R/bayessurvreg1.priorInit.R

Defines functions bayessurvreg1.priorInit

Documented in bayessurvreg1.priorInit

####################################################
#### AUTHOR:     Arnost Komarek                 ####
####             (2005)                         ####
####                                            ####
#### FILE:       bayessurvreg1.priorInit.R      ####
####                                            ####
#### FUNCTIONS:  bayessurvreg1.priorInit        ####
####################################################

### ======================================
### bayessurvreg1.priorInit
### ======================================
## Subfunction for bayessurvreg1.R
##  -> just to make it more readable
##
## Manipulation with initial values and prior specifications
##
## 14/01/2005: check using is.null changed to check using match and is.na
##
bayessurvreg1.priorInit <- function(prior, init, Yinit, Xinit, n, nX, nrandom, ncluster, indb, randomInt, toler.chol){

   if(length(prior) == 0) inprior <- "arnost"
   else                   inprior <- names(prior)
   if(length(init) == 0) ininit <- "arnost"
   else                  ininit <- names(init)   
  
   ## ============================================================================================
   ## Prior parameters (calculate these that were not given by the user and change the notation)
   ## part 1
   ## ============================================================================================
   prior.pari <- numeric(3)
   names(prior.pari) <- c("kmax", "k.prior", "Eb0.depend.mix")

   tmp <- match("kmax", inprior, nomatch=NA)
   if(is.na(tmp)) prior$kmax <- 5
   tmp <- match("k.prior", inprior, nomatch=NA)
   if(is.na(tmp)) prior$k.prior <- "poisson"
   tmp <- match("Eb0.depend.mix", inprior, nomatch=NA)
   if(is.na(tmp)) prior$Eb0.depend.mix <- FALSE
   tmp <- match("poisson.k", inprior, nomatch=NA)
   if(is.na(tmp)) prior$poisson.k <- 3
   prior.pari["k.prior"] <- pmatch(prior$k.prior, c("poisson", "uniform", "fixed"), nomatch = -1) - 1
               ## 0 = Poisson, 1 = Uniform, 2 = Fixed
   if (prior.pari["k.prior"] < 0) stop("Prior for k (number of mixture components) must be either poisson, uniform or fixed.")
   prior.pari["kmax"] <- prior$kmax
   prior.pari["Eb0.depend.mix"] <- 1*(prior$Eb0.depend.mix)

   prior.pard <- numeric(2*prior.pari["kmax"] + 7)
   names(prior.pard) <- c(paste("pi.split", 1:prior.pari["kmax"], sep = ""), paste("pi.birth", 1:prior.pari["kmax"], sep = ""),
                          "lambda", "delta", "xi", "kappa", "zeta", "g", "h")
   prior.pard["lambda"] <- prior$poisson.k


   ## =========================================================
   ## Get initial estimates
   ## =========================================================
   init.error <- "Something is wrong with your initials."
   fit.init <- survreg(Yinit ~ Xinit - 1, dist = "lognormal")          ## intercept is already included in Xinit

     ## index of the first iteration
   tmp <- match("iter", ininit, nomatch=NA)
   if(is.na(tmp)) init$iter <- 0
   if (is.na(init$iter))   init$iter <- 0
   init$iter <- init$iter[1]
   
