model-classes: Regularized transformation model classes

In tramnet: Penalized Transformation Models

Regularized transformation model classes

Description

Regularized transformation model classes

Usage

LmNET(
  formula,
  data,
  lambda = 0,
  alpha = 1,
  tram_args = NULL,
  constraints = NULL,
  ...
)

BoxCoxNET(
  formula,
  data,
  lambda = 0,
  alpha = 1,
  tram_args = NULL,
  constraints = NULL,
  ...
)

ColrNET(
  formula,
  data,
  lambda = 0,
  alpha = 1,
  tram_args = NULL,
  constraints = NULL,
  ...
)

SurvregNET(
  formula,
  data,
  lambda = 0,
  alpha = 1,
  tram_args = NULL,
  constraints = NULL,
  ...
)

CoxphNET(
  formula,
  data,
  lambda = 0,
  alpha = 1,
  tram_args = NULL,
  constraints = NULL,
  ...
)

LehmannNET(
  formula,
  data,
  lambda = 0,
  alpha = 1,
  tram_args = NULL,
  constraints = NULL,
  ...
)

PolrNET(
  formula,
  data,
  lambda = 0,
  alpha = 1,
  tram_args = NULL,
  constraints = NULL,
  ...
)

Arguments

formula	Formula specifying the regression. See tram.
data	Object of class "data.frame" containing the variables referred to in the formula model.
lambda	A positive penalty parameter for the whole penalty function.
alpha	A mixing parameter (between zero and one) defining the fraction between lasso and ridge penalties, where alpha = 1 corresponds to a pure lasso and alpha = 0 to a pure ridge penalty.
tram_args	Additional arguments (besides model and data) passed to tram_fun.
constraints	An optional list containing a matrix of linear inequality contraints on the regression coefficients and a vector specifying the rhs of the inequality.
...	Additional arguments passed to solve.

Value

Object of class "tramnet".

tramnet documentation built on Nov. 4, 2023, 3 p.m.