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SET_analyses_lat.Rmd
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SET_analyses_lat.Rmd
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---
title: "SET"
author: "Maria Eckstein"
date: "January 3, 2015"
output:
pdf_document:
fig_caption: yes
number_sections: yes
toc: yes
html_document:
fig_caption: yes
number_sections: yes
toc: yes
---
```{r, echo = F, results = F, warning = F, message = F}
##################
# #
# USER INPUT #
# #
##################
# TDs
data_year = "2016"
item_presentation_start = c(.1, .3, .5)
item_presentation_end = c(.2, .4, .6)
response_prompt_time = .7
trial_duration = response_prompt_time + 3
analysis_model = "lmer" # choose between 'aov', 'lme', 'lmer', 'hierarchicallmer'
exclude_poor_subjects = T # Should be T
exclude_left_handers = T # Should be T
exclude_saccade_trials = T # Should be T
exclude_fast_trials = F # Should be T
exclude_slow_trials = F # Should be F because RTs are changing so much over the course of the experiment that it makes no sense to exclude all show trials; doing this, that would penalize early trials much more than late trials
analyze_saccade_trials = F # If TRUE, makes a plot of fixation location for each (excluded) saccade trial
preprocess_data = F # If TRUE, reads in raw data (.gazedata format), preprocesses it (removes outliers and interpolates pupil data, bins into 50ms pieces, etc.), and creates and saves an all_files data file that contains the raw data from all subjects
ggsave_figures = T
run_regression_models = T
allfiles_dir = "C:/Users/Maria/MEGAsync/Berkeley/R scripts/sequentialset/"
setwd(allfiles_dir)
figure_dir = paste("C:/Users/Maria/Dropbox/Patricia_Maria_Silvia/latSETPlots", data_year, "/", sep = "")
source("FUNCTIONS/dprime.R"); dprime_cutoff = dprime(hits = 0.6, false.alarms = 0.4)
######################
# #
# PREPARE DATA & #
# LOAD PACKAGES #
# #
######################
### Load packages
library("plyr"); library("zoo"); library("gridExtra"); library("reshape"); library("simpleboot"); library("ggplot2"); library("nlme"); library("lme4"); library("lmerTest"); library("car"); library("predictmeans"); library("boot"); library("robustlmm"); theme_set(theme_bw())
source("FUNCTIONS/median.cl.boot.R"); source("FUNCTIONS/mean.cl.boot.R"); source("FUNCTIONS/bootlme.R")
### Get plot colours
bw_colors = c("#999999", "#000000")
span_colors = colorRampPalette(c("white", "green", "black"))
span_colors = span_colors(9)[5:8]
if (data_year == "2017") {
span_colors = span_colors[c(2, 4)]
}
preprocessing_output_dir = file.path(allfiles_dir, paste("subj_files/data", data_year, sep = ""))
raw_file_dir = file.path(preprocessing_output_dir, "raw_data")
preprocessing_data_dir = file.path(preprocessing_output_dir, "preprocessing_data")
if (!file.exists(preprocessing_data_dir)) {
dir.create(preprocessing_data_dir)
}
FUNCTION_dir = file.path(allfiles_dir, "FUNCTIONS")
### Set plot dimensions
two_by_two = data.frame(width = 3.5, height = 5)
one_by_two = data.frame(width = 3.5, height = 2.5)
one_by_one = data.frame(width = 2, height = 2.3)
pupil_two = data.frame(width = 6, height = 2.5)
pupil_one = data.frame(width = 3.5, height = 2.5)
### Prepare / load data
if (preprocess_data) {
# Read in .gazedata files, filter&interpolate, cut into trials, calculate relative pupil dilation, etc.
source("FUNCTIONS/preprocess_gazedata_files.R")
filter_stats = preprocess_gazedata_files(inputfile_directory = raw_file_dir,
input_filename_pattern = "lateralizedSET_",
inputfile_format = ".gazedata",
m_trial_duration = trial_duration,
output_filename = "latSET",
outputfile_directory = preprocessing_output_dir,
FUNCTION_dir = FUNCTION_dir,
filter_and_interpolate_max_interp = 12,
smooth_times = 3,
relative_pupil_baseline_window = c(0, 0.2),
data_year = data_year,
mark_saccade_trials = T,
save_saccade_trials = T)
write.csv(filter_stats, file.path(preprocessing_data_dir, "filter_stats.csv"), row.names = F)
# Create allfiles, which contains all preprocessed subj_files (write .csv and get as object)
source("FUNCTIONS/create_allfiles.R")
allfiles = create_allfiles(inputfile_directory = preprocessing_output_dir,
outputfile_directory = allfiles_dir,
data_year = data_year)
write.csv(allfiles, file.path(allfiles_dir, paste("preprocessed_allfiles_", data_year, ".csv", sep = "")), row.names = F)
} else {
# Read allfiles from disk
allfiles = read.csv(file.path(allfiles_dir, paste("preprocessed_allfiles_", data_year, ".csv", sep = "")), header = T)
filter_stats = read.csv(file.path(preprocessing_data_dir, "filter_stats.csv"), header = T)
}
allfiles = subset(allfiles, bin_time <= trial_duration)
allfiles$bin_time= round(allfiles$bin_time, 2)
allfiles$set = factor(allfiles$set, levels = c("SET", "noSET"))
allfiles$lateralization = factor(allfiles$lateralization, levels = c("BI", "LL", "RR", "LR", "RL"), labels = c("BI", "RRR", "LLL", "RLR", "LRL")) # Make such that lateralization label determines hemisphere, not presentation side
if (data_year == "2016") {
## Add mouse_hand column
allfiles$mouse_hand = "right"
allfiles$mouse_hand[allfiles$Subject %in% c(seq(406, 442, 4), seq(407, 443, 4), 454)] = "left"
allfiles$mouse_hand[allfiles$Subject == 443] = "right"
## Add program_version column
allfiles$program_version = "A"
allfiles$program_version[allfiles$Subject %in% seq(407, 444, 2)] = "B"
} else if (data_year == "2017") {
## Add block column
allfiles$block = 6
allfiles$block[allfiles$TrialId <= 209] = 5
allfiles$block[allfiles$TrialId <= 167] = 4
allfiles$block[allfiles$TrialId <= 125] = 3
allfiles$block[allfiles$TrialId <= 83] = 2
allfiles$block[allfiles$TrialId <= 41] = 1
## Add mouse_hand column
allfiles$mouse_hand = "left"
allfiles$mouse_hand[allfiles$Subject %% 2 == 0 & allfiles$block %% 2 == 0] = "right" #if Subject id is even, even blocks should be "R"
allfiles$mouse_hand[allfiles$Subject %% 2 == 1 & allfiles$block %% 2 == 1] = "right" #if Subject id is odd, odd blocks should be "R"
## Add program_version column
allfiles$program_version = NA # TD
}
allfiles$mouse_hand = factor(allfiles$mouse_hand)
allfiles$program_version = factor(allfiles$program_version)
```
```{r Trials excluded because of saccades, echo = F, message = F, warning = F}
# Read in saccade data
if (analyze_saccade_trials) {
filenames = list.files(preprocessing_data_dir, pattern = "saccade_trials")
for (filename in filenames) {
subj_file = read.csv(file.path(preprocessing_data_dir, filename))
# Get datafile in the right shape
if (nrow(subj_file) > 1) {
subj_file$Time = with(subj_file, TimestampSec + TimestampMicrosec/1000000)
subj_file$Time = subj_file$Time - subj_file$Time[1]
subj_file_long = melt(subj_file, id.vars = c("Subject", "Time", "TrialId"), measure.vars = c("XGazePosLeftEye", "XGazePosRightEye"), variable_name = "Eye")
# Plot fixation locations for each saccade trial
for (trial in unique(subj_file$TrialId)) {
