blr_step_p_backward function

Stepwise backward regression

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

blr_step_p_backward(model, ...)

## Default S3 method:
blr_step_p_backward(model, prem = 0.3, details = FALSE, ...)

## S3 method for class 'blr_step_p_backward'
plot(x, model = NA, print_plot = TRUE, ...)

Arguments

• model: An object of class lm; the model should include all candidate predictor variables.
• ...: Other inputs.
• prem: p value; variables with p more than prem will be removed from the model.
• details: Logical; if TRUE, will print the regression result at each step.
• x: An object of class blr_step_p_backward.
• print_plot: logical; if TRUE, prints the plot else returns a plot object.

Returns

blr_step_p_backward returns an object of class "blr_step_p_backward". An object of class "blr_step_p_backward" is a list containing the following components:

• model: model with the least AIC; an object of class glm

• steps: total number of steps

• removed: variables removed from the model

• aic: akaike information criteria

• bic: bayesian information criteria

• dev: deviance

• indvar: predictors

Examples

## Not run:

# stepwise backward regression
model <- glm(honcomp ~ female + read + science + math + prog + socst,
  data = hsb2, family = binomial(link = 'logit'))
blr_step_p_backward(model)

# stepwise backward regression plot
model <- glm(honcomp ~ female + read + science + math + prog + socst,
  data = hsb2, family = binomial(link = 'logit'))
k <- blr_step_p_backward(model)
plot(k)

# final model
k$model
## End(Not run)

References

Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.

See Also

Other variable selection procedures: blr_step_aic_backward(), blr_step_aic_both(), blr_step_aic_forward(), blr_step_p_forward()