na.replace: Replace the missing entries in a matrix columnwise with the...

In glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models

Replace the missing entries in a matrix columnwise with the entries in a supplied vector

Description

Missing entries in any given column of the matrix are replaced by the column means or the values in a supplied vector.

Usage

na.replace(x, m = rowSums(x, na.rm = TRUE))

Arguments

x	A matrix with potentially missing values, and also potentially in sparse matrix format (i.e. inherits from "sparseMatrix")
m	Optional argument. A vector of values used to replace the missing entries, columnwise. If missing, the column means of 'x' are used

Details

This is a simple imputation scheme. This function is called by makeX if the na.impute=TRUE option is used, but of course can be used on its own. If 'x' is sparse, the result is sparse, and the replacements are done so as to maintain sparsity.

Value

A version of 'x' is returned with the missing values replaced.

Author(s)

Trevor Hastie
Maintainer: Trevor Hastie [e-mail]

See Also

makeX and glmnet

Examples

set.seed(101)
### Single data frame
X = matrix(rnorm(20), 10, 2)
X[3, 1] = NA
X[5, 2] = NA
X3 = sample(letters[1:3], 10, replace = TRUE)
X3[6] = NA
X4 = sample(LETTERS[1:3], 10, replace = TRUE)
X4[9] = NA
dfn = data.frame(X, X3, X4)

x = makeX(dfn)
m = rowSums(x, na.rm = TRUE)
na.replace(x, m)

x = makeX(dfn, sparse = TRUE)
na.replace(x, m)

glmnet documentation built on Aug. 22, 2023, 9:12 a.m.