sommer (version 4.3.5)

corImputation: Imputing a matrix using correlations

Description

corImputation imputes missing data based on the correlation that exists between row levels.

Usage

corImputation(wide, Gu=NULL, nearest=10, roundR=FALSE)

Value

$res

a list with the imputed matrix and the original matrix.

Arguments

wide

numeric matrix with individuals in rows and time variable in columns (e.g., environments, genetic markers, etc.).

Gu

optional correlation matrix between the individuals or row levels. If NULL it will be computed as the correlation of t(wide).

nearest

integer value describing how many nearest neighbours (the ones showing the highest correlation) should be used to average and return the imputed value.

roundR

a TRUE/FALSE statement describing if the average result should be rounded or not. This may be specifically useful for categorical data in the form of numbers (e.g., -1,0,1).

Author

Giovanny Covarrubias-Pazaran

References

Covarrubias-Pazaran G (2016) Genome assisted prediction of quantitative traits using the R package sommer. PLoS ONE 11(6): doi:10.1371/journal.pone.0156744

See Also

The core functions of the package mmer and mmec

Examples

####################################
### imputing genotype data example
####################################
# data(DT_cpdata)
# X <- GT_cpdata
# # add missing data
# v <- sample(1:length(X), 500)
# Xna <- X
# Xna[v]<- NA
# ## impute (can take some time)
# Y <- corImputation(wide=Xna, Gu=NULL, nearest=20, roundR=TRUE) 
# cm <- table(Y$imputed[v],X[v])
# ## calculate accuracy
# sum(diag(cm))/length(v)
####################################
### imputing phenotypic data example
####################################
# data(DT_h2)
# X <- reshape(DT_h2[,c("Name","Env","y")], direction = "wide", idvar = "Name",
#                 timevar = "Env", v.names = "y", sep= "_")
# rownames(X) <- X$Name
# X <- as.matrix(X[,-1])
# head(X)
# # add missing data
# v <- sample(1:length(X), 50)
# Xna <- X
# Xna[v]<- NA
# ## impute
# Y <- corImputation(wide=Xna, Gu=NULL, nearest=20, roundR=TRUE)
# plot(y=Y$imputed[v],x=X[v], xlab="true",ylab="predicted")
# cor(Y$imputed[v],X[v], use = "complete.obs")