# install.packages("ggplot2") # Install ggplot2 if you haven't already# install.packages("FactoMineR") # Install FactoMineR if you haven't alreadylibrary(ggplot2)library(FactoMineR)run_types=c("pain")for(run_typeinrun_types){print(run_type)filtered_df<-df[!(df$condition=="rating"|df$condition=="cue"|df$runtype!=run_type), ]parsed_df<-filtered_df%>%separate(condition, into =c("cue", "stim"), sep ="_", remove =FALSE)# --------------------- subset regions based on ROI ----------------------------df_long<-pivot_longer(parsed_df, cols =starts_with("tr"), names_to ="tr_num", values_to ="tr_value")# ----------------------------- clean factor -----------------------------------df_long$tr_ordered<-factor(df_long$tr_num, levels =c(paste0("tr", 1:TR_length)))df_long$stim_ordered<-factor(df_long$stim, levels =c("stimH", "stimM", "stimL"))# --------------------------- summary statistics -------------------------------subjectwise<-meanSummary(df_long,c("sub","tr_ordered", "stim_ordered"), "tr_value")# Assuming your original dataframe is named 'df'# Convert the dataframe to wide formatdf_wide<-pivot_wider(subjectwise, id_cols =c("tr_ordered", "stim_ordered"), names_from =c("sub"), values_from ="mean_per_sub")# df_wide <- pivot_wider(subjectwise,# id_cols = c("sub", "ROIindex","stim_ordered"),# names_from = "tr_ordered",# values_from = "mean_per_sub")stim_high.df<-df_wide[df_wide$stim_ordered=="stimH",]stim_med.df<-df_wide[df_wide$stim_ordered=="stimM",]stim_low.df<-df_wide[df_wide$stim_ordered=="stimL",]# selected_columns <- subset(stim_high.df, select = 2:(ncol(stim_high.df) - 1))meanhighdf<-data.frame(subset(stim_high.df, select =3:(ncol(stim_high.df)-1)))high.pca_result<-prcomp(meanhighdf)high.pca_scores<-as.data.frame(high.pca_result$x)# Access the proportion of variance explained by each principal componenthigh.variance_explained<-high.pca_result$sdev^2/sum(high.pca_result$sdev^2)plot(high.variance_explained)# Access the standard deviations of each principal componenthigh.stdev<-high.pca_result$sdevmeanmeddf<-data.frame(subset(stim_med.df, select =3:(ncol(stim_med.df)-1)))med.pca<-prcomp(meanmeddf)med.pca_scores<-as.data.frame(med.pca$x)meanlowdf<-data.frame(subset(stim_low.df, select =3:(ncol(stim_low.df)-1)))low.pca<-prcomp(meanlowdf)low.pca_scores<-as.data.frame(low.pca$x)library(plotly)# You can use plotly to create an interactive 3D plot# plot_ly(high.pca_scores, x = ~PC1, y = ~PC2, z = ~PC3, type = "scatter3d", mode = "markers")# plot_ly(low.pca_scores, x = ~PC1, y = ~PC2, z = ~PC3, type = "scatter3d", mode = "markers")combined_pca_scores<-rbind(high.pca_scores, med.pca_scores, low.pca_scores)# Add a new column to indicate the stim_ordered category (high_stim or low_stim)combined_pca_scores$stim_ordered<-c(rep("high_stim", nrow(high.pca_scores)),rep("med_stim", nrow(med.pca_scores)),rep("low_stim", nrow(low.pca_scores)))# Create the 3D PCA plotplot_ly(combined_pca_scores, x =~PC1, y =~PC2, z =~PC3, type ="scatter3d", mode ="markers", color =~stim_ordered)# data_matrix <- groupwise[groupwise$stim_ordered == "high_stim",c("tr_ordered", "mean_per_sub_norm_mean")]# sorted_indices <- order(data_matrix$tr_ordered)# df_ordered <- data_matrix[sorted_indices, ]# pca_result <- PCA(data_matrix$mean_per_sub_norm_mean)# datapoints <- df$datapoints}
## [1] "pain"
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plot_ly(combined_pca_scores, x =~PC1, y =~PC2, z =~PC3, type ="scatter3d", mode ="markers", color =~stim_ordered)
