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_aromanogsr/", 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$X2
# List to store all 2D matrices
interferon_matrices <- list()
# Loop through all .tsv files in the folder
files <- list.files(path = "conmats_filtered_aromanogsr/", pattern = "ses-I",full.names = TRUE)
for (file in files) {
mat <- read.table(file, sep = "\t", header = FALSE)
interferon_matrices[[file]] <- mat
}
# Combine all matrices into a single 3D matrix
result_interferon_matrix <- abind(interferon_matrices, along = 3)
# List to store all 2D matrices
placebo_matrices <- list()
# Loop through all .tsv files in the folder
files <- list.files(path = "conmats_filtered_aromanogsr/", pattern = "ses-P",full.names = TRUE)
for (file in files) {
mat <- read.table(file, sep = "\t", header = FALSE)
placebo_matrices[[file]] <- mat
}
# Combine all matrices into a single 3D matrix
result_placebo_matrix <- abind(placebo_matrices, along = 3)
# nnodes <- length(brainregions$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\\aromanogsr_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_aromanogsr/sub-0122025_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0122026_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0122027_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0222028_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0222029_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0322030_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0322031_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0322032_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0421002_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0422033_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0422034_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0422035_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0521003_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0522036_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0721005_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0821006_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0821007_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0921009_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0921010_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0921012_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0921014_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0921015_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0921016_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-0921017_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-1021019_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/sub-1121020_ses-I_task-rest_space-MNI152NLin2009cAsym_atlas-Combined_measure-pearsoncorrelation_conmat.tsv
## 0
## conmats_filtered_aromanogsr/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$double.xmin
g1 <- graph.adjacency(A, weighted=TRUE, mode = "upper", diag = FALSE)
g2 <- graph.adjacency(B, weighted=TRUE, mode = "upper", diag = FALSE)
if (nrow(get.data.frame(g1)) != nrow(get.data.frame(g2))) {
break
}
df <- bind_rows(get.data.frame(g1) %>% mutate(condition="Placebo"), get.data.frame(g2) %>% mutate(condition="Interferon")) %>% mutate(sub = i)
tmp = bind_rows(tmp,df)
}
# Perform Wilcoxon signed-rank test
knitr::kable(tmp %>% wilcox_test(weight ~ condition, paired = TRUE, detailed = TRUE, exact=TRUE))| estimate | .y. | group1 | group2 | n1 | n2 | statistic | p | conf.low | conf.high | method | alternative |
|---|---|---|---|---|---|---|---|---|---|---|---|
