20 lines
941 B
R
20 lines
941 B
R
if (!require("glmnet")) install.packages("glmnet")
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library(glmnet)
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glm_fcn <- function(train.X, train.y, alpha_p){
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glmnet.class.model<-cv.glmnet(as.matrix(train.X), train.y, alpha=alpha_p,
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family="binomial", type.measure="class")
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glmnet.class.model$lambda.1se
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glmnet.class.model$lambda.min
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plot(glmnet.class.model)
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glmnet.class.coeffs<-predict(glmnet.class.model,type="coefficients")
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#glmnet.cc.coeffs # maybe 3 is most important, Excess kurtosis
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model.class.terms <- colnames(train.X) # glmnet includes an intercept but we are going to ignore
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#nonzero.glmnet.qtrait.coeffs <- model.qtrait.terms[glmnet.qtrait.coeffs@i[which(glmnet.qtrait.coeffs@i!=0)]] # skip intercept if there, 0-based counting
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glmnet.df <- data.frame(as.matrix(glmnet.class.coeffs))
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glmnet.df$abs_scores <- abs(glmnet.df$lambda.1se)
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#dplyr::slice_max(glmnet.df,order_by=abs_scores,n=21)
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return(glmnet.df)
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}
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