manual(values = c("Top 50%" = "aquamarine4", "Bottom 50%" = "grey")) +
ylim(0.7, 1.02) +
annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7, face = "bold",color = "darkseagreen4", hjust = 0.5, vjust = 0, alpha = 0.8)## Warning in annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7,
## : Ignoring unknown parameters: `face`# Customizing the x-axis labels with color to signify significance
plot_y <- plot_y +
scale_x_discrete(labels = c(
"Block 1<br><span style='color:#333333; font-size:18pt;'>Initial<br>Exposure</span>",
"<span style='color:red;'>Block 9</span><span style='color:#333333; font-size:18pt;'><br>Extended<br>Practice</span>",
"Block 10"
))
print(plot_y)
# Plot for Hurst Exponent Z
plot_z <- ggplot(combined_effects_df %>% filter(Axis == "Acceleration Z"), aes(x = Block, y = fit, color = Group, group = Group)) +
geom_vline(xintercept = 3, color = "darkseagreen", size = 1.5, alpha = 0.25, linetype = "solid") +
geom_line(size = 2.5) +
geom_point(size = 5) +
geom_ribbon(aes(ymin = l83, ymax = u83, fill = Group), alpha = 0.5) +
labs(
title = " ",
subtitle = " Acceleration Z",
x = NULL,
y = "Estimated Hurst Exponent"
) +
theme_minimal() +
theme(
text = element_text(size = 20),
legend.title = element_text(size = 20, face = "bold"),
legend.text = element_text(size = 20, face = "bold"),
legend.position = "right",
plot.title = element_text(face = "bold", size = 24),
plot.subtitle = element_text(size = 26),
axis.title.y = element_text(size = 26),
axis.text.x = ggtext::element_markdown(size = 22, face = "bold", angle = 0, hjust = 0.5),
axis.text.y = element_text(size = 24),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major.y = element_line(color = "gray80", size = 0.5),
panel.grid.minor.y = element_line(color = "gray80", size = 0.25)
) +
scale_color_manual(values = c("Top 50%" = "aquamarine4", "Bottom 50%" = "grey")) +
scale_fill_manual(values = c("Top 50%" = "aquamarine4", "Bottom 50%" = "grey")) +
ylim(0.7, 1.02) +
annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7, face = "bold",color = "darkseagreen4", hjust = 0.5, vjust = 0, alpha = 0.8)## Warning in annotate("text", x = 3, y = 0.71, label = "New Sequences", size = 7,
## : Ignoring unknown parameters: `face`# Customizing the x-axis labels with color to signify significance
plot_z <- plot_z +
scale_x_discrete(labels = c(
"Block 1<br><span style='color:#333333; font-size:18pt;'>Initial<br>Exposure</span>",
"<span style='color:red;'>Block 9</span><span style='color:#333333; font-size:18pt;'><br>Extended<br>Practice</span>",
"Block 10"
))
print(plot_z)
# Save the plot for Hurst Exponent X
ggsave("Execution_Hurst_Exponent_X.png", plot = plot_x, width = 8.2, height = 7, dpi = 300)
# Save the plot for Hurst Exponent Y
ggsave("Execution_Hurst_Exponent_Y.png", plot = plot_y, width = 8.2, height = 7, dpi = 300)
# Save the plot for Hurst Exponent Z
ggsave("Execution_Hurst_Exponent_Z.png", plot = plot_z, width = 10, height = 7, dpi = 300)# Loop through axes (x, y, z), blocks, and groups for the preparation phase
axes <- c("hurst_acceleration_x", "hurst_acceleration_y", "hurst_acceleration_z")
blocks <- c("1", "9", "10")
# Function to perform the regression for hurst_acceleration axes
run_regression <- function(data, axis, phase, block, group) {
model <- lm(trial_total_rt ~ get(axis), data = data)
cat("\n============================\n")
cat(group, "Group - Block", block, "-", phase, "Phase -", axis, "\n")
cat("============================\n")
print(summary(model))
}# Loop through the PREPARATION phase data
for (axis in axes) {
for (block in blocks) {
# Filter for each block in preparation phase
block_data <- preparation_data_grouped_by_speed_and_accuracy %>% filter(Block == block)
# Top 50% group in preparation phase
top50_data <- block_data %>% filter(Group == "Top 50%")
run_regression(top50_data, axis, "Preparation", block, "Top 50%")
# Bottom 50% group in preparation phase
bottom50_data <- block_data %>% filter(Group == "Bottom 50%")
run_regression(bottom50_data, axis, "Preparation", block, "Bottom 50%")
}
}##
## ============================
## Top 50% Group - Block 1 - Preparation Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3155.2 -1455.6 -556.7 666.3 25376.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3678.0 822.4 4.472 9.5e-06 ***
## get(axis) 111.9 916.4 0.122 0.903
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2716 on 526 degrees of freedom