sacc_trial_data = subset(subj_file_long, TrialId == trial)
gg_saccades = ggplot(sacc_trial_data, aes(Time, value, color = (value > 0.375 & value < 0.625))) +
geom_point() +
ggtitle(paste("Subject ", subj_file_long$Subject[1])) +
theme(legend.position = "none") +
facet_grid(~ Eye)
if (ggsave_figures) {
plot(gg_saccades)
if (!dir.exists(paste(figure_dir, "/saccade_trials", sep = ""))) {
dir.create(paste(figure_dir, "/saccade_trials", sep = ""))
}
ggsave(filename = paste(figure_dir, "/saccade_trials/Subj", sacc_trial_data$Subject[1], "_Trial", sacc_trial_data$TrialId[1], ".png", sep = ""), plot = gg_saccades, width = 5, height = 3)
}
}
}
}
}
```
```{r Initial Results, echo = F, results = F, warning = F, message = F, fig.cap = "Performance of individual subjects"}
## Summarize ALL data (BEFORE excluding trials or participants)
raw_trial_data = ddply(allfiles, .(Subject, TrialId, set, span, lateralization, mouse_hand, program_version), summarize,
ACC = mean(ACC, na.rm = T),
RT = median(RT, na.rm = T))
## ACCs
subject_ACCs = ggplot(raw_trial_data, aes(Subject, ACC, color = span, fill = span)) +
stat_summary(fun.y = "mean", geom = "bar", position = "dodge") +
scale_x_continuous(breaks = seq(min(raw_trial_data$Subject), max(raw_trial_data$Subject))) +
scale_color_manual(values = span_colors) +
scale_fill_manual(values = span_colors) +
facet_wrap(~ set, nrow = 2)
## RTs
subject_RTs = ggplot(subset(raw_trial_data, ACC == 1), aes(Subject, RT, color = span, fill = span)) +
stat_summary(fun.y = "median", geom = "bar", position = "dodge") +
scale_x_continuous(breaks = seq(min(raw_trial_data$Subject), max(raw_trial_data$Subject))) +
scale_color_manual(values = span_colors) +
scale_fill_manual(values = span_colors) +
facet_wrap(~ set, nrow = 2)
if (ggsave_figures) {
ggsave(plot = subject_ACCs, filename = file.path(figure_dir, "01subject_ACCs.png"), width = 20, height = 5)
ggsave(plot = subject_RTs, filename = file.path(figure_dir, "01subject_RTs.png"), width = 20, height = 5)
}
```
```{r Exclude subjects and trials, echo = F, results = F, warning = F, message = F}
### Get performance data for SETs and noSETs for each subject (BEFORE excluding subjects)
# Get subject average accuracies
raw_subject_data = ddply(raw_trial_data, .(Subject, set), summarize,
ACC = mean(ACC, na.rm = T))
raw_subject_data_wide = reshape(subset(raw_subject_data, select = c("Subject", "set", "ACC")),
v.names = "ACC", idvar = "Subject", timevar = "set", direction = "wide")
raw_subject_data_wide$dprime = with(raw_subject_data_wide, dprime(hits = ACC.SET, false.alarms = (1 - ACC.noSET)))
# Get subject accuracies per span
raw_span_data = ddply(raw_trial_data, .(Subject, set, span), summarize,
ACC = mean(ACC, na.rm = T))
raw_span_data_cor = ddply(subset(raw_trial_data, ACC == 1), .(Subject, set, span), summarize,
RT = median(RT, na.rm = T))
raw_span_data_wide = reshape(subset(raw_span_data, select = c("Subject", "set", "span", "ACC")),
v.names = "ACC", idvar = c("Subject", "span"), timevar = "set", direction = "wide")
raw_span_data_wide$dprime = with(raw_span_data_wide, dprime(hits = ACC.SET, false.alarms = (1 - ACC.noSET)))
raw_span_data_wide$Hits_minus_FA = with(raw_span_data_wide, ACC.SET - (1 - ACC.noSET)) # same as d-prime!
raw_span_data_wide$SETACCnoSETACC = with(raw_span_data_wide, ACC.SET - ACC.noSET)
raw_span_data_cor_wide = reshape(subset(raw_span_data_cor, select = c("Subject", "set", "span", "RT")),
v.names = "RT", idvar = c("Subject", "span"), timevar = "set", direction = "wide")
raw_span_data_wide$SETRTnoSETRT = with(raw_span_data_cor_wide, RT.SET - RT.noSET)
# Get chance performers
span_chance_performers = unique(subset(raw_span_data_wide, dprime < dprime_cutoff)$Subject)
overall_chance_performers = unique(subset(raw_subject_data_wide, dprime < dprime_cutoff)$Subject)
chance_performers = unique(c(overall_chance_performers, span_chance_performers))
### Find subjects who are left handers
quest = read.csv(file.path(allfiles_dir, "subj_files/LatSetQuestionnaires.csv"))
quest$Handedness = as.numeric(as.character(quest$Handedness))
left_handers = subset(quest, Handedness < 10)$Sub
### Find extremely slow and fast trials
allfiles$toofast = F
allfiles$toofast[allfiles$RT < 150] = T
allfiles$tooslow = F
filter_stats$fast_trials = 0
filter_stats$slow_trials = 0
for (subj in unique(allfiles$Subject)) {
subj_dat = subset(allfiles, Subject == subj)
RT_median = median(subj_dat$RT, na.rm = T)
RT_sd = sd(subj_dat$RT, na.rm = T)
RT_cutoff = RT_median + 2 * RT_sd
allfiles$tooslow[allfiles$Subject == subj & allfiles$RT > RT_cutoff] = T
filter_stats$slow_trials[filter_stats$Subject == subj] = length(levels(factor(subset(allfiles, Subject == subj & tooslow == T)$TrialId)))
filter_stats$fast_trials[filter_stats$Subject == subj] = length(levels(factor(subset(allfiles, Subject == subj & toofast == T)$TrialId)))
}
### Remove excluded subjects
if (exclude_left_handers) {
allfiles = subset(allfiles, !Subject %in% c(left_handers, 523)) # 523 was "born left-handed but parents trained him to be right-handed"
}
if (exclude_poor_subjects) {
allfiles = allfiles[!allfiles$Subject %in% chance_performers,]
}
# Delete excluded trials
if (exclude_saccade_trials) {
allfiles = subset(allfiles, saccade_trial == F)
}
if (exclude_fast_trials) {
allfiles = subset(allfiles, toofast == F)
}
if (exclude_slow_trials) {
allfiles = subset(allfiles, tooslow == F)
}
allfiles$toofast = allfiles$tooslow = NULL
# How many trials did each S contribute, i.e., how many trials were not excluded?
subject_trials = ggplot(filter_stats, aes(Subject, (saccade_trials + fast_trials + slow_trials) / length(levels(factor(allfiles$TrialId))))) +
stat_summary(fun.y = "mean", geom = "bar", position = "dodge") +
scale_x_continuous(breaks = seq(min(raw_trial_data$Subject), max(raw_trial_data$Subject))) +
labs(y = "% excluded trials")
if (ggsave_figures) {
ggsave(plot = subject_trials, filename = file.path(figure_dir, "01subject_trials.png"), width = 10, height = 2.5)
}
### Summarize data again (AFTER excluding participants & trials)
trial_data = ddply(allfiles, .(Subject, block, TrialId, set, span, lateralization, mouse_hand, program_version), summarize,
ACC = mean(ACC, na.rm = T),
RT = median(RT, na.rm = T))
span_data = ddply(trial_data, .(Subject, set, span), summarize,
ACC = mean(ACC, na.rm = T))
span_data_cor = ddply(subset(trial_data, ACC == 1), .(Subject, set, span), summarize,
RT = median(RT, na.rm = T))
span_data_wide = reshape(subset(span_data, select = c("Subject", "set", "span", "ACC")),
v.names = "ACC", idvar = c("Subject", "span"), timevar = "set", direction = "wide")
source("FUNCTIONS/dprime.R")
span_data_wide$dprime = with(span_data_wide, dprime(hits = ACC.SET, false.alarms = (1 - ACC.noSET)))
span_data_wide$Hits_minus_FA = with(span_data_wide, ACC.SET - (1 - ACC.noSET)) # same as d-prime!