     ## initial mixture
   tmp <- match("mixture", ininit, nomatch=NA)
   if(is.na(tmp)){
     if (prior.pari["k.prior"] == 2) stop("init$mixture must be given when prior$k.prior is 'fixed'.")
     init$mixture <- numeric(1 + 3*prior$kmax)
     init$mixture[1] <- 1                                        ## initial k
     init$mixture[2] <- 1.0                                      ## initial weight of the first mixture component   
     init$mixture[2 + prior$kmax] <- fit.init$coefficients[1]    ## initial mean of the first mixture component
     init$mixture[2 + 2*prior$kmax] <- fit.init$scale^2          ## initial variance of the first mixture component
   }
   else{
     if (length(init$mixture) != 1 + 3*prior$kmax) stop("Incorrect init$mixture parameter supplied.")
     if (is.na(init$mixture[1])) stop("Incorrect init$mixture parameter supplied.")
     wi <- init$mixture[2:(1 + init$mixture[1])]
     mui <-init$mixture[(2 + prior$kmax):(1 + prior$kmax + init$mixture[1])]       
     sig2i <- init$mixture[(2 + 2*prior$kmax):(1 + 2*prior$kmax + init$mixture[1])]   
     if (sum(is.na(wi)) | sum(is.na(mui)) | sum(is.na(sig2i))) stop("Incorrect init$mixture parameter supplied.")
     if (sum(wi < 0)) stop("Incorrect init$mixture parameter supplied.")
     if (sum(sig2i <= 0)) stop("Incorrect init$mixture parameter supplied.")
     wi <- wi/sum(wi)                              ## to make sure that the sum is 1
     ordermu <- order(mui)
     k.temp <- init$mixture[1]
     init$mixture <- numeric(1 + 3*prior$kmax)     
     wi <- wi[ordermu]
     mui <- mui[ordermu]
     sig2i <- sig2i[ordermu]
     init$mixture[1] <- k.temp
     init$mixture[2:(1 + init$mixture[1])] <- wi
     init$mixture[(2 + prior$kmax):(1 + prior$kmax + init$mixture[1])] <- mui
     init$mixture[(2 + 2*prior$kmax):(1 + 2*prior$kmax + init$mixture[1])] <- sig2i     
   }

     ## initial beta parameter
   if (!nX){
     init$beta <- 0
     ininit <- names(init)
   }     
   else{
     tmp <- match("beta", ininit, nomatch=NA)
     if(is.na(tmp)){     
       init$beta <- fit.init$coefficients[-1]                  ## remove the intercept
     }
     else{
       if (length(init$beta) < nX) stop("Incorrect init$beta parameter supplied.")
       init$beta <- init$beta[1:nX]
     }
     if (sum(is.na(init$beta))) stop("Incorrect init$beta parameter supplied.")
   }     
   
     ## initial values of the random effects
   if (!nrandom) init$b <- 0
   else{
     tmp <- match("b", ininit, nomatch=NA)
     if(is.na(tmp)){          
       bb <- fit.init$coefficients[-1][indb > 0]
       if (randomInt) bb <- c(0, bb)
       init$b <- rep(bb, ncluster)
     } 
     else{
       if (length(init$b) == 0){
         bb <- fit.init$coefficients[-1][indb > 0]
         if (randomInt) bb <- c(0, bb)
         init$b <- rep(bb, ncluster)
       } 
       else{
         if (length(init$b) < nrandom*ncluster) stop("Incorrect init$b parameter supplied.")
         init$b <- init$b[1:(nrandom*ncluster)]
       }
     }  
     if (sum(is.na(init$b))) stop("Incorrect init$b parameter supplied.")
   }     
   
     ## initial values of the (transformed) latent response
   tmp <- match("y", ininit, nomatch=NA)
   if(is.na(tmp)){             
     init$y <- as.numeric(log(Yinit[,1]))          
   } 
   else{
     if (length(init$y) < n) stop("Incorrect init$y parameter supplied.")    
     init$y <- init$y[1:n]
   }
   if (sum(is.na(init$y))) stop("Incorrect init$y parameter supplied.")
   
     ## initial values of the component pertinences
   tmp <- match("r", ininit, nomatch=NA)
   if(is.na(tmp)){                
     init$r <- numeric(n) + 1         ## initially, everyone belongs to the first mixture component    
   }
   else{
     if (length(init$r) < n) stop("Incorrect init$r parameter supplied.")
     init$r <- init$r[1:n]
   } 
   if (sum(is.na(init$r)) | sum(init$r <= 0) | sum(init$r > init$mixture[1]))
     stop("Incorrect init$r parameter supplied.")
   
