Possible methodologies for missing value imputation of continuous variables in a data frame in R
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svlachavas ▴ 780
Last seen 8 weeks ago
Germany/Heidelberg/German Cancer Resear…

Dear All,

i have a data frame in R of 8 continuous variables in the rows and 60 paired observations in the columns. I want to use this data frame in a subsequent analysis regarding machine learning, but unfortunatery in 3 samples the values from both the 8 variables are missing(not zero, totally missing). Thus, because if i exclude these samples from any downstream analysis, i also have to exclude their paired observations, and in totally loose 6 samples-which might seem not significant but i could loose information. Thus, i would like to ask if there is an package in R with an appropriate methodology of missing value imputation, to implement it in this current purpose. Im aware of the R package impute with k nearest neighbors, but as far as i know it is intended for gene expression data. Moreover, after searching i found also the R package MICE (https://cran.r-project.org/web/packages/mice/mice.pdf) but i have never used it. 

Any ideas or suggestions on how could i deal with this issue ??


missing data missing value imputation impute MICE bioconductor • 992 views

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