## ---- include = FALSE--------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>" ) ## ----setup-------------------------------------------------------------------- library(DMRnet) ## ----miete-------------------------------------------------------------------- data("miete") X <- miete[,-1] y <- miete$rent head(X) ## ----DMRnet------------------------------------------------------------------- models <- DMRnet(X, y, family="gaussian") models ## ----plot, fig.height=4, fig.width=6, small.mar=TRUE-------------------------- plot(models, xlim=c(1, 10), lwd=2) ## ----coef--------------------------------------------------------------------- coef(models, df=10) ## ----GIC, fig.height=4, fig.width=6, small.mar=TRUE--------------------------- gic.model <- gic.DMR(models) plot(gic.model) ## ----gic.df.min--------------------------------------------------------------- gic.model$df.min ## ----gic.coef----------------------------------------------------------------- coef(gic.model) ## ----cross-validation, fig.height=4, fig.width=6, small.mar=TRUE-------------- cv.model <- cv.DMRnet(X, y) plot(cv.model) ## ----cv.df.min---------------------------------------------------------------- cv.model$df.min ## ----cv.df.1se---------------------------------------------------------------- cv.model$df.1se ## ----cv.coef------------------------------------------------------------------ coef(cv.model)==coef(gic.model) ## ----predict------------------------------------------------------------------ predict(gic.model, newx=head(X)) predict(cv.model, newx=head(X)) ## ----cv predict--------------------------------------------------------------- predict(cv.model, md="df.min", newx=head(X)) # the default, best model predict(cv.model, md="df.1se", newx=head(X)) # the alternative df.1se model ## ----seq-predict-------------------------------------------------------------- predict(models, newx=head(X)) ## ----binomial----------------------------------------------------------------- binomial_y <- factor(y > mean(y)) #changing Miete response var y into a binomial factor with 2 classes binomial_models <- DMRnet(X, binomial_y, family="binomial") gic.binomial_model <- gic.DMR(binomial_models) gic.binomial_model$df.min ## ----predict-binomial--------------------------------------------------------- predict(gic.binomial_model, newx=head(X), type="class")