span_data_wide$SETACCnoSETACC = with(span_data_wide, ACC.SET - ACC.noSET)
span_data_cor_wide = reshape(subset(span_data_cor, select = c("Subject", "set", "span", "RT")),
v.names = "RT", idvar = c("Subject", "span"), timevar = "set", direction = "wide")
span_data_wide$SETRTnoSETRT = with(span_data_cor_wide, RT.SET - RT.noSET)
```
Participants performing at chance: `r chance_performers`
Left handers: `r left_handers`
```{r ACC, RT by span, echo = F, results = F, warning = F, message = F, fig.cap = "Effect of span"}
## ACCs
ACCs = ggplot(span_data, aes(span, (1 - ACC) * 100, color = set, group = set)) +
# geom_point(alpha = 0.5, position = "jitter") +
stat_summary(fun.data = "mean_se", geom = "pointrange") +
scale_color_manual(values = bw_colors) +
# scale_x_discrete(labels = c("0sp", "1sp", "2sp", "3sp")) +
coord_cartesian(ylim = c(0, 30)) +
labs(x = "", y = "Errors (%)") +
theme(legend.position = "none") +
facet_grid(~ set)
SETACCnoSETACC = ggplot(span_data_wide, aes(span, SETACCnoSETACC * 100)) +
# geom_point(alpha = 0.5, position = "jitter") +
stat_summary(fun.data = "mean_se", geom = "pointrange") +
theme(legend.position = "none") +
# scale_x_discrete(labels = c("0sp", "1sp", "2sp", "3sp")) +
coord_cartesian(ylim = c(-18, 10)) +
labs(x = "", y = "SET ACC - noSET ACC")
dprime = ggplot(span_data_wide, aes(span, dprime, fill = span)) +
# geom_point(alpha = 0.5, position = "jitter") +
stat_summary(fun.data = "mean_se", geom = "pointrange") +
theme(legend.position = "none") +
# scale_x_discrete(labels = c("0sp", "1sp", "2sp", "3sp")) +
coord_cartesian(ylim = c(2.35, 3.4)) +
labs(x = "", y = "d-prime")
## RTs
RTs = ggplot(span_data_cor, aes(span, RT, color = set)) +
# geom_point(alpha = 0.5, position = "jitter") +
stat_summary(fun.data = "median.cl.boot", fun.args = list(B = 2000, tr = .2, ci = .68), geom = "pointrange") +
scale_color_manual(values = bw_colors) +
# scale_x_discrete(labels = c("0sp", "1sp", "2sp", "3sp")) +
coord_cartesian(ylim = c(0, 800)) +
theme(legend.position = "none") +
labs(x = "", y = "RT (msec)") +
facet_grid(~ set)
SETRTnoSETRT = ggplot(span_data_wide, aes(span, SETRTnoSETRT)) +
# geom_point(alpha = 0.5, position = "jitter") +
stat_summary(fun.data = "median.cl.boot", fun.args = list(B = 2000, tr = .2, ci = .68), geom = "pointrange") +
scale_color_manual(values = bw_colors) +
# scale_x_discrete(labels = c("0sp", "1sp", "2sp", "3sp")) +
coord_cartesian(ylim = c(-130, 130)) +
theme(legend.position = "none") +
labs(x = "", y = "SET RT - noSET RT (msec)")
if (ggsave_figures) {
ggsave(plot = ACCs, filename = file.path(figure_dir, "02ACCs.png"), width = one_by_two$width, height = one_by_two$height)
ggsave(plot = RTs, filename = file.path(figure_dir, "02RTs.png"), width = one_by_two$width, height = one_by_two$height)
ggsave(plot = SETACCnoSETACC, filename = file.path(figure_dir, "03SETACCnoSETACC.png"), width = one_by_one$width, height = one_by_two$height)
ggsave(plot = SETRTnoSETRT, filename = file.path(figure_dir, "03SETRTnoSETRT.png"), width = one_by_one$width, height = one_by_two$height)
ggsave(plot = dprime, filename = file.path(figure_dir, "03dprime.png"), width = one_by_one$width, height = one_by_two$height)
}
```
```{r Performance over blocks}
if (data_year == "2017") {
trial_data$block_trial = NA
trial_data = trial_data[order(trial_data$Subject, trial_data$set, trial_data$span, trial_data$TrialId),] # , trial_data$lateralization
block_trial = 0
block = 1
for (row in 1:nrow(trial_data)) {
if (trial_data$block[row] == block) {
block_trial = block_trial + 1
} else {
block_trial = 1
}
trial_data$block_trial[row] = block_trial
block = trial_data$block[row]
}
gg_block_ACC = ggplot(trial_data, aes(block_trial, (1 - ACC) * 100, color = mouse_hand, group = mouse_hand)) +
stat_summary(fun.data = mean_se, geom = "pointrange") +
stat_summary(fun.data = mean_se, geom = "line") +
labs(x = "Trial", y = "Errors (%)", color = "Mouse hand") +
facet_grid(paste("Block", block) ~ set)
gg_block_RT = gg_block_ACC
gg_block_RT$data = subset(trial_data, ACC == 1)
gg_block_RT = gg_block_RT +
aes(y = RT) +
labs(y = "RT (msec)")
gg_block_ACC2 = ggplot(subset(trial_data, block != 1), aes(2 * round(block_trial/2), (1 - ACC) * 100, color = mouse_hand, group = mouse_hand)) +
stat_summary(fun.data = mean_se, geom = "pointrange") +
stat_summary(fun.data = mean_se, geom = "line") +
labs(x = "Trial", y = "Errors (%)", color = "Mouse hand") +
facet_grid( ~ set)
gg_block_RT2 = gg_block_ACC2
gg_block_RT2$data = subset(gg_block_ACC2$data, ACC == 1)
gg_block_RT2 = gg_block_RT2 +
aes(y = RT) +
labs(y = "RT (msec)")
if (ggsave_figures) {
ggsave(file.path(figure_dir, "03gg_block_ACC.png"), gg_block_ACC)
ggsave(file.path(figure_dir, "03gg_block_RT.png"), gg_block_RT)
ggsave(file.path(figure_dir, "03gg_block_ACC2.png"), gg_block_ACC2, width = 6, height = 3)
ggsave(file.path(figure_dir, "03gg_block_RT2.png"), gg_block_RT2, width = 6, height = 3)
}
}
```
```{r ACC, RT by lateralization - raw, echo = F, results = F, warning = F, message = F, fig.cap = "Effect of lateralization"}
### Prepare data
source("FUNCTIONS/dprime.R")
lat_data = ddply(subset(trial_data, !span %in% c("0span", "2span")),
.(Subject, set, lateralization), summarize,
ACC = mean(ACC, na.rm = T))
lat_data_cor = ddply(subset(trial_data, !span %in% c("0span", "2span") & ACC == 1),
.(Subject, set, lateralization), summarize,
RT = median(RT, na.rm = T))
lat_data_wide = reshape(lat_data,
v.names = "ACC", idvar = c("Subject", "lateralization"), timevar = "set", direction = "wide")
lat_data_wide$dprime = with(lat_data_wide, dprime(ACC.SET, (1 - ACC.noSET)))
lat_data_wide$ACCSETminusnoSET = with(lat_data_wide, ACC.SET - ACC.noSET)
