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_p(model, ...) # S3 method for default ols_step_forward_p(model, penter = 0.3, progress = FALSE, details = FALSE, ...) # S3 method for ols_step_forward_p plot(x, model = NA, print_plot = TRUE, ...)
model | An object of class |
---|---|
... | Other arguments. |
penter | p value; variables with p value less than |
progress | Logical; if |
details | Logical; if |
x | An object of class |
print_plot | logical; if |
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:
final model; an object of class lm
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
ols_step_forward()
has been deprecated. Instead use ols_step_forward_p()
.
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
#> #> Selection Summary #> ------------------------------------------------------------------------------ #> Variable Adj. #> Step Entered R-Square R-Square C(p) AIC RMSE #> ------------------------------------------------------------------------------ #> 1 liver_test 0.4545 0.4440 62.5119 771.8753 296.2992 #> 2 alc_heavy 0.5667 0.5498 41.3681 761.4394 266.6484 #> 3 enzyme_test 0.6590 0.6385 24.3379 750.5089 238.9145 #> 4 pindex 0.7501 0.7297 7.5373 735.7146 206.5835 #> 5 bcs 0.7809 0.7581 3.1925 730.6204 195.4544 #> ------------------------------------------------------------------------------# stepwise forward regression plot model <- lm(y ~ ., data = surgical) k <- ols_step_forward_p(model) plot(k)# final model k$model#> #> Call: #> lm(formula = paste(response, "~", paste(preds, collapse = " + ")), #> data = l) #> #> Coefficients: #> (Intercept) liver_test alc_heavy enzyme_test pindex bcs #> -1178.330 58.064 317.848 9.748 8.924 59.864 #>