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P203_DataAnalysis.Rmd
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P203_DataAnalysis.Rmd
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---
title: "P203 Subjective Perception and ERP Amplitude (the N170)"
author: "[Haiyang Jin](https://haiyangjin.github.io/)"
date: "`r format(Sys.time(), '%d-%m-%Y')`"
output:
html_document:
code_folding: hide
df_print: paged
number_sections: true
toc: true
toc_depth: 4
toc_float:
collapsed: true
smooth_scroll: false
---
<style>
pre {
overflow-x: auto;
}
pre code {
word-wrap: normal;
white-space: pre;
}
</style>
```{r global_options, echo = FALSE, include = FALSE}
options(width = 1500)
knitr::opts_chunk$set(echo = TRUE, warning = FALSE, message = FALSE,
include = TRUE, cache = FALSE, tidy = FALSE,
size = "big", fig.width=8, fig.asp=0.7)
xaringanExtra::use_clipboard()
```
# General introduction
## Introduction
**Linear Mixed Model** were used.
## Experiment design
* **Independent variables**:
+ Stimulus Type (`Type`: *intact* vs. *scrambled*)
+ Stimulus Category (`Category`: *faces* vs. *houses*)
+ Hemisphere (`Hemisphere`: *left* vs. *right*)
+ Duration (`Duration`: *33*[*17*] vs. *216*[*200*])
## Preparations
```{r setup, message=FALSE}
## load libraries
library(tidyverse)
library(magrittr)
library(lme4)
library(lmerTest)
library(emmeans)
# options for calculating estimated marginal means
# lmer.df = c("kenward-roger", "satterthwaite", "asymptotic")
emm_options(lmer.df = "satterthwaite",
lmerTest.limit = 1e6,
opt.digits = FALSE) # , pbkrtest.limit =, disable.pbkrtest = FALSE
```
```{r foldes and load R files}
set.seed(42)
# folders
folder_R <- "R"
folder_data <- "data"
folder_nesi <- "NeSI"
folder_bmm <- "bmmfit"
# load all the R files
tmp <- sapply(list.files(folder_R, "*.R", full.names = TRUE, recursive = TRUE), source)
```
```{r settings}
# general setting
saveCSV <- FALSE
ratio_count <- 0.8
RT_min <- 200 # minimum response times
# set up the theme for plot and rainclound plot
plot_theme <- {
general_theme +
theme(legend.position = "right")
}
erp_theme <- {
general_theme +
theme(legend.position = "bottom")
}
ylimit_p1 <- c(2.5, -0.5)
ylimit_n170 <- c(1.5, -5.5) # y axis for plotting N170 amplitude
resp_labels = c("sure faces", "not sure faces", "guessing", "not sure houses", "sure houses")
```
# Hypotheses
Previous studies showed that the N170 amplitudes evoked by faces preseneted for longer durations were larger:
```{r fig.height=6, fig.width=4}
Duration <- c("17", "200")
Amplitudes <- c(-2.5, -5)
Hypotheses <- rep("means", 2)
df_lit <- tibble(Duration, Amplitudes, Hypotheses)
plot_lit <- {
ggplot(data = df_lit, aes(y = Amplitudes, x = Duration, fill = Duration)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(color = "black") +
facet_grid(. ~ Hypotheses) +
scale_fill_manual(values = c(gray(.7), gray(.3))) +
scale_y_reverse(limits =c(0, -5.5), breaks = seq(0, -5, -1), expand= c(0, 0)) + # set the limit for y axis
geom_hline(yintercept = -2.5, linetype = "dashed", color = "red", size = 1) +
labs(x = "", y = expression(paste("Amplitudes (", mu, "V)"))) + # set the names for main, x and y axises , title = "Means",
# theme_bw() +
general_theme +
theme( # strip.text = element_text(color = "white"),
legend.position = "none",
axis.line = element_line(size = 3, colour = "black"))
}
plot_lit
```
There are two possible explainations for the aggregated mean amplitudes:
```{r}
Duration <- rep(c(rep("17", 5), rep("200", 5) ), 2)
Amplitudes <- c(c(rep(-2.5, 5), rep(-5, 5)), c(rep(-.83, 3), rep(-5, 7)))
TrialNum <- rep(as.character(rep(c(1:5), 2)), 2)
Hypotheses <- c(rep("Possibility 1", 10), rep("Possibility 2", 10))
df_st <- tibble(Duration, Amplitudes, TrialNum, Hypotheses)
plot_st <- {
ggplot(data = df_st, aes(y = Amplitudes, x = Duration, fill = Duration, color = TrialNum)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(position = "dodge") +
scale_fill_manual(values = c(gray(.7), gray(.3))) +
scale_color_manual(values = rep("black", 5)) +
facet_grid(. ~ Hypotheses) +
geom_hline(yintercept = -2.5, linetype = "dashed", color = "red", size = 1) +
scale_y_reverse(limits =c(0, -5.5), breaks = seq(0, -5, -1), expand= c(0, 0)) + # set the limit for y axis
labs(x = "Duration (ms)") + # set the names for main, x and y axises , title = "At the single-trial level:", , y = expression(paste("Amplitudes (", mu, "V)"))
theme_bw() +
general_theme +
theme(legend.position = "none",
axis.title.y = element_blank(),
axis.line = element_line(size = 3, colour = "black"))
}
plot_st
```
```{r combine the aggregated mean amp plots and the explanation plots, include=FALSE}
# library(ggpubr)
# plot.hypo <- ggarrange(plot_lit, plot_st, widths = c(1.1, 4))
#
# # ggsave('plot_hypo.pdf', plot.hypo, width = 7, height = 3)
#
# plot_hypo
```
# Brief introduction to the analysis
## Equivalence tests
In this study, we do predict that there will be no differences between some conditions. For example, we expect that N170 amplitudes for faces presented for various duration are comparable as long as they are perceived with high subjective confidence. Thus, we employ Equivalence tests to explore if the differences are equivalent to a null region, which we defined as [-0.5, 0.5] in this study. Essentially, the equivalence test performs two one-sided tests to examine if the differences are smaller than the upper boundary of the region and larger than the lower boundary.