lat_data_cor_wide = reshape(lat_data_cor,
v.names = "RT", idvar = c("Subject", "lateralization"), timevar = "set", direction = "wide")
lat_data_cor_wide$RTSETminusnoSET = with(lat_data_cor_wide, RT.SET - RT.noSET)
### Plot
## ACCs
base_plot = ggplot() +
# geom_point(alpha = 0.5, position = "jitter") +
stat_summary(fun.data = "mean_se", geom = "pointrange")
base_plot$data = lat_data
lat_ACCs = base_plot +
aes(lateralization, (1 - ACC) * 100 ) +
coord_cartesian(ylim = c(0, 24)) +
labs(x = "", y = "Errors (%)") +
facet_grid(~ set)
base_plot$data = lat_data_wide
lat_SETACCnoSETACC = base_plot +
aes(lateralization, ACCSETminusnoSET * 100) +
coord_cartesian(ylim = c(-18, 10)) +
labs(x = "", y = "SET ACC - noSET ACC")
lat_dprime = lat_SETACCnoSETACC +
aes(lateralization, dprime) +
coord_cartesian(ylim = c(2.2, 2.9)) +
labs(x = "", y = "d-prime")
## RTs
base_plot$data = lat_data_cor
lat_RTs = base_plot +
aes(lateralization, RT) +
coord_cartesian(ylim = c(500, 1000)) +
# stat_summary(fun.data = "median.cl.boot", fun.args = list(B = 2000, tr = .2, ci = .68), geom = "pointrange") +
labs(x = "", y = "RT (msec)") +
facet_grid(~ set)
base_plot$data = lat_data_cor_wide
lat_SETRTnoSETRT = base_plot +
aes(lateralization, RTSETminusnoSET) +
coord_cartesian(ylim = c(-50, 350)) +
labs(x = "", y = "SET RT - noSET RT")
gg_ACC_lat_mouse = ggplot(trial_data, aes(lateralization, (1 - ACC) * 100, color = mouse_hand)) +
stat_summary(fun.data = mean_se, geom = "pointrange") +
labs(x = "", y = "Errors (%)", color = "Mouse hand") +
facet_grid(~ set)
gg_RT_lat_mouse = gg_ACC_lat_mouse +
aes(y = RT) +
labs(y = "RT (msec)")
if (ggsave_figures) {
ggsave(plot = lat_ACCs, filename = file.path(figure_dir, "05lat_ACCs.png"), width = one_by_two$width + 1, height = one_by_two$height)
ggsave(plot = lat_RTs, filename = file.path(figure_dir, "05lat_RTs.png"), width = one_by_two$width + 1, height = one_by_two$height)
ggsave(plot = lat_SETACCnoSETACC, filename = file.path(figure_dir, "06lat_SETACCnoSETACC.png"), width = one_by_one$width + 1, height = one_by_one$height)
ggsave(plot = lat_SETRTnoSETRT, filename = file.path(figure_dir, "06lat_SETRTnoSETRT.png"), width = one_by_one$width + 1, height = one_by_one$height)
ggsave(plot = lat_dprime, filename = file.path(figure_dir, "06lat_dprime.png"), width = one_by_one$width + 1, height = one_by_one$height)
ggsave(plot = gg_ACC_lat_mouse, filename = file.path(figure_dir, "05gg_ACC_lat_mouse.png"), width = one_by_two$width + 1, height = one_by_two$height)
ggsave(plot = gg_RT_lat_mouse, filename = file.path(figure_dir, "05gg_RT_lat_mouse.png"), width = one_by_two$width + 1, height = one_by_two$height)
}
```
```{r Effects of span and lateralization, echo = F, results = F, warning = F, message = F, fig.cap = "Effect of lateralization by span"}
### Prepare data
sum_data = ddply(trial_data, .(Subject, set, span, lateralization, mouse_hand), summarize,
ACC = mean(ACC, na.rm = T))
sum_data_cor = ddply(subset(trial_data, ACC == 1), .(Subject, set, span, lateralization, mouse_hand), summarize,
RT = median(RT, na.rm = T))
sum_data_wide = reshape(sum_data, v.names = "ACC", idvar = c("Subject", "mouse_hand", "lateralization", "span"), timevar = "set", direction = "wide")
sum_data_wide$dprime = with(sum_data_wide, dprime(ACC.SET, (1 - ACC.noSET)))
sum_data_wide$ACCSETminusnoSET = with(sum_data_wide, ACC.SET - ACC.noSET)
sum_data_cor_wide = reshape(sum_data_cor, v.names = "RT", idvar = c("Subject", "mouse_hand", "lateralization", "span"), timevar = "set", direction = "wide")
sum_data_cor_wide$RTSETminusnoSET = with(sum_data_cor_wide, RT.SET - RT.noSET)
### Plot
## ACCs
lat_ACCs_big_subj = ggplot(trial_data, aes(span, lateralization, color = factor(ACC), shape = span)) +
# geom_point(position = "jitter") +
facet_grid(Subject ~ set)
ACC_base_plot = ggplot() +
# geom_point(alpha = 0.5, position = "jitter") +
stat_summary(fun.data = "mean_se", geom = "pointrange") +
stat_summary(fun.data = "mean_se", geom = "line") +
scale_color_manual(values = span_colors) +
scale_fill_manual(values = span_colors) +
theme(legend.position = "none") #c(.8, .8))
ACC_base_plot$data = sum_data
lat_ACCs_big = ACC_base_plot +
aes(lateralization, (1 - ACC) * 100, color = span, group = span) +
labs(x = "", y = "Errors (%)", color = "") +
facet_grid( ~ set)
ACC_base_plot$data = sum_data_wide
lat_dprime_big = ACC_base_plot +
aes(lateralization, dprime, color = span, group = span) +
labs(x = "", y = "d-prime", color = "")
lat_SETACCnoSETACC_big = lat_dprime_big +
aes(lateralization, ACCSETminusnoSET * 100, color = span, group = span) +
labs(x = "", y = "SET ACC - noSET ACC")
## RTs
RT_base_plot = ggplot() +
# geom_point(alpha = 0.5, position = "jitter") +
stat_summary(fun.data = "median.cl.boot", fun.args = list(B = 2000, tr = .2, ci = .68), geom = "pointrange") +
stat_summary(fun.data = "median.cl.boot", fun.args = list(B = 2000, tr = .2, ci = .68), geom = "line") +
scale_color_manual(values = span_colors) +
scale_fill_manual(values = span_colors) +
theme(legend.position = "none")
RT_base_plot$data = sum_data_cor
lat_RTs_big = RT_base_plot +
aes(lateralization, RT, color = span, group = span) +
labs(x = "", y = "RT") +
facet_grid(~ set)
RT_base_plot$data = sum_data_cor_wide
lat_SETRTnoSETRT_big = RT_base_plot +
aes(lateralization, RTSETminusnoSET, color = span, group = span) +
labs(x = "", y = "SET RT - noSET RT")
if (ggsave_figures) {
ggsave(plot = lat_ACCs_big_subj, filename = file.path(figure_dir, "07lat_ACCs_big_by_subj.png"), width = one_by_two$width + 1, height = 49)
