Package 'palasso'

Arguments

Description

Checks the validity of the provided arguments.

Usage

.args(...)

Arguments

...	Arguments supplied to palasso, other than y, X and max.

Value

Returns the arguments as a list, including default values for missing arguments.

Examples

NA

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Combining p-values

Description

This function combines local $p$-values to a global $p$-value.

Usage

.combine(x, method = "simes")

Arguments

x	local $p$-values: numeric vector of length $k$
method	character $"fisher"$, $"tippet"$, $"sidak"$, or $"simes"$

Value

These functions return a numeric vector of length $p$ (main effects), or a numeric matrix with $p$ rows and $p$ columns (interaction effects).

References

Westfall, P. H. (2005). "Combining p-values". Encyclopedia of Biostatistics doi:10.1002/0470011815.b2a15181

Examples

# independence
p <- runif(10)
palasso:::.combine(p)

## dependence 
#runif <- function(n,cor=0){
#    Sigma <- matrix(cor,nrow=n,ncol=n)
#     diag(Sigma) <- 1
#     mu <- rep(0,times=n)
#     q <- MASS::mvrnorm(n=1,mu=mu,Sigma=Sigma)
#     stats::pnorm(q=q)
#}
#p <- runif(n=10,cor=0.8)
#combine(p)

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Correlation

Description

Calculates the correlation between the response and the covariates. Shrinks the correlation coefficients for each covariate set separately.

Usage

.cor(y, x, args)

Arguments

y	vector of length $n$
x	matrix with $n$ rows and $p$ columns
args	options for paired lasso: list of arguments (output from .dims and .args)

Value

list of vectors

Examples

NA

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Cross-validation

Description

Repeatedly leaves out samples, and predicts their response.

Usage

.cv(y, x, foldid, lambda, args)

Arguments

y	response: vector of length $n$
x	covariates: matrix with $n$ rows (samples) and $k * p$ columns (variables)
foldid	fold identifiers: vector of length $n$, with entries from $1$ to nfolds
lambda	lambda sequence: vector of decreasing positive values
args	options for paired lasso: list of arguments (output from .dims and .args)

Value

Returns matrix of predicted values (except "cox")

Examples

NA

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Dimensionality

Description

This function extracts the dimensions.

Usage

.dims(y, X, args = NULL)

Arguments

y	response: vector of length $n$
X	covariates: list of matrices, each with $n$ rows (samples) and $p$ columns (variables)
args	options for paired lasso: list of arguments (output from .dims and .args)

Value

The function .dims extracts the dimensionality. It returns the numbers of samples, covariate pairs and covariate sets. It also returns the number of weighting schemes, and the names of these weighting schemes.

Examples

NA

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Extraction

Description

Extracts cv.glmnet-like object.

Usage

.extract(fit, lambda, cvm, type.measure)

Arguments

fit	matrix with one row for each sample ("gaussian", "binomial" and "poisson"), or one row for each fold (only "cox"), and one column for each lambda (output from .fit)
lambda	lambda sequence: vector of decreasing positive values
cvm	mean cross-validated loss: vector of same length as lambda (output from .loss)
type.measure	... loss function: character "deviance", "mse", "mae", "class", or "auc"

Examples

NA

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Model bag

Description

Fits all models from the chosen bag.

Usage

.fit(y, x, args)

Arguments

y	response: vector of length $n$
x	covariates: matrix with $n$ rows (samples) and $k * p$ columns (variables)
args	options for paired lasso: list of arguments (output from .dims and .args)

Value

list of glmnet-like objects

Examples

NA

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Cross-validation folds

Description

Assigns samples to cross-validation folds, balancing the folds in the case of a binary or survival response.

Usage

.folds(y, nfolds, foldid = NULL)

Arguments

y	response: vector of length $n$
nfolds	number of folds: positive integer ($>= 10$ recommended)
foldid	fold identifiers: vector of length $n$, with entries from $1$ to nfolds

Value

Returns the fold identifiers.

Examples

NA

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Cross-validation loss

Description

Calculates mean cross-validated loss

Usage

.loss(y, fit, family, type.measure, foldid = NULL)

Arguments

y	response: vector of length $n$
fit	matrix with one row for each sample ("gaussian", "binomial" and "poisson"), or one row for each fold (only "cox"), and one column for each lambda (output from .fit)
family	model family: character "gaussian", "binomial", "poisson", or "cox"
type.measure	... loss function: character "deviance", "mse", "mae", "class", or "auc"
foldid	fold identifiers: vector of length $n$, with entries from $1$ to nfolds

Value

Returns list of vectors, one for each model.

Examples

NA

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Weighting schemes

Description

Calculates the weighting schemes.

Usage

.weight(cor, args)

Arguments

cor	correlation coefficients: list of $k$ vectors of length $p$ (one vector for each covariate set with one entry for each covariate)
args	options for paired lasso: list of arguments (output from .dims and .args)

Value

list of named vectors (one for each weighting scheme)

Examples

NA

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Arguments for "palasso"

Description

This page lists the arguments for the (internal) "palasso" function(s).

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
x	covariates: matrix with $n$ rows (samples) and $k * p$ columns (variables)
args	options for paired lasso: list of arguments (output from .dims and .args)
nfolds	number of folds: positive integer ($>= 10$ recommended)
foldid	fold identifiers: vector of length $n$, with entries from $1$ to nfolds
cor	correlation coefficients: list of $k$ vectors of length $p$ (one vector for each covariate set with one entry for each covariate)
lambda	lambda sequence: vector of decreasing positive values
family	model family: character "gaussian", "binomial", "poisson", or "cox"
type.measure	... loss function: character "deviance", "mse", "mae", "class", or "auc"
fit	matrix with one row for each sample ("gaussian", "binomial" and "poisson"), or one row for each fold (only "cox"), and one column for each lambda (output from .fit)
cvm	mean cross-validated loss: vector of same length as lambda (output from .loss)

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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

A Rauschenberger, I Ciocanea-Teodorescu, RX Menezes, MA Jonker, and MA 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, pdf, [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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