regressions: Implemented regression methods

In comets: Covariance Measure Tests for Conditional Independence

Implemented regression methods

Description

Implemented regression methods

Usage

rf(y, x, ...)

survforest(y, x, ...)

qrf(y, x, ...)

lrm(y, x, ...)

glrm(y, x, ...)

lasso(y, x, s = "lambda.min", ...)

ridge(y, x, s = "lambda.min", ...)

postlasso(y, x, s = "lambda.min", ...)

cox(y, x, ...)

tuned_rf(
  y,
  x,
  max.depths = 1:5,
  mtrys = list(1, function(p) ceiling(sqrt(p)), identity),
  verbose = FALSE,
  ...
)

xgb(y, x, nrounds = 2L, verbose = 0L, ...)

tuned_xgb(
  y,
  x,
  nfold,
  folds,
  etas = c(0.1, 0.5, 1),
  max_depths = 1:5,
  nrounds = c(2, 10, 50),
  verbose = 0,
  metrics = list("rmse"),
  ...
)

lgbm(y, x, nrounds = 100L, verbose = -1L, ...)

Arguments

y	Vector (or matrix) of response values.
x	Design matrix of predictors.
...	Additional arguments passed to the underlying regression method. In case of "rf", "tuned_rf", "survforest" and "qrf", this is ranger. In case of "lasso" and "ridge", this is glmnet. In case of "cox", this is coxph. In case of "xgb" and "tuned_xgb" this is xgboost.
s	Which lambda to use for prediction, defaults to "lambda.min". See cv.glmnet
max.depths	Values for max.depth to tune out-of-bag. See ranger.
mtrys	for mtry to tune out-of-bag. See ranger.
verbose	See xgboost.
nrounds	See xgboost.
nfold	Number of folds for nfold-cross validation.
folds	Specify folds for cross validation.
etas	Values for eta to cross-validate. See xgboost.
max_depths	Values for max_depth to cross-validate. See xgboost.
metrics	See xgboost.

Details

The implemented choices are "rf" for random forests as implemented in ranger, "lasso" for cross-validated Lasso regression (using the one-standard error rule), "ridge" for cross-validated ridge regression (using the one-standard error rule), "cox" for the Cox proportional hazards model as implemented in survival, "qrf" or "survforest" for quantile and survival random forests, respectively. The option "postlasso" option refers to a cross-validated LASSO (using the one-standard error rule) and subsequent OLS regression. The "lrm" option implements a standard linear regression model. The "xgb" and "tuned_xgb" options require the xgboost package.

The "tuned_rf" regression method tunes the mtry and max.depth parameters in ranger out-of-bag. The "tuned_xgb" regression method uses k-fold cross-validation to tune the nrounds, mtry and max_depth parameters in xgb.cv.

New regression methods can be implemented and supplied as well and need the following structure. The regression method "custom_reg" needs to take arguments y, x, ..., fit the model using y and x as matrices and return an object of a user-specified class, for instance, 'custom'. For the GCM test, implementing a residuals.custom method is sufficient, which should take arguments object, response = NULL, data = NULL, .... For the PCM test, a predict.custom method is necessary for out-of-sample prediction and computation of residuals.

comets documentation built on Nov. 5, 2025, 5:09 p.m.