ggsave(plot = lat_ACCs_big, filename = file.path(figure_dir, "07lat_ACCs_big.png"), width = one_by_two$width + 1, height = one_by_two$height)
ggsave(plot = lat_RTs_big, filename = file.path(figure_dir, "07lat_RTs_big.png"), width = one_by_two$width + 1, height = one_by_two$height)
ggsave(plot = lat_SETACCnoSETACC_big, filename = file.path(figure_dir, "08lat_SETACCnoSETACC_big.png"), width = one_by_one$width + 1, height = one_by_one$height)
ggsave(plot = lat_SETRTnoSETRT_big, filename = file.path(figure_dir, "08lat_SETRTnoSETRT_big.png"), width = one_by_one$width + 1, height = one_by_one$height)
ggsave(plot = lat_dprime_big, filename = file.path(figure_dir, "08lat_dprime_big.png"), width = one_by_one$width + 1, height = one_by_one$height)
}
```
```{r Regression models, echo = F, warning = F, message = F}
# Prepare data
sum_data_reg = merge(sum_data, sum_data_cor) # Get ACC & RT in the same dataframe
## Check assumptions
### Normality
hist(sum_data_reg$ACC) # ACC is very bad
hist(sum_data_reg$RT); qplot(sample = sum_data_reg$RT, stat = 'qq') # RT can be transformed
shapiro.test(sum_data_reg$RT)
sum_data_reg$log_RT = log(sum_data_reg$RT)
hist(sum_data_reg$log_RT); qplot(sample = sum_data_reg$log_RT, stat = 'qq')
shapiro.test(sum_data_reg$log_RT)
### Homogeneity of variances
ggplot(sum_data_reg, aes(span, log_RT)) + stat_summary(fun.y = var, geom = "point") + facet_grid(set ~ lateralization) # RTs are ok
condition = factor(paste(sum_data_reg$set, sum_data_reg$span, sum_data_reg$lateralization, sep = "."))
bartlett.test(sum_data_reg$log_RT ~ condition); leveneTest(sum_data_reg$log_RT, condition, center = median)
ggplot(sum_data_reg, aes(span, ACC)) + stat_summary(fun.y = var, geom = "point") + facet_grid(set ~ lateralization) # ACCs are NOT ok
bartlett.test(sum_data_reg$ACC ~ condition); leveneTest(sum_data_reg$ACC, condition, center = median)
### Outliers
ggplot(sum_data_reg, aes(span, log_RT)) + facet_grid(set ~ lateralization) + geom_boxplot() # More RT outliers in SET than noSET
sum_data_reg$log_RT_z = (sum_data_reg$log_RT - mean(sum_data_reg$log_RT, na.rm = T)) / sd(sum_data_reg$log_RT, na.rm = T)
ggplot(sum_data_reg, aes(log_RT_z)) + geom_histogram(aes(y = ..density..)) + geom_vline(xintercept = c(-2.58, -1.96, 0, 1.96, 2.58)) # More too fast trials than too slow
ggplot(sum_data_reg, aes(span, ACC)) + facet_grid(set ~ lateralization) + geom_boxplot()
sum_data_reg$ACC_z = (sum_data_reg$ACC - mean(sum_data_reg$ACC, na.rm = T)) / sd(sum_data_reg$ACC, na.rm = T) # ACCs look just horrible
ggplot(sum_data_reg, aes(ACC_z)) + geom_histogram(aes(y = ..density..)) + geom_vline(xintercept = c(-2.58, -1.96, 0, 1.96, 2.58)) # Horrible
# FAZIT: RTs are fine after log transform, regression models should work; ACCs are really bad, should use robust test
## Get data ready for regression
### Balanced design?
with(sum_data_reg, table(set, span, lateralization)) # Some cells have fewer values than others => Problem!
with(sum_data_reg, table(Subject, span)) # Subjects 428, 429, 432 have missing data
incomplete_subj = c(428, 429, 432)
sum_data_reg = subset(sum_data_reg, !Subject %in% incomplete_subj) # Remove these subjects
with(sum_data_reg, table(set, span, lateralization)) # Check that all cells have the same number now
### Code factors and define effect contrasts (sum up to 1)
sum_data_reg$Subject = factor(sum_data_reg$Subject)
sum_data_reg$span = factor(sum_data_reg$span)
sum_data_reg$mouse_hand = factor(sum_data_reg$mouse_hand)
sum_data_reg$across = NA # Break the 4 lateralization conditions into 2 independent factors (across & L_dom)
sum_data_reg$across[sum_data_reg$lateralization %in% c("RLR", "LRL")] = "across"
sum_data_reg$across[sum_data_reg$lateralization %in% c("LLL", "RRR")] = "within"
sum_data_reg$across = factor(sum_data_reg$across)
sum_data_reg$L_dom = NA
sum_data_reg$L_dom[sum_data_reg$lateralization %in% c("LLL", "LRL")] = "R_dom"
sum_data_reg$L_dom[sum_data_reg$lateralization %in% c("RRR", "RLR")] = "L_dom"
sum_data_reg$L_dom = factor(sum_data_reg$L_dom)
if (run_regression_models) {
BIvRRR = c(-1, 1, 0, 0, 0)
BIvLLL = c(-1, 0, 1, 0, 0)
BIvRLR = c(-1, 0, 0, 1, 0)
BIvLRL = c(-1, 0, 0, 0, 1)
BIvALL = c(-4, 1, 1, 1, 1)
across = c( 0,-1,-1, 1, 1)
LLLvRRR= c( 0,-1, 1, 0, 0)
LRLvRLR= c( 0, 0, 0,-1, 1)
l_dom = c( 0,-1, 1,-1, 1)
RRRvRLR= c( 0,-1, 0, 1, 0)
LLLvLRL= c( 0, 0,-1, 0, 1)
# contrasts(sum_data_reg$lateralization) = cbind(BIvRRR, BIvLLL, BIvRLR, BIvLRL)
# contrasts(sum_data_reg$lateralization) = cbind(BIvALL, across, LLLvRRR, LRLvRLR)
contrasts(sum_data_reg$lateralization) = cbind(BIvALL, l_dom, LLLvLRL, RRRvRLR)
contrasts(sum_data_reg$set) = contr.sum(2)
contrasts(sum_data_reg$span) = contr.poly(ifelse(data_year == "2016", 4, 2))
contrasts(sum_data_reg$across) = contr.sum(2)
contrasts(sum_data_reg$L_dom) = contr.sum(2)
contrasts(sum_data_reg$mouse_hand) = contr.sum(2)
trial_data$log_RT = log(trial_data$RT)
hist(trial_data$RT)
hist(trial_data$log_RT)
trial_data$across = NA
trial_data$across[trial_data$lateralization %in% c("RLR", "LRL")] = "across"
trial_data$across[trial_data$lateralization %in% c("LLL", "RRR")] = "within"
trial_data$across = factor(trial_data$across)
trial_data$L_dom = NA
trial_data$L_dom[trial_data$lateralization %in% c("LLL", "LRL")] = "L_dom"
trial_data$L_dom[trial_data$lateralization %in% c("RRR", "RLR")] = "R_dom"
trial_data$L_dom = factor(trial_data$L_dom)
trial_data$mouse_hand = factor(trial_data$mouse_hand)
contrasts(trial_data$lateralization) = cbind(BIvALL, l_dom, LLLvLRL, RRRvRLR)
contrasts(trial_data$set) = contr.sum(2)
contrasts(trial_data$span) = contr.poly(ifelse(data_year == "2016", 4, 2))
contrasts(trial_data$across) = contr.sum(2)
contrasts(trial_data$L_dom) = contr.sum(2)