## Multiple R-squared: 2.834e-05, Adjusted R-squared: -0.001873
## F-statistic: 0.01491 on 1 and 526 DF, p-value: 0.9029
##
##
## ============================
## Bottom 50% Group - Block 1 - Preparation Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4789 -2557 -1179 965 75147
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1586 1332 1.190 0.234546
## get(axis) 5220 1551 3.365 0.000822 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5275 on 526 degrees of freedom
## Multiple R-squared: 0.02107, Adjusted R-squared: 0.01921
## F-statistic: 11.32 on 1 and 526 DF, p-value: 0.0008215
##
##
## ============================
## Top 50% Group - Block 9 - Preparation Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1948.5 -530.3 -235.8 129.8 20968.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3042.1 570.8 5.33 1.46e-07 ***
## get(axis) -1461.0 616.4 -2.37 0.0181 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1498 on 526 degrees of freedom
## Multiple R-squared: 0.01057, Adjusted R-squared: 0.008688
## F-statistic: 5.619 on 1 and 526 DF, p-value: 0.01813
##
##
## ============================
## Bottom 50% Group - Block 9 - Preparation Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1804.9 -634.9 -98.4 463.9 5934.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1560.0 272.3 5.730 1.69e-08 ***
## get(axis) 1244.5 314.5 3.958 8.61e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 997.4 on 526 degrees of freedom
## Multiple R-squared: 0.02892, Adjusted R-squared: 0.02707
## F-statistic: 15.66 on 1 and 526 DF, p-value: 8.607e-05
##
##
## ============================
## Top 50% Group - Block 10 - Preparation Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1576.4 -515.4 -217.8 213.9 11618.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1863.62 427.50 4.359 1.57e-05 ***
## get(axis) -45.69 458.54 -0.100 0.921
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1075 on 526 degrees of freedom
## Multiple R-squared: 1.888e-05, Adjusted R-squared: -0.001882
## F-statistic: 0.009929 on 1 and 526 DF, p-value: 0.9207
##
##
## ============================
## Bottom 50% Group - Block 10 - Preparation Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2334.2 -800.5 -198.6 457.1 8173.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3992.7 346.8 11.512 <2e-16 ***
## get(axis) -602.6 399.7 -1.507 0.132
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1297 on 526 degrees of freedom
## Multiple R-squared: 0.004301, Adjusted R-squared: 0.002408
## F-statistic: 2.272 on 1 and 526 DF, p-value: 0.1323
##
##
## ============================
## Top 50% Group - Block 1 - Preparation Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3275.5 -1415.0 -569.6 727.5 25241.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2798.6 725.7 3.856 0.000129 ***
## get(axis) 1131.9 828.1 1.367 0.172228
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2712 on 526 degrees of freedom
## Multiple R-squared: 0.00354, Adjusted R-squared: 0.001645
## F-statistic: 1.869 on 1 and 526 DF, p-value: 0.1722
##
##
## ============================
## Bottom 50% Group - Block 1 - Preparation Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4891 -2544 -1186 953 75094
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1923 1177 1.634 0.102874
## get(axis) 4980 1409 3.534 0.000445 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5269 on 526 degrees of freedom
## Multiple R-squared: 0.0232, Adjusted R-squared: 0.02134
## F-statistic: 12.49 on 1 and 526 DF, p-value: 0.0004446
##
##
## ============================
## Top 50% Group - Block 9 - Preparation Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1791.5 -524.9 -253.3 123.0 20969.1
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2804.9 473.8 5.920 5.82e-09 ***
## get(axis) -1238.1 524.9 -2.359 0.0187 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1498 on 526 degrees of freedom
## Multiple R-squared: 0.01046, Adjusted R-squared: 0.008583
## F-statistic: 5.563 on 1 and 526 DF, p-value: 0.01871
##
##
## ============================
## Bottom 50% Group - Block 9 - Preparation Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1790.3 -617.5 -147.5 496.7 6068.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1906.2 231.2 8.244 1.34e-15 ***
## get(axis) 878.0 277.8 3.160 0.00167 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1003 on 526 degrees of freedom
## Multiple R-squared: 0.01863, Adjusted R-squared: 0.01677
## F-statistic: 9.987 on 1 and 526 DF, p-value: 0.001667
##
##
## ============================
## Top 50% Group - Block 10 - Preparation Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1580.3 -516.9 -217.1 217.9 11614.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1808.99 343.24 5.270 1.99e-07 ***
## get(axis) 13.72 379.56 0.036 0.971
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1075 on 526 degrees of freedom