| -0.0620572 | weight | Interferon | Placebo | 2263815 | 2263815 | 1.028623e+12 | 0 | -0.0624888 | -0.0615767 | Wilcoxon | two.sided |
# Perform Wilcoxon signed-rank test
print(wilcox.test(tmp %>% filter(condition=="Placebo")%>%.$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") %>% .$weight
## V = 1.5338e+12, p-value < 2.2e-16
## alternative hypothesis: true location shift is not equal to 0rm(tmp)List of most Interferon affected connections in graph
Where is the most condition related change happening?
library(igraph)
g_diff_pMinusI <- graph.adjacency(z_mean_diff_funcconn,weighted=TRUE,mode = "upper", diag = FALSE)
df_diff_pMinusI <- get.data.frame(g_diff_pMinusI) %>% mutate(from = gsub("V", "ROI_", from), to = gsub("V", "ROI_", to)) %>% rowwise() %>% mutate(from_network = ifelse(is.na(match(from, network.combined$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 | 0.01 | 5.36 * | .030 | .029 |
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 | 0.01 | 9.78 ** | .044 | .004 |
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 | 0.06 | 1.41 | .046 | .247 |
| condition | 1, 25 | 0.01 | 5.74 * | .033 | .024 |
| Age_cat:condition | 1, 25 | 0.01 | 1.75 | .010 | .198 |
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 | 0.35 | 5.71 * | .026 | .024 |
| from2to | 35, 910 | 0.01 | 81.13 *** | .298 | <.001 |
| condition:from2to | 35, 910 | 0.01 | 3.04 *** | .008 | <.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 | -0.0815160 | 0.0304683 | 26 | -2.6754390 | 0.0127396 | 0.0585078 |
| Interferon - Placebo | Default2Dorsal.Attention | -0.0666573 | 0.0382110 | 26 | -1.7444525 | 0.0928927 | 0.1520062 |
| Interferon - Placebo | Default2Frontoparietal | -0.0506589 | 0.0285547 | 26 | -1.7740987 | 0.0877599 | 0.1504456 |
| Interferon - Placebo | Default2Limbic | -0.0540492 | 0.0210194 | 26 | -2.5713963 | 0.0161985 | 0.0585078 |
| Interferon - Placebo | Default2Somatomotor | -0.0747728 | 0.0381428 | 26 | -1.9603374 | 0.0607544 | 0.1286563 |
| Interferon - Placebo | Default2Subcortical | -0.0228950 | 0.0209601 | 26 | -1.0923129 | 0.2847141 | 0.3416569 |
| Interferon - Placebo | Default2Ventral.Attention | -0.0842890 | 0.0366841 | 26 | -2.2976969 | 0.0298771 | 0.0768269 |
| Interferon - Placebo | Default2Visual | -0.0553459 | 0.0347972 | 26 | -1.5905295 | 0.1238027 | 0.1782759 |
| Interferon - Placebo | Dorsal.Attention2Dorsal.Attention | -0.0947438 | 0.0408481 | 26 | -2.3194173 | 0.0284922 | 0.0768269 |
| Interferon - Placebo | Dorsal.Attention2Frontoparietal | -0.0667919 | 0.0349703 | 26 | -1.9099605 | 0.0672287 | 0.1344575 |
| Interferon - Placebo | Dorsal.Attention2Limbic | -0.0613602 | 0.0273448 | 26 | -2.2439410 | 0.0335718 | 0.0805723 |
| Interferon - Placebo | Dorsal.Attention2Somatomotor | -0.1194299 | 0.0419414 | 26 | -2.8475392 | 0.0084935 | 0.0585078 |
| Interferon - Placebo | Dorsal.Attention2Subcortical | -0.0424110 | 0.0301826 | 26 | -1.4051451 | 0.1718145 | 0.2304044 |
| Interferon - Placebo | Dorsal.Attention2Ventral.Attention | -0.1016978 | 0.0355715 | 26 | -2.8589660 | 0.0082651 | 0.0585078 |
| Interferon - Placebo | Dorsal.Attention2Visual | -0.0687718 | 0.0382276 | 26 | -1.7990106 | 0.0836360 | 0.1504456 |
| Interferon - Placebo | Frontoparietal2Frontoparietal | -0.0340307 | 0.0293319 | 26 | -1.1601926 | 0.2565134 | 0.3184304 |
| Interferon - Placebo | Frontoparietal2Limbic | -0.0327962 | 0.0233959 | 26 | -1.4017917 | 0.1728033 | 0.2304044 |
| Interferon - Placebo | Frontoparietal2Somatomotor | -0.0634794 | 0.0354959 | 26 | -1.7883579 | 0.0853787 | 0.1504456 |
| Interferon - Placebo | Frontoparietal2Subcortical | -0.0177767 | 0.0235041 | 26 | -0.7563213 | 0.4562553 | 0.4977331 |
