SSGL: Spike-and-Slab Group Lasso Regression

In sparseGAM: Sparse Generalized Additive Models

Description

This is a stand-alone function for group-regularized regression models in the exponential dispersion family with the spike-and-slab group lasso (SSGL) penalty. Let y_i denote the ith response and x_i denote a p-dimensional vector of covariates. We fit models of the form,

g(E(y_i)) = β_0 + x_i^T β, i = 1, ..., n,

where g is a monotone increasing link function. The identity link function is used for Gaussian regression, the logit link is used for binomial regression, and the log link is used for Poisson, negative binomial, and gamma regression.

If the covariates in each x_i are grouped according to known groups g=1, ..., G, then this function may estimate some of the G groups of coefficients as all zero, depending on the amount of regularization.

Another implementation of the SSGL model for Gaussian regression models is available on Github at https://github.com/jantonelli111/SSGL. This package sparseGAM also implements the SSGL model for binomial, Poisson, negative binomial, and gamma regression.

Usage

SSGL(y, X, X.test, groups, 
     family=c("gaussian","binomial","poisson","negativebinomial","gamma"), 
     nb.size=1, gamma.shape=1, weights, nlambda0=20, lambda0, lambda1, a, b, 
     max.iter=100, tol = 1e-6, print.iter=TRUE) 

Arguments

y	n \times 1 vector of responses for training data.
X	n \times p design matrix for training data, where the jth column of X corresponds to the jth overall covariate.
X.test	n_{test} \times p design matrix for test data to calculate predictions. X.test must have the same number of columns as X, but not necessarily the same number of rows. If no test data is provided or if in-sample predictions are desired, then the function automatically sets X.test=X in order to calculate in-sample predictions.
groups	p-dimensional vector of group labels. The jth entry in groups should contain either the group number or the name of the factor level that the jth covariate belongs to. groups must be either a vector of integers or factors.
family	exponential dispersion family. Allows for "gaussian", "binomial", "poisson", "negativebinomial", and "gamma". Note that for "negativebinomial", the size parameter must be specified, while for "gamma", the shape parameter must be specified.
nb.size	known size parameter α in NB(α,μ_i) distribution for the negative binomial responses. Default is nb.size=1. Ignored if family is not "negativebinomial".
gamma.shape	known shape parameter ν in Gamma(μ_i,ν) distribution for gamma responses. Default is gamma.shape=1. Ignored if family is not "gamma".
weights	group-specific, nonnegative weights for the penalty. Default is to use the square roots of the group sizes.
nlambda0	number of spike hyperparameters L. Default is nlambda0=20.
lambda0	grid of L spike hyperparameters λ_0. The user may specify either a scalar or a vector. If the user does not provide this, the program chooses the grid automatically.
lambda1	slab hyperparameter λ_1 in the SSGL prior. Default is lambda1=1.
a	shape hyperparameter for the Beta(a,b) prior on the mixing proportion in the SSGL prior. Default is a=1.
b	shape hyperparameter for the Beta(a,b) prior on the mixing proportion in the SSGL prior. Default is b=dim(X)[2].
max.iter	maximum number of iterations in the algorithm. Default is max.iter=100.
tol	convergence threshold for algorithm. Default is tol=1e-6.
print.iter	Boolean variable for whether or not to print the current nlambda0 in the algorithm. Default is print.iter=TRUE.

Value

The function returns a list containing the following components:

lambda0	L \times 1 vector of spike hyperpameters lambda0 used to fit the model. lambda0 is displayed in descending order.
beta0	L \times 1 vector of estimated intercepts. The kth entry in beta0 corresponds to the kth spike hyperparameter in lambda0.
beta	p \times L matrix of estimated regression coefficients. The kth column in beta corresponds to the kth spike hyperparameter in lambda0.
mu.pred	n_{test} \times L matrix of predicted mean response values μ_{test} = E(Y_{test}) based on the test data in X.test (or training data X if no argument was specified forX.test). The kth column in mu.pred corresponds to the predictions for the kth spike hyperparameter in lambda0.
classifications	G \times L matrix of classifications, where G is the number of groups. An entry of "1" indicates that the group was classified as nonzero, and an entry of "0" indicates that the group was classified as zero. The kth column of classifications corresponds to the kth spike hyperparameter in lambda0.
loss	vector of either the residual sum of squares ("gaussian") or the negative log-likelihood ("binomial", "poisson", "negativebinomial", "gamma") of the fitted model. The kth entry in loss corresponds to the kth spike hyperparameter in lambda0.

References

Bai R. (2021). "Spike-and-slab group lasso for consistent Bayesian estimation and variable selection in non-Gaussian generalized additive models." arXiv pre-print arXiv:2007.07021.

Bai, R., Moran, G. E., Antonelli, J. L., Chen, Y., and Boland, M.R. (2021). "Spike-and-slab group lassos for grouped regression and sparse generalized additive models." Journal of the American Statistical Association, in press.

Examples

## Generate data
set.seed(12345)
X = matrix(runif(100*10), nrow=100)
n = dim(X)[1]
groups = c("A","A","A","B","B","B","C","C","D","D")
groups = as.factor(groups)
true.beta = c(-2.5,1.5,1.5,0,0,0,2,-2,0,0)

## Generate responses from Gaussian distribution
y = crossprod(t(X),true.beta) + rnorm(n)

## Generate test data
n.test = 50
X.test = matrix(runif(n.test*10), nrow=n.test)

## Fit SSGL model with 10 spike hyperparameters
## Note that if user does not specify lambda0, the SSGL function chooses a grid automatically.

SSGL.mod = SSGL(y, X, X.test, groups, family="gaussian", lambda0=seq(from=50,to=5,by=-5))

## Regression coefficient estimates
SSGL.mod$beta

# Predicted n.test-dimensional vectors mu=E(Y.test) based on test data, X.test. 
# The kth column of 'mu.pred' corresponds to the kth entry in 'lambda.'
SSGL.mod$mu.pred 

# Classifications of the 8 groups. The kth column of 'classifications'
# corresponds to the kth entry in 'lambda.'
SSGL.mod$classifications


## Example with binomial regression

## Generate binary responses
eta = crossprod(t(X), true.beta)
y = rbinom(n, size=1, prob=1/(1+exp(-eta)))

## Fit SSGL model with 10 spike hyperparameters
## Note that if user does not specify lambda0, the SSGL function chooses a grid automatically.

SSGL.mod = SSGL(y, X, X.test, groups, family="binomial", 
		lambda0=seq(from=10,to=1,by=-1))

## Predicted probabilities of success mu=E(Y.test) based on test data, X.test
SSGL.mod$mu.pred

## Classifications of the 8 groups. 
SSGL.mod$classifications

sparseGAM documentation built on May 31, 2021, 5:09 p.m.