Package 'palasso'

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 $n$ rows (samples) and $p$ 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.

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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 $n$
X	covariates: list of matrices, each with $n$ rows (samples) and $p$ 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)

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