16.0.2 PCA groupwise
# install.packages("ggplot2") # Install ggplot2 if you haven't already# install.packages("FactoMineR") # Install FactoMineR if you haven't alreadylibrary(ggplot2)library(FactoMineR)# Assuming your original dataframe is named 'df'# Convert the dataframe to wide formatdf_wide.group<-pivot_wider(subjectwise, id_cols =c("tr_ordered", "stim_ordered"), names_from ="sub", values_from ="mean_per_sub")# ------# data_matrix <- groupwise[groupwise$stim_ordered == "high_stim",c("tr_ordered", "mean_per_sub_norm_mean")]# sorted_indices <- order(data_matrix$tr_ordered)# df_ordered <- data_matrix[sorted_indices, ]# datapoints <- df_ordered$mean_per_sub_norm_mean# data_df <- data.frame(Dim1 = datapoints, Dim2 = datapoints, Dim3 = datapoints)# pca <- prcomp(data_df)# pca_scores <- as.data.frame(pca$x)# plot_ly(pca_scores, x = ~PC1, y = ~PC2, z = ~PC3, type = "scatter3d", mode = "markers")# -------stim_high.df<-df_wide[df_wide$stim_ordered=="stimH",]stim_low.df<-df_wide[df_wide$stim_ordered=="stimL",]# selected_columns <- subset(stim_high.df, select = 2:(ncol(stim_high.df) - 1))meanhighdf<-data.frame(subset(stim_high.df, select =3:(ncol(stim_high.df)-1)))high.pca<-prcomp(meanhighdf)high.pca_scores<-as.data.frame(high.pca$x)meanlowdf<-data.frame(subset(stim_low.df, select =3:(ncol(stim_low.df)-1)))low.pca<-prcomp(meanlowdf)low.pca_scores<-as.data.frame(low.pca$x)library(plotly)# You can use plotly to create an interactive 3D plot# plot_ly(high.pca_scores, x = ~PC1, y = ~PC2, z = ~PC3, type = "scatter3d", mode = "markers")# plot_ly(low.pca_scores, x = ~PC1, y = ~PC2, z = ~PC3, type = "scatter3d", mode = "markers")combined_pca_scores<-rbind(high.pca_scores, low.pca_scores)# Add a new column to indicate the stim_ordered category (high_stim or low_stim)combined_pca_scores$stim_ordered<-c(rep("high_stim", nrow(high.pca_scores)), rep("low_stim", nrow(low.pca_scores)))# Create the 3D PCA plotplot_ly(combined_pca_scores, x =~PC1, y =~PC2, z =~PC3, type ="scatter3d", mode ="markers", color =~stim_ordered)
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
## Warning in RColorBrewer::brewer.pal(N, "Set2"): minimal value for n is 3, returning requested palette with 3 different levels
# data_matrix <- groupwise[groupwise$stim_ordered == "high_stim",c("tr_ordered", "mean_per_sub_norm_mean")]# sorted_indices <- order(data_matrix$tr_ordered)# df_ordered <- data_matrix[sorted_indices, ]# pca_result <- PCA(data_matrix$mean_per_sub_norm_mean)# datapoints <- df$datapoints# Assuming you have a dataframe named 'data' containing the 20 data points, 'x' and 'y' values, and corresponding standard deviations 'sd'# Load the ggplot2 library# install.packages("ggplot2")library(ggplot2)# Create the plot# y = "mean_per_sub_mean"z# combined_pca <- combined_pca_scores %>%# mutate(group_index = group_indices(., stim_ordered))combined_pca<-combined_pca_scores%>%group_by(stim_ordered)%>%mutate(group_index =row_number())ggplot(combined_pca, aes(x=group_index,y=PC1, group =stim_ordered, colour=stim_ordered))+stat_smooth(method="loess", span=0.25, se=TRUE, aes(color=stim_ordered), alpha=0.3)+theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
