Nested Cross-Validation with 'glmnet' and 'caret'


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Documentation for package ‘nestedcv’ version 0.2.3

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anova_filter ANOVA filter
boruta_filter Boruta filter
boxplot_model Boxplot model predictors
coef.nestcv.glmnet Extract coefficients from nestcv.glmnet object
collinear Filter to reduce collinearity in predictors
combo_filter Combo filter
correls2 Correlation between a vector and a matrix
correl_filter Correlation filter
cva.glmnet Cross-validation of alpha for glmnet
glmnet_coefs glmnet coefficients
glmnet_filter glmnet filter
innercv_roc Build ROC curve from left-out folds from inner CV
innercv_roc.nestcv.glmnet Build ROC curve from left-out folds from inner CV
innercv_roc.nestcv.train Build ROC curve from left-out folds from inner CV
lm_filter Linear model filter
model.hsstan hsstan model for cross-validation
nestcv.glmnet Nested cross-validation with glmnet
nestcv.train Nested cross-validation for caret
outercv Outer cross-validation of selected models
outercv.default Outer cross-validation of selected models
outercv.formula Outer cross-validation of selected models
plot.cva.glmnet Plot lambda across range of alphas
plot_alphas Plot cross-validated glmnet alpha
plot_caret Plot caret tuning
plot_lambdas Plot cross-validated glmnet lambdas across outer folds
predict.hsstan Predict from hsstan model fitted within cross-validation
predict.nestcv.glmnet Predict method for nestcv.glmnet fits
predSummary Summarise prediction performance metrics
relieff_filter ReliefF filter
rf_filter Random forest filter
ttest_filter t-test filter
wilcoxon_filter Wilcoxon test filter