| Interferon - Placebo | Frontoparietal2Ventral.Attention | -0.0833424 | 0.0346003 | 26 | -2.4087182 | 0.0233922 | 0.0701765 |
| Interferon - Placebo | Frontoparietal2Visual | -0.0545244 | 0.0338889 | 26 | -1.6089172 | 0.1197111 | 0.1782759 |
| Interferon - Placebo | Limbic2Limbic | -0.0507585 | 0.0246055 | 26 | -2.0628958 | 0.0492427 | 0.1107962 |
| Interferon - Placebo | Limbic2Somatomotor | -0.0869891 | 0.0303276 | 26 | -2.8683153 | 0.0080826 | 0.0585078 |
| Interferon - Placebo | Limbic2Subcortical | -0.0108675 | 0.0195128 | 26 | -0.5569417 | 0.5823303 | 0.5989683 |
| Interferon - Placebo | Limbic2Ventral.Attention | -0.0408933 | 0.0249445 | 26 | -1.6393700 | 0.1131819 | 0.1771542 |
| Interferon - Placebo | Limbic2Visual | -0.0701318 | 0.0271168 | 26 | -2.5862831 | 0.0156550 | 0.0585078 |
| Interferon - Placebo | Somatomotor2Somatomotor | -0.1653478 | 0.0555821 | 26 | -2.9748401 | 0.0062556 | 0.0585078 |
| Interferon - Placebo | Somatomotor2Subcortical | -0.0197026 | 0.0341184 | 26 | -0.5774766 | 0.5685851 | 0.5989683 |
| Interferon - Placebo | Somatomotor2Ventral.Attention | -0.1142752 | 0.0444659 | 26 | -2.5699529 | 0.0162522 | 0.0585078 |
| Interferon - Placebo | Somatomotor2Visual | -0.1003946 | 0.0379336 | 26 | -2.6465854 | 0.0136224 | 0.0585078 |
| Interferon - Placebo | Subcortical2Subcortical | 0.0052534 | 0.0301009 | 26 | 0.1745270 | 0.8628033 | 0.8628033 |
| Interferon - Placebo | Subcortical2Ventral.Attention | -0.0322010 | 0.0321531 | 26 | -1.0014873 | 0.3258214 | 0.3665491 |
| Interferon - Placebo | Subcortical2Visual | -0.0281104 | 0.0273554 | 26 | -1.0275998 | 0.3136038 | 0.3641851 |
| Interferon - Placebo | Ventral.Attention2Ventral.Attention | -0.1232454 | 0.0359941 | 26 | -3.4240502 | 0.0020561 | 0.0585078 |
| Interferon - Placebo | Ventral.Attention2Visual | -0.0808948 | 0.0329065 | 26 | -2.4583232 | 0.0209353 | 0.0685156 |
| Interferon - Placebo | Visual2Visual | -0.0557824 | 0.0421366 | 26 | -1.3238459 | 0.1970764 | 0.2533840 |
# 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, 25 | 0.47 | 9.18 ** | .050 | .006 |
| from2to | 35, 875 | 0.00 | 86.31 *** | .059 | <.001 |
| condition:from2to | 35, 875 | 0.00 | 1.47 * | <.001 | .040 |
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 | -0.0875643 | 0.0296459 | 25 | -2.953670 | 0.0067472 | 0.0116009 |
| Interferon - Placebo | Default2Dorsal.Attention | -0.0948400 | 0.0312645 | 25 | -3.033474 | 0.0055708 | 0.0116009 |
| Interferon - Placebo | Default2Frontoparietal | -0.0968643 | 0.0303833 | 25 | -3.188072 | 0.0038261 | 0.0116009 |
| Interferon - Placebo | Default2Limbic | -0.0900159 | 0.0327840 | 25 | -2.745730 | 0.0110233 | 0.0137268 |
| Interferon - Placebo | Default2Somatomotor | -0.1018386 | 0.0319537 | 25 | -3.187067 | 0.0038356 | 0.0116009 |
| Interferon - Placebo | Default2Subcortical | -0.0895642 | 0.0326953 | 25 | -2.739365 | 0.0111878 | 0.0137268 |
| Interferon - Placebo | Default2Ventral.Attention | -0.0977249 | 0.0309474 | 25 | -3.157775 | 0.0041203 | 0.0116009 |
| Interferon - Placebo | Default2Visual | -0.0876043 | 0.0329325 | 25 | -2.660115 | 0.0134403 | 0.0146622 |
| Interferon - Placebo | Dorsal.Attention2Dorsal.Attention | -0.0976720 | 0.0322773 | 25 | -3.026026 | 0.0056717 | 0.0116009 |
| Interferon - Placebo | Dorsal.Attention2Frontoparietal | -0.0986600 | 0.0296995 | 25 | -3.321945 | 0.0027517 | 0.0116009 |
| Interferon - Placebo | Dorsal.Attention2Limbic | -0.1054951 | 0.0344958 | 25 | -3.058206 | 0.0052479 | 0.0116009 |
| Interferon - Placebo | Dorsal.Attention2Somatomotor | -0.1028056 | 0.0326582 | 25 | -3.147924 | 0.0042205 | 0.0116009 |