## Multiple R-squared: 2.483e-06, Adjusted R-squared: -0.001899
## F-statistic: 0.001306 on 1 and 526 DF, p-value: 0.9712
##
##
## ============================
## Bottom 50% Group - Block 10 - Preparation Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2349.6 -775.1 -177.6 441.8 8213.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4183.6 295.7 14.148 <2e-16 ***
## get(axis) -855.0 351.2 -2.434 0.0153 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1292 on 526 degrees of freedom
## Multiple R-squared: 0.01114, Adjusted R-squared: 0.00926
## F-statistic: 5.926 on 1 and 526 DF, p-value: 0.01525
##
##
## ============================
## Top 50% Group - Block 1 - Preparation Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3182.8 -1471.7 -568.0 642.4 25346.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3463.5 690.3 5.018 7.16e-07 ***
## get(axis) 357.4 774.4 0.462 0.645
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2716 on 526 degrees of freedom
## Multiple R-squared: 0.0004048, Adjusted R-squared: -0.001496
## F-statistic: 0.213 on 1 and 526 DF, p-value: 0.6446
##
##
## ============================
## Bottom 50% Group - Block 1 - Preparation Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5005 -2502 -1197 1010 74928
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1839 1031 1.784 0.0751 .
## get(axis) 5190 1254 4.140 4.04e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5246 on 526 degrees of freedom
## Multiple R-squared: 0.03156, Adjusted R-squared: 0.02972
## F-statistic: 17.14 on 1 and 526 DF, p-value: 4.038e-05
##
##
## ============================
## Top 50% Group - Block 9 - Preparation Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1833.2 -537.0 -241.9 128.0 20898.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2570.2 437.5 5.874 7.54e-09 ***
## get(axis) -966.3 479.3 -2.016 0.0443 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1500 on 526 degrees of freedom
## Multiple R-squared: 0.007667, Adjusted R-squared: 0.005781
## F-statistic: 4.064 on 1 and 526 DF, p-value: 0.04431
##
##
## ============================
## Bottom 50% Group - Block 9 - Preparation Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1790.2 -587.0 -150.8 502.5 6094.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2161.8 189.4 11.412 <2e-16 ***
## get(axis) 582.4 232.4 2.506 0.0125 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1006 on 526 degrees of freedom
## Multiple R-squared: 0.0118, Adjusted R-squared: 0.009924
## F-statistic: 6.282 on 1 and 526 DF, p-value: 0.0125
##
##
## ============================
## Top 50% Group - Block 10 - Preparation Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1568.3 -518.9 -214.6 214.2 11626.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1935.4 308.7 6.270 7.51e-10 ***
## get(axis) -125.7 336.1 -0.374 0.708
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1075 on 526 degrees of freedom
## Multiple R-squared: 0.000266, Adjusted R-squared: -0.001635
## F-statistic: 0.14 on 1 and 526 DF, p-value: 0.7085
##
##
## ============================
## Bottom 50% Group - Block 10 - Preparation Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2491.7 -801.7 -186.2 456.3 8230.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4188.5 241.7 17.332 < 2e-16 ***
## get(axis) -883.2 291.8 -3.027 0.00259 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1288 on 526 degrees of freedom
## Multiple R-squared: 0.01712, Adjusted R-squared: 0.01526
## F-statistic: 9.164 on 1 and 526 DF, p-value: 0.002589# Loop through axes (x, y, z), blocks, and groups for the EXECUTION phase
for (axis in axes) {
for (block in blocks) {
# Filter for each block in execution phase
block_data <- execution_data_grouped_by_speed_and_accuracy %>% filter(Block == block)
# Top 50% group in execution phase
top50_data <- block_data %>% filter(Group == "Top 50%")
run_regression(top50_data, axis, "Execution", block, "Top 50%")
# Bottom 50% group in execution phase
bottom50_data <- block_data %>% filter(Group == "Bottom 50%")
