squeezeVarRob: Robustly Squeeze Sample Variances

In statOmics/MSqRob: Robust statistical inference for quantitative LC-MS proteomics

Robustly Squeeze Sample Variances

Description

This function squeezes a set of sample variances together by computing empirical Bayes posterior means in a way that is robust against the presence of very small non-integer degrees of freedom values.

Usage

squeezeVarRob(var, df, covariate = NULL, robust = FALSE,
  winsor.tail.p = c(0.05, 0.1), min_df = 1)

Arguments

var	A numeric vector of independent sample variances.
df	A numeric vector of degrees of freedom for the sample variances.
covariate	If non-NULL, var.prior will depend on this numeric covariate. Otherwise, var.prior is constant.
robust	A logical indicating wheter the estimation of df.prior and var.prior should be robustified against outlier sample variances. Defaults to FALSE.
winsor.tail.p	A numeric vector of length 1 or 2, giving left and right tail proportions of x to Winsorize. Used only when robust=TRUE.
min_df	A numeric value indicating the minimal degrees of freedom that will be taken into account for calculating the prior degrees of freedom and prior variance.
k	A numeric value indicating that the calculation of the robust squeezed variances should Winsorize at k standard deviations.

Value

A list with components: var.post A numeric vector of posterior variances. var.prior The location of prior distribution. A vector if covariate is non-NULL, otherwise a scalar. df.prior The degrees of freedom of prior distribution. A vector if robust=TRUE, otherwise a scalar.

statOmics/MSqRob documentation built on Dec. 8, 2022, 6 a.m.