| Interferon - Placebo | Dorsal.Attention2Subcortical | -0.0865863 | 0.0339517 | 25 | -2.550281 | 0.0172703 | 0.0182861 |
| Interferon - Placebo | Dorsal.Attention2Ventral.Attention | -0.1101701 | 0.0324692 | 25 | -3.393067 | 0.0023063 | 0.0116009 |
| Interferon - Placebo | Dorsal.Attention2Visual | -0.0930802 | 0.0314987 | 25 | -2.955052 | 0.0067250 | 0.0116009 |
| Interferon - Placebo | Frontoparietal2Frontoparietal | -0.0916145 | 0.0292997 | 25 | -3.126806 | 0.0044433 | 0.0116009 |
| Interferon - Placebo | Frontoparietal2Limbic | -0.0994302 | 0.0339011 | 25 | -2.932947 | 0.0070895 | 0.0116009 |
| Interferon - Placebo | Frontoparietal2Somatomotor | -0.0974657 | 0.0342780 | 25 | -2.843392 | 0.0087674 | 0.0126251 |
| Interferon - Placebo | Frontoparietal2Subcortical | -0.0872243 | 0.0317403 | 25 | -2.748065 | 0.0109634 | 0.0137268 |
| Interferon - Placebo | Frontoparietal2Ventral.Attention | -0.0995183 | 0.0302164 | 25 | -3.293519 | 0.0029521 | 0.0116009 |
| Interferon - Placebo | Frontoparietal2Visual | -0.0919560 | 0.0319877 | 25 | -2.874732 | 0.0081414 | 0.0122121 |
| Interferon - Placebo | Limbic2Limbic | -0.0672763 | 0.0354489 | 25 | -1.897841 | 0.0693226 | 0.0693226 |
| Interferon - Placebo | Limbic2Somatomotor | -0.1054085 | 0.0335075 | 25 | -3.145816 | 0.0042423 | 0.0116009 |
| Interferon - Placebo | Limbic2Subcortical | -0.0927505 | 0.0330045 | 25 | -2.810239 | 0.0094791 | 0.0131249 |
| Interferon - Placebo | Limbic2Ventral.Attention | -0.1057917 | 0.0339639 | 25 | -3.114831 | 0.0045747 | 0.0116009 |
| Interferon - Placebo | Limbic2Visual | -0.0995749 | 0.0320278 | 25 | -3.109016 | 0.0046398 | 0.0116009 |
| Interferon - Placebo | Somatomotor2Somatomotor | -0.1260985 | 0.0393978 | 25 | -3.200646 | 0.0037101 | 0.0116009 |
| Interferon - Placebo | Somatomotor2Subcortical | -0.0943096 | 0.0349695 | 25 | -2.696913 | 0.0123461 | 0.0138894 |
| Interferon - Placebo | Somatomotor2Ventral.Attention | -0.1093486 | 0.0367210 | 25 | -2.977819 | 0.0063683 | 0.0116009 |
| Interferon - Placebo | Somatomotor2Visual | -0.0893942 | 0.0327473 | 25 | -2.729821 | 0.0114390 | 0.0137268 |
| Interferon - Placebo | Subcortical2Subcortical | -0.0938103 | 0.0324883 | 25 | -2.887511 | 0.0078985 | 0.0122121 |
| Interferon - Placebo | Subcortical2Ventral.Attention | -0.0806648 | 0.0335897 | 25 | -2.401476 | 0.0240877 | 0.0247759 |
| Interferon - Placebo | Subcortical2Visual | -0.0910929 | 0.0337008 | 25 | -2.702991 | 0.0121737 | 0.0138894 |
| Interferon - Placebo | Ventral.Attention2Ventral.Attention | -0.1121883 | 0.0324130 | 25 | -3.461215 | 0.0019456 | 0.0116009 |
| Interferon - Placebo | Ventral.Attention2Visual | -0.0953847 | 0.0320365 | 25 | -2.977372 | 0.0063751 | 0.0116009 |
| Interferon - Placebo | Visual2Visual | -0.0889294 | 0.0302238 | 25 | -2.942359 | 0.0069320 | 0.0116009 |
- 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 | 0.44 | 7.95 ** | .041 | .009 |
| from2to | 35, 910 | 0.00 | 98.27 *** | .101 | <.001 |
| condition:from2to | 35, 910 | 0.00 | 2.59 *** | .001 | <.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 | -0.0855354 | 0.0282202 | 26 | -3.030996 | 0.0054581 | 0.0183278 |
| Interferon - Placebo | Default2Dorsal.Attention | -0.0928198 | 0.0294128 | 26 | -3.155765 | 0.0040193 | 0.0183278 |
| Interferon - Placebo | Default2Frontoparietal | -0.0803523 | 0.0287010 | 26 | -2.799638 | 0.0095172 | 0.0210624 |
| Interferon - Placebo | Default2Limbic | -0.0757287 | 0.0289449 | 26 | -2.616300 | 0.0146103 | 0.0236781 |
| Interferon - Placebo | Default2Somatomotor | -0.0973355 | 0.0322256 | 26 | -3.020442 | 0.0056002 | 0.0183278 |
