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. doi:10.1371/journal.pcbi.1010420 .

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]]
#>           A         B
#> A 0.1911347 0.0000000
#> B 0.0000000 0.1911347
#> 
#> [[2]]
#>           A         B
#> A 0.1265196 0.0000000
#> B 0.0000000 0.1265196
#> 
#> [[3]]
#>           A         B
#> A 0.2441502 0.0000000
#> B 0.0000000 0.2441502
#> 
#> [[4]]
#>           A         B
#> A 0.1465614 0.0000000
#> B 0.0000000 0.1465614
#> 
#> [[5]]
#>           A         B
#> A 0.1341801 0.0000000
#> B 0.0000000 0.1341801
#>