# Assuming you have a dataframe named 'data' containing the 20 data points, 'x' and 'y' values, and corresponding standard deviations 'sd'# Load the ggplot2 library# install.packages("ggplot2")library(ggplot2)# Create the plot# y = "mean_per_sub_mean"zggplot(groupwise, aes(x=tr_ordered,y=mean_per_sub_mean, group =stim_ordered, colour=stim_ordered))+stat_smooth(method="loess", span=0.25, se=TRUE, aes(color=stim_ordered), alpha=0.3)+theme_bw()
## `geom_smooth()` using formula = 'y ~ x'
# ggplot(data=groupwise, aes(x=tr_ordered, y=mean_per_sub_mean, ymin=se, ymax=se, fill=stim_ordered, linetype=stim_ordered)) +# geom_line() +# geom_ribbon(alpha=0.5)# Assuming you have a dataframe named 'data' containing the 20 mean data points and corresponding standard errors# 'x' represents the x-values (e.g., time points)# 'mean_y' represents the mean y-values# 'se_y' represents the standard errors of the mean y-values# Load the ggplot2 library# install.packages("ggplot2")library(ggplot2)# groupwise$x <- as.numeric(groupwise$x)## # Sort the dataframe by the 'x' variable (if it's not already sorted)# data <- data[order(data$x), ]# Create the plot# Create the plot with custom span and smoothing methodggplot(groupwise, aes(x=tr_ordered,y=mean_per_sub_mean))+geom_line()+# Plot the smooth line for the meangeom_ribbon(aes(ymin =mean_per_sub_mean-se, ymax =mean_per_sub_mean+se), alpha =0.3)+# Add the ribbon for standard errorgeom_smooth(method ="loess", span =0.1, se =FALSE)+# Add the loess smoothing curvelabs(x ="X-axis Label", y ="Y-axis Label", title ="Smooth Line with Standard Error Ribbon")+theme_minimal()
## `geom_smooth()` using formula = 'y ~ x'
16.1 DEP: epoch: stim, high cue vs low cue
# filtered_df <- subset(df, condition != "rating")filtered_df<-df[!(df$condition=="rating"|df$condition=="cue"), ]parsed_df<-filtered_df%>%separate(condition, into =c("cue", "stim"), sep ="_", remove =FALSE)TR_length<-42# --------------------- subset regions based on ROI ----------------------------df_long<-pivot_longer(parsed_df, cols =starts_with("tr"), names_to ="tr_num", values_to ="tr_value")# ----------------------------- clean factor -----------------------------------df_long$tr_ordered<-factor(df_long$tr_num, levels =c(paste0("tr", 1:TR_length)))df_long$cue_ordered<-factor(df_long$cue, levels =c("cueH","cueL"))# --------------------------- summary statistics -------------------------------subjectwise<-meanSummary(df_long,c("sub", "tr_ordered", "cue_ordered"), "tr_value")groupwise<-summarySEwithin( data =subjectwise, measurevar ="mean_per_sub", withinvars =c("cue_ordered", "tr_ordered"), idvar ="sub")groupwise$task<-taskname# https://stackoverflow.com/questions/29402528/append-data-frames-together-in-a-for-loop/29419402# --------------------------------- plot ---------------------------------------LINEIV1="tr_ordered"LINEIV2="cue_ordered"MEAN="mean_per_sub_norm_mean"ERROR="se"dv_keyword="actual"sorted_indices<-order(groupwise$tr_ordered)groupwise_sorted<-groupwise[sorted_indices, ]p1=plot_timeseries_bar(groupwise,LINEIV1, LINEIV2, MEAN, ERROR, xlab ="Runs" , ylab="Epoch: stimulus, High cue vs. Low cue", ggtitle="time_series", color=c("red", "blue"))time_points<-seq(1, 0.46*TR_length, 0.46)p1+scale_x_discrete(labels =setNames(time_points, colnames(df_long)[7:7+TR_length]))+theme_classic()
