Build regression model from a set of candidate predictor variables by entering and removing predictors based on p values, in a stepwise manner until there is no variable left to enter or remove any more.
ols_step_both_p(model, ...) # S3 method for default ols_step_both_p(model, pent = 0.1, prem = 0.3, progress = FALSE, details = FALSE, ...) # S3 method for ols_step_both_p plot(x, model = NA, print_plot = TRUE, ...)
model | An object of class |
---|---|
... | Other arguments. |
pent | p value; variables with p value less than |
prem | p value; variables with p more than |
progress | Logical; if |
details | Logical; if |
x | An object of class |
print_plot | logical; if |
ols_step_both_p
returns an object of class "ols_step_both_p"
.
An object of class "ols_step_both_p"
is a list containing the
following components:
final model; an object of class lm
candidate predictor variables according to the order by which they were added or removed from the model
addition/deletion
total number of steps
variables retained in the model (after addition)
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_stepwise()
has been deprecated. Instead use ols_step_both_p()
.
Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.
#> #> Stepwise Selection Summary #> ------------------------------------------------------------------------------------------ #> Added/ Adj. #> Step Variable Removed R-Square R-Square C(p) AIC RMSE #> ------------------------------------------------------------------------------------------ #> 1 liver_test addition 0.455 0.444 62.5120 771.8753 296.2992 #> 2 alc_heavy addition 0.567 0.550 41.3680 761.4394 266.6484 #> 3 enzyme_test addition 0.659 0.639 24.3380 750.5089 238.9145 #> 4 pindex addition 0.750 0.730 7.5370 735.7146 206.5835 #> 5 bcs addition 0.781 0.758 3.1920 730.6204 195.4544 #> ------------------------------------------------------------------------------------------# 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 #>