Build regression model from a set of candidate predictor variables by removing predictors based on p values, in a stepwise manner until there is no variable left to remove any more.
ols_step_backward_p(model, ...) # S3 method for default ols_step_backward_p(model, prem = 0.3, progress = FALSE, details = FALSE, ...) # S3 method for ols_step_backward_p plot(x, model = NA, print_plot = TRUE, ...)
model | An object of class |
---|---|
... | Other inputs. |
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_backward_p
returns an object of class "ols_step_backward_p"
.
An object of class "ols_step_backward_p"
is a list containing the
following components:
final model; an object of class lm
total number of steps
variables removed from 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_backward()
has been deprecated. Instead use ols_step_backward_p()
.
Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.
Other variable selection procedures: ols_step_all_possible
,
ols_step_backward_aic
,
ols_step_best_subset
,
ols_step_both_aic
,
ols_step_forward_aic
,
ols_step_forward_p
#> #> #> Elimination Summary #> -------------------------------------------------------------------------- #> Variable Adj. #> Step Removed R-Square R-Square C(p) AIC RMSE #> -------------------------------------------------------------------------- #> 1 alc_mod 0.7818 0.7486 7.0141 734.4068 199.2637 #> 2 gender 0.7814 0.7535 5.0870 732.4942 197.2921 #> 3 age 0.7809 0.7581 3.1925 730.6204 195.4544 #> --------------------------------------------------------------------------# stepwise backward regression plot model <- lm(y ~ ., data = surgical) k <- ols_step_backward_p(model) plot(k)# final model k$model#> #> Call: #> lm(formula = paste(response, "~", paste(preds, collapse = " + ")), #> data = l) #> #> Coefficients: #> (Intercept) bcs pindex enzyme_test liver_test alc_heavy #> -1178.330 59.864 8.924 9.748 58.064 317.848 #>