blr_step_p_forward function

Stepwise forward regression

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

blr_step_p_forward(model, ...)

## Default S3 method:
blr_step_p_forward(model, penter = 0.3, details = FALSE, ...)

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

Arguments

• model: An object of class lm; the model should include all candidate predictor variables.
• ...: Other arguments.
• penter: p value; variables with p value less than penter will enter into the model
• details: Logical; if TRUE, will print the regression result at each step.
• x: An object of class blr_step_p_forward.
• print_plot: logical; if TRUE, prints the plot else returns a plot object.

Returns

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

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

• steps: number of steps

• predictors: variables added to the model

• aic: akaike information criteria

• bic: bayesian information criteria

• dev: deviance

• indvar: predictors

Examples

## Not run:

# stepwise forward regression
model <- glm(honcomp ~ female + read + science, data = hsb2,
  family = binomial(link = 'logit'))
blr_step_p_forward(model)

# stepwise forward regression plot
model <- glm(honcomp ~ female + read + science, data = hsb2,
  family = binomial(link = 'logit'))
k <- blr_step_p_forward(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.

Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.

See Also

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