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Rescale standardized coefficients back to their original range after model fitting

Source: R/rescale_coefficients.R
rescale_coefficients.Rd

Predictor variables are often standardized to be included in statistical models and allow comparison of the effect sizes for different predictors. This functions scales the fitted models coefficients back to the original scale of the predictors, to allow ecological interpretation.

Usage

rescale_coefficients(model, data)

Arguments

model


Fitted model, i.e. the object created by a model fit funtion such as "lm", "glm", or "coxph".

data


The original data.frame with the data used to fit the model.

Value

A vector of rescaled coefficients for the input model.

Examples

library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:terra’:
#> 
#>     intersect, union
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

# standardize predictors
iris_std <- iris |>
  dplyr::mutate(across(2:4, ~ scale(.x)))
# fit model
m1 <- lm(Sepal.Length ~ Petal.Length + Species, data = iris_std)
summary(m1)
#> 
#> Call:
#> lm(formula = Sepal.Length ~ Petal.Length + Species, data = iris_std)
#> 
#> Residuals:
#>      Min       1Q   Median       3Q      Max 
#> -0.75310 -0.23142 -0.00081  0.23085  1.03100 
#> 
#> Coefficients:
#>                   Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)         7.0829     0.1562  45.333  < 2e-16 ***
#> Petal.Length        1.5968     0.1144  13.962  < 2e-16 ***
#> Speciesversicolor  -1.6010     0.1935  -8.275 7.37e-14 ***
#> Speciesvirginica   -2.1177     0.2735  -7.744 1.48e-12 ***
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 0.338 on 146 degrees of freedom
#> Multiple R-squared:  0.8367,	Adjusted R-squared:  0.8334 
#> F-statistic: 249.4 on 3 and 146 DF,  p-value: < 2.2e-16
#> 

# rescale coefficients
(resc_cf <- rescale_coefficients(m1, iris))
#>       (Intercept)      Petal.Length Speciesversicolor  Speciesvirginica 
#>         7.0828803         0.9045646        -1.6009717        -2.1176692 

# compare with model with no standardization of predictors
coef(lm(Sepal.Length ~ Petal.Length + Species, data = iris))
#>       (Intercept)      Petal.Length Speciesversicolor  Speciesvirginica 
#>         3.6835266         0.9045646        -1.6009717        -2.1176692