aroma graph measures
Functional connectivity
Mean correlation maps
library(abind)
# List to store all 2D matrices
matrices <- list()
# Loop through all .tsv files in the folder
files <- list.files(path = "conmats_filtered_aroma/", pattern = "*.tsv",full.names = TRUE)
for (file in files) {
mat <- read.table(file, sep = "\t", header = FALSE)
matrices[[file]] <- mat
}
# Combine all matrices into a single 3D matrix
result_matrix <- abind(matrices, along = 3)
dim(result_matrix)## [1] 410 410 54results_brain_labs <- brainregions$X2
# List to store all 2D matrices
interferon_matrices <- list()
# Loop through all .tsv files in the folder
files <- list.files(path = "conmats_filtered_aroma/", pattern = "ses-I",full.names = TRUE)
for (file in files) {
mat <- read.table(file, sep = "\t", header = FALSE)
interferon_matrices[[file]] <- mat
}
# Combine all matrices into a single 3D matrix
result_interferon_matrix <- abind(interferon_matrices, along = 3)
# List to store all 2D matrices
placebo_matrices <- list()
# Loop through all .tsv files in the folder
files <- list.files(path = "conmats_filtered_aroma/", pattern = "ses-P",full.names = TRUE)
for (file in files) {
mat <- read.table(file, sep = "\t", header = FALSE)
placebo_matrices[[file]] <- mat
}
# Combine all matrices into a single 3D matrix
result_placebo_matrix <- abind(placebo_matrices, along = 3)
# nnodes <- length(brainregions$X2)
# tri_pos <- which(upper.tri(matrix(nrow = nnodes, ncol = nnodes)), arr.ind = T)# Load the R.matlab package
library(R.matlab)
# Specify the file path and name
file <- "C:\\Users\\saptaf1\\Downloads\\archives\\NBS_benchmarking-master\\interferon_files\\aroma_funccon_54.mat"
# Write the 3D matrix to the .mat file
writeMat(funccon=atanh(result_matrix), file)
file <- "C:\\Users\\saptaf1\\Downloads\\archives\\NBS_benchmarking-master\\interferon_files\\yeo_410.mat"
writeMat(yeo410=adjacency_matrix, file)n_missing <- sum(is.na(result_placebo_matrix))
mean_placebo_funcconn <- apply(result_placebo_matrix, c(1, 2), function(x) mean(x, na.rm = TRUE))
diag(mean_placebo_funcconn) <- 0
z_mean_placebo_funcconn <- atanh(mean_placebo_funcconn)
z_mean_placebo_funcconn[z_mean_placebo_funcconn == 0] <- .Machine$double.xmin
# problems with 1121023_ses-I,0821008_ses-I,1121022_ses-I
n_missing <- sum(is.na(result_interferon_matrix))
n_missing <- apply(result_interferon_matrix, c(3), function(x) sum(is.na(x)))
print(n_missing)## conmats_filtered_aroma/sub-0122025_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0122026_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0122027_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0222028_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0222029_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0322030_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0322031_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0322032_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0421002_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0422033_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0422034_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0422035_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0521003_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0522036_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0721005_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0821006_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0821007_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0921009_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0921010_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0921012_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0921014_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0921015_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0921016_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-0921017_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-1021019_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-1121020_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aroma/sub-1121021_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0mean_interferon_funcconn <- apply(result_interferon_matrix, c(1, 2), function(x) mean(x, na.rm = TRUE))
diag(mean_interferon_funcconn) <- 0
z_mean_interferon_funcconn <- atanh(mean_interferon_funcconn)
z_mean_interferon_funcconn[z_mean_interferon_funcconn == 0] <- .Machine$double.xmin
mean_diff_funcconn <- apply(result_placebo_matrix-result_interferon_matrix, c(1, 2), mean)
diag(mean_diff_funcconn) <- 0
#z_mean_diff_funcconn <- atanh(mean_diff_funcconn)
z_mean_diff_funcconn <- z_mean_placebo_funcconn-z_mean_interferon_funcconn
h1 <- Heatmap(z_mean_placebo_funcconn, name = "Fisher Z", row_split = network.combined$name, column_split = network.combined$name, row_title_rot = 0, show_column_dend = FALSE, show_row_dend = FALSE, show_column_names = FALSE, column_title = "Placebo", column_title_gp = gpar(fontsize = 20, fontface = "bold"), clustering_distance_rows="pearson", clustering_distance_columns ="pearson")
h2 <- Heatmap(z_mean_interferon_funcconn, name = "Fisher Z", row_split= factor(network.combined$name, levels = (names(row_order(h1)))), column_split= factor(network.combined$name, levels = (names(row_order(h1)))), row_title_rot = 0, show_column_dend = FALSE, show_row_dend = FALSE, show_column_names = FALSE, column_title = "Interferon", column_title_gp = gpar(fontsize = 20, fontface = "bold"), cluster_rows = FALSE, cluster_columns = FALSE, cluster_row_slices = FALSE, cluster_column_slices = FALSE, row_order = unlist(row_order(h1)), column_order = unlist(row_order(h1)) )
h3_v2 <- Heatmap(z_mean_diff_funcconn, name = "Fisher Z", row_split= factor(network.combined$name, levels = (names(row_order(h1)))), column_split= factor(network.combined$name, levels = (names(row_order(h1)))), row_title_rot = 0, show_column_dend = FALSE, show_row_dend = FALSE, show_column_names = FALSE, column_title = "Placebo-Interferon", column_title_gp = gpar(fontsize = 20, fontface = "bold"), cluster_rows = FALSE, cluster_columns = FALSE, cluster_row_slices = FALSE, cluster_column_slices = FALSE, row_order = unlist(row_order(h1)), column_order = unlist(row_order(h1)))
h3_v2_adjusted <- Heatmap(z_mean_diff_funcconn, name = "Fisher Z", row_split= factor(network.combined$name, levels = (names(row_order(h1)))), column_split= factor(network.combined$name, levels = (names(row_order(h1)))), row_title_rot = 0, show_column_dend = FALSE, show_row_dend = FALSE, show_column_names = FALSE, column_title = "Placebo-Interferon", column_title_gp = gpar(fontsize = 20, fontface = "bold"), cluster_rows = FALSE, cluster_columns = FALSE, cluster_row_slices = FALSE, cluster_column_slices = FALSE, row_order = unlist(row_order(h1)), column_order = unlist(row_order(h1)), col=colorRamp2(c(-1, 0, 1), c("blue", "white", "red")))
h1+h2+h3_v2_adjusted
h1+h2
h3_v2
hist(z_mean_diff_funcconn[upper.tri(z_mean_diff_funcconn, diag = FALSE)], main = "Histogram of functional connectivity difference values")
g1 <- graph.adjacency(z_mean_placebo_funcconn,weighted=TRUE,mode = "upper", diag = FALSE)
g2 <- graph.adjacency(z_mean_interferon_funcconn,weighted=TRUE,mode = "upper", diag = FALSE)
df <- bind_rows(get.data.frame(g1) %>% mutate(condition="Placebo"), get.data.frame(g2) %>% mutate(condition="Interferon"))
ggplot(df, aes(x=weight, fill=condition)) +
geom_density(alpha=.25)
df %>% mutate(from = gsub("V", "ROI_", from), to = gsub("V", "ROI_", to)) %>% rowwise() %>% mutate(from_network = ifelse(is.na(match(from, network.combined$ROI)), from, network.combined$name[match(from, network.combined$ROI)]), to_network = ifelse(is.na(match(to, network.combined$ROI)), to, network.combined$name[match(to, network.combined$ROI)]), from2to = paste0(sort(c(from_network,to_network))[1],"2",sort(c(from_network,to_network))[2])) -> df_ext
ggplot(df_ext, aes(x=weight, fill=condition)) +
geom_density(alpha=.25) + facet_wrap(~from2to)
df_ext %>% mutate(fromNum=parse_number(from), toNum = parse_number(to)) -> df_ext
df_ext$from2toIndex <- as.integer(factor(df_ext$from2to))
nodes <- unique(c(df_ext$fromNum, df_ext$toNum))
adjacency_matrix <- matrix(0, nrow = length(nodes), ncol = length(nodes))
colnames(adjacency_matrix) <- rownames(adjacency_matrix) <- nodes
adjacency_matrix[cbind(match(df_ext$fromNum, nodes), match(df_ext$toNum, nodes))] <- df_ext$from2toIndex
write.csv(adjacency_matrix, file = "yeo_edge_group.csv", row.names = FALSE)Wilcoxon
tmp = data.frame()
# run np test within-subject differences in edges
for (i in seq(1,dim(result_placebo_matrix)[3])) {
# print(i)
A <- atanh(result_placebo_matrix[,,i])
A[A == 0] <- .Machine$double.xmin
B <- atanh(result_interferon_matrix[,,i])
B[B == 0] <- .Machine$double.xmin
g1 <- graph.adjacency(A, weighted=TRUE, mode = "upper", diag = FALSE)
g2 <- graph.adjacency(B, weighted=TRUE, mode = "upper", diag = FALSE)
if (nrow(get.data.frame(g1)) != nrow(get.data.frame(g2))) {
break
}
df <- bind_rows(get.data.frame(g1) %>% mutate(condition="Placebo"), get.data.frame(g2) %>% mutate(condition="Interferon")) %>% mutate(sub = i)
tmp = bind_rows(tmp,df)
}
# Perform Wilcoxon signed-rank test
knitr::kable(tmp %>% wilcox_test(weight ~ condition, paired = TRUE, detailed = TRUE, exact=TRUE))| estimate | .y. | group1 | group2 | n1 | n2 | statistic | p | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.0035356 | weight | Interferon | Placebo | 2263815 | 2263815 | 1.292945e+12 | 0 | 0.0029513 | 0.0041176 | Wilcoxon | two.sided |
# Perform Wilcoxon signed-rank test
print(wilcox.test(tmp %>% filter(condition=="Placebo")%>%.$weight, tmp %>% filter(condition=="Interferon")%>%.$weight, paired = TRUE))##
## Wilcoxon signed rank test with continuity correction
##
## data: tmp %>% filter(condition == "Placebo") %>% .$weight and tmp %>% filter(condition == "Interferon") %>% .$weight
## V = 1.2694e+12, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0rm(tmp)List of most Interferon affected connections in graph
Where is the most condition related change happening?