By performing the conventional null hypothesis significance tests and the equivalence tests, there are (at least) five scenarios for the results:
```{r possible results, fig.height=4.7, fig.width=8}
# equivalence interval
delta_null <- 0.5 # the equivalent interval will be [-delta_null, delta_null]
CI_size = .8
df_equi <- tibble(
Scenarios = paste("Scenario", 1:6),
CI_low = c(delta_null-.15, -delta_null-.5, -delta_null+.15, delta_null-.6, -delta_null-.25, -delta_null-.1),
CI_upp = CI_low + c(rep(CI_size, 5), 1.3)
) %>%
mutate(Scenarios = factor(Scenarios),
Scenarios = factor(Scenarios, levels = rev(levels(Scenarios))))
gg_equi <- ggplot(df_equi, aes(xmin = CI_low, xmax = CI_upp, y = Scenarios)) +
geom_errorbarh(height = .5, color = c("red", "red", "blue", rep("black", 3)), size = 1) +
geom_vline(xintercept = 0, linetype = "longdash") +
geom_vline(xintercept = c(-delta_null, delta_null), linetype = "dashed", color = "gray30") +
scale_x_continuous(limits = c(-1.15, 1.15), breaks = c(-delta_null, 0, delta_null)) +
xlab(expression(paste("Amplitudes (", mu, "V)"))) +
theme_bw() +
theme(
text = element_text(size = 10),
axis.title = element_text(size = 16),
axis.text = element_text(size = 15), # the size of the texts in plot
# axis.text.x = element_text(angle = 45, vjust = 0.5),
legend.title=element_text(size=16),
legend.text=element_text(size=16),
# legend.position = "bottom",
legend.key.width = unit(1.2, "cm"),
plot.title = element_text(lineheight=.8, face="bold", size = 17),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
# remove the facet background color
strip.text = element_text(face="bold", size=15, lineheight=5.0),
strip.background = element_rect(fill="white", colour="white", size=1),
strip.placement = "outside"
)
# ggsave("equivalence_test.png", gg_equi, width = 8, height = 4.7)
gg_equi
```
```{r}
gg_equi_bw <- ggplot(df_equi, aes(xmin = CI_low, xmax = CI_upp, y = Scenarios)) +
geom_errorbarh(height = .5, size = 1,
color = c("gray50", "gray50", "gray", rep("black", 3))) +
geom_vline(xintercept = 0, linetype = "longdash") +
geom_vline(xintercept = c(-delta_null, delta_null), linetype = "dashed", color = "gray30") +
scale_x_continuous(limits = c(-1.15, 1.15), breaks = c(-delta_null, 0, delta_null)) +
xlab(expression(paste("Amplitudes (", mu, "V)"))) +
theme_bw() +
theme(
text = element_text(size = 10),
axis.title = element_text(size = 16),
axis.text = element_text(size = 15), # the size of the texts in plot
axis.title.y=element_blank(), #remove y axis labels
# axis.text.x = element_text(angle = 45, vjust = 0.5),
legend.title=element_text(size=16),
legend.text=element_text(size=16),
# legend.position = "bottom",
legend.key.width = unit(1.2, "cm"),
plot.title = element_text(lineheight=.8, face="bold", size = 17),
panel.border = element_blank(),
panel.grid.minor = element_blank(),
panel.grid.major = element_blank(),
axis.line.x = element_line(colour = 'black', size=0.5, linetype='solid'),
axis.line.y = element_line(colour = 'black', size=0.5, linetype='solid'),
# remove the facet background color
strip.text = element_text(face="bold", size=15, lineheight=5.0),
strip.background = element_rect(fill="white", colour="white", size=1),
strip.placement = "outside"
)
# ggsave("equivalence_test_bw.png", gg_equi_pw, width = 8, height = 4.7)
gg_equi_bw
```
# Behavior results
```{r load behavioral results of 205, message=FALSE}
raw_beha_205 <- {
"E205_35_Behavior.csv" %>%
file.path(folder_data, .) %>%
read_csv()
}
```
```{r select certain rows and columns from the data, and calculate the Z value for reaction times (205)}
clean_beha_205 <- {
raw_beha_205 %>%
filter(`Procedure[Trial]` == "expProc") %>% # remove rows for practice and countdown
