Nested Cross Validation for the Relaxed Lasso and Other Machine Learning Models

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Documentation for package ‘glmnetr’ version 0.4-6

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aicreg Identify model based upon AIC criteria from a stepreg() putput
ann_tab_cv Fit an Artificial Neural Network model on "tabular" provided as a matrix, optionally allowing for an offset term
ann_tab_cv_best Fit multiple Artificial Neural Network models on "tabular" provided as a matrix, and keep the best one.
best.preds Get the best models for the steps of a stepreg() fit
bsint Construct the bias terms for going from model layer to layer to carry forward an offset to mimic a linear model
calceloss calculate cross-entry for multinomial outcomes
calplot Construct calibration plots for a nested.glmnetr output object
calplot0 Construct a single calibration plot for a nested.glmnetr output object
cox.sat.dev Calculate the CoxPH saturated log-likelihood
cv.glmnetr Get a cross validation informed relaxed lasso model fit.
cv.stepreg Cross validation informed stepwise regression model fit.
devrat_ Calculate deviance ratios for CV based
diff_time Output to console the elapsed and split times
diff_time1 Get elapsed time in c(hour, minute, secs)
dtstndrz Standardize a data set
factor.foldid Generate foldid's by factor levels
get.foldid Get foldid's with branching for cox, binomial and gaussian models
getlamgam get numerical values for lam and gam
glmnetr Fit relaxed part of lasso model
glmnetr.cis Calculate performance measure CI's and p's
glmnetr.cis0 Calculate performance measure CI's and p's
glmnetr.compcv Compare cross validation fits from a nested.glmnetr output.
glmnetr.compcv0 Calculate agreement differences with CI and p
glmnetr.simdata Generate example data
glmnetrll_1fold Evaluate fit of leave out fold
glmnetr_devratio Get Deviance ratio.
glmnetr_seed Get seeds to store, facilitating replicable results
myaxis Un-log the log(HR)'s for plotting
myrug A customized rug
nested.glmnetr Using (nested) cross validation, describe and compare some machine learning model performances
plot.cv.glmnetr Plot cross-validation deviances, or model coefficients.
plot.glmnetr Plot the relaxed lasso coefficients.
plot.nested.glmnetr Plot results from a nested.glmnetr() output
plot_perf_glmnetr Plot nested cross validation performance summaries
predict.cv.glmnetr Give predicteds based upon a cv.glmnetr() output object.
predict.cv.stepreg Beta's or predicteds based upon a cv.stepreg() output object.
predict.glmnetr Get predicteds or coefficients using a glmnetr output object
predict.nested.glmnetr Give predicteds based upon the cv.glmnet output object contained in the nested.glmnetr output object.
predict_ann_tab Get predicteds for an Artificial Neural Network model fit in nested.glmnetr()
predict_nested_rf Get predicteds for a rf fit from nested.glmnetr() output object
predict_nested_xgb Get predicteds for a XGB fit from a nested.glmnetr() output object
prednn_tl predicted values from an ann_tab_cv output object based upon the model and its lasso model used for generating an offset
preds_1 Get predictors form a stepwise regression model.
print.nested.glmnetr A redirect to the summary() function for nested.glmnetr() output objects
print.rf_tune Print output from rf_tune() function
rederive_rf Rederive Random Forest models not kept in nested.glmnetr() output
rederive_xgb Rederive XGB models not kept in nested.glmnetr() output
rf_tune Fit a Random Forest model on data provided in matrix and vector formats.
rf_xbhat get XBeta from an rfsrc output object
roundperf round elements of a summary.glmnetr() output
rpart_xbhat get XBeta from an rpart output object
stepreg Fit the steps of a stepwise regression.
summary.cv.glmnetr Output summary of a cv.glmnetr() output object.
summary.cv.stepreg Summarize results from a cv.stepreg() output object.
summary.nested.glmnetr Summarize a nested.glmnetr() output object
summary.rf_tune Summarize output from rf_tune() function
summary.stepreg Briefly summarize steps in a stepreg() output object, i.e. a stepwise regression fit
wtlast Construct the weights for going from the last hidden layer to the last layer of the model, not counting any activation, to carry forward an offset to mimic a linear model
wtmiddle Construct the weights for going between two hidden layers, carrying forward an offset term to mimic a linear model
wtzero Construct the weights for going from the observed data with an offset in column 1 to the first hidden layer
xgb.simple Get a simple XGBoost model fit (no tuning)
xgb.tuned Get a tuned XGBoost model fit
xgb_xbhat get XBeta from an XGB.train output object