fudge2LRT: Heuristic to choose the value of the hyperparameter (fudge...
In DAPAR: Tools for the Differential Analysis of Proteins Abundance with R

Description Usage Arguments Value Author(s)

fudge2LRT: heuristic to choose the value of the hyperparameter (fudge factor) used to regularize the variance estimator in the likelihood ratio statistic (as implemented in samLRT). We follow the heuristic described in [1] and adapt the code of the fudge2 function in the siggene R package. [1] Tusher, Tibshirani and Chu, Significance analysis of microarrays applied to the ionizing radiation response, PNAS 2001 98: 5116-5121, (Apr 24).

fudge2LRT(
  lmm.res.h0,
  lmm.res.h1,
  cc,
  n,
  p,
  s,
  alpha = seq(0, 1, 0.05),
  include.zero = TRUE
)

`lmm.res.h0`	a vector of object containing the estimates (used to compute the statistic) under H0 for each connected component. If the fast version of the estimator was used (as implemented in this package), lmm.res.h0 is a vector containing averages of squared residuals. If a fixed effect model was used, it is a vector of lm objects and if a mixed effect model was used it is a vector or lmer object.
`lmm.res.h1`	similar to lmm.res.h0, a vector of object containing the estimates (used to compute the statistic) under H1 for each protein.
`cc`	a list containing the indices of peptides and proteins belonging to each connected component.
`n`	the number of samples used in the test
`p`	the number of proteins in the experiment
`s`	a vector containing the maximum likelihood estimate of the variance for the chosen model. When using the fast version of the estimator implemented in this package, this is the same thing as the input lmm.res.h1. For other models (e.g. mixed models) it can be obtained from samLRT.
`alpha`	A vector of proportions used to build candidate values for the regularizer. We use quantiles of s with these proportions. Default to seq(0, 1, 0.05)
`include.zero`	logical value indicating if 0 should be included in the list of candidates. Default to TRUE.

(same as the fudge2 function of siggene): s.zero: the value of the fudge factor s0. alpha.hat: the optimal quantile of the 's' values. If s0=0, 'alpha.hat' will not be returned. vec.cv: the vector of the coefficients of variations. Following Tusher et al. (2001), the optimal 'alpha' quantile is given by the quantile that leads to the smallest CV of the modified test statistics. msg: a character string summarizing the most important information about the fudge factor.

Thomas Burger, Laurent Jacob

DAPAR documentation built on April 11, 2021, 6 p.m.