select(ExperimentName, SubjCode = Subject, blockID, Block, Trial, Age, Sex, Handedness, Type = `stimCategory[Trial]`, Category = `FH[Trial]`, Duration = `stimDuration[Trial]`, ACC = `Resp.ACC[Trial]`, Resp.RT = `Resp.RT[Trial]`, Resp = `Resp.RESP[Trial]`, Stimuli = `stimName[Trial]`) %>% # select and rename the columns (remove unuseful columns)
mutate(
SubjCode = factor(paste("P", SubjCode, sep = "")), # save SubjCode as factor
Type = substr(Type, 1, 1),
Type = recode_factor(Type, "N" = "intact", "S" = "scrambled"), # rename the levels of Type
Category = recode(Category, "hosue" = "house", "house" = "house", "face" = "face", .default = "face"), # rename "hosue" as "house"
Duration = factor(Duration), # save Duration as factor
Category = as.factor(Category),
Resp = as.factor(Resp),
blockID = as.factor(blockID)
) %>%
group_by(SubjCode, Type, Category, Duration, ACC) %>% # divide the data based on these conditions
mutate(
RT = Resp.RT + as.numeric(levels(Duration))[Duration],
RT.Z = scale(RT), # calculate the Z value for reaction times within each group
RT.Within3Z = ifelse(RT.Z <= 3 & RT.Z >= -3, 1, ifelse(RT.Z < -3 | RT.Z > 3, 0, NaN)) # if the Z values are between -3 and 3, will be marked as 1
) %>%
ungroup() # ungroup the data
}
# behavior.tidyup
head(clean_beha_205, 10)
```
```{r check the number of trials for 205}
# check the number of trials for 205
sum.count.205 <- {
clean_beha_205 %>%
group_by(SubjCode, Type, Category, Duration) %>%
summarize(Count_sum = n())
}
valid.count.205 <- {
clean_beha_205 %>%
filter(RT > RT_min) %>%
group_by(SubjCode, Type, Category, Duration) %>%
summarize(Count = n()) %>%
right_join(sum.count.205, by = c("SubjCode", "Type", "Category", "Duration")) %>%
mutate(ratio = Count / Count_sum) %>%
filter(ratio > ratio_count) %$%
SubjCode %>%
unique()
}
# remove the participants
clean_beha_205 %<>% filter(SubjCode %in% valid.count.205)
```
```{r remove participants whose performance in Key 1 was lower than 95%}
# remove participants whose performance in Key 1 was lower than 95%
valid.17 <- {
clean_beha_205 %>%
filter(Duration == 17, Resp == 1) %>% # Type == "intact",
group_by(SubjCode, Duration, Resp) %>% # Type,
summarize(Accuracy = mean(ACC), Count = n()) %>%
filter(Accuracy > 0.95) %$%
SubjCode
}
# clean_beha_205 %>%
# filter(Duration == 17, Resp == 1) %>% # Type == "intact",
# group_by(SubjCode, Duration, Resp) %>% # Type,
# summarize(Accuracy = mean(ACC), Count = n()) %>%
# filter(Accuracy < 0.95) %>%
# ungroup() %>%
# summarize(mean(Accuracy), min(Accuracy), max(Accuracy))
clean_beha_205 %<>% filter(SubjCode %in% valid.17)
```
## Participant demographic information
```{r participant information E205}
subj.info.205 <- {
clean_beha_205 %>%
select(SubjCode, Age, Sex, Handedness) %>% # select the columns of participant inforamtion
distinct() # only keep the unique rows
}
subj.info.205
```
```{r}
mean(subj.info.205$Age)
range(subj.info.205$Age)
sum(subj.info.205$Sex == "female")
```
## Allocation of responses in each condition
```{r}
tmp.count <- {
clean_beha_205 %>%
group_by(SubjCode, Type, Category, Duration) %>%
summarize(Count_sum = n())
}
avg.dist.long.205 <- {
clean_beha_205 %>%
group_by(SubjCode, Type, Category, Duration, Resp) %>%
summarize(Count = n(), .groups = "drop") %>%
right_join(., tidyr::expand(., SubjCode, Type, Category, Duration, Resp),
by = c("SubjCode", "Type", "Category", "Duration", "Resp")) %>% # make missing rows explicit
mutate(Count = if_else(is.na(Count), 0L, Count)) %>%
right_join(tmp.count, by = c("SubjCode", "Type", "Category", "Duration")) %>%
mutate(RespRate = Count / Count_sum)
}
# descriptive statistics of accuracy for plotting
desc.dist.205 <- {
avg.dist.long.205 %>%
group_by(Type, Category, Duration, Resp) %>%
summarize(N = n()
, Mean = sum(RespRate)/N
, SD = sd(RespRate)