contrasts(trial_data$mouse_hand) = contr.sum(2)
if (data_year == "2016") {
contrasts(trial_data$program_version) = contr.sum(2)
}
### Regression formulas
if (data_year == "2016") {
lmer_com_formula = "~ (set + span + L_dom + across + mouse_hand) ^ 2 + ((set + span + L_dom + across) ^ 2 | Subject)" # interactions up to third order
lmer_SET_formula = "~ (span + L_dom + across + mouse_hand) ^ 2 + ((span + L_dom + across) ^ 2 | Subject)"
} else if (data_year == "2017") {
lmer_com_formula = "~ (set + span + L_dom + across + mouse_hand) ^ 2 + ((set + span + L_dom + across + mouse_hand) ^ 2 | Subject)" # interactions up to third order
lmer_SET_formula = "~ (span + L_dom + across + mouse_hand) ^ 2 + ((span + L_dom + across + mouse_hand) ^ 2 | Subject)"
}
lmer_com_RT = paste("RT", lmer_com_formula)
lmer_SET_RT = paste("RT", lmer_SET_formula)
lmer_com_ACC = paste("ACC", lmer_com_formula)
lmer_SET_ACC = paste("ACC", lmer_SET_formula)
### RT models
lmer_complete = lmer(as.formula(lmer_com_RT), data = subset(trial_data, ACC == 1), REML = F, na.action = na.omit, control = lmerControl(optCtrl = list(maxfun = 1e6)), verbose = T)
write.csv(as.data.frame(summary(lmer_complete)$coef), paste(figure_dir, "/RT_lmer_complete_summary.csv", sep = ""))
write.csv(as.data.frame(anova(lmer_complete, type = 3)), paste(figure_dir, "/RT_lmer_complete_aov.csv", sep = ""))
lmer_SET = lmer(as.formula(lmer_SET_RT), data = subset(trial_data, set == "SET" & ACC == 1), REML = F, na.action = na.omit, control = lmerControl(optCtrl = list(maxfun = 1e6)), verbose = T)
write.csv(as.data.frame(summary(lmer_SET)$coef), paste(figure_dir, "/RT_lmer_SET_summary.csv", sep = ""))
write.csv(as.data.frame(anova(lmer_SET, type = 3)), paste(figure_dir, "/RT_lmer_SET_aov.csv", sep = ""))
# rlmer_SET = rlmer(as.formula(lmer_SET_RT), data = subset(trial_data, set == "SET" & ACC == 1), REML = F, na.action = na.omit, control = lmerControl(optCtrl = list(maxfun = 1e6)), verbose = T)
# anova(rlmer_SET, type = 3)
lmer_noSET = update(lmer_SET, data = subset(trial_data, set == "noSET" & ACC == 1)) #lmer(as.formula(lmer_SET_RT), data = subset(trial_data, set == "noSET" & ACC == 1), REML = F, na.action = na.omit, control = lmerControl(optCtrl = list(maxfun = 1e6)), verbose = T)
write.csv(as.data.frame(summary(lmer_noSET)$coef), paste(figure_dir, "/RT_lmer_noSET_summary.csv", sep = ""))
write.csv(as.data.frame(anova(lmer_noSET, type = 3)), paste(figure_dir, "/RT_lmer_noSET_aov.csv", sep = ""))
### ACC models
glmer_complete = glmer(as.formula(lmer_com_ACC), data = trial_data, family = "binomial", verbose = T, control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e6)))
# Anova(glmer_complete, type = "III")
write.csv(as.data.frame(summary(glmer_complete)$coef), paste(figure_dir, "/ACC_glmer_complete_summary.csv", sep = ""))
write.csv(as.data.frame(anova(glmer_complete, type = 3)), paste(figure_dir, "/ACC_glmer_complete_aov.csv", sep = ""))
glmer_SET = glmer(as.formula(lmer_SET_ACC), data = subset(trial_data, set == "SET"), family = "binomial", verbose = T, control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e6)))
write.csv(as.data.frame(summary(glmer_SET)$coef), paste(figure_dir, "/ACC_glmer_SET_summary.csv", sep = ""))
write.csv(as.data.frame(anova(glmer_SET, type = 3)), paste(figure_dir, "/ACC_glmer_SET_aov.csv", sep = ""))
glmer_noSET = update(glmer_SET, data = subset(trial_data, set == "noSET")) #glmer(as.formula(lmer_SET_ACC), data = subset(trial_data, set == "noSET"), family = "binomial", verbose = T, control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun = 1e6)))
write.csv(as.data.frame(summary(glmer_noSET)$coef), paste(figure_dir, "/ACC_glmer_noSET_summary.csv", sep = ""))
write.csv(as.data.frame(anova(glmer_noSET, type = 3)), paste(figure_dir, "/ACC_glmer_noSET_aov.csv", sep = ""))
# Plot results (regr_data_full)
gg_regr_full = ggplot(regr_data_full, aes(measure, chi2, fill = effect, alpha = p < 0.05)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
labs(x = "", y = "Regression chi^2", fill = "") +
facet_grid(~ set)
regr_data_select$effect = factor(regr_data_select$effect, levels = levels(regr_data_select$effect)[c(2,5,1,4,3)])
gg_regr_select = ggplot(regr_data_select, aes(measure, chi2, fill = effect, alpha = p < 0.05)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
labs(x = "", y = "Regression chi^2", fill = "") +
scale_fill_manual(values = c("steelblue4", "steelblue1", "coral4", "coral1", "mediumpurple")) +
facet_grid(~ set)
## SETACCnoSETACC & dprime
# Prepare data frame to save the regression results
regr_data_select2 = expand.grid(
effect = c("left_dominant", "across", "L_dom*across", "L_dom*span", "across*span"),
measure = c("ACCSETminusnoSET", "RTSETminusnoSET", "dprime"),
chi2 = NA,
p = NA)
regr_data_full2 = expand.grid(
effect = c("left_dominant", "across", "mouse_hand", "L_dom*hand", "L_dom*span", "L_dom*across", "across*span", "across*hand"),
measure = c("ACCSETminusnoSET", "RTSETminusnoSET", "dprime"),
chi2 = NA,
p = NA)
}
# Prepare data
sum_data_wide_reg = merge(subset(sum_data_wide, lateralization != "BI"),# & span != "0span"
subset(sum_data_cor_wide, lateralization != "BI"))# & span != "0span"
# hist(sum_data_wide_reg$ACCSETminusnoSET)
# hist(sum_data_wide_reg$RTSETminusnoSET)
# hist(sum_data_wide_reg$dprime)
sum_data_wide_reg$across = F
sum_data_wide_reg$across[sum_data_wide_reg$lateralization %in% c("RLR", "LRL")] = T
sum_data_wide_reg$across = factor(sum_data_wide_reg$across)
sum_data_wide_reg$L_dom = F
sum_data_wide_reg$L_dom[sum_data_wide_reg$lateralization %in% c("LLL", "LRL")] = T
sum_data_wide_reg$L_dom = factor(sum_data_wide_reg$L_dom)