| Interferon - Placebo | Default2Subcortical | -0.0753463 | 0.0291738 | 26 | -2.582669 | 0.0157854 | 0.0236781 |
| Interferon - Placebo | Default2Ventral.Attention | -0.0918269 | 0.0301183 | 26 | -3.048870 | 0.0052253 | 0.0183278 |
| Interferon - Placebo | Default2Visual | -0.0910042 | 0.0294374 | 26 | -3.091448 | 0.0047084 | 0.0183278 |
| Interferon - Placebo | Dorsal.Attention2Dorsal.Attention | -0.0975653 | 0.0314085 | 26 | -3.106329 | 0.0045395 | 0.0183278 |
| Interferon - Placebo | Dorsal.Attention2Frontoparietal | -0.0867298 | 0.0291966 | 26 | -2.970549 | 0.0063209 | 0.0189628 |
| Interferon - Placebo | Dorsal.Attention2Limbic | -0.0879343 | 0.0325801 | 26 | -2.699017 | 0.0120583 | 0.0225117 |
| Interferon - Placebo | Dorsal.Attention2Somatomotor | -0.1041896 | 0.0335683 | 26 | -3.103809 | 0.0045677 | 0.0183278 |
| Interferon - Placebo | Dorsal.Attention2Subcortical | -0.0802048 | 0.0323001 | 26 | -2.483114 | 0.0197987 | 0.0263983 |
| Interferon - Placebo | Dorsal.Attention2Ventral.Attention | -0.0989879 | 0.0317584 | 26 | -3.116906 | 0.0044231 | 0.0183278 |
| Interferon - Placebo | Dorsal.Attention2Visual | -0.0823756 | 0.0309218 | 26 | -2.664000 | 0.0130830 | 0.0225117 |
| Interferon - Placebo | Frontoparietal2Frontoparietal | -0.0750047 | 0.0258955 | 26 | -2.896431 | 0.0075565 | 0.0191252 |
| Interferon - Placebo | Frontoparietal2Limbic | -0.0759694 | 0.0321210 | 26 | -2.365100 | 0.0257684 | 0.0318013 |
| Interferon - Placebo | Frontoparietal2Somatomotor | -0.0869983 | 0.0335606 | 26 | -2.592276 | 0.0154410 | 0.0236781 |
| Interferon - Placebo | Frontoparietal2Subcortical | -0.0666030 | 0.0304656 | 26 | -2.186169 | 0.0379992 | 0.0404439 |
| Interferon - Placebo | Frontoparietal2Ventral.Attention | -0.0918914 | 0.0287749 | 26 | -3.193452 | 0.0036617 | 0.0183278 |
| Interferon - Placebo | Frontoparietal2Visual | -0.0801337 | 0.0320051 | 26 | -2.503781 | 0.0188950 | 0.0261624 |
| Interferon - Placebo | Limbic2Limbic | -0.0449193 | 0.0318295 | 26 | -1.411246 | 0.1700270 | 0.1700270 |
| Interferon - Placebo | Limbic2Somatomotor | -0.0874129 | 0.0328324 | 26 | -2.662397 | 0.0131318 | 0.0225117 |
| Interferon - Placebo | Limbic2Subcortical | -0.0715271 | 0.0323647 | 26 | -2.210034 | 0.0361101 | 0.0404439 |
| Interferon - Placebo | Limbic2Ventral.Attention | -0.0766561 | 0.0332114 | 26 | -2.308126 | 0.0292047 | 0.0339151 |
| Interferon - Placebo | Limbic2Visual | -0.0843513 | 0.0314519 | 26 | -2.681912 | 0.0125491 | 0.0225117 |
| Interferon - Placebo | Somatomotor2Somatomotor | -0.1399491 | 0.0414672 | 26 | -3.374935 | 0.0023272 | 0.0183278 |
| Interferon - Placebo | Somatomotor2Subcortical | -0.0855542 | 0.0340732 | 26 | -2.510896 | 0.0185929 | 0.0261624 |
| Interferon - Placebo | Somatomotor2Ventral.Attention | -0.1041441 | 0.0374484 | 26 | -2.781001 | 0.0099461 | 0.0210624 |
| Interferon - Placebo | Somatomotor2Visual | -0.0917021 | 0.0319048 | 26 | -2.874245 | 0.0079688 | 0.0191252 |
| Interferon - Placebo | Subcortical2Subcortical | -0.0669558 | 0.0306612 | 26 | -2.183732 | 0.0381970 | 0.0404439 |
| Interferon - Placebo | Subcortical2Ventral.Attention | -0.0678052 | 0.0317615 | 26 | -2.134825 | 0.0423677 | 0.0435782 |
| Interferon - Placebo | Subcortical2Visual | -0.0739030 | 0.0311656 | 26 | -2.371299 | 0.0254177 | 0.0318013 |
| Interferon - Placebo | Ventral.Attention2Ventral.Attention | -0.1060082 | 0.0341916 | 26 | -3.100414 | 0.0046060 | 0.0183278 |
| Interferon - Placebo | Ventral.Attention2Visual | -0.0880488 | 0.0304776 | 26 | -2.888971 | 0.0076928 | 0.0191252 |
