#-------------------------------------------------------------------
## function for cross validation using linear model
#-------------------------------------------------------------------
runlinear <- function(x,
y,
nPredics,
fwerRate = 0.25,
adjust_method = "fdr") {
results <- list()
bootResu <- lm_sparse(x = x, y = y)
p_value_est <- bootResu[, 4]
p_value_est_noint <-
p_value_est[-seq(1, length(p_value_est), by = (nPredics + 1))]
p_value_est_noint_adj <- p.adjust(p_value_est_noint, adjust_method)
p_value_est_noint_adj[is.na(p_value_est_noint_adj)] <- 1
coef_est <- abs(bootResu[, 1])
coef_est_noint <- coef_est[-seq(1, length(coef_est), by = (nPredics +
1))]
coef_est_noint[is.na(coef_est_noint)] <-
max(coef_est_noint, na.rm = TRUE)
# return
results$betaNoInt <- p_value_est_noint_adj < fwerRate
results$betaInt <- p_value_est
results$coef_est_noint <- coef_est_noint
return(results)
}
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