VIF function for assessing VIF.

Source: R/VIF.R

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.

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

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

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

# S3 method for 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))

See also

fill_NA fill_NA_N

Examples

if (FALSE) {
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"]]
}