| Interferon - Placebo | Visual2Visual | -0.0727204 | 0.0309134 | 26 | -2.352394 | 0.0265011 | 0.0318013 |
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 | 1.96 | 1.22 | .032 | .279 |
| condition | 1, 25 | 0.34 | 6.12 * | .028 | .021 |
| Age_cat:condition | 1, 25 | 0.34 | 1.78 | .008 | .195 |
| from2to | 35, 875 | 0.01 | 84.28 *** | .309 | <.001 |
| Age_cat:from2to | 35, 875 | 0.01 | 2.11 *** | .011 | <.001 |
| condition:from2to | 35, 875 | 0.01 | 3.06 *** | .008 | <.001 |
| Age_cat:condition:from2to | 35, 875 | 0.01 | 0.84 | .002 | .738 |
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 | -0.0653514 | 0.0264275 | 25 | -2.472862 | 0.0205555 | 0.0205555 |
# 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
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 | 5.94 | 7.35 * | .089 | .012 |
| threshold | 1, 26 | 2.32 | 121615.21 *** | .998 | <.001 |
| condition:threshold | 1, 26 | 1.57 | 14.97 *** | .050 | <.001 |
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 | 7.24 | 1.40 | .023 | .248 |
| condition | 1, 25 | 6.17 | 7.08 * | .092 | .013 |
| Age_cat:condition | 1, 25 | 6.17 | 0.01 | <.001 | .941 |
| threshold | 1, 25 | 2.31 | 122179.17 *** | .998 | <.001 |
| Age_cat:threshold | 1, 25 | 2.31 | 1.15 | .006 | .294 |
| condition:threshold | 1, 25 | 1.61 | 14.79 *** | .052 | <.001 |
| Age_cat:condition:threshold | 1, 25 | 1.61 | 0.41 | .002 | .529 |
#
# 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 | 50.57 | 4.96 * | .006 | .035 |
| name | 5.11, 132.83 | 136.59 | 1.74 | .028 | .129 |
| threshold | 1, 26 | 21.77 | 103702.63 *** | .982 | <.001 |
| condition:name | 5.44, 141.41 | 87.59 | 0.53 | .006 | .772 |
| condition:threshold | 1, 26 | 13.29 | 13.27 ** | .004 | .001 |
| name:threshold | 5.32, 138.45 | 31.72 | 1.16 | .005 | .331 |
| condition:name:threshold | 5.45, 141.62 | 21.72 | 0.89 | .003 | .494 |
#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 | 25.29 | 4.96 * | .007 | .035 |
| name | 5.11, 132.83 | 68.30 | 1.74 | .035 | .129 |
| condition:name | 5.44, 141.41 | 43.79 | 0.53 | .007 | .772 |
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 | -2.3071364 | 1.353114 | 26 | -1.7050576 | 0.1001058 | 0.3916545 |
| Placebo - Interferon | Dorsal.Attention | 0.1031145 | 1.544432 | 26 | 0.0667653 | 0.9472793 | 0.9472793 |
| Placebo - Interferon | Frontoparietal | -2.2592593 | 1.510885 | 26 | -1.4953219 | 0.1468704 | 0.3916545 |
| Placebo - Interferon | Limbic | 0.1915709 | 1.904128 | 26 | 0.1006082 | 0.9206333 | 0.9472793 |
| Placebo - Interferon | Somatomotor | -0.2603075 | 1.596335 | 26 | -0.1630656 | 0.8717276 | 0.9472793 |
| Placebo - Interferon | Subcortical | -0.7014815 | 1.469710 | 26 | -0.4772923 | 0.6371418 | 0.9472793 |
| Placebo - Interferon | Ventral.Attention | -0.7500000 | 1.550439 | 26 | -0.4837341 | 0.6326239 | 0.9472793 |
| Placebo - Interferon | Visual | -2.6377903 | 1.509883 | 26 | -1.7470168 | 0.0924388 | 0.3916545 |
# 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 | 52.98 | 8.00 ** | .012 | .009 |
| name | 5.36, 139.38 | 132.72 | 1.58 | .031 | .164 |
| condition:name | 5.49, 142.71 | 90.53 | 0.63 | .009 | .695 |
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 | 50.57 | 4.96 * | .006 | .035 |
| name | 5.11, 132.83 | 136.59 | 1.74 | .028 | .129 |
| threshold | 1, 26 | 21.77 | 103702.63 *** | .982 | <.001 |
| condition:name | 5.44, 141.41 | 87.59 | 0.53 | .006 | .772 |
| condition:threshold | 1, 26 | 13.29 | 13.27 ** | .004 | .001 |
| name:threshold | 5.32, 138.45 | 31.72 | 1.16 | .005 | .331 |
| condition:name:threshold | 5.45, 141.62 | 21.72 | 0.89 | .003 | .494 |
