This function inserts NA's to the provided matrix according to the MAR (Missing At Random) patterns.
Arguments
- dat
a matrix or data.frame of data to be filled with some NA's.
- ratio
a number from 0 to 1 denoting the ratio of data to be exchanged into NA's
- thresh
a value from 0 to 1: limit value indicating maximum ratio of missing observations in one column
Details
This function uses ampute. It firstly tries to
ampute missing data by metabolites (columns) and if it fails, it switches to
introduce missing values by samples (rows).
Examples
set.seed(1)
m <- as.data.frame(matrix(rnorm(10), 50, 5))
insert_MAR(m, ratio = 0.1)
#> V1 V2 V3 V4 V5
#> 1 -0.6264538 -0.6264538 -0.6264538 NA -0.6264538
#> 2 0.1836433 0.1836433 0.1836433 0.1836433 0.1836433
#> 3 -0.8356286 -0.8356286 NA -0.8356286 -0.8356286
#> 4 NA NA 1.5952808 1.5952808 1.5952808
#> 5 0.3295078 0.3295078 0.3295078 0.3295078 0.3295078
#> 6 -0.8204684 -0.8204684 NA -0.8204684 -0.8204684
#> 7 0.4874291 0.4874291 0.4874291 0.4874291 0.4874291
#> 8 NA 0.7383247 0.7383247 0.7383247 0.7383247
#> 9 0.5757814 0.5757814 0.5757814 0.5757814 0.5757814
#> 10 -0.3053884 -0.3053884 -0.3053884 -0.3053884 -0.3053884
#> 11 -0.6264538 -0.6264538 -0.6264538 -0.6264538 -0.6264538
#> 12 0.1836433 0.1836433 0.1836433 0.1836433 0.1836433
#> 13 -0.8356286 -0.8356286 NA -0.8356286 -0.8356286
#> 14 NA NA 1.5952808 1.5952808 1.5952808
#> 15 0.3295078 0.3295078 0.3295078 0.3295078 0.3295078
#> 16 -0.8204684 -0.8204684 -0.8204684 NA -0.8204684
#> 17 0.4874291 0.4874291 0.4874291 0.4874291 0.4874291
#> 18 NA 0.7383247 0.7383247 0.7383247 0.7383247
#> 19 0.5757814 0.5757814 0.5757814 0.5757814 0.5757814
#> 20 -0.3053884 -0.3053884 -0.3053884 -0.3053884 -0.3053884
#> 21 -0.6264538 -0.6264538 -0.6264538 -0.6264538 -0.6264538
#> 22 0.1836433 0.1836433 0.1836433 0.1836433 0.1836433
#> 23 -0.8356286 -0.8356286 NA -0.8356286 -0.8356286
#> 24 NA NA 1.5952808 1.5952808 1.5952808
#> 25 0.3295078 0.3295078 0.3295078 0.3295078 0.3295078
#> 26 -0.8204684 -0.8204684 -0.8204684 NA -0.8204684
#> 27 0.4874291 0.4874291 0.4874291 0.4874291 0.4874291
#> 28 NA 0.7383247 0.7383247 0.7383247 0.7383247
#> 29 0.5757814 0.5757814 0.5757814 0.5757814 0.5757814
#> 30 -0.3053884 -0.3053884 -0.3053884 -0.3053884 -0.3053884
#> 31 -0.6264538 -0.6264538 -0.6264538 -0.6264538 -0.6264538
#> 32 0.1836433 0.1836433 0.1836433 0.1836433 0.1836433
#> 33 -0.8356286 -0.8356286 NA -0.8356286 -0.8356286
#> 34 NA NA 1.5952808 1.5952808 1.5952808
#> 35 0.3295078 0.3295078 0.3295078 0.3295078 0.3295078
#> 36 -0.8204684 -0.8204684 -0.8204684 NA -0.8204684
#> 37 0.4874291 0.4874291 0.4874291 0.4874291 0.4874291
#> 38 NA 0.7383247 0.7383247 0.7383247 0.7383247
#> 39 0.5757814 0.5757814 0.5757814 0.5757814 0.5757814
#> 40 -0.3053884 -0.3053884 -0.3053884 -0.3053884 -0.3053884
#> 41 -0.6264538 -0.6264538 -0.6264538 -0.6264538 -0.6264538
#> 42 0.1836433 0.1836433 0.1836433 0.1836433 0.1836433
#> 43 -0.8356286 -0.8356286 NA -0.8356286 -0.8356286
#> 44 NA 1.5952808 1.5952808 1.5952808 1.5952808
#> 45 0.3295078 0.3295078 0.3295078 0.3295078 0.3295078
#> 46 -0.8204684 -0.8204684 -0.8204684 NA -0.8204684
#> 47 0.4874291 0.4874291 0.4874291 0.4874291 0.4874291
#> 48 NA 0.7383247 0.7383247 0.7383247 0.7383247
#> 49 0.5757814 0.5757814 0.5757814 0.5757814 0.5757814
#> 50 -0.3053884 -0.3053884 -0.3053884 -0.3053884 -0.3053884