network_lme

mass univariate linear mixed effects analysis at the network level

Usage

network_lme(
  model,
  contrast,
  random,
  FC_data,
  threshold.method = "fdr",
  perm = T,
  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=T.

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".

Value

Returns a data.frame object with coef and corrected p values

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