run_regression(bottom50_data, axis, "Execution", block, "Bottom 50%")
}
}##
## ============================
## Top 50% Group - Block 1 - Execution Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2738.5 -1436.1 -642.4 618.7 25250.4
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2015 1331 -1.514 0.131
## get(axis) 5989 1371 4.369 1.5e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2668 on 526 degrees of freedom
## Multiple R-squared: 0.03502, Adjusted R-squared: 0.03318
## F-statistic: 19.09 on 1 and 526 DF, p-value: 1.505e-05
##
##
## ============================
## Bottom 50% Group - Block 1 - Execution Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5292 -2489 -1509 894 75679
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 14222 4379 3.248 0.00124 **
## get(axis) -8357 4445 -1.880 0.06066 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5313 on 526 degrees of freedom
## Multiple R-squared: 0.006674, Adjusted R-squared: 0.004786
## F-statistic: 3.534 on 1 and 526 DF, p-value: 0.06066
##
##
## ============================
## Top 50% Group - Block 9 - Execution Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1449.7 -505.2 -237.1 143.9 21111.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1746.84 467.56 3.736 0.000207 ***
## get(axis) -53.88 510.59 -0.106 0.915998
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1506 on 526 degrees of freedom
## Multiple R-squared: 2.117e-05, Adjusted R-squared: -0.00188
## F-statistic: 0.01114 on 1 and 526 DF, p-value: 0.916
##
##
## ============================
## Bottom 50% Group - Block 9 - Execution Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1747.1 -617.4 -223.2 483.7 6301.7
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -2460.6 779.0 -3.159 0.00168 **
## get(axis) 5213.7 797.7 6.536 1.49e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 973.4 on 526 degrees of freedom
## Multiple R-squared: 0.07512, Adjusted R-squared: 0.07336
## F-statistic: 42.72 on 1 and 526 DF, p-value: 1.495e-10
##
##
## ============================
## Top 50% Group - Block 10 - Execution Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1422.7 -550.5 -200.0 219.5 11459.5
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 84.28 312.32 0.270 0.787
## get(axis) 1948.82 346.68 5.621 3.08e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1044 on 526 degrees of freedom
## Multiple R-squared: 0.05667, Adjusted R-squared: 0.05488
## F-statistic: 31.6 on 1 and 526 DF, p-value: 3.079e-08
##
##
## ============================
## Bottom 50% Group - Block 10 - Execution Phase - hurst_acceleration_x
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2415.0 -801.9 -179.7 500.3 8111.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5019 1209 4.151 3.86e-05 ***
## get(axis) -1571 1230 -1.277 0.202
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1297 on 526 degrees of freedom
## Multiple R-squared: 0.00309, Adjusted R-squared: 0.001195
## F-statistic: 1.63 on 1 and 526 DF, p-value: 0.2022
##
##
## ============================
## Top 50% Group - Block 1 - Execution Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2720.9 -1448.7 -594.7 555.2 25268.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -262.3 988.3 -0.265 0.791
## get(axis) 4210.0 1022.7 4.116 4.47e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2674 on 526 degrees of freedom
## Multiple R-squared: 0.03121, Adjusted R-squared: 0.02937
## F-statistic: 16.94 on 1 and 526 DF, p-value: 4.468e-05
##
##
## ============================
## Bottom 50% Group - Block 1 - Execution Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8256 -2424 -1469 853 75547
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 20062 4193 4.784 2.23e-06 ***
## get(axis) -14262 4247 -3.358 0.000842 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5275 on 526 degrees of freedom
## Multiple R-squared: 0.02099, Adjusted R-squared: 0.01913
## F-statistic: 11.28 on 1 and 526 DF, p-value: 0.0008419
##
##
## ============================
## Top 50% Group - Block 9 - Execution Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1454.4 -501.1 -233.0 142.6 21113.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1795.5 446.1 4.025 6.52e-05 ***
## get(axis) -107.1 484.4 -0.221 0.825
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1506 on 526 degrees of freedom
