PHInfiniteEstimates (version 1.6)

reduceLR: 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)

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

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

Details

This function implements version of kolassa97;textualPHInfiniteEstimates. It is intended for use with extensions to multinomial regression as in kolassa97;textualPHInfiniteEstimates and to survival analysis as in kz19;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, kolassa97;textualPHInfiniteEstimates removed regressors that became linearly dependent using orthogonalization, but on further reflection this computation is unnecessary. Data in the examples are from mehtapatel;textualPHInfiniteEstimates, citing goorinetal87;textualPHInfiniteEstimates.

References

mehtapatelPHInfiniteEstimates

goorinetal87PHInfiniteEstimates

kolassa97PHInfiniteEstimates

kolassa16PHInfiniteEstimates

kz19PHInfiniteEstimates

Examples

# NOT RUN {
#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")
# }