squeezeVarRob: Robustly Squeeze Sample Variances

Description Usage Arguments Value

View source: R/squeezeVarRob.R View source: R/squeezeVarRob.R

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

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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 Nov. 24, 2018, 6:52 a.m.