network_lme: network_lme

In FCtools: Functional Connectivity Tools

network_lme

Description

mass univariate linear mixed effects analysis at the network level

Usage

network_lme(
  model,
  contrast,
  random,
  FC_data,
  threshold.method = "fdr",
  perm = TRUE,
  nperm = 1000,
  perm_type = "within_between",
  nthread = 4
)

Arguments

model	A data.frame or matrix containing all the predictors in the model
contrast	The predictor of interest. The edge- and network-wise statistics will only be estimated for this predictor
random	A N x 1 numeric vector or object containing the values of the random variable (optional). Its length should be equal to the number of subjects in model (it should NOT be inside the model data.frame).
FC_data	An N x E matrix containing the vectorized edges; where N = number of subjects, E=number of edges
threshold.method	method for correcting for multiple tests. set to fdr by default
perm	If set to TRUE, p values will be calculated using a permutation approach by shuffling subjects' labels, before correcting for FDR. Set to TRUE by default
nperm	number of permutations to use if perm=TRUE.
perm_type	A string object specifying whether to permute the rows ("row"), between subjects ("between"), within subjects ("within") or between and within subjects ("within_between") for random subject effects. Default is "row".
nthread	The number of CPU threads to use. Default is 4.

Details

This function first summarizes the FC edges into their respective networks and then carry out mass univariate linear mixed effect analyses on each of the network to network connection

Value

A data.frame object with coef and corrected p values

Examples

demomat=get('demomat')[,1:7021] 
contrast=c(1,1,2,2)
random=c('sub1','sub2','sub3','sub4')
model1=network_lme(model=contrast, 
                   contrast=contrast, 
                   random=random, 
                   FC_data=demomat, 
                   nperm=1, 
                   nthread=2)

FCtools documentation built on Sept. 15, 2026, 5:09 p.m.