Gibbs Sampler Based Left-Censored Missing Value Imputation.
Usage
impute_gsimp(
missdf,
iters_each = 100,
iters_all = 20,
initial = "qrilc",
lo = -Inf,
hi = "min",
imp_model = "glmnet_pred",
gibbs = data.frame(row = integer(), col = integer())
)Arguments
- missdf
a data frame with missing values to be imputed containing features in columns and samples in rows.
- iters_each
number (100); vector of numbers, e.g. rep(100, 20)
- iters_all
= 20
- initial
character ('qrilc'/'lysm') initialized data matrix
- lo
number; vector; functions like min/max/median/mean
- hi
number; vector; functions like min/max/median/mean
- imp_model
=
glmnet_pred,- gibbs
= data.frame(row = integer(), col=integer())
Details
A function to replace NA in the data frame by GSimp method.
This function and its documentation was copied from https://github.com/WandeRum/GSimp and contains the GSimp algorithm and related functions developed by Rum Wei (10.1371/journal.pcbi.1005973).
References
Wei R, Wang J, Jia E, Chen T, Ni Y, Jia W (2018). “GSimp: A Gibbs Sampler Based Left-Censored Missing Value Imputation Approach for Metabolomics Studies.” PLOS Computational Biology, 14(1), e1005973. ISSN 1553-7358, doi:10.1371/journal.pcbi.1005973 .
Examples
data(sim_miss)
impute_gsimp(sim_miss)
#> X1 X2 X3 X4 X5 X6
#> 1 0.155050833 0.88115677 0.1747543 0.1194841210 0.600405471 0.01306738
#> 2 0.968378809 0.60748407 0.6337790 0.6181194666 0.008132938 0.75207300
#> 3 0.468263086 0.57452956 0.2476089 -0.3708286661 0.370125689 -0.16734818
#> 4 0.776819652 0.80313329 0.5513393 0.1459775318 0.724248714 0.84777606
#> 5 0.407885741 0.79911692 0.2347611 0.1897135850 0.418270750 0.70873497
#> 6 0.538797149 0.80186265 0.2586147 0.5754945197 0.238248518 -0.16104460
#> 7 0.830082966 0.75207300 0.9528881 0.0001881735 0.887881226 0.48833525
#> 8 0.187103555 0.57546133 0.8568746 0.3283282877 0.577775384 0.34722847
#> 9 0.779969688 0.94421829 0.1286374 0.0782780354 0.739950257 0.20304801
#> 10 0.193943927 0.21898736 0.5545242 0.6041721753 0.160679622 0.73995026
#> 11 0.434231178 0.47799791 0.8769438 0.3267990556 0.529571799 0.89369752
#> 12 0.002274518 0.04466879 0.8018626 0.7904525734 0.574529562 0.57452956
#> 13 0.834692139 0.16848571 0.5467373 0.8300829662 0.960086967 0.08444870
#> 14 -1.493134156 0.36828087 0.2327532 0.8100510929 0.398616886 0.14825833
#> 15 0.956967818 0.56331137 0.6022678 0.2027530000 0.408468068 0.37028128
#> 16 0.948497073 -0.19814971 0.7125391 0.5060450307 0.613321482 0.86117762
#> 17 0.600729976 0.73432757 0.6489036 0.5206679751 0.706590850 0.83668180
#> 18 0.261807405 0.70365677 0.1875624 0.0842680801 0.799116918 0.74054705
#> 19 0.643034251 0.73682406 0.2383929 0.2687653562 0.161331222 0.98576580
#> 20 0.526233507 -0.20306946 -0.1130160 0.8147157354 0.885932416 0.03208182