# Reduce a logistic regression with monotone likelihood to a conditional regression with double descending likelihood. ```r 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. ## Returns 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 if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="kolassa97;textual",package="PHInfiniteEstimates",cached_env=.Rdpack.currefs) . It is intended for use with extensions to multinomial regression as in if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="kolassa97;textual",package="PHInfiniteEstimates",cached_env=.Rdpack.currefs) and to survival analysis as in if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="kz19;textual",package="PHInfiniteEstimates",cached_env=.Rdpack.currefs) . 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, if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="kolassa97;textual",package="PHInfiniteEstimates",cached_env=.Rdpack.currefs) removed regressors that became linearly dependent using orthogonalization, but on further reflection this computation is unnecessary. Data in the examples are from if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="mehtapatel;textual",package="PHInfiniteEstimates",cached_env=.Rdpack.currefs) , citing if(!exists(".Rdpack.currefs")) .Rdpack.currefs \<-new.env();Rdpack::insert_citeOnly(keys="goorinetal87;textual",package="PHInfiniteEstimates",cached_env=.Rdpack.currefs) . ## Examples ```r #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") ``` ## References Rdpack::insert_ref(key="mehtapatel",package="PHInfiniteEstimates") Rdpack::insert_ref(key="goorinetal87",package="PHInfiniteEstimates") Rdpack::insert_ref(key="kolassa97",package="PHInfiniteEstimates") Rdpack::insert_ref(key="kolassa16",package="PHInfiniteEstimates") Rdpack::insert_ref(key="kz19",package="PHInfiniteEstimates")

reduceLR function