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.

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.

See also

Other variable selection procedures: ols_step_all_possible, ols_step_backward_aic, ols_step_backward_p, ols_step_best_subset, ols_step_both_aic, ols_step_forward_aic

Examples

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

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