, SE = SD/sqrt(N)
, SE.lo = Mean - SE, SE.hi = Mean + SE
, CI_lo = Mean - SE * 1.96
, CI_hi = Mean + SE * 1.96
, Median = median(RespRate)
, Lower = Mean - SD, Upper = Mean + SD # for rainclound plot
, .groups = "drop"
)
}
avg.dist.wide.205 <- {
avg.dist.long.205 %>%
mutate(Conditions = paste(Type, Category, Duration, Resp, sep = "_")) %>%
dplyr::select(SubjCode, Conditions, RespRate) %>%
spread(Conditions, RespRate)
}
```
```{r rainclound plot of the RT data E205, warning = FALSE}
desc.dist.205
dist.RainPlot.205 <- {
ggplot(data = avg.dist.long.205, aes(y = RespRate, x = as.factor(Resp), fill = Category)) +
geom_flat_violin(position = position_nudge(x = .2, y = 0), alpha = .7) +
geom_point(aes(y = RespRate, color = Category), position = position_jitter(width = .15), size = .5, alpha = .8) +
geom_boxplot(aes(y = RespRate), width = .2, outlier.shape = NA, alpha = .7) +
geom_point(data = desc.dist.205, aes(y = Mean, x = Resp, color = Category), position = position_nudge(x = 0.3), size = 2.5) +
geom_errorbar(data = desc.dist.205, aes(y = Mean, ymin = Lower, ymax = Upper), position = position_nudge(x = 0.3), width = 0) +
facet_grid(Type ~ Duration) +
# facet_grid(. ~ Type, switch = "x") + # create two panels to show data
# scale_colour_grey() + # start = .1, end = .6, color for the contour
# scale_fill_grey() + # start = .3, end = .6, color for the fill
# scale_color_brewer(palette = "Set1") + # palette = "RdBu"
# scale_fill_brewer(palette = "Set1") + # palette = "RdBu"
labs(title = "Reaction times for 205", x = "Stimulus Type", y = "Reaction times (ms)", fill = "Duration(ms)", color = "Duration(ms)") + # set the names for main, x and y axises and the legend
# scale_x_discrete(labels=resp_labels) +
# coord_cartesian(ylim = c(0, 1.05)) + # set the limit for y axis
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
theme_bw() + # the backgroud color
plot_theme
}
```
```{r display the RainCloud plot for reaction times, eval=FALSE, include=FALSE}
dist.RainPlot.205
```
```{r}
dura.labels <- c("17ms", "200ms")
names(dura.labels) <- c(17, 200)
dist.ColuPlot.205 <- {
ggplot(data = desc.dist.205, aes(y = Mean, x = Resp, fill = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(position = "dodge", color = "black", alpha = .7) + # position of columns and countour of columns
facet_grid(Type ~ Duration,
labeller = labeller(Duration = dura.labels)) +
geom_errorbar(mapping = aes(ymin = CI_lo, ymax = CI_hi), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5,
position = position_dodge(width=0.9)) +
geom_hline(yintercept = c(1), linetype = 5, alpha = 0.5) + # add the line for 0.5 and 1 (y)
coord_cartesian(ylim = c(0,1.1)) + # set the limit for y axis
scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(title = "", x = "Responses", y = "Percentage", fill = "Category") + # set the names for main, x and y axises Subjective Responses for E205
scale_fill_grey() + # set the color for columns
# geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
erp_theme
}
dist.ColuPlot.205
```
## Percentage of face stimuli
```{r}
df_ratio_205 <- {
clean_beha_205 %>%
mutate(isFace = if_else(Category == "face", 1, 0)) %>%
group_by(SubjCode, Type, Duration, Resp) %>%
summarize(ratio = mean(isFace), Count = n())
}
desc_ratio_205 <- {
df_ratio_205 %>%
group_by(Type, Duration, Resp) %>%
summarize(Mean = mean(ratio)
, N = n()
, SD = sd(ratio)
, SE = SD/sqrt(N)
, SE.lo = Mean - SE, SE.hi = Mean + SE
, CI_lo = Mean - SE * 1.96
, CI_hi = Mean + SE * 1.96
, Median = median(ratio)
, Lower = Mean - SD, Upper = Mean + SD # for rainclound plot
) %>%
ungroup()
}
```
```{r }
rate.ColuPlot.205 <- {
ggplot(data = desc_ratio_205, aes(y = Mean, x = Resp)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(position = "dodge", color = "black", alpha = .7) + # position of columns and countour of columns
