# Indexing parameter objective function Objective used for optimizing indexing parameters via `optim`. ```r index_optim(in.par, data, data.id, br_s, X1, XX1, event_time_name = 'years', time_name = 'year', event_name = 'status2', b, t, true.haz) ``` ## Arguments - `in.par`: Numeric vector length 2. - `data`: Data frame. - `data.id`: Id-level data frame. - `br_s`: user-supplied grid points ranging from the minimum to the maximum of the time_name argument - `X1`: List of vectors for indexing: each vector corresponds to a biomarker and contains one summary measurement per individual. - `XX1`: List of vectors for indexing: each vector corresponds to a single biomarker and contains its longitudinal measurements across all individuals/time points. - `event_time_name`: Name of event time column. - `time_name`: Name of observation time column. - `event_name`: Name of event indicator column. - `b`: Bandwidth. - `t`: Evaluation point or grid. - `true.haz`: Numeric vector of true hazard values for CV. ## Returns Scalar cross-validation score. ## Examples ```r marker_name1 <- 'albumin' marker_name2 <- 'serBilir' event_time_name <- 'years' time_name <- 'year' event_name <- 'status2' id<-'id' par.x1 <- 0.0702 #0.149 par.x2 <- 0.0856 #0.10 t.x1 = 0 # refers to zero mean variables - slightly high t.x2 = 1.9 # refers to zero mean variable - high b = 0.42 t = par.x1 * t.x1 + par.x2 *t.x2 # first simulate true HR function: xin <- pbc2[,c(id, marker_name1, marker_name2, event_time_name, time_name, event_name)] ls<-50 # 50 grid points to evaluate the estimates s.out<- xin[, 'year'] s.out.use <- seq(0, max(s.out), max(s.out)/( ls-1)) n <- length(xin$id) nn<-max( as.double(xin[,'id']) ) ################### Create bootstrap samples by group #################### set.seed(1) B<- 10 # 400 #50 Boot.samples<-list() for(j in 1:B) { i.use<-c() id.use<-c() index.nn <- sample (nn, replace = TRUE) for(l in 1:nn) { i.use2<-which(xin[,id]==index.nn[l]) i.use<-c(i.use, i.use2) id.use2<-rep(index.nn[l], times=length(i.use2)) id.use<-c(id.use, id.use2) } xin.i<-xin[i.use,] xin.i<-xin[i.use,] Boot.samples[[j]]<- xin.i[order(xin.i$id),] #xin[i.use,] } true.hazard<- Sim.True.Hazard(Boot.samples, id='id', size_s_grid=ls, marker_name1=marker_name1, marker_name2= marker_name2, event_time_name = event_time_name, time_name = time_name, event_name = event_name, in.par = c(par.x1, par.x2), b) ########################################################################## # Then run the optimization for the indexing parameters: data.use<-Boot.samples[[1]] data.use.id<-to_id(data.use) data.use.id<-data.use.id[complete.cases(data.use.id), ] X1t=data.use[,marker_name1] -mean(data.use[, marker_name1]) XX1t=data.use.id[,marker_name1] -mean(data.use.id[, marker_name1]) X2t=data.use[,marker_name2] -mean(data.use[, marker_name2]) XX2t=data.use.id[,marker_name2] -mean(data.use.id[, marker_name2]) X1=list(X1t, X2t) XX1=list(XX1t, XX2t) s.out<- data.use[, time_name] s.out.use <- seq(0, max(s.out), max(s.out)/( ls-1)) res<- optim(par=c(par.x1, par.x2), fn=index_optim, data=data.use, data.id=data.use.id, br_s=s.out.use, X1=X1, XX1=XX1, event_time_name = event_time_name, time_name = time_name, event_name = event_name, b=b, t=t, true.haz=true.hazard, method="Nelder-Mead") ```

index_optim function