# sum_data_wide_reg$span = factor(sum_data_wide_reg$span)
contrasts(sum_data_wide_reg$span) = contr.poly(4)
sum_data_wide_reg$mouse_hand = factor(sum_data_wide_reg$mouse_hand)
# Run Models
if (run_regression_models) {
for (measure in c("ACCSETminusnoSET", "RTSETminusnoSET", "dprime")) {
regr_formula = paste(measure, " ~ 1 + (1|Subject) + (1|span)")
baseline_mod =
lmer(as.formula(regr_formula), data = sum_data_wide_reg, REML = F, na.action = na.omit)
# Reduced model
span_mod =
update(baseline_mod, .~. + span)
Ldom_mod =
update(baseline_mod, .~. + span + L_dom)
across_mod =
update(baseline_mod, .~. + span + L_dom + across)
Ldom_x_across_mod =
update(baseline_mod, .~. + span + L_dom + across + L_dom:across)
Ldom_x_span_mod =
update(baseline_mod, .~. + span + L_dom + across + L_dom:across + L_dom:span)
across_x_span_mod =
update(baseline_mod, .~. + span + L_dom + across + L_dom:across + L_dom:span + across:span)
ANOVA = anova(baseline_mod, span_mod, Ldom_mod, across_mod, Ldom_x_across_mod, Ldom_x_span_mod, across_x_span_mod)
coefs = data.frame(coef(summary(across_x_span_mod)))
coefs$p = round(2 * (1 - pnorm(abs(coefs$t.value))), 3)
# Add results to data frame
regr_data_select2[regr_data_select2$measure == measure, "chi2"] = ANOVA$Chisq[3:length(ANOVA$Chisq)]
regr_data_select2[regr_data_select2$measure == measure, "p"] = ANOVA$`Pr(>Chisq)`[3:length(ANOVA$Chisq)]
# Complete model
span_mod =
update(baseline_mod, .~. + span)
Ldom_mod =
update(baseline_mod, .~. + span + L_dom)
# summary(Ldom_mod)
across_mod =
update(baseline_mod, .~. + span + L_dom + across)
hand_mod =
update(baseline_mod, .~. + span + L_dom + across + mouse_hand)
Ldom_x_hand_mod =
update(baseline_mod, .~. + span + L_dom + across + mouse_hand + L_dom:mouse_hand)
Ldom_x_span_mod =
update(baseline_mod, .~. + span + L_dom + across + mouse_hand + L_dom:mouse_hand + L_dom:span)
Ldom_x_across_mod =
update(baseline_mod, .~. + span + L_dom + across + mouse_hand + L_dom:mouse_hand + L_dom:span + L_dom:across)
across_x_span_mod =
update(baseline_mod, .~. + span + L_dom + across + mouse_hand + L_dom:mouse_hand + L_dom:span + L_dom:across + across:span)
across_x_hand_mod =
update(baseline_mod, .~. + span + L_dom + across + mouse_hand + L_dom:mouse_hand + L_dom:span + L_dom:across + across:span + across:mouse_hand)
ANOVA = anova(baseline_mod, span_mod, Ldom_mod, across_mod, hand_mod, Ldom_x_hand_mod, Ldom_x_span_mod, Ldom_x_across_mod, across_x_span_mod, across_x_hand_mod)
# Add results to data frame
regr_data_full2[regr_data_full2$measure == measure, "chi2"] = ANOVA$Chisq[3:10]
regr_data_full2[regr_data_full2$measure == measure, "p"] = ANOVA$`Pr(>Chisq)`[3:10]
}
}
# Plot results (regr_data_full2)
gg_regr_full2 = ggplot(subset(regr_data_full2), aes(measure, chi2, fill = effect, alpha = p < 0.05)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
labs(x = "", y = "Regression chi^2", fill = "")
regr_data_select2$effect = factor(regr_data_select2$effect, levels = levels(regr_data_select2$effect)[c(2,5,1,4,3)])
gg_regr_select2 = ggplot(regr_data_select2, aes(measure, chi2, fill = effect, alpha = p < 0.05)) +
geom_bar(stat = "identity", position = position_dodge(width = 0.9)) +
scale_fill_manual(values = c("steelblue4", "steelblue1", "coral4", "coral1", "mediumpurple")) +
labs(x = "", y = "Regression chi^2", fill = "")
## t-tests RRR vs LLL
# ACC ~ SnS ~ span (RRR vs LLL)
for (spani in levels(factor(sum_data_wide_reg$span))) {
dat = subset(sum_data_wide_reg, lateralization %in% c("LLL", "RRR") & span == spani)
print(t.test(ACC.SET ~ lateralization, data=dat), paired = T)
}
# RT ~ SnS (RRR vs LLL)
for (spani in levels(factor(sum_data_wide_reg$span))) {
dat = subset(sum_data_wide_reg, lateralization %in% c("LLL", "RRR") & span == spani)
print(t.test(RT.SET ~ lateralization, data=dat), paired = T)
}
# bias ~ SnS (RRR vs LLL)
for (spani in levels(factor(sum_data_wide_reg$span))) {
dat = subset(sum_data_wide_reg, lateralization %in% c("LLL", "RRR") & span == spani)
print(t.test(ACCSETminusnoSET ~ lateralization, data=dat), paired = T)
}
if (ggsave_figures) {
# Print to pdf (when knitting)
plot(gg_regr_full)
plot(gg_regr_full2)
plot(gg_regr_select)
plot(gg_regr_select2)
# Safe into folder
ggsave(plot = gg_regr_full, filename = file.path(figure_dir, "12gg_regr_full.png"), width = one_by_two$width + 1, height = one_by_two$height)
ggsave(plot = gg_regr_full2, filename = file.path(figure_dir, "12gg_regr_full2.png"), width = one_by_two$width + 1, height = one_by_two$height)
ggsave(plot = gg_regr_select, filename = file.path(figure_dir, "12gg_regr_select.png"), width = one_by_two$width + 1, height = one_by_two$height)
ggsave(plot = gg_regr_select2, filename = file.path(figure_dir, "12gg_regr_select2.png"), width = one_by_two$width + 1, height = one_by_two$height)
}
```
```{r ACC, RT by lateralization - relative to BI condition (within-subjects), echo = F, warning = F, message = F}
### Prepare data
# Subtract ACC of lateralized trials (LL, RR, RL, LR) from ACC of BI trials
sum_data_wide_l = reshape(data = sum_data, direction = "wide", timevar = "lateralization", idvar = c("Subject", "set", "span", "mouse_hand"))
sum_data_wide_l$LLL = with(sum_data_wide_l, ACC.LLL - ACC.BI)
sum_data_wide_l$RRR = with(sum_data_wide_l, ACC.RRR - ACC.BI)
sum_data_wide_l$LRL = with(sum_data_wide_l, ACC.LRL - ACC.BI)
sum_data_wide_l$RLR = with(sum_data_wide_l, ACC.RLR - ACC.BI)
sum_data_l = melt(sum_data_wide_l, id.vars = c("Subject", "set", "span", "mouse_hand"), measure.vars = c("RRR", "LLL", "RLR", "LRL"), variable_name = "lateralization")
colnames(sum_data_l)[colnames(sum_data_l) == "value"] = "relACC"
