Detect aliased variables in a linear model

This function identifies aliased (linearly dependent) variables in a linear model by fitting a linear model, and then using the stats::alias function to detect aliased variables.

Usage

detect_alias(data, verbose = FALSE)

Arguments

data: A data frame or tibble containing the variables to be checked for aliasing.
verbose: Logical. Whether to print the aliased variables found (if any). If TRUE, aliased variables are printed to the console. Defaults to FALSE.

Value

Returns a character vector of aliased variable names if any are found; otherwise, returns NULL invisibly. If verbose is TRUE, the function will also print a message to the console.

Author

Ahmed El-Gabbas

Examples

load_packages(car)

x1 <- rnorm(100)
x2 <- 2 * x1
x3 <- rnorm(100)
y <- rnorm(100)

model <- lm(y ~ x1 + x2 + x3)
summary(model)
#> 
#> Call:
#> lm(formula = y ~ x1 + x2 + x3)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -2.39881 -0.75212  0.05363  0.55473  2.64535 
#> 
#> Coefficients: (1 not defined because of singularities)
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.13647    0.09791  -1.394    0.167
#> x1          -0.04277    0.10526  -0.406    0.685
#> x2                NA         NA      NA       NA
#> x3           0.08659    0.11303   0.766    0.445
#> 
#> Residual standard error: 0.9686 on 97 degrees of freedom
#> Multiple R-squared:  0.006927,	Adjusted R-squared:  -0.01355 
#> F-statistic: 0.3383 on 2 and 97 DF,  p-value: 0.7138
#> 

# there are aliased coefficients in the model
try(car::vif(model))
#> Error in vif.default(model) : there are aliased coefficients in the model

# The function identifies the aliased variables
detect_alias(data = cbind.data.frame(x1, x2, x3))
#> [1] "x2"

detect_alias(data = cbind.data.frame(x1, x2, x3), verbose = TRUE)
#> aliased variables: x2
#> [1] "x2"

# excluding x2 and refit the model
model <- lm(y ~ x1 + x3)

summary(model)
#> 
#> Call:
#> lm(formula = y ~ x1 + x3)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -2.39881 -0.75212  0.05363  0.55473  2.64535 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)
#> (Intercept) -0.13647    0.09791  -1.394    0.167
#> x1          -0.04277    0.10526  -0.406    0.685
#> x3           0.08659    0.11303   0.766    0.445
#> 
#> Residual standard error: 0.9686 on 97 degrees of freedom
#> Multiple R-squared:  0.006927,	Adjusted R-squared:  -0.01355 
#> F-statistic: 0.3383 on 2 and 97 DF,  p-value: 0.7138
#> 

try(car::vif(model))
#>       x1       x3 
#> 1.020971 1.020971