tmixture: Estimate Scale Factor in Mixture of t-Distributions
In hdeberg/limma: Linear Models for Microarray Data

Description Usage Arguments Details Value Author(s) See Also

These functions estimate the unscaled standard deviation of the true (unobserved) log fold changes for differentially expressed genes. They are used by the functions ebayes and eBayes and are not intended to be called directly by users.

1 2	tmixture.vector(tstat, stdev.unscaled, df, proportion, v0.lim = NULL) tmixture.matrix(tstat, stdev.unscaled, df, proportion, v0.lim = NULL)

`tstat`	numeric vector or matrix of t-statistics. `tmixture.vector` assumes a vector while `tmixture.matrix` assumes a matrix.
`stdev.unscaled`	numeric vector or matrix, conformal with `tstat`, containing the unscaled standard deviations of the coefficients used to compute the t-statistics.
`df`	numeric vector giving the degrees of freedom associated with `tstat`.
`proportion`	assumed proportion of genes that are differentially expressed.
`v0.lim`	numeric vector of length 2 giving the lower and upper limits for the estimated unscaled standard deviations.

The values in each column of tstat are assumed to follow a mixture of an ordinary t-distribution, with mixing proportion 1-proportion, and (v0+v1)/v1 times a t-distribution, with mixing proportion proportion. Here v1 is stdev.unscaled^2 and v0 is the value to be estimated.

Numeric vector, of length equal to the number of columns of tstat, containing estimated v0 values.

Gordon Smyth

eBayes

hdeberg/limma documentation built on Dec. 20, 2021, 3:43 p.m.