View source: R/drop1SignifReg.R
drop1SignifReg | R Documentation |
drop1SignifReg removes from the model the predictor, out of the current predictors, which minimizes the criterion (AIC, BIC, r-ajd, PRESS, max p-value) when a) the p-values of the predictors in the current model do not pass the multiple testing correction (Bonferroni, FDR, None, etc) or b) when the p-values of both current and prospective models pass the correction but the criterion of the prospective model is smaller.
max_pvalue
indicates the maximum p-value from the multiple t-tests for each predictor. More specifically, the algorithm computes the prospective models with each predictor included, and all p-values of this prospective model. Then, the predictor selected to be added to the model is the one whose generating model has the smallest p-values, in fact, the minimum of the maximum p-values in each prospective model.
drop1SignifReg(fit, scope, alpha = 0.05, criterion = "p-value", adjust.method = "fdr", override = FALSE, print.step = FALSE)
fit |
an lm or glm object representing a model. |
scope |
defines the range of models examined in the stepwise search. This should be either a single formula, or a list containing components upper and lower, both formulae. See the details for how to specify the formulae and how they are used. |
alpha |
Significance level. Default value is 0.05. |
criterion |
Criterion to select predictor variables. |
adjust.method |
Correction for multiple testing accumulation of error. See |
override |
If |
print.step |
If true, information is printed for each step of variable selection.
Default is |
drop1SifnifReg returns an object of the class lm
or glm
for a generalized regression model with the additional component steps.info
, which shows the steps taken during the variable selection and model metrics: Deviance, Resid.Df, Resid.Dev, AIC, BIC, adj.rsq, PRESS, max_pvalue, max.VIF, and whether it passed the chosen p-value correction.
Jongwook Kim <jongwook226@gmail.com>
Adriano Zanin Zambom <adriano.zambom@gmail.com>
Zambom A Z, Kim J. Consistent significance controlled variable selection in high-dimensional regression. Stat.2018;7:e210. https://doi.org/10.1002/sta4.210
SignifReg
, add1summary
, add1SignifReg
, drop1summary
,
##mtcars data is used as an example. data(mtcars) fit <- lm(mpg~., mtcars) drop1SignifReg(fit, print.step = TRUE)
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