Computes the 2nd Rubin's rule (all peptides)

Computes the total variance-covariance component in the 2nd Rubin's rule for all peptides.

rubin2.all(
  data,
  metacond,
  funcmean = meanImp_emmeans,
  funcvar = within_variance_comp_emmeans,
  is.parallel = FALSE,
  verbose = FALSE
)

Arguments

data	dataset
metacond	a factor to specify the groups
funcmean	function that should be used to compute the mean
funcvar	function that should be used to compute the variance
is.parallel	should parallel computing be used?
verbose	should messages be displayed?

Value

List of variance-covariance matrices.

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. arxiv:2108.07086. https://arxiv.org/abs/2108.07086.

Author

Frédéric Bertrand

Examples

library(mi4p)
data(datasim)
datasim_imp <- multi.impute(data = datasim[,-1], conditions = 
attr(datasim,"metadata")$Condition, method = "MLE")
rubin2.all(datasim_imp[1:5,,],attr(datasim,"metadata")$Condition)
#> [[1]]
#>           [,1]      [,2]
#> [1,] 0.1911347 0.0000000
#> [2,] 0.0000000 0.1911347
#> 
#> [[2]]
#>           [,1]      [,2]
#> [1,] 0.1265196 0.0000000
#> [2,] 0.0000000 0.1265196
#> 
#> [[3]]
#>           [,1]      [,2]
#> [1,] 0.2441502 0.0000000
#> [2,] 0.0000000 0.2441502
#> 
#> [[4]]
#>           [,1]      [,2]
#> [1,] 0.1465614 0.0000000
#> [2,] 0.0000000 0.1465614
#> 
#> [[5]]
#>           [,1]      [,2]
#> [1,] 0.1341801 0.0000000
#> [2,] 0.0000000 0.1341801
#>