penMSM.

Share:

Description

L1 penalized estimation of multistate models.

Usage

1
2
3
penMSM(type = "fused", d, X, PSM1, PSM2, lambda1, lambda2, w, betastart, nu = 0.5, 
tol = 1e-10, max.iter = 50, trace = TRUE, diagnostics = TRUE, family = "coxph", 
poissonresponse = NULL, poissonoffset = NULL, constant.approx = 1e-8)

Arguments

type

character defining the type of penalty, either fused or lasso.

d

data set with variables (mandatory) entry, exit, trans, and event.

X

design matrix.

PSM1

penalty structure matrix containing the penalty structure vectors psv as rows (lasso part).

PSM2

penalty structure matrix containing the penalty structure vectors psv as rows (fusion part).

lambda1

vector with penalty parameters for the respective penalty components (lasso part).

lambda2

vector with penalty parameters for the respective penalty components (fusion part).

w

vector containing weights for the respective penalty components.

betastart

vector containing starting values for beta.

nu

numeric value denoting the weight, i.e. a value between 0 and 1, of the Fisher scoring updates.

tol

relative update tolerance for stopping of the estimation algorithm.

max.iter

number of maximum iterations if tlerance is not reached.

trace

logical triggering printout of status information during the fitting process. .

diagnostics

logical triggering that Fisher matrix, score vector, and approximated penalty matrix are returned with the results.

family

character defining the likelihood to be used.

poissonresponse

response values for poisson likelihood (if used).

poissonoffset

offset values for poisson likelihood (if used).

constant.approx

constant for locally squared approximation of the absolute value penalty function.

Details

This function is the core function of this package. It implements L1 penalized estimation of multistate models, with the penalty applied to absolute effects and absolute effect differences on transition-type specific hazard rates.

Value

A list with elements B (matrix with estimated effects), aic (Akaike Information Criterion), gcv (GCV criterion), df (degrees of freedom), and (if diagnostics are requested) F (Fisher matrix), s (score vector), and A (approximated penalty matrix).

Author(s)

Holger Reulen

Examples

1
2
3
4
5
## Not run: penMSMtype = "fused", d, X, PSM1, PSM2, lambda1, lambda2, w, 
betastart, nu = 0.5, tol = 1e-10, max.iter = 50, trace = TRUE, 
diagnostics = TRUE, family = "coxph", poissonresponse = NULL, 
poissonoffset = NULL, constant.approx = 1e-8)
## End(Not run)