# VIF: 'VIF' function for assessing VIF. In miceFast: Fast Imputations Using 'Rcpp' and 'Armadillo'

 VIF R Documentation

## `VIF` function for assessing VIF.

### Description

VIF measure how much the variance of the estimated regression coefficients are inflated. It helps to identify when the predictor variables are linearly related. You have to decide which variable should be delete. Usually values higher than 10 (around), mean a collinearity problem.

### Usage

```VIF(x, posit_y, posit_x, correct = FALSE)

## S3 method for class 'data.frame'
VIF(x, posit_y, posit_x, correct = FALSE)

## S3 method for class 'data.table'
VIF(x, posit_y, posit_x, correct = FALSE)

## S3 method for class 'matrix'
VIF(x, posit_y, posit_x, correct = FALSE)
```

### Arguments

 `x` a numeric matrix or data.frame/data.table (factor/character/numeric) - variables `posit_y` an integer/character - a position/name of dependent variable. This variable is taken into account only for getting complete cases. `posit_x` an integer/character vector - positions/names of independent variables `correct` a boolean - basic or corrected - Default: FALSE

### Value

load a numeric vector with VIF for all variables provided by posit_x

### Methods (by class)

• `VIF(data.frame)`:

• `VIF(data.table)`:

• `VIF(matrix)`:

### Note

vif_corrected = vif_basic^(1/(2*df))

`fill_NA` `fill_NA_N`

### Examples

```## Not run:
library(miceFast)
library(data.table)

airquality2 <- airquality
airquality2\$Temp2 <- airquality2\$Temp**2
airquality2\$Month <- factor(airquality2\$Month)
data_DT <- data.table(airquality2)
data_DT[, .(vifs = VIF(
x = .SD,
posit_y = "Ozone",
posit_x = c("Solar.R", "Wind", "Temp", "Month", "Day", "Temp2"),
correct = FALSE
))][["vifs.V1"]]

data_DT[, .(vifs = VIF(
x = .SD,
posit_y = 1,
posit_x = c(2, 3, 4, 5, 6, 7),
correct = TRUE
))][["vifs.V1"]]

## End(Not run)

```

miceFast documentation built on Nov. 18, 2022, 1:07 a.m.