facet_grid(Type ~ Duration,
labeller = labeller(Duration = dura.labels)) +
geom_errorbar(mapping = aes(ymin = SE.lo, ymax = SE.hi), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5,
position = position_dodge(width=0.9)) +
geom_hline(yintercept = c(0.5, 1), linetype = 5, alpha = 0.5) + # add the line for 0.5 and 1 (y)
# coord_cartesian(ylim = c(0,1.1)) + # set the limit for y axis
scale_y_continuous(expand= c(0, 0), limits = c(-0.1, 1.15), breaks = seq(0, 1, 0.25)) + # remove the space between columns and x axis
labs(x = "Responses", y = "Percentage") + # set the names for main, x and y axises Subjective Responses for E205
scale_fill_grey() + # set the color for columns
# geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
plot_theme
}
rate.ColuPlot.205
```
Combine the two plots (for publication):
```{r include=TRUE, fig.asp=.5}
dura16.labels <- c("33 ms", "216 ms")
names(dura16.labels) <- c(17, 200)
# combine the two behavior response plots
dist_ColuPlot_205 <-
desc.dist.205 %>%
# mutate(Duration = fct_recode(Duration, `33` = "17", `216` = "200")) %>%
ggplot(aes(y = Mean, x = Resp, fill = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(position = "dodge", color = "black", alpha = .7) + # position of columns and countour of columns
facet_grid(Type ~ Duration,
labeller = labeller(Duration = dura16.labels)) +
geom_errorbar(mapping = aes(ymin = CI_lo, ymax = CI_hi), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5,
position = position_dodge(width=0.9)) +
geom_hline(yintercept = c(1), linetype = 5, alpha = 0.5) + # add the line for 0.5 and 1 (y)
# coord_cartesian(ylim = c(0,1.1)) + # set the limit for y axis
scale_y_continuous(expand= c(0, 0), limits = c(-0.1, 1.15), breaks = seq(0, 1, 0.25)) +# remove the space between columns and x axis
labs(x = "Responses", y = "Percentage", fill = "Category") + # set the names for main, x and y axises Subjective Responses for E205
scale_fill_grey() + # set the color for columns
# geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
erp_theme +
theme(legend.position = c(0.53, 0.3))
resp_ColuPlot_205 <- {
desc_ratio_205 %>%
# mutate(Duration = fct_recode(Duration, "33" = "17", "216" = "200")) %>%
ggplot(aes(y = Mean, x = Resp)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_col(position = "dodge", color = "black", alpha = .7) + # position of columns and countour of columns
facet_grid(Type ~ Duration,
labeller = labeller(Duration = dura16.labels)) +
geom_errorbar(mapping = aes(ymin = SE.lo, ymax = SE.hi), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5,
position = position_dodge(width=0.9)) +
geom_hline(yintercept = c(0.5, 1), linetype = 5, alpha = 0.5) + # add the line for 0.5 and 1 (y)
# coord_cartesian(ylim = c(0,1.1)) + # set the limit for y axis
scale_y_continuous(expand= c(0, 0), limits = c(-0.1, 1.15), breaks = seq(0, 1, 0.25)) + # remove the space between columns and x axis
labs(x = "Responses", y = "Percentage") + # set the names for main, x and y axises Subjective Responses for E205
scale_fill_grey() + # set the color for columns
# geom_text(label = c("***", "***", "", ""), color = c("red", "red", "red", "red"), size = 6, nudge_y = 0.05, nudge_x = rep(c(-0.225, 0.225), 2)) + # add starts to the significant columns
plot_theme
}
plot_two_response <- ggpubr::ggarrange(dist_ColuPlot_205, resp_ColuPlot_205, nrow = 1,
widths = c(1, .7),
labels = c("A", "B"),
font.label = list(size = 24)) # ratio between the two subplots)
# ggsave('plot_two_response.pdf', plot_two_response, width = 10, height = 5)
```
# ERP amplitude analysis
## Load mean amplitude for single trials
The raw mean amplitude of each trial for all participants were loaded.