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 | 3.2859079 | 1.892675 | 26 | 1.7361188 | 0.0943806 | 0.2516817 |
| Interferon - Placebo | Dorsal.Attention | 0.4747475 | 2.303777 | 26 | 0.2060735 | 0.8383386 | 0.9725700 |
| Interferon - Placebo | Frontoparietal | 4.2880658 | 2.233913 | 26 | 1.9195312 | 0.0659543 | 0.2516817 |
| Interferon - Placebo | Limbic | 0.3793103 | 2.841336 | 26 | 0.1334972 | 0.8948281 | 0.9725700 |
| Interferon - Placebo | Somatomotor | 0.0768693 | 2.214126 | 26 | 0.0347177 | 0.9725700 | 0.9725700 |
| Interferon - Placebo | Subcortical | 1.2533333 | 2.039670 | 26 | 0.6144785 | 0.5442395 | 0.8707832 |
| Interferon - Placebo | Ventral.Attention | 1.5000000 | 2.243949 | 26 | 0.6684644 | 0.5097289 | 0.8707832 |
| Interferon - Placebo | Visual | 4.5900816 | 2.163629 | 26 | 2.1214731 | 0.0435750 | 0.2516817 |
# 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 | 66.89 | 0.67 | .001 | .420 |
| condition | 1, 25 | 52.52 | 4.80 * | .006 | .038 |
| Age_cat:condition | 1, 25 | 52.52 | 0.04 | <.001 | .853 |
| name | 5.16, 128.88 | 137.25 | 1.76 | .029 | .124 |
| Age_cat:name | 5.16, 128.88 | 137.25 | 0.64 | .011 | .673 |
| threshold | 1, 25 | 21.98 | 102602.39 *** | .982 | <.001 |
| Age_cat:threshold | 1, 25 | 21.98 | 0.75 | <.001 | .393 |
| condition:name | 5.33, 133.17 | 89.96 | 0.51 | .006 | .782 |
| Age_cat:condition:name | 5.33, 133.17 | 89.96 | 0.85 | .010 | .525 |
| condition:threshold | 1, 25 | 13.47 | 13.28 ** | .004 | .001 |
| Age_cat:condition:threshold | 1, 25 | 13.47 | 0.64 | <.001 | .433 |
| name:threshold | 5.33, 133.16 | 32.62 | 1.12 | .005 | .352 |
| Age_cat:name:threshold | 5.33, 133.16 | 32.62 | 0.27 | .001 | .937 |
| condition:name:threshold | 5.39, 134.74 | 22.25 | 0.88 | .003 | .502 |
| Age_cat:condition:name:threshold | 5.39, 134.74 | 22.25 | 0.65 | .002 | .671 |
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 | 33.45 | 0.67 | .001 | .420 |
| condition | 1, 25 | 26.26 | 4.80 * | .008 | .038 |
| Age_cat:condition | 1, 25 | 26.26 | 0.04 | <.001 | .853 |
| name | 5.16, 128.88 | 68.63 | 1.76 | .037 | .124 |
| Age_cat:name | 5.16, 128.88 | 68.63 | 0.64 | .014 | .673 |
| condition:name | 5.33, 133.17 | 44.98 | 0.51 | .007 | .782 |
| Age_cat:condition:name | 5.33, 133.17 | 44.98 | 0.85 | .012 | .525 |
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 | -1.3027674 | 2.749397 | 25 | -0.4738374 | 0.6397279 | 0.9112563 |
| Placebo - Interferon | Y - O | Dorsal.Attention | -4.8753122 | 2.997600 | 25 | -1.6264049 | 0.1164043 | 0.5514486 |
| Placebo - Interferon | Y - O | Frontoparietal | -1.1170330 | 3.075625 | 25 | -0.3631889 | 0.7195167 | 0.9112563 |
| Placebo - Interferon | Y - O | Limbic | 0.0832702 | 3.886306 | 25 | 0.0214266 | 0.9830755 | 0.9830755 |
| Placebo - Interferon | Y - O | Somatomotor | 3.3207029 | 3.189726 | 25 | 1.0410621 | 0.3078106 | 0.8208283 |
| Placebo - Interferon | Y - O | Subcortical | -0.7774725 | 2.995658 | 25 | -0.2595331 | 0.7973492 | 0.9112563 |
| Placebo - Interferon | Y - O | Ventral.Attention | -1.3258929 | 3.153328 | 25 | -0.4204742 | 0.6777314 | 0.9112563 |
| Placebo - Interferon | Y - O | Visual | 4.5163904 | 2.946330 | 25 | 1.5328871 | 0.1378622 | 0.5514486 |
# 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 | 66.89 | 0.67 | .001 | .420 |
| condition | 1, 25 | 52.52 | 4.80 * | .006 | .038 |
| Age_cat:condition | 1, 25 | 52.52 | 0.04 | <.001 | .853 |
| name | 5.16, 128.88 | 137.25 | 1.76 | .029 | .124 |
| Age_cat:name | 5.16, 128.88 | 137.25 | 0.64 | .011 | .673 |
| threshold | 1, 25 | 21.98 | 102602.39 *** | .982 | <.001 |
| Age_cat:threshold | 1, 25 | 21.98 | 0.75 | <.001 | .393 |
