reduceLR: Reduce a logistic regression with monotone likelihood to a...

In PHInfiniteEstimates: Tools for Inference in the Presence of a Monotone Likelihood

Reduce a logistic regression with monotone likelihood to a conditional regression with double descending likelihood.

Description

Reduce a logistic regression with monotone likelihood to a conditional regression with double descending likelihood.

Usage

reduceLR(Z, nvec = NULL, yvec = NULL, keep, sst = NULL, verybig = 1e+07)

Arguments

Z	regression matrix
nvec	vector of sample sizes
yvec	vector of responses
keep	vector of variable names to block from consideration for removal.
sst	vector of sufficient statistics
verybig	threshold for condition number to declare colinearity.

Details

This function implements version of \insertCitekolassa97;textualPHInfiniteEstimates. It is intended for use with extensions to multinomial regression as in \insertCitekolassa97;textualPHInfiniteEstimates and to survival analysis as in \insertCitekz19;textualPHInfiniteEstimates. The method involves linear optimization that is potentially repeated. Initial calculations were done using a proprietary coding of the simplex, in a way that allowed for later iterations to be restarted from earlier iterations; this computational advantage is not employed here, in favor of computational tools in the public domain and included in the R package lpSolve. Furthermore, \insertCitekolassa97;textualPHInfiniteEstimates removed regressors that became linearly dependent using orthogonalization, but on further reflection this computation is unnecessary. Data in the examples are from \insertCitemehtapatel;textualPHInfiniteEstimates, citing \insertCitegoorinetal87;textualPHInfiniteEstimates.

Value

a list with components

• keepme indicators of which variables are retained in the reduced data set

• moderate indicatiors of which observations are retained in the reduced data set

• extreme indicators of which observations are removed in the reduced data set

• toosmall indicator of whether resulting data set is too small to fit the proportional hazards regression

References

\insertRef

mehtapatelPHInfiniteEstimates

\insertRef

goorinetal87PHInfiniteEstimates

\insertRef

kolassa97PHInfiniteEstimates

\insertRef

kolassa16PHInfiniteEstimates

\insertRef

kz19PHInfiniteEstimates

Examples

#Cancer Data
Z<-cbind(rep(1,8),c(rep(0,4),rep(1,4)),rep(c(0,0,1,1),2),rep(c(0,1),4))
dimnames(Z)<-list(NULL,c("1","LI","SEX","AOP"))
nvec<-c(3,2,4,1,5,5,9,17); yvec<-c(3,2,4,1,5,3,5,6)
reduceLR(Z,nvec,yvec,c("SEX","AOP"))
#CD4, CD8 data
Z<-cbind(1,c(0,0,1,1,0,0,1,0),c(0,0,0,0,1,1,0,1),c(0,0,0,0,0,1,1,0),c(0,1,0,1,0,0,0,1))
dimnames(Z)<-list(NULL,c("1","CD41","CD42","CD81","CD82"))
nvec<-c(7,1,7,2,2,13,12,3); yvec<-c(4,1,2,2,0,0,4,1)
reduceLR(Z,nvec,yvec,"CD41")

PHInfiniteEstimates documentation built on Nov. 24, 2023, 9:06 a.m.