library(igraph)
g_diff_pMinusI <- graph.adjacency(z_mean_diff_funcconn,weighted=TRUE,mode = "upper", diag = FALSE)
df_diff_pMinusI <- get.data.frame(g_diff_pMinusI) %>% mutate(from = gsub("V", "ROI_", from), to = gsub("V", "ROI_", to)) %>% rowwise() %>% mutate(from_network = ifelse(is.na(match(from, network.combined$ROI)), from, network.combined$name[match(from, network.combined$ROI)]), to_network = ifelse(is.na(match(to, network.combined$ROI)), to, network.combined$name[match(to, network.combined$ROI)]), from2to = paste0(sort(c(from_network,to_network))[1],"2",sort(c(from_network,to_network))[2])) %>% mutate(from_roi = ifelse(is.na(match(from, brainregions$ROI_X)), from, brainregions$X2[match(from, brainregions$ROI_X)]), to_roi = ifelse(is.na(match(to, brainregions$ROI_X)), from, brainregions$X2[match(to, brainregions$ROI_X)]), abs_weight = abs(weight))
df_diff_pMinusI$abs_weight_decile <- ntile(df_diff_pMinusI$abs_weight, 10)
DT::datatable(df_diff_pMinusI %>% filter(abs_weight_decile >= 10))# top 20% of affected edges
df_diff_pMinusI %>% filter(abs_weight_decile >= 9) %>% ggplot(., aes(abs_weight, fill = from2to)) +
geom_histogram(binwidth = 0.07)
df_diff_pMinusI %>% filter(abs_weight_decile >= 9) %>% ggplot(., aes(from2to)) +
geom_bar() + coord_flip()
# top 10% of affected edges
df_diff_pMinusI %>% filter(abs_weight_decile >= 10) %>% ggplot(., aes(abs_weight, fill = from2to)) +
geom_histogram(binwidth = 0.1)
df_diff_pMinusI %>% filter(abs_weight_decile >= 10) %>% ggplot(., aes(from2to)) +
geom_bar() + coord_flip()
library(igraph)
df_list <- list()
for (i in 1:dim(result_matrix)[3]) {
#print(i)
g <- graph.adjacency(result_matrix[,,i],weighted=TRUE,mode = "upper", diag = FALSE)
df_list[[i]] <- get.data.frame(g) %>% mutate(fileIndex=i)
}
data.funccon <- bind_rows(df_list) %>% left_join(.,variables_ext)Grouped by scale, condition, subject
Unthresholded
data.funccon %>% mutate(weight = atanh(weight)) %>% group_by(Subj_ID,condition) %>% summarise(meanFuncCon = mean(weight)) -> data.aggr
anova_summary <- nice(aov_ez("Subj_ID", "meanFuncCon", data.aggr, within ="condition"))
knitr::kable(anova_summary)| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| condition | 1, 26 | 0.00 | 0.63 | .007 | .436 |
ggwithinstats(
data = data.aggr,
x = condition,
y = meanFuncCon,
type = "np",
bf.message = FALSE)
Only positive weight > 0
data.funccon %>% mutate(weight = atanh(weight)) %>% group_by(Subj_ID,condition) %>% filter(weight>0) %>% summarise(meanFuncCon = mean(weight))-> data.aggr
anova_summary <- nice(aov_ez("Subj_ID", "meanFuncCon", data.aggr, within ="condition"))
knitr::kable(anova_summary)| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| condition | 1, 26 | 0.00 | 11.85 ** | .078 | .002 |
ggwithinstats(
data = data.aggr,
x = condition,
y = meanFuncCon,
type = "np",
bf.message = FALSE)
Proportional thresholding based on on weight (not abs_weight)
# total edges per session: EDGES
plots_list <- list()
x <- c(0.1,0.15,0.2,0.25,0.3,0.35)
for (i in 1:6) {
p <- ggwithinstats(
data = data.funccon %>% mutate(weight = atanh(weight), abs_weight = abs(weight)) %>% group_by(Subj_ID,condition) %>% top_n(round(EDGES*x[i]),weight) %>% dplyr::summarise(meanFuncCon = mean(weight)),
x = condition,
y = meanFuncCon,
type = "np",
title =x[i] ,
bf.message = FALSE)
plots_list[[i]] <- p
}
# arrange the plots in a grid using grid.arrange from gridExtra
gridExtra::grid.arrange(grobs = plots_list)
Grouped by scale, condition, subject and age_cat
data.funccon %>% mutate(weight = atanh(weight)) %>% group_by(Subj_ID,condition,Age_cat) %>% summarise(meanFuncCon = mean(weight)) -> data.aggr
anova_summary <- nice(aov_ez("Subj_ID", "meanFuncCon", data.aggr, within ="condition", between="Age_cat"))
knitr::kable(anova_summary)| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| Age_cat | 1, 25 | 0.00 | 0.66 | .018 | .425 |
| condition | 1, 25 | 0.00 | 0.74 | .009 | .398 |
| Age_cat:condition | 1, 25 | 0.00 | 2.06 | .024 | .164 |
Grouped by scale, condition, subject and network:yeo
- Unthresholded
data.funccon %>% mutate(weight = atanh(weight), from = gsub("V", "ROI_", from), to = gsub("V", "ROI_", to)) %>% rowwise() %>% mutate(from_network = ifelse(is.na(match(from, network.combined$ROI)), from, network.combined$name[match(from, network.combined$ROI)]), to_network = ifelse(is.na(match(to, network.combined$ROI)), to, network.combined$name[match(to, network.combined$ROI)]), from2to = paste0(sort(c(from_network,to_network))[1],"2",sort(c(from_network,to_network))[2])) -> data.funccon.ext
data.funccon.ext %>% mutate(abs_weight = abs(weight)) %>% group_by(Subj_ID,condition,from2to) -> tmp
# Calculate the number of occurrences of each network within each condition
df_counts <- table(tmp$from2to, tmp$condition)
knitr::kable(df_counts)| Interferon | Placebo | |
|---|---|---|
| Default2Default | 89667 | 89667 |
| Default2Dorsal Attention | 97416 | 97416 |
| Default2Frontoparietal | 99630 | 99630 |
| Default2Limbic | 64206 | 64206 |
| Default2Somatomotor | 117342 | 117342 |
| Default2Subcortical | 109962 | 110700 |
| Default2Ventral Attention | 106272 | 106272 |
| Default2Visual | 130626 | 130626 |
| Dorsal Attention2Dorsal Attention | 25542 | 25542 |
| Dorsal Attention2Frontoparietal | 53460 | 53460 |
| Dorsal Attention2Limbic | 34452 | 34452 |
| Dorsal Attention2Somatomotor | 62964 | 62964 |
| Dorsal Attention2Subcortical | 59004 | 59400 |
| Dorsal Attention2Ventral Attention | 57024 | 57024 |
| Dorsal Attention2Visual | 70092 | 70092 |
| Frontoparietal2Frontoparietal | 26730 | 26730 |
| Frontoparietal2Limbic | 35235 | 35235 |
| Frontoparietal2Somatomotor | 64395 | 64395 |
| Frontoparietal2Subcortical | 60345 | 60750 |
| Frontoparietal2Ventral Attention | 58320 | 58320 |
| Frontoparietal2Visual | 71685 | 71685 |
| Limbic2Limbic | 10962 | 10962 |
| Limbic2Somatomotor | 41499 | 41499 |
| Limbic2Subcortical | 38889 | 39150 |
| Limbic2Ventral Attention | 37584 | 37584 |
| Limbic2Visual | 46197 | 46197 |
| Somatomotor2Somatomotor | 37206 | 37206 |
| Somatomotor2Subcortical | 71073 | 71550 |
| Somatomotor2Ventral Attention | 68688 | 68688 |
| Somatomotor2Visual | 84429 | 84429 |
| Subcortical2Subcortical | 32670 | 33075 |
| Subcortical2Ventral Attention | 64368 | 64800 |
| Subcortical2Visual | 79119 | 79650 |
| Ventral Attention2Ventral Attention | 30456 | 30456 |
| Ventral Attention2Visual | 76464 | 76464 |
| Visual2Visual | 46197 | 46197 |
df_counts <- table(tmp$from2to)
# Calculate the number of occurrences of each network within each condition
df_counts <- table(tmp$from2to)
# Convert the counts to a data frame and add column names
df_counts <- as.data.frame(df_counts)
names(df_counts) <- c("network", "count")
# Create pie chart faceted by condition
ggplot(df_counts, aes(x = "", y = count, fill = network)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0) +
theme_void()
data.funccon.ext %>% group_by(Subj_ID,condition,from2to) %>% summarise(meanFuncConn = mean(weight)) -> data.funccon.aggr
# grouped_ggwithinstats(
# data = data.aggr,
# x = condition,
# y = meanFuncConn,
# grouping.var = from2to,
# type = "p",
# bf.message = FALSE)
# main anova
main_anova_summary <- aov_ez("Subj_ID", "meanFuncConn", data.funccon.aggr,within =c("condition","from2to"))
knitr::kable(nice(main_anova_summary))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| condition | 1, 26 | 0.01 | 0.46 | <.001 | .504 |
| from2to | 35, 910 | 0.01 | 94.04 *** | .682 | <.001 |
| condition:from2to | 35, 910 | 0.01 | 2.53 *** | .032 | <.001 |
emmip(main_anova_summary, from2to ~ condition)
EMM <- emmeans(main_anova_summary, ~ condition | from2to) # where treat has 2 levels
EMM.unadj <- summary(pairs(EMM), by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(pairs(EMM), by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
knitr::kable(left_join(EMM.unadj,EMM.fdr))| contrast | from2to | estimate | SE | df | t.ratio | p.value.unadj | p.value.fdr |
|---|---|---|---|---|---|---|---|
| Interferon - Placebo | Default2Default | -0.0115062 | 0.0174494 | 26 | -0.6594067 | 0.5154333 | 0.8127516 |
| Interferon - Placebo | Default2Dorsal.Attention | 0.0058752 | 0.0176813 | 26 | 0.3322848 | 0.7423383 | 0.8127516 |
| Interferon - Placebo | Default2Frontoparietal | 0.0049899 | 0.0124984 | 26 | 0.3992413 | 0.6929759 | 0.8127516 |
| Interferon - Placebo | Default2Limbic | 0.0156683 | 0.0178635 | 26 | 0.8771134 | 0.3884579 | 0.8127516 |
| Interferon - Placebo | Default2Somatomotor | 0.0116184 | 0.0152453 | 26 | 0.7620945 | 0.4528620 | 0.8127516 |
| Interferon - Placebo | Default2Subcortical | 0.0061428 | 0.0143315 | 26 | 0.4286237 | 0.6717274 | 0.8127516 |
| Interferon - Placebo | Default2Ventral.Attention | -0.0013334 | 0.0192044 | 26 | -0.0694306 | 0.9451781 | 0.9451781 |
| Interferon - Placebo | Default2Visual | 0.0079070 | 0.0162587 | 26 | 0.4863247 | 0.6308110 | 0.8127516 |
