PenalisedRegression: Penalised regression

In sharp: Stability-enHanced Approaches using Resampling Procedures

Penalised regression

Description

Runs penalised regression using implementation from glmnet. This function is not using stability.

Usage

PenalisedRegression(
  xdata,
  ydata,
  Lambda = NULL,
  family,
  penalisation = c("classic", "randomised", "adaptive"),
  gamma = NULL,
  ...
)

Arguments

xdata	matrix of predictors with observations as rows and variables as columns.
ydata	optional vector or matrix of outcome(s). If family is set to "binomial" or "multinomial", ydata can be a vector with character/numeric values or a factor.
Lambda	matrix of parameters controlling the level of sparsity.
family	type of regression model. This argument is defined as in glmnet. Possible values include "gaussian" (linear regression), "binomial" (logistic regression), "multinomial" (multinomial regression), and "cox" (survival analysis).
penalisation	type of penalisation to use. If penalisation="classic" (the default), penalised regression is done with the same regularisation parameter, or using penalty.factor, if specified. If penalisation="randomised", the regularisation for each of the variables is uniformly chosen between lambda and lambda/gamma. If penalisation="adaptive", the regularisation for each of the variables is weighted by 1/abs(beta)^gamma where beta is the regression coefficient obtained from unpenalised regression.
gamma	parameter for randomised or adaptive regularisation. Default is gamma=0.5 for randomised regularisation and gamma=2 for adaptive regularisation. The parameter gamma should be between 0 and 1 for randomised regularisation.
...	additional parameters passed to glmnet.

Value

A list with:

selected	matrix of binary selection status. Rows correspond to different model parameters. Columns correspond to predictors.
beta_full	array of model coefficients. Rows correspond to different model parameters. Columns correspond to predictors. Indices along the third dimension correspond to outcome variable(s).

References

\insertRef

AdaptiveLassosharp

\insertRef

lassosharp

See Also

SelectionAlgo, VariableSelection

Other underlying algorithm functions: CART(), ClusteringAlgo(), PenalisedGraphical(), PenalisedOpenMx()

Examples

# Data simulation
set.seed(1)
simul <- SimulateRegression(pk = 50)

# Running the LASSO
mylasso <- PenalisedRegression(
  xdata = simul$xdata, ydata = simul$ydata,
  Lambda = c(0.1, 0.2), family = "gaussian"
)

# Using glmnet arguments
mylasso <- PenalisedRegression(
  xdata = simul$xdata, ydata = simul$ydata,
  Lambda = c(0.1), family = "gaussian",
  penalty.factor = c(rep(0, 10), rep(1, 40))
)
mylasso$beta_full

sharp documentation built on April 11, 2025, 5:44 p.m.