# na.replace

0th

Percentile

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

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

Keywords
models
##### 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.

##### See Also

makeX and glmnet

• na.replace
##### Examples
# NOT RUN {
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)

# }

Documentation reproduced from package glmnet, version 3.0-2, License: GPL-2

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