Implemented regression methods
rf(y, x, ...)survforest(y, x, ...)
qrf(y, x, ...)
lrm(y, x, ...)
glrm(y, x, ...)
lasso(y, x, ...)
ridge(y, x, ...)
postlasso(y, x, ...)
cox(y, x, ...)
Vector (or matrix) of response values.
Design matrix of predictors.
Additional arguments passed to the underlying regression method.
In case of "rf", "survforest" and "qrf", this is
ranger. In case of "lasso" and
"ridge", this is glmnet. In case of
"cox", this is coxph.
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.
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.