permute.residuals.iter:

In brentp/bootstrip: Resampling residuals of a linear model to determine significance.

Usage

permute.residuals.iter(mat, mod, mod0, iterations = c(200, 5000, 1e+05, 2e+06), p_samples = 1, mc.cores = 12)

Arguments

mat
mod
mod0
iterations
p_samples
mc.cores

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (mat, mod, mod0, iterations = c(200, 5000, 1e+05, 2e+06), 
    p_samples = 1, mc.cores = 12) 
{
    stopifnot(nrow(mod) == ncol(mat))
    subset = rep(TRUE, nrow(mat))
    df = NA
    for (it in iterations) {
        message(paste("sampling", sum(subset), "rows", it, "times"))
        if (any(is.na(df))) {
            df = permute.residuals(mat, mod, mod0, it, p_samples, 
                mc.cores)
        }
        else {
            df[subset, ] = permute.residuals(mat[subset, ], mod, 
                mod0, it, p_samples, mc.cores)
        }
        df[subset, "samples"] = it
        subset = (df$pvalue <= (2/it))
        if (sum(subset) == 0) {
            break
        }
    }
    df
  }

brentp/bootstrip documentation built on May 13, 2019, 5:11 a.m.