na.replace {glmnet} | R Documentation |

## 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 hastie@stanford.edu

### 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*version 4.1-8 Index]