Missing values imputation using the LSimpute algorithm.

This method is a wrapper to the function impute.mi() of the package imp4p adapted to an object of class SummarizedExperiment.

Usage

wrapper.dapar.impute.mi(
  obj,
  design,
  nb.iter = 3,
  nknn = 15,
  selec = 600,
  siz = 500,
  weight = 1,
  ind.comp = 1,
  progress.bar = FALSE,
  x.step.mod = 300,
  x.step.pi = 300,
  nb.rei = 100,
  method = 4,
  gridsize = 300,
  q = 0.95,
  q.min = 0,
  q.norm = 3,
  eps = 0,
  methodi = "slsa",
  lapala = TRUE,
  distribution = "unif"
)

Arguments

obj

An object of class SummarizedExperiment.

design

xxx

nb.iter

Same as the function mi.mix in the package imp4p

nknn

Same as the function mi.mix in the package imp4p

selec

Same as the function mi.mix in the package imp4p

siz

Same as the function mi.mix in the package imp4p

weight

Same as the function mi.mix in the package imp4p

ind.comp

Same as the function mi.mix in the package imp4p

progress.bar

Same as the function mi.mix in the package imp4p

x.step.mod

Same as the function estim.mix in the package imp4p

x.step.pi

Same as the function estim.mix in the package imp4p

nb.rei

Same as the function estim.mix in the package imp4p

method

Same as the function estim.mix in the package imp4p

gridsize

Same as the function estim.mix in the package imp4p

q

Same as the function mi.mix in the package imp4p

q.min

Same as the function impute.pa in the package imp4p

q.norm

Same as the function impute.pa in the package imp4p

eps

Same as the function impute.pa in the package imp4p

methodi

Same as the function mi.mix in the package imp4p

lapala

xxxxxxxxxxx

distribution

The type of distribution used. Values are unif (default) or beta.

Value

The Biobase::exprs(obj) matrix with imputed values instead of missing values.

Author

Samuel Wieczorek

Examples

# \donttest{
utils::data(subR25pept)
design <- design.qf(subR25pept)
obj.imp.na <- wrapper.dapar.impute.mi(subR25pept[[2]], design, nb.iter = 1, lapala = TRUE)
#> Warning: tab.imp contains missing values
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_C_R1
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_C_R2
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_C_R3
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_D_R1
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_D_R2
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_D_R3
#> 
#>  Iterations: 
#> 
#>  1 / 1  - Imputation MNAR OK - 
#> Imputation MCAR in progress - 
#> 3 % - 6 % - 9 % - 12 % - 15 % - 18 % - 21 % - 24 % - 27 % - 30 % - 33 % - 36 % - 39 % - 42 % - 45 % - 48 % - 51 % - 54 % - 57 % - 60 % - 63 % - 66 % - 69 % - 72 % - 75 % - 78 % - 81 % - 84 % - 87 % - 90 % - 93 % - 96 % - 99 % - 
obj.imp.pov <- wrapper.dapar.impute.mi(subR25pept[[2]], design, nb.iter = 1, lapala = FALSE)
#> Warning: tab.imp contains missing values
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_C_R1
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_C_R2
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_C_R3
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_D_R1
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_D_R2
#> Warning: Few missing values are found as imputed in the input imputed matrix (<20) in sample Intensity_D_R3
#> 
#>  Iterations: 
#> 
#>  1 / 1  - Imputation MNAR OK - 
#> Imputation MCAR in progress - 
#> 3 % - 6 % - 9 % - 12 % - 15 % - 18 % - 21 % - 24 % - 27 % - 30 % - 33 % - 36 % - 39 % - 42 % - 45 % - 48 % - 51 % - 54 % - 57 % - 60 % - 63 % - 66 % - 69 % - 72 % - 75 % - 78 % - 81 % - 84 % - 87 % - 90 % - 93 % - 96 % - 99 % - 
# }