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, ...)
| model | An object of class |
|---|---|
| ... | Other arguments. |
| penter | p value; variables with p value less than |
| details | Logical; if |
| x | An object of class |
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:
number of steps
variables added to the model
coefficient of determination
akaike information criteria
bayesian information criteria
sawa's bayesian information criteria
adjusted r-square
root mean square error
mallow's Cp
predictors
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.
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
# 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) # }