Stepwise R-Squared backward regression

Build regression model from a set of candidate predictor variables by removing predictors based on r-squared, in a stepwise manner until there is no variable left to remove any more.

Usage

ols_step_backward_r2(model, ...)

# S3 method for default
ols_step_backward_r2(
  model,
  include = NULL,
  exclude = NULL,
  progress = FALSE,
  details = FALSE,
  ...
)

# S3 method for ols_step_backward_r2
plot(x, print_plot = TRUE, details = TRUE, digits = 3, ...)

Arguments

model

An object of class lm; the model should include all candidate predictor variables.

...

Other arguments.

include

Character or numeric vector; variables to be included in selection process.

exclude

Character or numeric vector; variables to be excluded from selection process.

progress

Logical; if TRUE, will display variable selection progress.

details

Logical; if TRUE, will print the regression result at each step.

x

An object of class ols_step_backward_*.

print_plot

logical; if TRUE, prints the plot else returns a plot object.

digits

Number of decimal places to display.

Value

List containing the following components:

model

final model; an object of class lm

metrics

selection metrics

others

list; info used for plotting and printing

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See also

Other backward selection procedures: ols_step_backward_adj_r2(), ols_step_backward_aic(), ols_step_backward_p(), ols_step_backward_sbc(), ols_step_backward_sbic()

Examples

# stepwise backward regression
model <- lm(y ~ ., data = surgical)
ols_step_backward_r2(model)
#> [1] "No variables have been removed from the model."

# final model and selection metrics
k <- ols_step_backward_aic(model)
k$metrics
#>   step variable        r2    adj_r2      aic      sbc     sbic
#> 1    1  alc_mod 0.7817703 0.7485615 734.4068 752.3077 583.8836
#> 2    2   gender 0.7814169 0.7535127 732.4942 748.4061 581.2896
#> 3    3      age 0.7809054 0.7580831 730.6204 744.5433 578.8438
k$model
#> 
#> Call:
#> lm(formula = paste(response, "~", paste(preds, collapse = " + ")), 
#>     data = l)
#> 
#> Coefficients:
#> (Intercept)          bcs       pindex  enzyme_test   liver_test    alc_heavy  
#>   -1178.330       59.864        8.924        9.748       58.064      317.848  
#> 

# include or exclude variable
# force variables to be included in the selection process
ols_step_backward_r2(model, include = c("alc_mod", "gender"))
#> [1] "No variables have been removed from the model."

# use index of variable instead of name
ols_step_backward_r2(model, include = c(7, 6))
#> [1] "No variables have been removed from the model."

# force variable to be excluded from selection process
ols_step_backward_r2(model, exclude = c("alc_heavy", "bcs"))
#> [1] "No variables have been removed from the model."

# use index of variable instead of name
ols_step_backward_r2(model, exclude = c(8, 1))
#> [1] "No variables have been removed from the model."