| Interferon - Placebo | Dorsal.Attention2Dorsal.Attention | -0.0245047 | 0.0273831 | 26 | -0.8948828 | 0.3790598 | 0.8127516 |
| Interferon - Placebo | Dorsal.Attention2Frontoparietal | -0.0049723 | 0.0130610 | 26 | -0.3806954 | 0.7065221 | 0.8127516 |
| Interferon - Placebo | Dorsal.Attention2Limbic | 0.0606555 | 0.0203700 | 26 | 2.9776862 | 0.0062127 | 0.1118277 |
| Interferon - Placebo | Dorsal.Attention2Somatomotor | -0.0498630 | 0.0232124 | 26 | -2.1481199 | 0.0411953 | 0.2471719 |
| Interferon - Placebo | Dorsal.Attention2Subcortical | 0.0057870 | 0.0176063 | 26 | 0.3286907 | 0.7450223 | 0.8127516 |
| Interferon - Placebo | Dorsal.Attention2Ventral.Attention | -0.0234611 | 0.0189484 | 26 | -1.2381565 | 0.2267219 | 0.6278452 |
| Interferon - Placebo | Dorsal.Attention2Visual | 0.0139635 | 0.0235296 | 26 | 0.5934464 | 0.5580103 | 0.8127516 |
| Interferon - Placebo | Frontoparietal2Frontoparietal | 0.0123844 | 0.0161737 | 26 | 0.7657112 | 0.4507439 | 0.8127516 |
| Interferon - Placebo | Frontoparietal2Limbic | 0.0307797 | 0.0152877 | 26 | 2.0133594 | 0.0545369 | 0.2699736 |
| Interferon - Placebo | Frontoparietal2Somatomotor | 0.0081089 | 0.0144743 | 26 | 0.5602266 | 0.5801205 | 0.8127516 |
| Interferon - Placebo | Frontoparietal2Subcortical | 0.0134101 | 0.0105220 | 26 | 1.2744915 | 0.2137628 | 0.6278452 |
| Interferon - Placebo | Frontoparietal2Ventral.Attention | -0.0125953 | 0.0180966 | 26 | -0.6960015 | 0.4926031 | 0.8127516 |
| Interferon - Placebo | Frontoparietal2Visual | 0.0030929 | 0.0152130 | 26 | 0.2033043 | 0.8404800 | 0.8765290 |
| Interferon - Placebo | Limbic2Limbic | -0.0565078 | 0.0247568 | 26 | -2.2825205 | 0.0308809 | 0.2409969 |
| Interferon - Placebo | Limbic2Somatomotor | 0.0589345 | 0.0254405 | 26 | 2.3165596 | 0.0286710 | 0.2409969 |
| Interferon - Placebo | Limbic2Subcortical | 0.0154161 | 0.0201529 | 26 | 0.7649594 | 0.4511837 | 0.8127516 |
| Interferon - Placebo | Limbic2Ventral.Attention | 0.0810324 | 0.0187819 | 26 | 4.3143798 | 0.0002051 | 0.0073850 |
| Interferon - Placebo | Limbic2Visual | -0.0033502 | 0.0178010 | 26 | -0.1882002 | 0.8521809 | 0.8765290 |
| Interferon - Placebo | Somatomotor2Somatomotor | -0.0793181 | 0.0353259 | 26 | -2.2453238 | 0.0334718 | 0.2409969 |
| Interferon - Placebo | Somatomotor2Subcortical | 0.0335466 | 0.0170585 | 26 | 1.9665575 | 0.0599941 | 0.2699736 |
| Interferon - Placebo | Somatomotor2Ventral.Attention | -0.0336031 | 0.0254148 | 26 | -1.3221876 | 0.1976202 | 0.6278452 |
| Interferon - Placebo | Somatomotor2Visual | -0.0165790 | 0.0233846 | 26 | -0.7089722 | 0.4846509 | 0.8127516 |
| Interferon - Placebo | Subcortical2Subcortical | 0.0087950 | 0.0246437 | 26 | 0.3568855 | 0.7240578 | 0.8127516 |
| Interferon - Placebo | Subcortical2Ventral.Attention | 0.0148183 | 0.0157127 | 26 | 0.9430742 | 0.3543243 | 0.8127516 |
| Interferon - Placebo | Subcortical2Visual | 0.0104272 | 0.0184842 | 26 | 0.5641145 | 0.5775104 | 0.8127516 |
| Interferon - Placebo | Ventral.Attention2Ventral.Attention | -0.0328725 | 0.0261226 | 26 | -1.2583928 | 0.2194331 | 0.6278452 |
| Interferon - Placebo | Ventral.Attention2Visual | 0.0055992 | 0.0167858 | 26 | 0.3335692 | 0.7413800 | 0.8127516 |
| Interferon - Placebo | Visual2Visual | 0.0475949 | 0.0330474 | 26 | 1.4402019 | 0.1617442 | 0.6278452 |
# aov_test <- aov(meanFuncConn ~ condition*from2to + Error(Subj_ID/(condition*from2to)), data=data.funccon.aggr)
# report(aov_test)
# uncorrected p values
# data.funccon.aggr %>% ungroup() %>%
# nest_by(from2to) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meanFuncConn", data, within ="condition")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# knitr::kable(anova_summary)- 10% threshold
data.funccon.ext %>% mutate(abs_weight = abs(weight)) %>% group_by(Subj_ID,condition) %>% top_n(round(EDGES*0.1),weight) %>% group_by(Subj_ID,condition, from2to) %>% dplyr::summarise(meanFuncConn = mean(weight)) -> data.funccon.aggr
data.funccon.ext %>% mutate(abs_weight = abs(weight)) %>% group_by(Subj_ID,condition,from2to) %>% top_n(round(EDGES*0.1),weight) -> tmp
# Calculate the number of occurrences of each network within each condition
df_counts <- table(tmp$from2to, tmp$condition)
knitr::kable(df_counts)| Interferon | Placebo | |
|---|---|---|
| Default2Default | 89667 | 89667 |
| Default2Dorsal Attention | 97416 | 97416 |
| Default2Frontoparietal | 99630 | 99630 |
| Default2Limbic | 64206 | 64206 |
| Default2Somatomotor | 117342 | 117342 |
| Default2Subcortical | 109962 | 110700 |
| Default2Ventral Attention | 106272 | 106272 |
| Default2Visual | 130626 | 130626 |
| Dorsal Attention2Dorsal Attention | 25542 | 25542 |
| Dorsal Attention2Frontoparietal | 53460 | 53460 |
| Dorsal Attention2Limbic | 34452 | 34452 |
| Dorsal Attention2Somatomotor | 62964 | 62964 |
| Dorsal Attention2Subcortical | 59004 | 59400 |
| Dorsal Attention2Ventral Attention | 57024 | 57024 |
| Dorsal Attention2Visual | 70092 | 70092 |
| Frontoparietal2Frontoparietal | 26730 | 26730 |
| Frontoparietal2Limbic | 35235 | 35235 |
| Frontoparietal2Somatomotor | 64395 | 64395 |
| Frontoparietal2Subcortical | 60345 | 60750 |
| Frontoparietal2Ventral Attention | 58320 | 58320 |
| Frontoparietal2Visual | 71685 | 71685 |
| Limbic2Limbic | 10962 | 10962 |
| Limbic2Somatomotor | 41499 | 41499 |
| Limbic2Subcortical | 38889 | 39150 |
| Limbic2Ventral Attention | 37584 | 37584 |
| Limbic2Visual | 46197 | 46197 |
| Somatomotor2Somatomotor | 37206 | 37206 |
| Somatomotor2Subcortical | 71073 | 71550 |
| Somatomotor2Ventral Attention | 68688 | 68688 |
| Somatomotor2Visual | 84429 | 84429 |
| Subcortical2Subcortical | 32670 | 33075 |
| Subcortical2Ventral Attention | 64368 | 64800 |
| Subcortical2Visual | 79119 | 79650 |
| Ventral Attention2Ventral Attention | 30456 | 30456 |
| Ventral Attention2Visual | 76464 | 76464 |
| Visual2Visual | 46197 | 46197 |
df_counts <- table(tmp$from2to)
# Calculate the number of occurrences of each network within each condition
df_counts <- table(tmp$from2to)
# Convert the counts to a data frame and add column names
df_counts <- as.data.frame(df_counts)
names(df_counts) <- c("network", "count")
# Create pie chart faceted by condition
ggplot(df_counts, aes(x = "", y = count, fill = network)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0) +
theme_void()
# main anova
main_anova_summary <- aov_ez("Subj_ID", "meanFuncConn", data.funccon.aggr,within =c("condition","from2to"))
knitr::kable(nice(main_anova_summary))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| condition | 1, 26 | 0.26 | 10.71 ** | .070 | .003 |
| from2to | 35, 910 | 0.00 | 52.07 *** | .100 | <.001 |
| condition:from2to | 35, 910 | 0.00 | 1.16 | .002 | .240 |
emmip(main_anova_summary, from2to ~ condition)
EMM <- emmeans(main_anova_summary, ~ condition | from2to) # where treat has 2 levels
EMM.unadj <- summary(pairs(EMM), by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(pairs(EMM), by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
knitr::kable(left_join(EMM.unadj,EMM.fdr))| contrast | from2to | estimate | SE | df | t.ratio | p.value.unadj | p.value.fdr |
|---|---|---|---|---|---|---|---|
| Interferon - Placebo | Default2Default | -0.0589396 | 0.0206592 | 26 | -2.852944 | 0.0083847 | 0.0116096 |
| Interferon - Placebo | Default2Dorsal.Attention | -0.0735613 | 0.0222787 | 26 | -3.301865 | 0.0027955 | 0.0093939 |
| Interferon - Placebo | Default2Frontoparietal | -0.0686236 | 0.0212679 | 26 | -3.226626 | 0.0033724 | 0.0093939 |
| Interferon - Placebo | Default2Limbic | -0.0669526 | 0.0254463 | 26 | -2.631134 | 0.0141184 | 0.0169030 |
| Interferon - Placebo | Default2Somatomotor | -0.0818709 | 0.0238866 | 26 | -3.427480 | 0.0020384 | 0.0093939 |
| Interferon - Placebo | Default2Subcortical | -0.0689410 | 0.0236654 | 26 | -2.913157 | 0.0072591 | 0.0115845 |
| Interferon - Placebo | Default2Ventral.Attention | -0.0798188 | 0.0234698 | 26 | -3.400919 | 0.0021797 | 0.0093939 |
| Interferon - Placebo | Default2Visual | -0.0748779 | 0.0232476 | 26 | -3.220890 | 0.0034208 | 0.0093939 |
| Interferon - Placebo | Dorsal.Attention2Dorsal.Attention | -0.0722445 | 0.0251699 | 26 | -2.870276 | 0.0080448 | 0.0115845 |
| Interferon - Placebo | Dorsal.Attention2Frontoparietal | -0.0663514 | 0.0216901 | 26 | -3.059066 | 0.0050968 | 0.0115355 |
| Interferon - Placebo | Dorsal.Attention2Limbic | -0.0602085 | 0.0274557 | 26 | -2.192935 | 0.0374548 | 0.0374548 |
| Interferon - Placebo | Dorsal.Attention2Somatomotor | -0.0820076 | 0.0230971 | 26 | -3.550562 | 0.0014914 | 0.0093939 |
| Interferon - Placebo | Dorsal.Attention2Subcortical | -0.0623837 | 0.0246492 | 26 | -2.530859 | 0.0177688 | 0.0193841 |
| Interferon - Placebo | Dorsal.Attention2Ventral.Attention | -0.0728382 | 0.0244924 | 26 | -2.973916 | 0.0062696 | 0.0115355 |
| Interferon - Placebo | Dorsal.Attention2Visual | -0.0700796 | 0.0236368 | 26 | -2.964854 | 0.0064086 | 0.0115355 |
