clogitLasso {clogitLasso} R Documentation

## fit lasso for conditional logistic regression for matched case-control studies

### Description

Fit a sequence of conditional logistic regression with lasso penalty, for small to large sized samples

### Usage

clogitLasso(X, y, strata, fraction = NULL, nbfraction = 100,
nopenalize = NULL, BACK = TRUE, standardize = FALSE, maxit = 100,
maxitB = 500, thr = 1e-10, tol = 1e-10, epsilon = 1e-04,
trace = TRUE, log = TRUE, adaptive = FALSE, separate = FALSE,
ols = FALSE, p.fact = NULL, remove = FALSE)


### Arguments

 X Input matrix, of dimension nobs x nvars; each row is an observation vector y Binary response variable, with 1 for cases and 0 for controls strata Vector with stratum membership of each observation fraction Sequence of lambda values nbfraction The number of lambda values - default is 100 nopenalize List of coefficients not to penalize starting at 0 BACK If TRUE, use Backtracking-line search -default is TRUE standardize Logical flag for x variable standardization, prior to fitting the model sequence. maxit Maximum number of iterations of outer loop - default is 100 maxitB Maximum number of iterations in Backtracking-line search - default is 100 thr Threshold for convergence in lassoshooting. Default value is 1e-10. Iterations stop when max absolute parameter change is less than thr tol Threshold for convergence-default value is 1e-10 epsilon ratio of smallest to largest value of regularisation parameter at which we find parameter estimates trace If TRUE the algorithm will print out information as iterations proceed -default is TRUE log If TRUE, fraction are spaced uniformly on the log scale adaptive If TRUE adaptive lasso is fitted-default is FALSE separate If TRUE, the weights in adaptive lasso are build separately using univariate models. Default is FALSE, weights are build using multivariate model ols If TRUE, weights less than 1 in adaptive lasso are set to 1. Default is FALSE p.fact Weights for adaptive lasso remove If TRUE, invariable covariates are removed-default is FALSE

### Details

The sequence of models implied by fraction is fit by IRLS (iteratively reweighted least squares) algorithm. by coordinate descent with warm starts and sequential strong rules

### Value

An object of type clogitLasso which is a list with the following components:

 beta nbfraction-by-ncol matrix of estimated coefficients. First row has all 0s fraction A sequence of regularisation parameters at which we obtained the fits nz A vector of length nbfraction containing the number of nonzero parameter estimates for the fit at the corresponding regularisation parameter arg List of arguments

### Author(s)

Marta Avalos, Helene Pouyes, Marius Kwemou and Binbin Xu

### References

Avalos, M., Pouyes, H., Grandvalet, Y., Orriols, L., & Lagarde, E. (2015). Sparse conditional logistic regression for analyzing large-scale matched data from epidemiological studies: a simple algorithm. BMC bioinformatics, 16(6), S1. doi: 10.1186/1471-2105-16-S6-S1.

### Examples

## Not run:
# generate data
y <- rep(c(1,0), 100)
X <- matrix (rnorm(20000, 0, 1), ncol = 100) # pure noise
strata <- sort(rep(1:100, 2))

# 1:1
fitLasso <- clogitLasso(X,y,strata,log=TRUE)

## End(Not run)


[Package clogitLasso version 1.1 Index]