Transcription of Package ‘leaps’ - R
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Package ‘leaps’ - R
Package leaps January 16, 2020 TitleRegression Subset Lumley based on Fortran code by Alan MillerDescriptionRegression subset selection, including exhaustive (>= 2)MaintainerThomas 17:50:05 UTCR topics documented:leaps ..4regsubsets ..5 Index8leapsall-subsets regressiomDescriptionleaps() performs an exhaustive search for the best subsets of the variables in x for predicting y inlinear regression, using an efficient branch-and-bound algorithm. It is a compatibility wrapper forregsubsetsdoes the same thing the algorithm returns a best model of each size, the results do not depend on a penalty modelfor model size: it doesn t make any difference whether you want to use AIC, BIC, CIC, DIC, ..12leapsUsageleaps(x=, y=, wt=rep(1, NROW(x)), int=TRUE, method=c("Cp", "adjr2", "r2"), nbest=10,names=NULL, df=NROW(x), )ArgumentsxA matrix of predictorsyA response vectorwtOptional weight vectorintAdd an intercept to the modelmethodCalculate Cp, adjusted R-squared or R-squarednbestNumber of subsets of each size to reportnamesvector of names for columns ofxdfTotal degrees of freedom to use instead ofnrow(x)in calculating Cp and ad-justed misfeatures of leaps() in SValueA list with componentswhichlogical matrix.
leaps() performs an exhaustive search for the best subsets of the variables in x for predicting y in linear regression, using an efficient branch-and-bound algorithm. It is a compatibility wrapper for
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