```{r load trial mean amplitude E2, message = FALSE}
# read the trial mean amplitude data
df.erp.E2 <- {
"E205_MeanAmp.csv" %>%
file.path(folder_data, .) %>%
read_csv() %>%
substr_Event() %>%
filter(SubjCode %in% valid.17) %>%
mutate(SubjCode = as.factor(SubjCode),
Hemisphere = as.factor(Hemisphere),
urResponse = as.factor(urResponse),
Stimuli = substr(stimName, 2, 6))
}
df.erp.E2 %>%
group_by(SubjCode, Stimuli, Hemisphere, Type, Category, Duration) %>%
summarize(count = n())
```
## Linear mixed model across responses
### Set the successive difference coding
```{r set the dummy coding for ERP data E2}
# dummy coding
df.erp.E2.all <- sdif_coding_P203_erp(df.erp.E2)
```
### Amplitudes of the P1
```{r erp df for the P1 E2}
# only keep the data for amplitudes of the P1 (correct and incorrect erps)
df.erp.P1.E2 <- {
df.erp.E2.all %>%
filter(Component == "P1")
}
if (saveCSV) {
output.erp.P1.E2 <- file.path(folder_nesi, "E205_erp_P1.RData")
save(df.erp.P1.E2, file = output.erp.P1.E2)
}
# the structure of this dataset
head(df.erp.P1.E2, 10)
```
#### The maximal model
```{r maximal lmm for P1 E2}
file.max.P1.E2 <- file.path(folder_nesi, "E205_P1_lmm_max.RData")
# fit the maximal model
if (!file.exists(file.max.P1.E2)) {
lmm.max.P1.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi +
Type_Cate_Dura_Hemi
| SubjCode) +
(1 + Type_C + Dura_C + Hemi_C +
Type_Dura + Type_Hemi + Dura_Hemi +
Type_Dura_Hemi
| Stimuli),
data = df.erp.P1.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa", # nloptwrap Nelder_Mead
optCtrl = list(maxfun = 1e7)))
} else {
load(file.max.P1.E2)
}
print(summary(lmm.max.P1.E2), corr = FALSE)
```
#### The zero-correlation-parameter model
```{r zcp lmm for P1 E2}
file.zcp.P1.E2 <- file.path(folder_nesi, "E205_P1_lmm_zcp.RData")
# fit the zcp model
if (!file.exists(file.zcp.P1.E2)) {
lmm.zcp.P1.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi +
Type_Cate_Dura_Hemi
|| SubjCode) +
(1 + Type_C + Dura_C + Hemi_C +
Type_Dura + Type_Hemi + Dura_Hemi +
Type_Dura_Hemi
|| Stimuli),
data = df.erp.P1.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa", # nloptwrap Nelder_Mead
optCtrl = list(maxfun = 1e7)))
} else {
load(file.zcp.P1.E2)
}
print(summary(lmm.zcp.P1.E2), corr = FALSE)
```
#### The reduced model
```{r PCA analysis for zcp lmm for E2 P1}
summary(rePCA(lmm.zcp.P1.E2))
```
```{r }
file.rdc.P1.E2 <- file.path(folder_nesi, "E205_P1_lmm_rdc.RData")
# fit the rdc model
if (!file.exists(file.rdc.P1.E2)) {
# lmm.rdc.P1.E2.step <- step(lmm.rdc.P1.E2, reduce.fixed = FALSE)
lmm.rdc.P1.E2 <- update(lmm.zcp.P1.E2,
formula = MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Dura_Hemi
|| SubjCode) +
(1 + Type_C + Dura_C +
Type_Dura
|| Stimuli))
} else {
load(file.rdc.P1.E2)
}
print(summary(lmm.rdc.P1.E2), corr = FALSE)
```
```{r}
anova(lmm.rdc.P1.E2, lmm.zcp.P1.E2, refit = FALSE)
```
#### The extended model
```{r }
file.etd.P1.E2 <- file.path(folder_nesi, "E205_P1_lmm_etd.RData")
# fit the etd model
if (!file.exists(file.etd.P1.E2)) {
lmm.etd.P1.E2 <- update(lmm.rdc.P1.E2,
formula = MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Dura_Hemi
| SubjCode) +
(1 + Type_C + Dura_C +
Type_Dura
| Stimuli))
lmm.etd.P1.E2.2 <- re_fit(lmm.etd.P1.E2)
} else {
load(file.etd.P1.E2)
}
print(summary(lmm.etd.P1.E2.2), corr = FALSE)
```
```{r}
anova(lmm.etd.P1.E2.2, lmm.rdc.P1.E2, refit = FALSE)
```
The reduced model (`lmm.rdc.P1.E2`) explained the data better than the extended model (`lmm.rdc.P1.E2.2`).
