olsrr (version 0.4.0)

ols_step_forward: 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(model, ...)
# S3 method for default
ols_step_forward(model, penter = 0.3, details = FALSE,
  ...)
# S3 method for ols_step_forward
plot(x, model = NA, ...)

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 ols_step_forward

Value

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

steps

number of steps

predictors

variables added to the model

rsquare

coefficient of determination

aic

akaike information criteria

sbc

bayesian information criteria

sbic

sawa's bayesian information criteria

adjr

adjusted r-square

rmse

root mean square error

mallows_cp

mallow's Cp

indvar

predictors

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.

Examples

# NOT RUN {
# stepwise forward regression
model <- lm(y ~ ., data = surgical)
ols_step_forward(model)
# }
# NOT RUN {
# }
# NOT RUN {
# stepwise forward regression plot
model <- lm(y ~ ., data = surgical)
k <- ols_step_forward(model)
plot(k)
# }
# NOT RUN {
# }