This function inserts NA's to the provided metabolomic matrix according to the MNAR (Missing Not At Random) mechanism.
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
LOD missing data is simulated by sampling possible limit of detection (LOD) for each metabolite and truncates the observations below this values. Thus, each metabolite has different truncation threshold. However, all the removed data corresponds to the provided fraction.
Examples
set.seed(1)
m <- as.data.frame(matrix(rnorm(200), 10, 20))
insert_MNAR(m, ratio = 0.1)
#> V1 V2 V3 V4 V5 V6
#> 1 -0.6264538 1.51178117 0.91897737 1.35867955 -0.1645236 0.3981059
#> 2 0.1836433 0.38984324 0.78213630 -0.10278773 -0.2533617 -0.6120264
#> 3 NA -0.62124058 0.07456498 0.38767161 0.6969634 0.3411197
#> 4 1.5952808 -2.21469989 NA -0.05380504 0.5566632 NA
#> 5 0.3295078 1.12493092 0.61982575 NA NA 1.4330237
#> 6 -0.8204684 -0.04493361 -0.05612874 NA NA 1.9803999
#> 7 0.4874291 -0.01619026 -0.15579551 -0.39428995 0.3645820 -0.3672215
#> 8 0.7383247 0.94383621 NA -0.05931340 0.7685329 NA
#> 9 0.5757814 0.82122120 -0.47815006 1.10002537 -0.1123462 0.5697196
#> 10 -0.3053884 0.59390132 0.41794156 0.76317575 0.8811077 -0.1350546
#> V7 V8 V9 V10 V11 V12
#> 1 2.40161776 0.475509529 -0.5686687 -0.5425200 -0.62036668 -0.6357365
#> 2 -0.03924000 -0.709946431 -0.1351786 1.2078678 0.04211587 -0.4616447
#> 3 0.68973936 0.610726353 1.1780870 1.1604026 NA 1.4322822
#> 4 0.02800216 -0.934097632 -1.5235668 0.7002136 0.15802877 NA
#> 5 -0.74327321 NA 0.5939462 1.5868335 NA -0.2073807
#> 6 0.18879230 0.291446236 0.3329504 0.5584864 1.76728727 -0.3928079
#> 7 -1.80495863 -0.443291873 1.0630998 NA 0.71670748 -0.3199929
#> 8 1.46555486 0.001105352 -0.3041839 -0.5732654 0.91017423 -0.2791133
#> 9 0.15325334 0.074341324 0.3700188 NA 0.38418536 0.4941883
#> 10 2.17261167 -0.589520946 0.2670988 -0.4734006 1.68217608 -0.1773305
#> V13 V14 V15 V16 V17 V18
#> 1 -0.50595746 0.06016044 NA 0.45018710 0.4251004 2.30797840
#> 2 1.34303883 -0.58889449 1.17658331 -0.01855983 -0.2386471 0.10580237
#> 3 -0.21457941 0.53149619 -1.66497244 -0.31806837 1.0584830 0.45699881
#> 4 -0.17955653 -1.51839408 -0.46353040 -0.92936215 0.8864227 -0.07715294
#> 5 -0.10019074 0.30655786 -1.11592011 -1.48746031 -0.6192430 -0.33400084
#> 6 0.71266631 NA -0.75081900 -1.07519230 2.2061025 -0.03472603
#> 7 -0.07356440 -0.30097613 2.08716655 1.00002880 -0.2550270 0.78763961
#> 8 -0.03763417 -0.52827990 0.01739562 -0.62126669 -1.4244947 2.07524501
#> 9 NA -0.65209478 -1.28630053 -1.38442685 -0.1443996 1.02739244
#> 10 -0.32427027 -0.05689678 -1.64060553 1.86929062 0.2075383 1.20790840
#> V19 V20
#> 1 -1.2313234 -0.1771040
#> 2 0.9838956 0.4020118
#> 3 0.2199248 -0.7317482
#> 4 NA 0.8303732
#> 5 0.5210227 NA
#> 6 -0.1587546 -1.0479844
#> 7 1.4645873 1.4411577
#> 8 -0.7660820 -1.0158475
#> 9 -0.4302118 0.4119747
#> 10 -0.9261095 -0.3810761