Description Usage Arguments Details Value Author(s) References See Also Examples
imputeHD
performs multiple hot-deck imputation on an input data frame
with missing rows. Each missing row is imputed with a unique donor. This
method requires an auxiliary dataset to compute similaritities between
individuals and create the pool of donors.
1 |
X |
n x p numeric matrix containing RNA-seq expression with missing rows (numeric matrix or data frame) |
Y |
auxiliary dataset (n' x q numeric matrix or data frame) |
sigma |
threshold for hot-deck imputation (numeric, positive) |
m |
number of replicates in multiple imputation (integer). Default to 50 |
seed |
single value, interpreted as an in integer, used to initialize
the random number generation state. Default to |
Missing values are identified by matching rownames in X
and
Y
. If rownames are not provided the missing rows in X
are
supposed to correspond to the last rows of Y
.
S3 object of class HDImputed
: a list consisting of
donors
a list. Each element of this list contains the donor
pool for every missing observations
draws
a data frame which indicates which donor was chosen
for each missing samples
data
a list of m
imputed datasets
Alyssa Imbert, alyssa.imbert@gmail.com
Nathalie Vialaneix, nathalie.vialaneix@inrae.fr
Imbert, A., Valsesia, A., Le Gall, C., Armenise, C., Lefebvre, G. Gourraud, P.A., Viguerie, N. and Villa-Vialaneix, N. (2018) Multiple hot-deck imputation for network inference from RNA sequencing data. Bioinformatics. doi: 10.1093/bioinformatics/btx819.
chooseSigma
, imputedGLMnetwork
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