| Interferon - Placebo | Frontoparietal2Frontoparietal | -0.0700674 | 0.0213011 | 26 | -3.289372 | 0.0028842 | 0.0093939 |
| Interferon - Placebo | Frontoparietal2Limbic | -0.1101104 | 0.0257725 | 26 | -4.272406 | 0.0002291 | 0.0082467 |
| Interferon - Placebo | Frontoparietal2Somatomotor | -0.0797478 | 0.0239682 | 26 | -3.327239 | 0.0026234 | 0.0093939 |
| Interferon - Placebo | Frontoparietal2Subcortical | -0.0767858 | 0.0267317 | 26 | -2.872463 | 0.0080029 | 0.0115845 |
| Interferon - Placebo | Frontoparietal2Ventral.Attention | -0.0709281 | 0.0222040 | 26 | -3.194390 | 0.0036532 | 0.0093939 |
| Interferon - Placebo | Frontoparietal2Visual | -0.0779449 | 0.0229678 | 26 | -3.393665 | 0.0022200 | 0.0093939 |
| Interferon - Placebo | Limbic2Limbic | -0.0724899 | 0.0304471 | 26 | -2.380851 | 0.0248859 | 0.0259270 |
| Interferon - Placebo | Limbic2Somatomotor | -0.0873169 | 0.0303709 | 26 | -2.875020 | 0.0079541 | 0.0115845 |
| Interferon - Placebo | Limbic2Subcortical | -0.0697146 | 0.0268323 | 26 | -2.598162 | 0.0152335 | 0.0171377 |
| Interferon - Placebo | Limbic2Ventral.Attention | -0.0755877 | 0.0318255 | 26 | -2.375065 | 0.0252068 | 0.0259270 |
| Interferon - Placebo | Limbic2Visual | -0.0901010 | 0.0302892 | 26 | -2.974691 | 0.0062579 | 0.0115355 |
| Interferon - Placebo | Somatomotor2Somatomotor | -0.1114999 | 0.0298851 | 26 | -3.730953 | 0.0009392 | 0.0093939 |
| Interferon - Placebo | Somatomotor2Subcortical | -0.0843894 | 0.0290090 | 26 | -2.909074 | 0.0073307 | 0.0115845 |
| Interferon - Placebo | Somatomotor2Ventral.Attention | -0.0872196 | 0.0262567 | 26 | -3.321804 | 0.0026594 | 0.0093939 |
| Interferon - Placebo | Somatomotor2Visual | -0.0674193 | 0.0257529 | 26 | -2.617933 | 0.0145554 | 0.0169030 |
| Interferon - Placebo | Subcortical2Subcortical | -0.0761860 | 0.0269132 | 26 | -2.830806 | 0.0088386 | 0.0117848 |
| Interferon - Placebo | Subcortical2Ventral.Attention | -0.0759520 | 0.0282943 | 26 | -2.684353 | 0.0124779 | 0.0154898 |
| Interferon - Placebo | Subcortical2Visual | -0.0710146 | 0.0259422 | 26 | -2.737411 | 0.0110215 | 0.0141705 |
| Interferon - Placebo | Ventral.Attention2Ventral.Attention | -0.0840783 | 0.0238015 | 26 | -3.532478 | 0.0015617 | 0.0093939 |
| Interferon - Placebo | Ventral.Attention2Visual | -0.0738669 | 0.0245242 | 26 | -3.011999 | 0.0057164 | 0.0115355 |
| Interferon - Placebo | Visual2Visual | -0.0686427 | 0.0229487 | 26 | -2.991141 | 0.0060133 | 0.0115355 |
- 35% threshold
data.funccon.ext %>% mutate(abs_weight = abs(weight)) %>% group_by(Subj_ID,condition) %>% top_n(round(EDGES*0.35),weight) %>% group_by(Subj_ID,condition, from2to) %>% dplyr::summarise(meanFuncConn = mean(weight)) -> data.funccon.aggr
data.funccon.ext %>% mutate(abs_weight = abs(weight)) %>% group_by(Subj_ID,condition,from2to) %>% top_n(round(EDGES*0.35),weight) -> tmp
# Calculate the number of occurrences of each network within each condition
df_counts <- table(tmp$from2to, tmp$condition)
knitr::kable(df_counts)| Interferon | Placebo | |
|---|---|---|
| Default2Default | 89667 | 89667 |
| Default2Dorsal Attention | 97416 | 97416 |
| Default2Frontoparietal | 99630 | 99630 |
| Default2Limbic | 64206 | 64206 |
| Default2Somatomotor | 117342 | 117342 |
| Default2Subcortical | 109962 | 110700 |
| Default2Ventral Attention | 106272 | 106272 |
| Default2Visual | 130626 | 130626 |
| Dorsal Attention2Dorsal Attention | 25542 | 25542 |
| Dorsal Attention2Frontoparietal | 53460 | 53460 |
| Dorsal Attention2Limbic | 34452 | 34452 |
| Dorsal Attention2Somatomotor | 62964 | 62964 |
| Dorsal Attention2Subcortical | 59004 | 59400 |
| Dorsal Attention2Ventral Attention | 57024 | 57024 |
| Dorsal Attention2Visual | 70092 | 70092 |
| Frontoparietal2Frontoparietal | 26730 | 26730 |
| Frontoparietal2Limbic | 35235 | 35235 |
| Frontoparietal2Somatomotor | 64395 | 64395 |
| Frontoparietal2Subcortical | 60345 | 60750 |
| Frontoparietal2Ventral Attention | 58320 | 58320 |
| Frontoparietal2Visual | 71685 | 71685 |
| Limbic2Limbic | 10962 | 10962 |
| Limbic2Somatomotor | 41499 | 41499 |
| Limbic2Subcortical | 38889 | 39150 |
| Limbic2Ventral Attention | 37584 | 37584 |
| Limbic2Visual | 46197 | 46197 |
| Somatomotor2Somatomotor | 37206 | 37206 |
| Somatomotor2Subcortical | 71073 | 71550 |
| Somatomotor2Ventral Attention | 68688 | 68688 |
| Somatomotor2Visual | 84429 | 84429 |
| Subcortical2Subcortical | 32670 | 33075 |
| Subcortical2Ventral Attention | 64368 | 64800 |
| Subcortical2Visual | 79119 | 79650 |
| Ventral Attention2Ventral Attention | 30456 | 30456 |
| Ventral Attention2Visual | 76464 | 76464 |
| Visual2Visual | 46197 | 46197 |
df_counts <- table(tmp$from2to)
# Calculate the number of occurrences of each network within each condition
df_counts <- table(tmp$from2to)
# Convert the counts to a data frame and add column names
df_counts <- as.data.frame(df_counts)
names(df_counts) <- c("network", "count")
# Create pie chart faceted by condition
ggplot(df_counts, aes(x = "", y = count, fill = network)) +
geom_bar(stat = "identity", width = 1) +
coord_polar("y", start = 0) +
theme_void()
# main anova
main_anova_summary <- aov_ez("Subj_ID", "meanFuncConn", data.funccon.aggr,within =c("condition","from2to"))
knitr::kable(nice(main_anova_summary))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| condition | 1, 26 | 0.13 | 8.83 ** | .048 | .006 |
| from2to | 35, 910 | 0.00 | 63.42 *** | .256 | <.001 |
| condition:from2to | 35, 910 | 0.00 | 1.03 | .003 | .422 |
emmip(main_anova_summary, from2to ~ condition)
EMM <- emmeans(main_anova_summary, ~ condition | from2to) # where treat has 2 levels
EMM.unadj <- summary(pairs(EMM), by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(pairs(EMM), by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
knitr::kable(left_join(EMM.unadj,EMM.fdr))| contrast | from2to | estimate | SE | df | t.ratio | p.value.unadj | p.value.fdr |
|---|---|---|---|---|---|---|---|
| Interferon - Placebo | Default2Default | -0.0452756 | 0.0158522 | 26 | -2.856103 | 0.0083218 | 0.0259050 |
| Interferon - Placebo | Default2Dorsal.Attention | -0.0453066 | 0.0160511 | 26 | -2.822658 | 0.0090115 | 0.0259050 |
| Interferon - Placebo | Default2Frontoparietal | -0.0420201 | 0.0147892 | 26 | -2.841264 | 0.0086214 | 0.0259050 |
| Interferon - Placebo | Default2Limbic | -0.0511946 | 0.0169922 | 26 | -3.012825 | 0.0057049 | 0.0259050 |
| Interferon - Placebo | Default2Somatomotor | -0.0552391 | 0.0184712 | 26 | -2.990549 | 0.0060220 | 0.0259050 |
| Interferon - Placebo | Default2Subcortical | -0.0393645 | 0.0165371 | 26 | -2.380373 | 0.0249123 | 0.0407655 |
| Interferon - Placebo | Default2Ventral.Attention | -0.0480927 | 0.0166644 | 26 | -2.885948 | 0.0077488 | 0.0259050 |
| Interferon - Placebo | Default2Visual | -0.0426345 | 0.0158754 | 26 | -2.685571 | 0.0124426 | 0.0263489 |
| Interferon - Placebo | Dorsal.Attention2Dorsal.Attention | -0.0495034 | 0.0221644 | 26 | -2.233465 | 0.0343383 | 0.0475453 |
| Interferon - Placebo | Dorsal.Attention2Frontoparietal | -0.0402500 | 0.0148068 | 26 | -2.718336 | 0.0115257 | 0.0259328 |
| Interferon - Placebo | Dorsal.Attention2Limbic | -0.0418653 | 0.0219521 | 26 | -1.907119 | 0.0676112 | 0.0737577 |
| Interferon - Placebo | Dorsal.Attention2Somatomotor | -0.0585235 | 0.0207325 | 26 | -2.822787 | 0.0090087 | 0.0259050 |
| Interferon - Placebo | Dorsal.Attention2Subcortical | -0.0421641 | 0.0180573 | 26 | -2.335016 | 0.0275339 | 0.0413008 |
| Interferon - Placebo | Dorsal.Attention2Ventral.Attention | -0.0491621 | 0.0166846 | 26 | -2.946563 | 0.0066981 | 0.0259050 |
| Interferon - Placebo | Dorsal.Attention2Visual | -0.0286605 | 0.0217582 | 26 | -1.317224 | 0.1992549 | 0.2049479 |
| Interferon - Placebo | Frontoparietal2Frontoparietal | -0.0368437 | 0.0132807 | 26 | -2.774216 | 0.0101068 | 0.0259050 |
| Interferon - Placebo | Frontoparietal2Limbic | -0.0658801 | 0.0214841 | 26 | -3.066466 | 0.0050054 | 0.0259050 |
| Interferon - Placebo | Frontoparietal2Somatomotor | -0.0481856 | 0.0182876 | 26 | -2.634877 | 0.0139967 | 0.0279934 |
| Interferon - Placebo | Frontoparietal2Subcortical | -0.0425054 | 0.0192832 | 26 | -2.204276 | 0.0365580 | 0.0487440 |
| Interferon - Placebo | Frontoparietal2Ventral.Attention | -0.0490681 | 0.0156623 | 26 | -3.132883 | 0.0042526 | 0.0259050 |
| Interferon - Placebo | Frontoparietal2Visual | -0.0390631 | 0.0166052 | 26 | -2.352462 | 0.0264971 | 0.0413008 |
| Interferon - Placebo | Limbic2Limbic | -0.0524214 | 0.0268639 | 26 | -1.951372 | 0.0618650 | 0.0695982 |
| Interferon - Placebo | Limbic2Somatomotor | -0.0535292 | 0.0222044 | 26 | -2.410747 | 0.0232867 | 0.0399201 |
| Interferon - Placebo | Limbic2Subcortical | -0.0517048 | 0.0248988 | 26 | -2.076599 | 0.0478611 | 0.0594138 |
| Interferon - Placebo | Limbic2Ventral.Attention | -0.0517423 | 0.0228231 | 26 | -2.267099 | 0.0319321 | 0.0459822 |
