olsrr (version 0.6.0)

ols_step_forward_p: Stepwise forward regression

Description

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.

Usage

ols_step_forward_p(model, ...)
# S3 method for default
ols_step_forward_p(
  model,
  p_val = 0.3,
  include = NULL,
  exclude = NULL,
  hierarchical = FALSE,
  progress = FALSE,
  details = FALSE,
  ...
)
# S3 method for ols_step_forward_p
plot(x, model = NA, print_plot = TRUE, details = TRUE, ...)

Value

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

model

final model; an object of class lm

metrics

selection metrics

Arguments

model

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

...

Other arguments.

p_val

p value; variables with p value less than p_val will enter into the model

include

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

exclude

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

hierarchical

Logical; if TRUE, performs hierarchical selection.

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_forward_p.

print_plot

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

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 forward selection procedures: ols_step_forward_adj_r2(), ols_step_forward_aic(), ols_step_forward_r2(), ols_step_forward_sbc(), ols_step_forward_sbic()

Examples

# stepwise forward regression
model <- lm(y ~ ., data = surgical)
ols_step_forward_p(model)

# stepwise forward regression plot
model <- lm(y ~ ., data = surgical)
k <- ols_step_forward_p(model)
plot(k)

# selection metrics
k$metrics

# final model
k$model

# include or exclude variables
# force variable to be included in selection process
ols_step_forward_p(model, include = c("age", "alc_mod"))

# use index of variable instead of name
ols_step_forward_p(model, include = c(5, 7))

# force variable to be excluded from selection process
ols_step_forward_p(model, exclude = c("pindex"))

# use index of variable instead of name
ols_step_forward_p(model, exclude = c(2))

# hierarchical selection
model <- lm(y ~ bcs + alc_heavy + pindex + enzyme_test, data = surgical)
ols_step_forward_p(model, 0.1, hierarchical = TRUE)

# plot
k <- ols_step_forward_p(model, 0.1, hierarchical = TRUE)
plot(k)