## Multiple R-squared: 9.29e-05, Adjusted R-squared: -0.001808
## F-statistic: 0.04887 on 1 and 526 DF, p-value: 0.8251
##
##
## ============================
## Bottom 50% Group - Block 9 - Execution Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1829.6 -565.7 -212.8 561.1 6073.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4876 1526 3.195 0.00148 **
## get(axis) -2279 1544 -1.476 0.14051
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1010 on 526 degrees of freedom
## Multiple R-squared: 0.004125, Adjusted R-squared: 0.002232
## F-statistic: 2.179 on 1 and 526 DF, p-value: 0.1405
##
##
## ============================
## Top 50% Group - Block 10 - Execution Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1352.3 -516.2 -202.9 207.5 11509.3
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -48.09 381.42 -0.126 0.9
## get(axis) 2002.97 405.73 4.937 1.07e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1051 on 526 degrees of freedom
## Multiple R-squared: 0.04428, Adjusted R-squared: 0.04246
## F-statistic: 24.37 on 1 and 526 DF, p-value: 1.068e-06
##
##
## ============================
## Bottom 50% Group - Block 10 - Execution Phase - hurst_acceleration_y
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2399.7 -797.8 -179.0 478.2 8108.0
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4773.4 841.4 5.673 2.31e-08 ***
## get(axis) -1323.7 857.1 -1.544 0.123
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1297 on 526 degrees of freedom
## Multiple R-squared: 0.004515, Adjusted R-squared: 0.002622
## F-statistic: 2.385 on 1 and 526 DF, p-value: 0.1231
##
##
## ============================
## Top 50% Group - Block 1 - Execution Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3152.6 -1455.0 -553.8 657.7 25388.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4469.6 4427.8 1.009 0.313
## get(axis) -697.4 4459.4 -0.156 0.876
##
## Residual standard error: 2716 on 526 degrees of freedom
## Multiple R-squared: 4.649e-05, Adjusted R-squared: -0.001855
## F-statistic: 0.02446 on 1 and 526 DF, p-value: 0.8758
##
##
## ============================
## Bottom 50% Group - Block 1 - Execution Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6861 -2439 -1523 852 75956
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 16225 3976 4.081 5.17e-05 ***
## get(axis) -10367 4025 -2.576 0.0103 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 5298 on 526 degrees of freedom
## Multiple R-squared: 0.01246, Adjusted R-squared: 0.01058
## F-statistic: 6.635 on 1 and 526 DF, p-value: 0.01027
##
##
## ============================
## Top 50% Group - Block 9 - Execution Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1371.4 -500.9 -253.4 129.2 21113.2
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 101.1 2529.7 0.040 0.968
## get(axis) 1619.8 2565.1 0.631 0.528
##
## Residual standard error: 1505 on 526 degrees of freedom
## Multiple R-squared: 0.0007575, Adjusted R-squared: -0.001142
## F-statistic: 0.3988 on 1 and 526 DF, p-value: 0.528
##
##
## ============================
## Bottom 50% Group - Block 9 - Execution Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1703.5 -576.2 -205.9 548.2 6068.8
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12559 2147 5.850 8.63e-09 ***
## get(axis) -9999 2160 -4.629 4.63e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 992.2 on 526 degrees of freedom
## Multiple R-squared: 0.03914, Adjusted R-squared: 0.03732
## F-statistic: 21.43 on 1 and 526 DF, p-value: 4.631e-06
##
##
## ============================
## Top 50% Group - Block 10 - Execution Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1478.2 -520.8 -231.0 188.1 11574.6
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3014 2060 -1.463 0.1440
## get(axis) 4900 2087 2.348 0.0192 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1069 on 526 degrees of freedom
## Multiple R-squared: 0.01037, Adjusted R-squared: 0.008492
## F-statistic: 5.513 on 1 and 526 DF, p-value: 0.01924
##
##
## ============================
## Bottom 50% Group - Block 10 - Execution Phase - hurst_acceleration_z
## ============================
##
## Call:
## lm(formula = trial_total_rt ~ get(axis), data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2372.7 -780.0 -169.0 483.1 8048.9
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 12988 2498 5.198 2.88e-07 ***
## get(axis) -9585 2517 -3.808 0.000157 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1282 on 526 degrees of freedom
## Multiple R-squared: 0.02682, Adjusted R-squared: 0.02497
## F-statistic: 14.5 on 1 and 526 DF, p-value: 0.0001569