| Interferon - Placebo | Limbic2Visual | -0.0540915 | 0.0220188 | 26 | -2.456609 | 0.0210161 | 0.0398200 |
| Interferon - Placebo | Somatomotor2Somatomotor | -0.1001769 | 0.0271876 | 26 | -3.684650 | 0.0010581 | 0.0259050 |
| Interferon - Placebo | Somatomotor2Subcortical | -0.0535636 | 0.0220983 | 26 | -2.423879 | 0.0226145 | 0.0399201 |
| Interferon - Placebo | Somatomotor2Ventral.Attention | -0.0617064 | 0.0220224 | 26 | -2.801982 | 0.0094645 | 0.0259050 |
| Interferon - Placebo | Somatomotor2Visual | -0.0429386 | 0.0208614 | 26 | -2.058281 | 0.0497160 | 0.0596592 |
| Interferon - Placebo | Subcortical2Subcortical | -0.0424470 | 0.0240902 | 26 | -1.761999 | 0.0898247 | 0.0951086 |
| Interferon - Placebo | Subcortical2Ventral.Attention | -0.0426144 | 0.0214374 | 26 | -1.987854 | 0.0574541 | 0.0667208 |
| Interferon - Placebo | Subcortical2Visual | -0.0395548 | 0.0190042 | 26 | -2.081369 | 0.0473883 | 0.0594138 |
| Interferon - Placebo | Ventral.Attention2Ventral.Attention | -0.0561329 | 0.0186665 | 26 | -3.007154 | 0.0057841 | 0.0259050 |
| Interferon - Placebo | Ventral.Attention2Visual | -0.0483860 | 0.0176186 | 26 | -2.746299 | 0.0107937 | 0.0259050 |
| Interferon - Placebo | Visual2Visual | -0.0311741 | 0.0242073 | 26 | -1.287800 | 0.2091602 | 0.2091602 |
Grouped by scale, condition, subject and network:yeo and age
#slow to calc
data.funccon %>% mutate(weight = atanh(weight), from = gsub("V", "ROI_", from), to = gsub("V", "ROI_", to)) %>% rowwise() %>% mutate(from_network = ifelse(is.na(match(from, network.combined$ROI)), from, network.combined$name[match(from, network.combined$ROI)]), to_network = ifelse(is.na(match(to, network.combined$ROI)), to, network.combined$name[match(to, network.combined$ROI)]), from2to = paste0(sort(c(from_network,to_network))[1],"2",sort(c(from_network,to_network))[2])) %>% group_by(Subj_ID,condition,from2to, Age_cat) %>% summarise(meanFuncConn = mean(weight)) -> data.funccon.network.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanFuncConn", data.funccon.network.aggr,within =c("condition","from2to"), between = "Age_cat")
knitr::kable(nice(main_anova_summary))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| Age_cat | 1, 25 | 0.02 | 0.86 | .001 | .363 |
| condition | 1, 25 | 0.01 | 0.55 | <.001 | .466 |
| Age_cat:condition | 1, 25 | 0.01 | 1.92 | .002 | .178 |
| from2to | 35, 875 | 0.01 | 93.19 *** | .689 | <.001 |
| Age_cat:from2to | 35, 875 | 0.01 | 0.82 | .019 | .765 |
| condition:from2to | 35, 875 | 0.01 | 2.50 *** | .033 | <.001 |
| Age_cat:condition:from2to | 35, 875 | 0.01 | 0.91 | .012 | .619 |
EMM <- emmeans(main_anova_summary, ~ condition)
EMM.unadj <- summary(pairs(EMM), by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(pairs(EMM), by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
knitr::kable(left_join(EMM.unadj,EMM.fdr))| contrast | estimate | SE | df | t.ratio | p.value.unadj | p.value.fdr |
|---|---|---|---|---|---|---|
| Interferon - Placebo | 0.0037629 | 0.0050778 | 25 | 0.7410429 | 0.4655717 | 0.4655717 |
# data.funccon.network.aggr %>% ungroup() %>%
# nest_by(from2to) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meanFuncConn", data, within ="condition", between = "Age_cat")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# knitr::kable(anova_summary)Summary
NBS
cNBS
cnbs.fdr01 <- read.table("C:\\Users\\saptaf1\\Downloads\\archives\\NBS_benchmarking-master\\aroma_cnbs_fdr01.csv", header = FALSE,sep = ",")
#
# Heatmap(as.matrix(cnbs.fdr01), name = "pFDR<0.01", row_split = network.combined$name, column_split = network.combined$name, row_title_rot = 0, show_column_dend = FALSE, show_row_dend = FALSE, show_column_names = FALSE, column_title = "Significant edges", column_title_gp = gpar(fontsize = 20, fontface = "bold"))
Heatmap(as.matrix(cnbs.fdr01), name = "pFDR<0.01", row_split = network.combined$name, column_split = network.combined$name,show_column_dend = FALSE, show_row_dend = FALSE, row_title_rot = 0, column_labels = rep("",410),cluster_columns=FALSE,cluster_rows =FALSE)
Node degree
Grouped by scale, condition, subject
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "degree") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition) %>% summarise(meanDegree = mean(degree)) -> data.aggr
anova_summary <- aov_ez("Subj_ID", "meanDegree", data.aggr, within =c("condition","threshold"))
knitr::kable(nice(anova_summary))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| condition | 1, 26 | 4.25 | 0.01 | <.001 | .939 |
| threshold | 1, 26 | 1.57 | 179476.85 *** | .999 | <.001 |
| condition:threshold | 1, 26 | 0.51 | 0.04 | <.001 | .835 |
emmip(anova_summary, condition~threshold )
grouped_ggwithinstats(
data = data.aggr,
x = condition,
y = meanDegree,
grouping.var = threshold,
type = "np",
bf.message = FALSE,
exact = FALSE
)
Grouped by scale, condition, subject and age_cat
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "degree") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition, Age_cat) %>% summarise(meanDegree = mean(degree)) -> data.aggr
anova_summary <- nice(aov_ez("Subj_ID", "meanDegree", data.aggr, within =c("condition","threshold"), between = "Age_cat"))
knitr::kable(anova_summary)| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| Age_cat | 1, 25 | 5.94 | 0.51 | .010 | .482 |
| condition | 1, 25 | 4.29 | 0.00 | <.001 | .965 |
| Age_cat:condition | 1, 25 | 4.29 | 0.79 | .011 | .383 |
| threshold | 1, 25 | 1.57 | 179834.82 *** | .999 | <.001 |
| Age_cat:threshold | 1, 25 | 1.57 | 1.08 | .005 | .308 |
| condition:threshold | 1, 25 | 0.53 | 0.04 | <.001 | .834 |
| Age_cat:condition:threshold | 1, 25 | 0.53 | 0.02 | <.001 | .892 |
#
# data.aggr %>% ungroup() %>%
# nest_by(threshold) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meanDegree", data, within ="condition", between="Age_cat")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# knitr::kable(anova_summary)
#
# # aw <- aov_ez("Subj_ID", "meanDegree", data.aggr, within ="condition", between="Age_cat")
# # p_an <- afex_plot(aw, x = "condition", trace = "Age_cat") + ggtitle("test") + theme(legend.position = c(0.87, 0.15),legend.key.size = unit(0.3, "cm"))
#
# # plot anovas
#
# d_nested <- data.aggr %>% ungroup() %>%
# nest_by(threshold)
#
# d_plots <-
# d_nested %>%
# mutate(aov = list(map(data,~aov_ez("Subj_ID", "meanDegree", data, within ="condition", between="Age_cat")))) %>%
# mutate(plot = list(map2(aov,threshold,~afex_plot(., x = "condition", trace = "Age_cat") +ggtitle(paste("threshold:",threshold)) + theme(legend.position = c(0.87, 0.15),legend.key.size = unit(0.3, "cm")))))
#
# d_plots$plot_one <- lapply(d_plots$plot, "[[", 1)
#
# library(gridExtra)
# do.call(grid.arrange, c(d_plots$plot_one))Grouped by scale, condition, subject and network:yeo
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "degree") %>% left_join(.,network.combined) %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition,name) %>% summarise(meanDegree = mean(degree)) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanDegree", data.aggr,within =c("condition","name","threshold"))
knitr::kable(nice(main_anova_summary))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| condition | 1, 26 | 41.18 | 0.03 | <.001 | .856 |
| name | 4.96, 128.90 | 53.83 | 2.02 + | .026 | .080 |
| threshold | 1, 26 | 12.59 | 179071.02 *** | .991 | <.001 |
| condition:name | 5.05, 131.26 | 56.12 | 0.41 | .006 | .840 |
| condition:threshold | 1, 26 | 5.68 | 0.14 | <.001 | .709 |
| name:threshold | 5.16, 134.20 | 10.22 | 0.84 | .002 | .523 |
| condition:name:threshold | 4.99, 129.85 | 10.90 | 0.30 | <.001 | .914 |
#emmip(main_anova_summary, condition~threshold)
# average across threshold
main_anova_summary <- aov_ez("Subj_ID", "meanDegree", data.aggr,within =c("condition","name"))
knitr::kable(nice(main_anova_summary))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| condition | 1, 26 | 20.59 | 0.03 | <.001 | .856 |
| name | 4.96, 128.90 | 26.91 | 2.02 + | .031 | .080 |
| condition:name | 5.05, 131.26 | 28.06 | 0.41 | .007 | .840 |
EMM <- emmeans(main_anova_summary, ~ condition | name) %>% contrast(interaction = c( "consec", "consec"))
EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
knitr::kable(left_join(EMM.unadj,EMM.fdr))| condition_consec | name | estimate | SE | df | t.ratio | p.value.unadj | p.value.fdr |
|---|---|---|---|---|---|---|---|
| Placebo - Interferon | Default | -0.7170280 | 1.0471181 | 26 | -0.6847632 | 0.4995527 | 0.799321 |
| Placebo - Interferon | Dorsal.Attention | -0.9494949 | 1.0314339 | 26 | -0.9205582 | 0.3657441 | 0.799321 |
| Placebo - Interferon | Frontoparietal | 0.8086420 | 1.3979838 | 26 | 0.5784344 | 0.5679480 | 0.799321 |
| Placebo - Interferon | Limbic | 0.4872286 | 1.6628758 | 26 | 0.2930036 | 0.7718452 | 0.799321 |
| Placebo - Interferon | Somatomotor | 0.9119497 | 0.8757177 | 26 | 1.0413740 | 0.3072892 | 0.799321 |