# Subtract dprime of lateralized trials from BI trials
sum_data_wide_l2 = reshape(subset(sum_data_wide, select = -c(ACC.SET, ACC.noSET)), direction = "wide", timevar = "lateralization", idvar = c("Subject", "span", "mouse_hand"))
sum_data_wide_l2$LLL = with(sum_data_wide_l2, dprime.LLL - dprime.BI)
sum_data_wide_l2$RRR = with(sum_data_wide_l2, dprime.RRR - dprime.BI)
sum_data_wide_l2$RLR = with(sum_data_wide_l2, dprime.RLR - dprime.BI)
sum_data_wide_l2$LRL = with(sum_data_wide_l2, dprime.LRL - dprime.BI)
sum_data_l2a = melt(sum_data_wide_l2, id.vars = c("Subject", "span", "mouse_hand"), measure.vars = c("RRR", "LLL", "RLR", "LRL"), variable_name = "lateralization")
colnames(sum_data_l2a)[colnames(sum_data_l2a) == "value"] = "reldprime"
# Subtract difference between SET and noSET ACC (ACCSETminusnoSET) of lateralized trials from BI trials
sum_data_wide_l2$LLL = with(sum_data_wide_l2, ACCSETminusnoSET.LLL - ACCSETminusnoSET.BI)
sum_data_wide_l2$RRR = with(sum_data_wide_l2, ACCSETminusnoSET.RRR - ACCSETminusnoSET.BI)
sum_data_wide_l2$RLR = with(sum_data_wide_l2, ACCSETminusnoSET.RLR - ACCSETminusnoSET.BI)
sum_data_wide_l2$LRL = with(sum_data_wide_l2, ACCSETminusnoSET.LRL - ACCSETminusnoSET.BI)
sum_data_l2b = melt(sum_data_wide_l2, id.vars = c("Subject", "span", "mouse_hand"), measure.vars = c("RRR", "LLL", "RLR", "LRL"), variable_name = "lateralization")
colnames(sum_data_l2b)[colnames(sum_data_l2b) == "value"] = "relSnS"
sum_data_l2_ACC = merge(sum_data_l2a, sum_data_l2b)
# Subtract RT of lateralized trials from BI trials
sum_data_cor_wide_l = reshape(data = sum_data_cor, direction = "wide", timevar = "lateralization", idvar = c("Subject", "set", "span", "mouse_hand"))
sum_data_cor_wide_l$LLL = with(sum_data_cor_wide_l, RT.LLL - RT.BI)
sum_data_cor_wide_l$RRR = with(sum_data_cor_wide_l, RT.RRR - RT.BI)
sum_data_cor_wide_l$LRL = with(sum_data_cor_wide_l, RT.LRL - RT.BI)
sum_data_cor_wide_l$RLR = with(sum_data_cor_wide_l, RT.RLR - RT.BI)
sum_data_cor_l = melt(sum_data_cor_wide_l, id.vars = c("Subject", "set", "span", "mouse_hand"), measure.vars = c("RRR", "LLL", "RLR", "LRL"), variable_name = "lateralization")
colnames(sum_data_cor_l)[colnames(sum_data_cor_l) == "value"] = "relRT"
# Subtract difference between SET and noSET RT (RTSETminusnoSET) of lateralized trials from BI trials
sum_data_cor_wide_l2 = reshape(subset(sum_data_cor_wide, select = -c(RT.SET, RT.noSET)), direction = "wide", timevar = "lateralization", idvar = c("Subject", "span", "mouse_hand"))
sum_data_cor_wide_l2$LLL = with(sum_data_cor_wide_l2, RTSETminusnoSET.LLL - RTSETminusnoSET.BI)
sum_data_cor_wide_l2$RRR = with(sum_data_cor_wide_l2, RTSETminusnoSET.RRR - RTSETminusnoSET.BI)
sum_data_cor_wide_l2$RLR = with(sum_data_cor_wide_l2, RTSETminusnoSET.RLR - RTSETminusnoSET.BI)
sum_data_cor_wide_l2$LRL = with(sum_data_cor_wide_l2, RTSETminusnoSET.LRL - RTSETminusnoSET.BI)
sum_data_l2_RT = melt(sum_data_cor_wide_l2, id.vars = c("Subject", "span", "mouse_hand"), measure.vars = c("RRR", "LLL", "RLR", "LRL"), variable_name = "lateralization")
colnames(sum_data_l2_RT)[colnames(sum_data_l2_RT) == "value"] = "relSnS"
### Plots
# ACC by lateralization
base_plot = ggplot() +
stat_summary(fun.data = "mean_se", geom = "line") +
stat_summary(fun.data = "mean_se", geom = "pointrange")
gg_rel_ACC = base_plot
gg_rel_ACC$data = sum_data_l
gg_rel_ACC = gg_rel_ACC +
aes(lateralization, -relACC, group = 1) +
labs(x = "", y = "errors (LAT - BI)") +
facet_grid(~ set)
# gg_rel_ACC = ggplot(sum_data_l, aes(lateralization, -relACC, group = 1)) +
# stat_summary(fun.data = "mean_se", geom = "line") +
# stat_summary(fun.data = "mean_se", geom = "pointrange") +
# labs(x = "", y = "errors (LAT - BI)") +
# facet_grid(~ set)
gg_rel_ACC_span = gg_rel_ACC + aes(color = span, group = span) +
scale_color_manual(values = span_colors) +
theme(legend.position = "none")
# RT by lateralization
gg_rel_RT = ggplot(sum_data_cor_l, aes(lateralization, relRT, group = 1)) +
stat_summary(fun.data = "mean_se", geom = "line") +
stat_summary(fun.data = "mean_se", geom = "pointrange") +
labs(x = "", y = "RT (LAT - BI)", color = "") +
facet_grid(~ set)
gg_rel_RT_span = gg_rel_RT + aes(color = span, group = span) +
scale_color_manual(values = span_colors) +
theme(legend.position = "none")
# dprime by lateralization
gg_rel_dprime = ggplot(sum_data_l2_ACC, aes(lateralization, reldprime, group = 1)) +
stat_summary(fun.data = "mean_se", geom = "line") +
stat_summary(fun.data = "mean_se", geom = "pointrange") +
coord_cartesian(ylim = c(-0.55, 0.25)) +
labs(x = "", y = "dprime (LAT - BI)")
gg_rel_dprime_span = gg_rel_dprime + aes(color = span, group = span) +
scale_color_manual(values = span_colors) +
theme(legend.position = "none")
# SET.ACC-noSET.ACC by lateralization
gg_rel_SnS_ACC = ggplot(sum_data_l2_ACC, aes(lateralization, relSnS, group = 1)) +
stat_summary(fun.data = "mean_se", geom = "line") +
stat_summary(fun.data = "mean_se", geom = "pointrange") +
labs(x = "", y = "SETACC - noSETACC (LAT - BI)")
gg_rel_SnS_ACC_span = gg_rel_SnS_ACC + aes(color = span, group = span) +
scale_color_manual(values = span_colors) +
theme(legend.position = "none")
# SET.RT-noSET.RT by lateralization
gg_rel_SnS_RT = ggplot(sum_data_l2_RT, aes(lateralization, relSnS, group = 1)) +
stat_summary(fun.data = "mean_se", geom = "line") +
stat_summary(fun.data = "mean_se", geom = "pointrange") +
labs(x = "", y = "SETRT - noSETRT (LAT - BI)")
gg_rel_SnS_RT_span = gg_rel_SnS_RT + aes(color = span, group = span) +
scale_color_manual(values = span_colors) +
theme(legend.position = "none")
### Save plots
if (ggsave_figures) {
# Print to pdf (when knitting)