Hi R-masters I read the article: Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews.
In this paper i proposed a bivariate mixed model and use SAS proc mixed to adjust the estimates. I thinks use R to make the same and try with this code: base<-read.csv("base.csv") adj<-.5 attach(base) sens<-(VP+adj)/(VP+FN+2*adj) log.S<-log(sens/(1-sens)) var.log.S<-1/(sens*(1-sens)*(VP+FN)) dis<-rep(1,length(log.S)) non.dis<-rep(0,length(log.S)) data.S<-data.frame(id,Modality,log.S,var.log.S,dis,non.dis) names(data.S)<-c("id","Modality","logit","var.logit","dis","non.dis") esp<-(VN+adj)/(VN+FP+2*adj) log.E<-log((1-esp)/esp) var.log.E<-1/(esp*(1-esp)*(VN+FP)) dis<-rep(0,length(log.E)) non.dis<-rep(1,length(log.E)) data.E<-data.frame(id,Modality,log.E,var.log.E,dis,non.dis) names(data.E)<-c("id","Modality","logit","var.logit","dis","non.dis") data.bi<-rbind(data.S,data.E) require(nlme) lme(logit~dis*Modality+non.dis*Modality, random=~dis|id+non.dis| id,data=data.bi) but i recive a erro msg : Error in MEEM(object, conLin, control$niterEM) : Singularity in backsolve at level 0, block 1 How in solve this problem? Whats is wrong? Thanks in advance -- Bernardo Rangel Tura, M.D,Ph.D National Institute of Cardiology Brazil
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