Package 'palasso'

Title: Sparse Regression with Paired Covariates
Description: Implements sparse regression with paired covariates (<doi:10.1007/s11634-019-00375-6>). The paired lasso is designed for settings where each covariate in one set forms a pair with a covariate in the other set (one-to-one correspondence). For the optional correlation shrinkage, install 'ashr' (<https://github.com/stephens999/ashr>) and 'CorShrink' (<https://github.com/kkdey/CorShrink>) from GitHub (see README).
Authors: Armin Rauschenberger [aut, cre]
Maintainer: Armin Rauschenberger <[email protected]>
License: GPL-3
Version: 1.0.0
Built: 2024-10-27 06:05:43 UTC
Source: https://github.com/rauschenberger/palasso

Help Index

Methods for class "palasso"

Description

This page lists the main methods for class "palasso".

Usage

## S3 method for class 'palasso'
predict(object, newdata, model = "paired", s = "lambda.min", max = NULL, ...)

## S3 method for class 'palasso'
coef(object, model = "paired", s = "lambda.min", max = NULL, ...)

## S3 method for class 'palasso'
weights(object, model = "paired", max = NULL, ...)

## S3 method for class 'palasso'
fitted(object, model = "paired", s = "lambda.min", max = NULL, ...)

## S3 method for class 'palasso'
residuals(object, model = "paired", s = "lambda.min", max = NULL, ...)

## S3 method for class 'palasso'
deviance(object, model = "paired", max = NULL, ...)

## S3 method for class 'palasso'
logLik(object, model = "paired", max = NULL, ...)

## S3 method for class 'palasso'
summary(object, model = "paired", ...)

Arguments

object

palasso object
newdata

covariates: list of matrices, each with nn rows (samples) and pp columns (variables)
model

character "paired", or an entry of names(object)
s

penalty parameter: character "lambda.min" or "lambda.1se", positive numeric, or NULL (entire sequence)
max

maximum number of non-zero coefficients, positive integer, or NULL
...

further arguments for predict.cv.glmnet, coef.cv.glmnet, or deviance.glmnet

Details

By default, the function predict returns the linear predictor (type="link"). Consider predicting the response (type="response").

See Also

Use palasso to fit the paired lasso.

Paired lasso

Description

The function palasso fits the paired lasso. Use this function if you have paired covariates and want a sparse model.

Usage

palasso(y = y, X = X, max = 10, ...)

Arguments

y

response: vector of length nn
X

covariates: list of matrices, each with nn rows (samples) and pp columns (variables)
max

maximum number of non-zero coefficients: positive numeric, or NULL (no sparsity constraint)
...

further arguments for cv.glmnet or glmnet

Details

Let x denote one entry of the list X. See glmnet for alternative specifications of y and x. Among the further arguments, family must equal "gaussian", "binomial", "poisson", or "cox", and penalty.factor must not be used.

Hidden arguments: Deactivate adaptive lasso by setting adaptive to FALSE, activate standard lasso by setting standard to TRUE, and activate shrinkage by setting shrink to TRUE.

Value

This function returns an object of class palasso. Available methods include predict, coef, weights, fitted, residuals, deviance, logLik, and summary.

References

Armin Rauschenberger, Iiuliana Ciocanea-Teodorescu, Marianne A. Jonker, Renee X. Menezes, and Mark A. van de Wiel (2020). "Sparse classification with paired covariates." Advances in Data Analysis and Classification 14:571-588. doi:10.1007/s11634-019-00375-6. (Click here to access PDF. Contact: [email protected].)

Examples

set.seed(1)
n <- 50; p <- 20
y <- rbinom(n=n,size=1,prob=0.5)
X <- lapply(1:2,function(x) matrix(rnorm(n*p),nrow=n,ncol=p))
object <- palasso(y=y,X=X,family="binomial") # adaptive=TRUE,standard=FALSE
names(object)