| Placebo - Interferon | Subcortical | -0.2992593 | 1.1651410 | 26 | -0.2568438 | 0.7993210 | 0.799321 |
| Placebo - Interferon | Ventral.Attention | -0.5385802 | 1.1123413 | 26 | -0.4841862 | 0.6323074 | 0.799321 |
| Placebo - Interferon | Visual | 0.9350282 | 1.3330593 | 26 | 0.7014153 | 0.4892750 | 0.799321 |
# focus on threshold=3.5
knitr::kable(nice(aov_ez("Subj_ID", "meanDegree", data.aggr %>% filter(threshold=="3.5"),within =c("condition","name"))))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| condition | 1, 26 | 28.91 | 0.00 | <.001 | .971 |
| name | 5.25, 136.39 | 38.86 | 1.61 | .024 | .158 |
| condition:name | 5.17, 134.54 | 44.42 | 0.29 | .005 | .924 |
main_anova_summary <- aov_ez("Subj_ID", "meanDegree", data.aggr,within =c("condition","name","threshold"))
knitr::kable(nice(main_anova_summary))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| condition | 1, 26 | 41.18 | 0.03 | <.001 | .856 |
| name | 4.96, 128.90 | 53.83 | 2.02 + | .026 | .080 |
| threshold | 1, 26 | 12.59 | 179071.02 *** | .991 | <.001 |
| condition:name | 5.05, 131.26 | 56.12 | 0.41 | .006 | .840 |
| condition:threshold | 1, 26 | 5.68 | 0.14 | <.001 | .709 |
| name:threshold | 5.16, 134.20 | 10.22 | 0.84 | .002 | .523 |
| condition:name:threshold | 4.99, 129.85 | 10.90 | 0.30 | <.001 | .914 |
EMM <- emmeans(main_anova_summary, ~ condition | name, at=list(threshold="X3.5"))
EMM.unadj <- summary(pairs(EMM), by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(pairs(EMM), by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
knitr::kable(left_join(EMM.unadj,EMM.fdr))| contrast | name | estimate | SE | df | t.ratio | p.value.unadj | p.value.fdr |
|---|---|---|---|---|---|---|---|
| Interferon - Placebo | Default | 0.5790425 | 1.338590 | 26 | 0.4325765 | 0.6688894 | 0.940674 |
| Interferon - Placebo | Dorsal.Attention | 1.1195286 | 1.337177 | 26 | 0.8372331 | 0.4100916 | 0.940674 |
| Interferon - Placebo | Frontoparietal | -0.1308642 | 1.741473 | 26 | -0.0751457 | 0.9406740 | 0.940674 |
| Interferon - Placebo | Limbic | -0.4584930 | 2.144796 | 26 | -0.2137700 | 0.8323940 | 0.940674 |
| Interferon - Placebo | Somatomotor | -1.0230608 | 1.058166 | 26 | -0.9668245 | 0.3425396 | 0.940674 |
| Interferon - Placebo | Subcortical | 0.2503704 | 1.565206 | 26 | 0.1599601 | 0.8741488 | 0.940674 |
| Interferon - Placebo | Ventral.Attention | 0.7199074 | 1.313644 | 26 | 0.5480233 | 0.5883506 | 0.940674 |
| Interferon - Placebo | Visual | -1.2059008 | 1.631061 | 26 | -0.7393352 | 0.4663266 | 0.940674 |
# data.aggr %>% ungroup() %>%
# nest_by(threshold,name) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meanDegree", data, within ="condition")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# knitr::kable(anova_summary)Grouped by scale, condition, subject, age_cat and network:yeo
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "degree") %>% left_join(.,network.combined) %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition,name,Age_cat) %>% summarise(meanDegree = mean(degree)) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanDegree", data.aggr,within =c("condition","name","threshold"), between = "Age_cat")
knitr::kable(nice(main_anova_summary)) | Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| Age_cat | 1, 25 | 55.68 | 0.23 | <.001 | .635 |
| condition | 1, 25 | 40.27 | 0.02 | <.001 | .891 |
| Age_cat:condition | 1, 25 | 40.27 | 1.59 | .003 | .219 |
| name | 4.84, 121.12 | 55.51 | 2.04 + | .027 | .080 |
| Age_cat:name | 4.84, 121.12 | 55.51 | 0.80 | .011 | .548 |
| threshold | 1, 25 | 12.86 | 175158.10 *** | .991 | <.001 |
| Age_cat:threshold | 1, 25 | 12.86 | 0.46 | <.001 | .502 |
| condition:name | 5.06, 126.41 | 56.82 | 0.40 | .006 | .850 |
| Age_cat:condition:name | 5.06, 126.41 | 56.82 | 0.64 | .009 | .671 |
| condition:threshold | 1, 25 | 5.79 | 0.16 | <.001 | .693 |
| Age_cat:condition:threshold | 1, 25 | 5.79 | 0.49 | <.001 | .492 |
| name:threshold | 5.10, 127.48 | 10.02 | 0.88 | .002 | .498 |
| Age_cat:name:threshold | 5.10, 127.48 | 10.02 | 1.86 | .005 | .105 |
| condition:name:threshold | 5.01, 125.33 | 11.00 | 0.31 | <.001 | .906 |
| Age_cat:condition:name:threshold | 5.01, 125.33 | 11.00 | 0.67 | .002 | .649 |
emmip(main_anova_summary, name~condition | Age_cat)
emmip(main_anova_summary, name~Age_cat)
main_anova_summary <- aov_ez("Subj_ID", "meanDegree", data.aggr,within =c("condition","name"),between = "Age_cat")
knitr::kable(nice(main_anova_summary))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| Age_cat | 1, 25 | 27.84 | 0.23 | <.001 | .635 |
| condition | 1, 25 | 20.14 | 0.02 | <.001 | .891 |
| Age_cat:condition | 1, 25 | 20.14 | 1.59 | .004 | .219 |
| name | 4.84, 121.12 | 27.75 | 2.04 + | .033 | .080 |
| Age_cat:name | 4.84, 121.12 | 27.75 | 0.80 | .013 | .548 |
| condition:name | 5.06, 126.41 | 28.41 | 0.40 | .007 | .850 |
| Age_cat:condition:name | 5.06, 126.41 | 28.41 | 0.64 | .011 | .671 |
EMM <- emmeans(main_anova_summary, ~ condition:Age_cat | name) %>% contrast(interaction = c( "consec", "consec"))
EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
knitr::kable(left_join(EMM.unadj,EMM.fdr))| condition_consec | Age_cat_consec | name | estimate | SE | df | t.ratio | p.value.unadj | p.value.fdr |
|---|---|---|---|---|---|---|---|---|
| Placebo - Interferon | Y - O | Default | 0.6627915 | 2.133062 | 25 | 0.3107231 | 0.7585871 | 0.9114497 |
| Placebo - Interferon | Y - O | Dorsal.Attention | -0.5104271 | 2.102688 | 25 | -0.2427498 | 0.8101798 | 0.9114497 |
| Placebo - Interferon | Y - O | Frontoparietal | -2.6672161 | 2.802987 | 25 | -0.9515619 | 0.3504290 | 0.8063462 |
| Placebo - Interferon | Y - O | Limbic | -4.4489390 | 3.275229 | 25 | -1.3583595 | 0.1864820 | 0.8063462 |
| Placebo - Interferon | Y - O | Somatomotor | -1.4984450 | 1.762043 | 25 | -0.8504020 | 0.4031731 | 0.8063462 |
| Placebo - Interferon | Y - O | Subcortical | 0.2670879 | 2.377462 | 25 | 0.1123416 | 0.9114497 | 0.9114497 |
| Placebo - Interferon | Y - O | Ventral.Attention | -2.0906021 | 2.231463 | 25 | -0.9368752 | 0.3577859 | 0.8063462 |
| Placebo - Interferon | Y - O | Visual | 1.5773887 | 2.702433 | 25 | 0.5836921 | 0.5646571 | 0.9034514 |
# focus on threshold=3.5
main_anova_summary <- aov_ez("Subj_ID", "meanDegree", data.aggr,within =c("condition","name","threshold"),between = "Age_cat")
knitr::kable(nice(main_anova_summary))| Effect | df | MSE | F | ges | p.value |
|---|---|---|---|---|---|
| Age_cat | 1, 25 | 55.68 | 0.23 | <.001 | .635 |
| condition | 1, 25 | 40.27 | 0.02 | <.001 | .891 |
| Age_cat:condition | 1, 25 | 40.27 | 1.59 | .003 | .219 |
| name | 4.84, 121.12 | 55.51 | 2.04 + | .027 | .080 |
| Age_cat:name | 4.84, 121.12 | 55.51 | 0.80 | .011 | .548 |
| threshold | 1, 25 | 12.86 | 175158.10 *** | .991 | <.001 |
| Age_cat:threshold | 1, 25 | 12.86 | 0.46 | <.001 | .502 |
| condition:name | 5.06, 126.41 | 56.82 | 0.40 | .006 | .850 |
| Age_cat:condition:name | 5.06, 126.41 | 56.82 | 0.64 | .009 | .671 |
| condition:threshold | 1, 25 | 5.79 | 0.16 | <.001 | .693 |
| Age_cat:condition:threshold | 1, 25 | 5.79 | 0.49 | <.001 | .492 |
| name:threshold | 5.10, 127.48 | 10.02 | 0.88 | .002 | .498 |
| Age_cat:name:threshold | 5.10, 127.48 | 10.02 | 1.86 | .005 | .105 |
| condition:name:threshold | 5.01, 125.33 | 11.00 | 0.31 | <.001 | .906 |
| Age_cat:condition:name:threshold | 5.01, 125.33 | 11.00 | 0.67 | .002 | .649 |
EMM <- emmeans(main_anova_summary, ~ condition:Age_cat | name, at=list(threshold="X3.5")) %>% contrast(interaction = c( "consec", "consec"))
EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
knitr::kable(left_join(EMM.unadj,EMM.fdr))| condition_consec | Age_cat_consec | name | estimate | SE | df | t.ratio | p.value.unadj | p.value.fdr |
|---|---|---|---|---|---|---|---|---|
| Placebo - Interferon | Y - O | Default | 1.3555347 | 2.718588 | 25 | 0.4986172 | 0.6224080 | 0.8082598 |
| Placebo - Interferon | Y - O | Dorsal.Attention | -1.0338412 | 2.721345 | 25 | -0.3799008 | 0.7072273 | 0.8082598 |
| Placebo - Interferon | Y - O | Frontoparietal | -3.4435897 | 3.486996 | 25 | -0.9875520 | 0.3328343 | 0.7663910 |
| Placebo - Interferon | Y - O | Limbic | -6.1788556 | 4.199497 | 25 | -1.4713321 | 0.1536779 | 0.7663910 |
| Placebo - Interferon | Y - O | Somatomotor | -1.5363881 | 2.137754 | 25 | -0.7186925 | 0.4789944 | 0.7663910 |
| Placebo - Interferon | Y - O | Subcortical | -0.2440659 | 3.194224 | 25 | -0.0764085 | 0.9397023 | 0.9397023 |
| Placebo - Interferon | Y - O | Ventral.Attention | -2.1596841 | 2.646136 | 25 | -0.8161650 | 0.4221147 | 0.7663910 |
| Placebo - Interferon | Y - O | Visual | 2.7032036 | 3.284815 | 25 | 0.8229394 | 0.4183234 | 0.7663910 |
# data.aggr %>% ungroup() %>%
# nest_by(threshold,name) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meanDegree", data, within ="condition",between="Age_cat")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# knitr::kable(anova_summary)Hubness?