#### The optimal model
```{r }
lmm.opt.P1.E205 <- lmm.rdc.P1.E2
# print(summary(lmm.opt.P1.E2), corr = FALSE)
summary(lmm.opt.P1.E205)
# confint(lmm.opt.P1.E205, method="Wald")
```
#### Diagnostic plots
```{r qqplot for lmm.opt.P1.E205}
# qqplot
qqplot_lmer(lmm.opt.P1.E205)
```
#### Estimated marginal means
```{r}
emm.P1.E205 <- emmeans(lmm.opt.P1.E205, ~ Type + Hemisphere + Category + Duration)
emm.P1.E205 %>%
as.data.frame() %>%
arrange(Type, Hemisphere, Category, Duration)
```
#### Plots
```{r}
# emmip(emm.P1.E205, Category ~ Duration | Type + Hemisphere, CIs = TRUE)
```
```{r p1.all.LinePlot, fig.asp=1}
hemiLabel <- c("left", "right")
names(hemiLabel) <- c("Left", "Right")
p1.all.LinePlot = {
ggplot(data = data.frame(emm.P1.E205), aes(y = emmean, x = Duration, color = Category, group = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_point(size = 2) +
geom_line(aes(linetype = Category), size = .85) + # position = "dodge", alpha = .7
scale_linetype_manual(values=c("solid", "dashed")) +
geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5) + # , position = position_dodge(width=0.9)
facet_grid(Type ~ Hemisphere, # switch = "x",
labeller = labeller(Hemisphere = hemiLabel)) +
scale_y_reverse(limits = ylimit_p1) + # set the limit for y axis
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(x = "Duration (ms)", y = expression(paste("P1 Amplitudes (", mu, "V)")), fill = "Category") + # set the names for main, x and y axises title = "",
# scale_x_discrete(labels = c("left", "right")) +
# geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
theme_bw() +
erp_theme
}
# p1.all.LinePlot.slide <- {
# p1.all.LinePlot +
# theme(legend.position="right",
# axis.line = element_line(color="black", size = 3))
# }
# ggsave('P1_all.png', p1.all.LinePlot.slide, width = 7, height = 4)
p1.all.LinePlot
```
```{r eval=FALSE}
library(RColorBrewer)
duration_color <- c(brewer.pal(9, 'Blues')[c(6:8)], brewer.pal(12, "Paired")[6])
# category_color <- brewer.pal(8, 'Dark2')[c(6,1)]
p1_all_LinePlot <- {
data.frame(emm.P1.E205) %>%
mutate(Duration = fct_recode(Duration, `33`="17", `216`="200")) %>%
ggplot(aes(y = emmean, x = Duration, group = Category)) + # set the data, varialbes for x and y axises, and the variable for dividing data
geom_point(aes(fill = Duration), size=4, shape=21, stroke=0) +
scale_fill_manual(values = duration_color[c(3, 4)], guide = "none") +
geom_line(aes(linetype = Category)) + # position = "dodge", alpha = .7
# scale_color_manual(values=category_color, limits = c("face", "house")) +
scale_linetype_manual(values=c("solid", "dashed")) +
geom_errorbar(mapping = aes(ymin = lower.CL, ymax = upper.CL), linetype = 1, # set the error bar
show.legend = FALSE, width = 0.25, alpha = .5) + # , position = position_dodge(width=0.9)
facet_grid(Type ~ Hemisphere, # switch = "x",
labeller = labeller(Hemisphere = hemiLabel)) +
scale_y_reverse(limits = ylimit_p1) + # set the limit for y axis
# scale_y_continuous(expand= c(0, 0)) + # remove the space between columns and x axis
labs(x = "Duration (ms)", y = expression(paste("P1 Amplitudes (", mu, "V)")), color = "Category", linetype = "Category") + # set the names for main, x and y axises title = "",
# scale_x_discrete(labels = c("left", "right")) +
# geom_text(label = c("", "", "", "", "***", "", "", ""), color = "red", size = 6, nudge_y = 0.5, nudge_x = 0.5) + # add starts to the significant columns
theme_bw() +
erp_theme +
theme(
legend.position = c(.5, .5),
legend.title=element_text(size=10),
legend.text=element_text(size=10),
) +
guides(linetype = guide_legend(title.position = "left", nrow = 1),
color = guide_legend(title.position = "left", nrow = 1))
}
ggsave('P1_all_pub.pdf', p1_all_LinePlot, width = 6, height = 5)
```
#### Contrasts
##### Main results for intact stimuli
Comparisons of P1 amplitudes for intact faces with different durations:
```{r P1 amplitudes for all}
contra_all_P1 <- contrast(emmeans(lmm.opt.P1.E205, ~ Duration | Hemisphere + Category + Type),
"pairwise", combine = TRUE, adjust = "Bonferroni", infer = TRUE)
contra_all_P1[1:2]
summary(contra_all_P1[1:2], delta = delta_null, level = .9)
summary(contra_all_P1[1:2], delta = delta_null, side = ">")
summary(contra_all_P1[1:2], delta = delta_null, side = "<")
```
