# na.replace

From glmnet v3.0-2
by Trevor Hastie

##### 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`

##### 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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