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "degree") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition) %>% mutate(quartile = ntile(degree, 4)) -> data.aggr
fit1 <- lmer(degree ~ condition*quartile + (1 | Subj_ID), data.aggr )
fit2 <- lmer(degree ~ condition*quartile + Age + (1 | Subj_ID), data.aggr )
fit3 <- lmer(degree ~ condition*quartile*Age + (1 | Subj_ID), data.aggr )
tab_model(fit1,fit2,fit3)| Â | degree | degree | degree | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 21.06 | 19.35 – 22.76 | <0.001 | 21.34 | 19.32 – 23.37 | <0.001 | 22.48 | 18.35 – 26.61 | <0.001 |
| condition [Placebo] | -0.42 | -2.83 – 1.99 | 0.733 | -0.42 | -2.83 – 1.99 | 0.733 | -1.47 | -7.31 – 4.38 | 0.623 |
| quartile | 28.44 | 27.81 – 29.06 | <0.001 | 28.44 | 27.81 – 29.06 | <0.001 | 28.25 | 26.74 – 29.76 | <0.001 |
|
condition [Placebo] × quartile |
0.18 | -0.70 – 1.06 | 0.688 | 0.18 | -0.70 – 1.06 | 0.688 | 0.06 | -2.08 – 2.20 | 0.958 |
| Age | -0.01 | -0.03 – 0.02 | 0.608 | -0.03 | -0.11 – 0.05 | 0.459 | |||
| condition [Placebo] × Age | 0.02 | -0.09 – 0.14 | 0.700 | ||||||
| quartile × Age | 0.00 | -0.03 – 0.03 | 0.793 | ||||||
|
(condition [Placebo] × quartile) × Age |
0.00 | -0.04 – 0.04 | 0.901 | ||||||
| Random Effects | |||||||||
| σ2 | 2803.66 | 2803.71 | 2803.79 | ||||||
| Ï„00 | 0.00 Subj_ID | 0.00 Subj_ID | 0.00 Subj_ID | ||||||
| N | 27 Subj_ID | 27 Subj_ID | 27 Subj_ID | ||||||
| Observations | 44280 | 44280 | 44280 | ||||||
| Marginal R2 / Conditional R2 | 0.266 / NA | 0.266 / NA | 0.266 / NA | ||||||
plot_model(fit1, type = "pred", terms = c("quartile[all]","condition") )
fit1 <- lmer(degree ~ condition*quartile + (1 | Subj_ID), data.aggr %>% filter(threshold==3.5))
fit2 <- lmer(degree ~ condition*quartile + Age + (1 | Subj_ID), data.aggr %>% filter(threshold==3.5))
fit3 <- lmer(degree ~ condition*quartile*Age + (1 | Subj_ID), data.aggr %>% filter(threshold==3.5))
tab_model(fit1,fit2,fit3)| Â | degree | degree | degree | ||||||
|---|---|---|---|---|---|---|---|---|---|
| Predictors | Estimates | CI | p | Estimates | CI | p | Estimates | CI | p |
| (Intercept) | 59.11 | 58.22 – 60.00 | <0.001 | 59.66 | 58.00 – 61.33 | <0.001 | 60.63 | 58.47 – 62.79 | <0.001 |
| condition [Placebo] | -0.15 | -1.07 – 0.77 | 0.745 | -0.15 | -1.07 – 0.77 | 0.745 | -0.81 | -3.04 – 1.42 | 0.475 |
| quartile | 33.68 | 33.44 – 33.92 | <0.001 | 33.68 | 33.44 – 33.92 | <0.001 | 33.58 | 33.00 – 34.16 | <0.001 |
|
condition [Placebo] × quartile |
0.06 | -0.27 – 0.40 | 0.718 | 0.06 | -0.27 – 0.40 | 0.718 | -0.25 | -1.06 – 0.57 | 0.553 |
| Age | -0.01 | -0.04 – 0.02 | 0.438 | -0.03 | -0.08 – 0.01 | 0.128 | |||
| condition [Placebo] × Age | 0.01 | -0.03 – 0.06 | 0.525 | ||||||
| quartile × Age | 0.00 | -0.01 – 0.01 | 0.703 | ||||||
|
(condition [Placebo] × quartile) × Age |
0.01 | -0.01 – 0.02 | 0.415 | ||||||
| Random Effects | |||||||||
| σ2 | 203.96 | 203.96 | 203.86 | ||||||
| Ï„00 | 2.57 Subj_ID | 2.61 Subj_ID | 2.61 Subj_ID | ||||||
| ICC | 0.01 | 0.01 | 0.01 | ||||||
| N | 27 Subj_ID | 27 Subj_ID | 27 Subj_ID | ||||||
| Observations | 22140 | 22140 | 22140 | ||||||
| Marginal R2 / Conditional R2 | 0.873 / 0.875 | 0.873 / 0.875 | 0.873 / 0.875 | ||||||
plot_model(fit1, type = "pred", terms = c("quartile[all]","condition") )
Summary
Betweenness centrality
Grouped by scale, condition, subject
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "bwc") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition) %>% summarise(meanBWC = mean(bwc)) -> data.aggr
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("condition","threshold"))
DT::datatable(nice(main_anova_summary))emmip(main_anova_summary, condition~threshold)
grouped_ggwithinstats(
data = data.aggr,
x = condition,
y = meanBWC,
grouping.var = threshold,
type = "np",
bf.message = FALSE,
)
Grouped by scale, condition, subject and age_cat
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "bwc") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition, Age_cat) %>% summarise(meanBWC = mean(bwc)) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("condition","threshold"), between = "Age_cat")
DT::datatable(nice(main_anova_summary)) emmip(main_anova_summary, condition~Age_cat)
# looking at threshold = 3.5
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("condition","threshold"), between = "Age_cat")
EMM <- emmeans(main_anova_summary, ~condition:Age_cat, at=list(threshold="X3.5")) %>% contrast(interaction = c( "consec", "consec"))
DT::datatable(EMM %>% as.data.frame()) # data.aggr %>% ungroup() %>%
# nest_by(threshold) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meanBWC", data, within ="condition", between="Age_cat")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# DT::datatable(anova_summary)
#
# # aw <- aov_ez("Subj_ID", "meanDegree", data.aggr, within ="condition", between="Age_cat")
# # p_an <- afex_plot(aw, x = "condition", trace = "Age_cat") + ggtitle("test") + theme(legend.position = c(0.87, 0.15),legend.key.size = unit(0.3, "cm"))
#
# # plot anovas
#
# d_nested <- data.aggr %>% ungroup() %>%
# nest_by(threshold)
#
# d_plots <-
# d_nested %>%
# mutate(aov = list(map(data,~aov_ez("Subj_ID", "meanBWC", data, within ="condition", between="Age_cat")))) %>%
# mutate(plot = list(map2(aov,threshold,~afex_plot(., x = "condition", trace = "Age_cat") +ggtitle(paste("threshold:",threshold)) + theme(legend.position = c(0.87, 0.15),legend.key.size = unit(0.3, "cm")))))
#
# d_plots$plot_one <- lapply(d_plots$plot, "[[", 1)
#
# library(gridExtra)
# do.call(grid.arrange, c(d_plots$plot_one))Grouped by scale, condition, subject and network:yeo
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "bwc") %>% left_join(.,network.combined) %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition,name) %>% summarise(meanBWC = mean(bwc)) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("condition","name","threshold"))
DT::datatable(nice(main_anova_summary))#emmip(main_anova_summary, condition~threshold)
# average across threshold
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("condition","name"))
DT::datatable(nice(main_anova_summary))EMM <- emmeans(main_anova_summary, ~ condition | name) %>% contrast(interaction = c( "consec", "consec"))
EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))# focus on threshold=3.5
DT::datatable(nice(aov_ez("Subj_ID", "meanBWC", data.aggr %>% filter(threshold=="3.5"),within =c("condition","name"))))main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("condition","name","threshold"))
DT::datatable(nice(main_anova_summary))EMM <- emmeans(main_anova_summary, ~ condition | name, at=list(threshold="X3.5"))
EMM.unadj <- summary(pairs(EMM), by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(pairs(EMM), by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))# data.aggr %>% ungroup() %>%
# nest_by(threshold,name) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meanBWC", data, within ="condition")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# DT::datatable(anova_summary)Grouped by scale, condition, subject, age_cat and network:yeo
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "bwc") %>% left_join(.,network.combined) %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition,name,Age_cat) %>% summarise(meanBWC = mean(bwc)) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("condition","name","threshold"), between = "Age_cat")
DT::datatable(nice(main_anova_summary)) emmip(main_anova_summary, name~Age_cat | threshold)
emmip(main_anova_summary, ~condition | Age_cat)
# average across threshold
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("condition","name"),between = "Age_cat")
DT::datatable(nice(main_anova_summary))EMM <- emmeans(main_anova_summary, ~ condition:Age_cat | name) %>% contrast(interaction = c( "consec", "consec"))
EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))# focus on threshold=3.5
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("condition","name","threshold"), between = "Age_cat")
DT::datatable(nice(main_anova_summary))EMM <- emmeans(main_anova_summary, ~ condition:Age_cat | name, at=list(threshold="X3.5")) %>% contrast(interaction = c( "consec", "consec"))
EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))Subgroup analysis: Placebo condition only
# testing
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "bwc") %>% left_join(.,network.combined) %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition,Age_cat) %>% summarise(meanBWC = mean(bwc)) %>% filter(condition=="Placebo") -> data.aggr.age
# regular anova
main_anova_summary_a <- aov_ez("Subj_ID", "meanBWC", data.aggr.age, between = "Age_cat")
nice(main_anova_summary_a)## Anova Table (Type 3 tests)
##
## Response: meanBWC
## Effect df MSE F ges p.value
## 1 Age_cat 1, 25 458.00 1.94 .072 .176
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1afex_plot(main_anova_summary_a, "Age_cat")
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "bwc") %>% left_join(.,network.combined) %>% left_join(.,variables_ext) %>% group_by(Subj_ID,condition,Age_cat) %>% summarise(meanBWC = mean(bwc)) %>% filter(condition=="Placebo") -> data.aggr.age
ggbetweenstats(
data = data.aggr.age,
x = Age_cat,
y = meanBWC,
type = "p",
bf.message = FALSE,
)
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "bwc") %>% left_join(.,network.combined) %>% left_join(.,variables_ext) %>% group_by(Subj_ID,condition,Age) %>% summarise(meanBWC = mean(bwc)) %>% filter(condition=="Placebo") -> data.aggr.age
ggscatterstats(
data = data.aggr.age, ## data frame from which variables are taken
x = Age, ## predictor/independent variable
y = meanBWC ## dependent variabl
)
grouped_ggbetweenstats(
data = data.aggr,
x = Age_cat,
y = meanBWC,
grouping.var = threshold,
type = "p",
bf.message = FALSE,
)
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("threshold"), between = "Age_cat")
DT::datatable(nice(main_anova_summary)) data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "bwc") %>% left_join(.,network.combined) %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition,name,Age_cat) %>% summarise(meanBWC = mean(bwc)) %>% filter(condition=="Placebo") -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("name","threshold"), between = "Age_cat")
DT::datatable(nice(main_anova_summary)) emmip(main_anova_summary, name~Age_cat | threshold)
emmip(main_anova_summary, ~ Age_cat)
# average across threshold
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("name"),between = "Age_cat")
DT::datatable(nice(main_anova_summary))EMM <- emmeans(main_anova_summary, ~ Age_cat | name) %>% contrast(interaction = c( "consec", "consec"))
EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))# focus on threshold=3.5
main_anova_summary <- aov_ez("Subj_ID", "meanBWC", data.aggr,within =c("name","threshold"), between = "Age_cat")
DT::datatable(nice(main_anova_summary))EMM <- emmeans(main_anova_summary, ~ Age_cat | name, at=list(threshold="X3.5")) %>% contrast(interaction = c( "consec", "consec"))
EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))Summary
Global efficiency
Grouped by scale, condition, subject
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "geff") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition) %>% summarise(meangeff = mean(geff)) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meangeff", data.aggr,within =c("condition","threshold"))
DT::datatable(nice(main_anova_summary) ) emmip(main_anova_summary, condition~threshold )
grouped_ggwithinstats(
data = data.aggr,
x = condition,
y = meangeff,
grouping.var = threshold,
type = "np",
bf.message = FALSE,
)
Grouped by scale, condition, subject and age_cat
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "geff") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition, Age_cat) %>% summarise(meangeff = mean(geff)) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meangeff", data.aggr,within =c("condition","threshold"), between = "Age_cat")
DT::datatable(nice(main_anova_summary))# anova averaging across threshold
main_anova_summary <- aov_ez("Subj_ID", "meangeff", data.aggr,within =c("condition"), between = "Age_cat")
DT::datatable(nice(main_anova_summary))# looking at threshold = 3.5
main_anova_summary <- aov_ez("Subj_ID", "meangeff", data.aggr,within =c("condition","threshold"), between = "Age_cat")
EMM <- emmeans(main_anova_summary, ~condition:Age_cat, at=list(threshold="X3.5")) %>% contrast(interaction = c( "consec", "consec"))
DT::datatable(EMM %>% as.data.frame()) # data.aggr %>% ungroup() %>%
# nest_by(threshold) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meangeff", data, within ="condition", between="Age_cat")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# DT::datatable(anova_summary)
# aw <- aov_ez("Subj_ID", "meanDegree", data.aggr, within ="condition", between="Age_cat")
# p_an <- afex_plot(aw, x = "condition", trace = "Age_cat") + ggtitle("test") + theme(legend.position = c(0.87, 0.15),legend.key.size = unit(0.3, "cm"))
# plot anovas
#
# d_nested <- data.aggr %>% ungroup() %>%
# nest_by(threshold)
#
# d_plots <-
# d_nested %>%
# mutate(aov = list(map(data,~aov_ez("Subj_ID", "meangeff", data, within ="condition", between="Age_cat")))) %>%
# mutate(plot = list(map2(aov,threshold,~afex_plot(., x = "condition", trace = "Age_cat") +ggtitle(paste("threshold:",threshold)) + theme(legend.position = c(0.87, 0.15),legend.key.size = unit(0.3, "cm")))))
#
# d_plots$plot_one <- lapply(d_plots$plot, "[[", 1)
#
# library(gridExtra)
# do.call(grid.arrange, c(d_plots$plot_one))Grouped by scale, condition, subject and network:yeo
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "n_geff") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "n_geff", data.aggr,within =c("network","condition","threshold"))
DT::datatable(nice(main_anova_summary))emmip(main_anova_summary, network~threshold)
emmip(main_anova_summary, condition~threshold)
emmip(main_anova_summary, condition~threshold | network)
# anova averaging across threshold
main_anova_summary <- aov_ez("Subj_ID", "n_geff", data.aggr,within =c("network","condition"))
DT::datatable(nice(main_anova_summary))main_anova_summary <- aov_ez("Subj_ID", "n_geff", data.aggr,within =c("network","condition"))
EMM <- emmeans(main_anova_summary, ~ condition | network)
EMM.unadj <- summary(pairs(EMM), by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(pairs(EMM), by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))# looking at threshold = 3.5
main_anova_summary <- aov_ez("Subj_ID", "n_geff", data.aggr,within =c("network","condition","threshold"))
EMM <- emmeans(main_anova_summary, ~ condition | network, at=list(threshold="X3.5"))
EMM.unadj <- summary(pairs(EMM), by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(pairs(EMM), by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))Grouped by scale, condition, subject, age_cat and network:yeo
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "n_geff") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "n_geff", data.aggr,within =c("network","condition","threshold"), between = "Age_cat")
DT::datatable(nice(main_anova_summary))emmip(main_anova_summary, condition~Age_cat | network)
# anova averaging across threshold
main_anova_summary <- aov_ez("Subj_ID", "n_geff", data.aggr,within =c("network","condition"), between = "Age_cat")
DT::datatable(nice(main_anova_summary))main_anova_summary <- aov_ez("Subj_ID", "n_geff", data.aggr,within =c("network","condition"))
EMM <- emmeans(main_anova_summary, ~ condition | network)
EMM.unadj <- summary(pairs(EMM), by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(pairs(EMM), by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))# looking at threshold = 3.5
main_anova_summary <- aov_ez("Subj_ID", "n_geff", data.aggr,within =c("condition","threshold","network"), between = "Age_cat")
EMM <- emmeans(main_anova_summary, ~condition:Age_cat | network, at=list(threshold="X3.5")) %>% contrast(interaction = c( "consec", "consec"))
DT::datatable(EMM %>% as.data.frame()) Summary
Local efficiency
Grouped by scale, condition, subject
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "leff") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition) %>% summarise(meanleff = mean(leff)) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanleff", data.aggr,within =c("condition","threshold"))
DT::datatable(nice(main_anova_summary))emmip(main_anova_summary, condition~threshold)
grouped_ggwithinstats(
data = data.aggr,
x = condition,
y = meanleff,
grouping.var = threshold,
type = "np",
bf.message = FALSE,
)
Grouped by scale, condition, subject and age_cat
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "leff") %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition, Age_cat) %>% summarise(meanleff = mean(leff)) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanleff", data.aggr,within =c("condition","threshold"), between = "Age_cat")
DT::datatable(nice(main_anova_summary))main_anova_summary <- aov_ez("Subj_ID", "meanleff", data.aggr,within =c("condition","threshold"), between = "Age_cat")
EMM <- emmeans(main_anova_summary, ~condition:Age_cat, at=list(threshold="X1")) %>% contrast(interaction = c( "consec", "consec"))
EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))# data.aggr %>% ungroup() %>%
# nest_by(threshold) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meanleff", data, within ="condition", between="Age_cat")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# DT::datatable(anova_summary)
# aw <- aov_ez("Subj_ID", "meanDegree", data.aggr, within ="condition", between="Age_cat")
# p_an <- afex_plot(aw, x = "condition", trace = "Age_cat") + ggtitle("test") + theme(legend.position = c(0.87, 0.15),legend.key.size = unit(0.3, "cm"))
# plot anovas
# d_nested <- data.aggr %>% ungroup() %>%
# nest_by(threshold)
#
# d_plots <-
# d_nested %>%
# mutate(aov = list(map(data,~aov_ez("Subj_ID", "meanleff", data, within ="condition", between="Age_cat")))) %>%
# mutate(plot = list(map2(aov,threshold,~afex_plot(., x = "condition", trace = "Age_cat") +ggtitle(paste("threshold:",threshold)) + theme(legend.position = c(0.87, 0.15),legend.key.size = unit(0.3, "cm")))))
#
# d_plots$plot_one <- lapply(d_plots$plot, "[[", 1)
#
# library(gridExtra)
# do.call(grid.arrange, c(d_plots$plot_one))Grouped by scale, condition, subject and network:yeo
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "leff") %>% left_join(.,network.combined) %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition,name) %>% summarise(meanleff = mean(leff)) -> data.aggr
# # regular anova
main_anova_summary <- afex::aov_ez("Subj_ID", "meanleff", data.aggr,within =c("condition","name","threshold"))
# DT::datatable(nice(main_anova_summary) )
#
# # anova averaging across threshold
# main_anova_summary <- aov_ez("Subj_ID", "meanleff", data.aggr,within =c("condition","name"))
# DT::datatable(nice(main_anova_summary))
#
# # looking at threshold = 1
# main_anova_summary <- aov_ez("Subj_ID", "meanleff", data.aggr,within =c("condition","name","threshold"))
# EMM <- emmeans(main_anova_summary, ~condition | name, at=list(threshold="X1")) %>% contrast(interaction = c( "consec", "consec"))
# EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
# EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
#
# # corrected and uncorrected
# DT::datatable(left_join(EMM.unadj,EMM.fdr))
# data.aggr %>% ungroup() %>%
# nest_by(threshold,name) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meanleff", data, within ="condition")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# DT::datatable(anova_summary)Grouped by scale, condition, subject, age_cat and network:yeo
data %>% pivot_longer(
cols = starts_with("ROI"),
names_to = "ROI",
values_to = "leff") %>% left_join(.,network.combined) %>% left_join(.,variables_ext) %>% group_by(threshold,Subj_ID,condition,name,Age_cat) %>% summarise(meanleff = mean(leff)) -> data.aggr
# regular anova
main_anova_summary <- aov_ez("Subj_ID", "meanleff", data.aggr,within =c("condition","name","threshold"), between = "Age_cat")
emmip(main_anova_summary, condition~Age_cat | name)
DT::datatable(nice(main_anova_summary))# anova averaging across threshold
main_anova_summary <- aov_ez("Subj_ID", "meanleff", data.aggr,within =c("condition","name"),between = "Age_cat")
DT::datatable(nice(main_anova_summary) )# looking at threshold = 1
main_anova_summary <- aov_ez("Subj_ID", "meanleff", data.aggr,within =c("condition","name","threshold"),between = "Age_cat")
EMM <- emmeans(main_anova_summary, ~condition:Age_cat | name, at=list(threshold="X1")) %>% contrast(interaction = c( "consec", "consec"))
EMM.unadj <- summary(EMM, by = NULL, adjust = "none") %>% as.data.frame() %>% dplyr::rename(.,p.value.unadj = p.value)
EMM.fdr <- summary(EMM, by = NULL, adjust = "fdr") %>% as.data.frame() %>% dplyr::rename(.,p.value.fdr = p.value)
# corrected and uncorrected
DT::datatable(left_join(EMM.unadj,EMM.fdr))# data.aggr %>% ungroup() %>%
# nest_by(threshold,name) %>%
# mutate(Model = list(nice(aov_ez("Subj_ID", "meanleff", data, within ="condition",between="Age_cat")))) %>% select(-data) %>% unnest(Model) -> anova_summary
#
# DT::datatable(anova_summary)