tune_xrnet {xrnet}

k-fold cross-validation for hierarchical regularized regression

Description

k-fold cross-validation for hierarchical regularized regression xrnet

Usage

tune_xrnet(
  x,
  y,
  external = NULL,
  unpen = NULL,
  family = c("gaussian", "binomial"),
  penalty_main = define_penalty(),
  penalty_external = define_penalty(),
  weights = NULL,
  standardize = c(TRUE, TRUE),
  intercept = c(TRUE, FALSE),
  loss = c("deviance", "mse", "mae", "auc"),
  nfolds = 5,
  foldid = NULL,
  parallel = FALSE,
  control = list()
)

Arguments

x

predictor design matrix of dimension n x p, matrix options include:

• matrix

• big.matrix

• filebacked.big.matrix

• sparse matrix (dgCMatrix)

y

outcome vector of length n

external

(optional) external data design matrix of dimension p x q, matrix options include:

• matrix

• sparse matrix (dgCMatrix)

unpen

(optional) unpenalized predictor design matrix, matrix options include:

• matrix

family

error distribution for outcome variable, options include:

• "gaussian"

• "binomial"

penalty_main

specifies regularization object for x. See define_penalty for more details.

penalty_external

specifies regularization object for external. See define_penalty for more details. See define_penalty for more details.

weights

optional vector of observation-specific weights. Default is 1 for all observations.

standardize

indicates whether x and/or external should be standardized. Default is c(TRUE, TRUE).

intercept

indicates whether an intercept term is included for x and/or external. Default is c(TRUE, FALSE).

loss

loss function for cross-validation. Options include:

• "deviance"

• "mse" (Mean Squared Error)

• "mae" (Mean Absolute Error)

• "auc" (Area under the curve)

nfolds

number of folds for cross-validation. Default is 5.

foldid

(optional) vector that identifies user-specified fold for each observation. If NULL, folds are automatically generated.

parallel

use foreach function to fit folds in parallel if TRUE, must register cluster (doParallel) before using.

control

specifies xrnet control object. See xrnet_control for more details.

Details

k-fold cross-validation is used to determine the 'optimal' combination of hyperparameter values, where optimal is based on the optimal value obtained for the user-selected loss function across the k folds. To efficiently traverse all possible combinations of the hyperparameter values, 'warm-starts' are used to traverse the penalty from largest to smallest penalty value(s). Note that the penalty grid for the folds is generated by fitting the model on the entire training data. Parallelization is enabled through the foreach and doParallel R packages. To use parallelization, parallel = TRUE, you must first create the cluster makeCluster and then register the cluster registerDoParallel. See the parallel, foreach, and/or doParallel R packages for more details on how to setup parallelization.

Value

A list of class tune_xrnet with components

cv_mean

mean cross-validated error for each penalty combination. Object returned is a vector if there is no external data (external = NULL) and matrix if there is external data.

cv_sd

estimated standard deviation for cross-validated errors. Object returned is a vector if there is no external data (external = NULL) and matrix if there is external data.

loss

loss function used to compute cross-validation error

opt_loss

the value of the loss function for the optimal cross-validated error

opt_penalty

first-level penalty value that achieves the optimal loss

opt_penalty_ext

second-level penalty value that achieves the optimal loss (if external data is present)

fitted_model

fitted xrnet object using all data, see xrnet for details of object

Examples

## cross validation of hierarchical linear regression model
data(GaussianExample)

## 5-fold cross validation
cv_xrnet <- tune_xrnet(
  x = x_linear,
  y = y_linear,
  external = ext_linear,
  family = "gaussian",
  control = xrnet_control(tolerance = 1e-6)
)

## contour plot of cross-validated error
plot(cv_xrnet)