mi4p (version 1.3)

rubin2bt.all: 2nd Rubin's rule Between-Imputation component (all peptides)

Description

Computes the between-imputation component in the 2nd Rubin's rule for all peptides.

Usage

rubin2bt.all(
  data,
  funcmean = meanImp_emmeans,
  metacond,
  is.parallel = FALSE,
  verbose = FALSE
)

Value

List of variance-covariance matrices.

Arguments

data

dataset

funcmean

function that should be used to compute the mean

metacond

a factor to specify the groups

is.parallel

should parallel computing be used?

verbose

should messages be displayed?

Author

Frédéric Bertrand

References

M. Chion, Ch. Carapito and F. Bertrand (2021). Accounting for multiple imputation-induced variability for differential analysis in mass spectrometry-based label-free quantitative proteomics. tools:::Rd_expr_doi("doi:10.1371/journal.pcbi.1010420").

Examples

library(mi4p)
data(datasim)
datasim_imp <- multi.impute(data = datasim[,-1], conditions = 
attr(datasim,"metadata")$Condition, method = "MLE")
rubin2bt.all(datasim_imp[1:5,,],funcmean = meanImp_emmeans,
attr(datasim,"metadata")$Condition)