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#' @name preproc
#' @aliases preproc
#' @title Optional preprocessing step for regressing out noise variables.
#' @description This function saves the residuals after regressing the user-defined
#' noise variables from the user-defined "signal" variables. Noise variables can
#' be nuisance regressors (such as cebral spinal fluid in fMRI), time vectors,
#' or any other vector that the user wishes to regress out.
#' @keywords internal
#' @param Y data: Y = length*dim
#' @param noise string vector indicating which variables (columns) are regressors
#' @param signal string vector indicating which variables to regress out the noise variables from
#' @return \strong{signalmatrix}
preproc <- function(Y, noise, signal) {
Y = as.data.frame(Y)
networkframe <- data.frame(Y[, which(colnames(Y) %in% noise)], seq(nrow(Y)))
nt1 <- as.matrix(networkframe, nrow = nrow(Y), byrow = TRUE)
signalmatrix <- NULL
for (i in signal) {
signalmatrix <- as.data.frame(cbind(networkmatrix, stats::lm(Y[, i] ~ 1 + nt1)$residuals))
}
colnames(signalmatrix) <- signal
return(signalmatrix)
}
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