Comparisons of face-specific P1 amplitude components for intact stimuli with different durations:
```{r}
contra_all_P1_spec <- contrast(emmeans(lmm.opt.P1.E205, ~ Category + Duration | Hemisphere + Type),
interaction = "pairwise", adjust = "Bonferroni", infer = TRUE)
contra_all_P1_spec[1:2]
summary(contra_all_P1_spec[1:2], delta = delta_null, level = .9)
summary(contra_all_P1_spec[1:2], delta = delta_null, side = ">")
summary(contra_all_P1_spec[1:2], delta = delta_null, side = "<")
```
##### Main results for scrambled stimuli
Comparisons of P1 amplitudes for scrambled faces with different durations:
```{r P1 amplitudes for all scrambled}
contra_all_P1[5:6]
summary(contra_all_P1[5:6], delta = delta_null, level = .9)
summary(contra_all_P1[5:6], delta = delta_null, side = ">")
summary(contra_all_P1[5:6], delta = delta_null, side = "<")
```
Comparisons of face-specific P1 amplitude components for scrambled stimuli with different durations:
```{r}
contra_all_P1_spec[3:4]
summary(contra_all_P1_spec[3:4], delta = delta_null, level = .9)
summary(contra_all_P1_spec[3:4], delta = delta_null, side = ">")
summary(contra_all_P1_spec[3:4], delta = delta_null, side = "<")
```
##### Other results
Comparisons of P1 amplitudes between Durations (without multiple comparison corrections):
```{r}
summary(contra_all_P1[1:8], adjust = "none")
summary(contra_all_P1[1:8], delta = delta_null, side = "two.sided", adjust = "none")
```
Comparisons of face-specific P1 amplitude components between Durations (without multiple comparison corrections):
```{r}
summary(contra_all_P1_spec[1:4], adjust = "none")
summary(contra_all_P1_spec[1:4], delta = delta_null, side = "two.sided", adjust = "none")
```
### Ampitudes of the N170
```{r erp df for the N170 E2}
# only keep the data for amplitudes of the N170 (all erps)
df.erp.N170.E2 <- {
df.erp.E2.all %>%
filter(Component == "N170")
}
if (saveCSV) {
output.erp.N170.E2 <- file.path(folder_nesi, "E205_erp_N170.RData")
save(df.erp.N170.E2, file = output.erp.N170.E2)
}
# the structure of this dataset
head(df.erp.N170.E2, 10)
```
#### The maximal model
```{r maximal lmm for E2 N170}
file.max.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_max.RData")
# fit the maximal model
if (!file.exists(file.max.N170.E2)) {
lmm.max.N170.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi +
Type_Cate_Dura_Hemi
| SubjCode) +
(1 + Type_C + Dura_C + Hemi_C +
Type_Dura + Type_Hemi + Dura_Hemi +
Type_Dura_Hemi
| Stimuli),
data = df.erp.N170.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa", # nloptwrap Nelder_Mead
optCtrl = list(maxfun = 1e7)))
} else {
load(file.max.N170.E2)
}
print(summary(lmm.max.N170.E2), corr = FALSE)
```
#### The zero-correlation-parameter model
```{r zcp lmm for E2 N170}
file.zcp.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_zcp.RData")
# fit the zcp model
if (!file.exists(file.zcp.N170.E2)) {
lmm.zcp.N170.E2 <- lmer(MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi + Cate_Dura_Hemi +
Type_Cate_Dura_Hemi
|| SubjCode) +
(1 + Type_C + Dura_C + Hemi_C +
Type_Dura + Type_Hemi + Dura_Hemi +
Type_Dura_Hemi
|| Stimuli),
data = df.erp.N170.E2,
verbose = TRUE,
control = lmerControl(optimizer = "bobyqa", # nloptwrap Nelder_Mead
optCtrl = list(maxfun = 1e7)))
} else {
load(file.zcp.N170.E2)
}
print(summary(lmm.zcp.N170.E2), corr = FALSE)
```
#### The reduced model
```{r PCA analysis for zcp lmm for E2 N170}
summary(rePCA(lmm.zcp.N170.E2))
```
```{r}
file.rdc.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_rdc.RData")
# fit the rdc model
if (!file.exists(file.rdc.N170.E2)) {
# lmm.rdc.N170.E2.step <- step(lmm.rdc.N170.E2, reduce.fixed = FALSE)
lmm.rdc.N170.E2 <- update(lmm.zcp.N170.E2,
formula = MeanAmp ~ Type * Category * Duration * Hemisphere +
(1 + Type_C + Cate_C + Dura_C + Hemi_C +
Type_Cate + Type_Dura + Cate_Dura + Type_Hemi + Cate_Hemi + Dura_Hemi +
Type_Cate_Dura + Type_Cate_Hemi + Type_Dura_Hemi
|| SubjCode) +
(1 + Type_C + Dura_C +
Type_Dura
|| Stimuli))
} else {
load(file.rdc.N170.E2)
}
print(summary(lmm.rdc.N170.E2), corr = FALSE)
```
```{r}
anova(lmm.rdc.N170.E2, lmm.zcp.N170.E2, refit = FALSE)
```
#### The extended model
```{r }
file.etd.N170.E2 <- file.path(folder_nesi, "E205_N170_lmm_etd.RData")
# fit the etd model
if (!file.exists(file.etd.N170.E2)) {
lmm.etd.N170.E2 <- update(lmm.rdc.N170.E2,