| condition:name | 5.33, 133.17 | 89.96 | 0.51 | .006 | .782 |
| Age_cat:condition:name | 5.33, 133.17 | 89.96 | 0.85 | .010 | .525 |
| condition:threshold | 1, 25 | 13.47 | 13.28 ** | .004 | .001 |
| Age_cat:condition:threshold | 1, 25 | 13.47 | 0.64 | <.001 | .433 |
| name:threshold | 5.33, 133.16 | 32.62 | 1.12 | .005 | .352 |
| Age_cat:name:threshold | 5.33, 133.16 | 32.62 | 0.27 | .001 | .937 |
| condition:name:threshold | 5.39, 134.74 | 22.25 | 0.88 | .003 | .502 |
| Age_cat:condition:name:threshold | 5.39, 134.74 | 22.25 | 0.65 | .002 | .671 |
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 | -2.321563 | 3.834958 | 25 | -0.6053684 | 0.5503908 | 0.8121239 |
| Placebo - Interferon | Y - O | Dorsal.Attention | -6.400849 | 4.524404 | 25 | -1.4147385 | 0.1694805 | 0.6779219 |
| Placebo - Interferon | Y - O | Frontoparietal | -2.151038 | 4.539093 | 25 | -0.4738915 | 0.6396898 | 0.8121239 |
| Placebo - Interferon | Y - O | Limbic | -1.391436 | 5.792509 | 25 | -0.2402130 | 0.8121239 | 0.8121239 |
| Placebo - Interferon | Y - O | Somatomotor | 4.929090 | 4.410212 | 25 | 1.1176537 | 0.2743408 | 0.7315754 |
| Placebo - Interferon | Y - O | Subcortical | -1.926593 | 4.145112 | 25 | -0.4647868 | 0.6461069 | 0.8121239 |
| Placebo - Interferon | Y - O | Ventral.Attention | -1.458791 | 4.570616 | 25 | -0.3191673 | 0.7522512 | 0.8121239 |
| Placebo - Interferon | Y - O | Visual | 6.053734 | 4.246766 | 25 | 1.4254929 | 0.1663818 | 0.6779219 |
# 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) | -11.40 | -13.22 – -9.57 | <0.001 | -10.78 | -12.95 – -8.61 | <0.001 | -10.17 | -14.60 – -5.74 | <0.001 |
| condition [Placebo] | -6.19 | -8.77 – -3.60 | <0.001 | -6.19 | -8.77 – -3.60 | <0.001 | -6.58 | -12.84 – -0.32 | 0.040 |
| quartile | 41.70 | 41.04 – 42.37 | <0.001 | 41.70 | 41.04 – 42.37 | <0.001 | 41.48 | 39.86 – 43.10 | <0.001 |
|
condition [Placebo] × quartile |
1.97 | 1.02 – 2.91 | <0.001 | 1.97 | 1.02 – 2.91 | <0.001 | 2.10 | -0.19 – 4.39 | 0.073 |
| Age | -0.01 | -0.04 – 0.01 | 0.301 | -0.03 | -0.11 – 0.06 | 0.552 | |||
| condition [Placebo] × Age | 0.01 | -0.11 – 0.13 | 0.892 | ||||||
| quartile × Age | 0.00 | -0.03 – 0.04 | 0.761 | ||||||
|
(condition [Placebo] × quartile) × Age |
-0.00 | -0.05 – 0.04 | 0.904 | ||||||
| Random Effects | |||||||||
| σ2 | 3218.54 | 3218.54 | 3218.75 | ||||||
| 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 | 0.415 / NA | 0.415 / NA | 0.415 / 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) | 3.03 | 1.83 – 4.23 | <0.001 | 4.08 | 2.04 – 6.11 | <0.001 | 5.39 | 2.49 – 8.29 | <0.001 |
| condition [Placebo] | -9.78 | -11.16 – -8.39 | <0.001 | -9.78 | -11.16 – -8.39 | <0.001 | -10.08 | -13.43 – -6.73 | <0.001 |
| quartile | 56.60 | 56.24 – 56.96 | <0.001 | 56.60 | 56.24 – 56.96 | <0.001 | 56.12 | 55.25 – 56.98 | <0.001 |
|
condition [Placebo] × quartile |
3.04 | 2.53 – 3.54 | <0.001 | 3.04 | 2.53 – 3.54 | <0.001 | 3.08 | 1.85 – 4.30 | <0.001 |
| Age | -0.02 | -0.06 – 0.01 | 0.211 | -0.05 | -0.11 – 0.01 | 0.080 | |||
| condition [Placebo] × Age | 0.01 | -0.06 – 0.07 | 0.847 | ||||||
| quartile × Age | 0.01 | -0.01 – 0.03 | 0.230 | ||||||
|
(condition [Placebo] × quartile) × Age |
-0.00 | -0.02 – 0.02 | 0.944 | ||||||
| Random Effects | |||||||||
| σ2 | 461.00 | 461.00 | 461.00 | ||||||
| Ï„00 | 3.43 Subj_ID | 3.34 Subj_ID | 3.34 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 | 0.901 / 0.902 | 0.901 / 0.902 | 0.901 / 0.902 | ||||||
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, 25 913.52 0.20 .008 .655
## ---
## 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)