geeboost: GEEBoost

Description

Thresholded boosting for correlated data via GEE

Usage

geeboost(Y, X, id = 1:length(Y), family = "gaussian", corstr = "ind", traceplot = FALSE, ...)

Arguments

Vector of (presumably correlated) outcomes

Matrix of predictors

Index indicating clusters of correlated observations

family

Outcome distribution to be used. "gaussian" (the default), "binomial", and "poisson" are currently implemented.

corstr

Working correlation structure to use. "ind" (for independence, the default) and "exch" (for exchanageable) are currently implemented.

traceplot

Option of whether or not to produce a traceplot of the coefficient values. See coef_traceplot for details.

...

Additional arguments to be passed to the threeboost function. See the threeboost help page for details.

Value

A list with three entries:

coefmat A matrix with maxit rows and ncol(X) columns, with each row containing the parameter vector from an iteration of EEBoost.
QICs A vector of QICs computed from the coefficients.
final.model The coefficients corresponding to the model (set of coefficients) yielding the smallest QIC.

Details

This function implements thresholded EEBoost for the Generalized Estimating Equations. The arguments are consistent with those used by geepack.

Examples

Run this code

# Generate some test data
library(mvtnorm)
library(Matrix)
n <- 30
n.var <- 50
clust.size <- 4

B <- c(rep(2,5),rep(0.2,5),rep(0.05,10),rep(0,n.var-20))
mn.X <- rep(0,n.var)
sd.X <- 0.5
rho.X <- 0.3
cov.sig.X <- sd.X^2*((1-rho.X)*diag(rep(1,10)) + rho.X*matrix(data=1,nrow=10,ncol=10))
sig.X <- as.matrix( Matrix::bdiag(lapply(1:(n.var/10),function(x) { cov.sig.X } ) ) )
sd.Y <- 0.5
rho.Y <- 0.3
indiv.Sig <- sd.Y^2*( (1-rho.Y)*diag(rep(1,4)) + rho.Y*matrix(data=1,nrow=4,ncol=4) )
sig.list <- list(length=n)
for(i in 1:n) { sig.list[[i]] <- indiv.Sig }
Sig <- Matrix::bdiag(sig.list)
indiv.index <- rep(1:n,each=clust.size)
sig.Y <- as.matrix(Sig)

if(require(mvtnorm)) {
X <- mvtnorm::rmvnorm(n*clust.size,mean=mn.X,sigma=sig.X)
mn.Y <- X %*% B
Y <- mvtnorm::rmvnorm(1,mean=mn.Y,sigma=sig.Y) ## Correlated continuous outcomes
expit <- function(x) { exp(x) / (1 + exp(x)) }
## Correlated binary outcomes
Y.bin <- rbinom(n*clust.size,1,p=expit(mvtnorm::rmvnorm(1,mean=mn.Y,sigma=sig.Y)))
Y.pois <- rpois(length(Y),lambda=exp(mn.Y)) ## Correlated Poisson outcomes
} else { stop('Need mvtnorm package to generate correlated data.')}

## Run EEBoost (w/ indep working correlation)
results.lin <- geeboost(Y,X,id=indiv.index,maxit=1000)
## Not run: 
#  results.bin <- geeboost(Y.bin,X,id=indiv.index,family="binomial",maxit=1000)
#  results.pois <- geeboost(Y.pois,X,id=indiv.index,family="poisson",maxit=1000,traceplot=TRUE)
# ## End(Not run)

print(results.lin$final.model)