     ## initial values of the matrix D (covariance matrix of the random effects)
     ## (lower triangle of the matrix D in column major order)
   if (!nrandom){
     init$D <- 0
     ininit <- names(init)     
   }     
   else{
     tmp <- match("D", ininit, nomatch=NA)
     if(is.na(tmp)){                     
       init$D <- diag(nrandom)[lower.tri(diag(nrandom), diag = TRUE)]   ## identity matrix as initial D
     }
     else{
       if (length(init$D) < 0.5*nrandom*(1+nrandom)) stop("Incorrect init$D parameter supplied.")
       init$D <- init$D[1:(0.5*nrandom*(1+nrandom))]
     }
     if (sum(is.na(init$D))) stop("Incorrect init$D parameter supplied.")
   }     
   
     ## initial values of the remaining parameters (only eta at this moment)
   tmp <- match("otherp", ininit, nomatch=NA)
   if(is.na(tmp)){                   
     init$otherp <- 1              ## initial of eta
   }
   else{
     init$otherp <- init$otherp[1]
   }
   if (sum(is.na(init$otherp))) stop("Incorrect init$otherp parameter supplied.")
   
     ## initial proposal vector for a split-combine move
   tmp <- match("u", ininit, nomatch=NA)
   if(is.na(tmp)){                   
     init$u <- c(runif(1), 0, 0, runif(3*(prior$kmax - 1)))
   }
   else{
     if (length(init$u) < 3*prior$kmax) stop("Incorrect init$u parameter supplied.")  
     init$u <- init$u[1:(3*prior$kmax)]
   }     
   if (sum(is.na(init$u))) stop("Incorrect init$u parameter supplied.")  
   if (sum(init$u < 0 | init$u > 1)) stop("Incorrect init$u parameter supplied.")  

   
   ## =========================================================================================
   ## Prior parameters (calculate these that were not given by the user and change the notation)
   ## part 2
   ## =========================================================================================
   tmp <- match("dirichlet.w", inprior, nomatch=NA)
   if(is.na(tmp)) prior$dirichlet.w <- 1
   if (prior$dirichlet.w < 1) stop ("prior$dirichlet.w must be at least 1.")
   prior.pard["delta"] <- prior$dirichlet.w

   tmp <- match("mean.mu", inprior, nomatch=NA)
   if(is.na(tmp)) prior$mean.mu <- init$mixture[2 + prior.pari["kmax"]]                 ## mean of the first comp.
   tmp <- match("var.mu", inprior, nomatch=NA)
   if(is.na(tmp)) prior$var.mu <- 2*init$mixture[2 + 2*prior.pari["kmax"]]              ## 2*variance of the first comp.
   if (prior$var.mu <= 0) stop("prior$var.mu must be positive.")
   prior.pard["xi"] <- prior$mean.mu
   prior.pard["kappa"] <- prior$var.mu

   tmp <- match("shape.invsig2", inprior, nomatch=NA)
   if(is.na(tmp)) prior$shape.invsig2 <- 1.5
   tmp <- match("shape.hyper.invsig2", inprior, nomatch=NA)
   if(is.na(tmp)) prior$shape.hyper.invsig2 <- 0.8
   tmp <- match("rate.hyper.invsig2", inprior, nomatch=NA)
   if(is.na(tmp)) prior$rate.hyper.invsig2 <- prior$var.mu
   if (prior$shape.invsig2 <= 0) stop("prior$shape.invsig2 must be positive.")
   if (prior$shape.hyper.invsig2 <= 0) stop("prior$shape.hyper.invsig2 must be positive.")
   if (prior$rate.hyper.invsig2 <= 0) stop("prior$rate.hyper.invsig2 must be positive.")      
   prior.pard["zeta"] <- prior$shape.invsig2
   prior.pard["g"] <- prior$shape.hyper.invsig2
   prior.pard["h"] <- prior$rate.hyper.invsig2

   k.is.fixed <- (prior.pari["k.prior"] == 2)
   tmp <- match("pi.split", inprior, nomatch=NA)
   tmp <- match("pi.birth", inprior, nomatch=NA)
   if (k.is.fixed){
     prior$pi.split <- 0
     prior$pi.birth <- 0
   }
   else{   
     if(is.na(tmp)) prior$pi.split <- c(1, rep(0.5, prior.pari["kmax"]-2), 0)
     if(is.na(tmp)) prior$pi.birth <- c(1, rep(0.5, prior.pari["kmax"]-2), 0)
     if (prior$pi.split[1] != 1) stop("prior$pi.split[1] must be equal to 1.")
     if (prior$pi.birth[1] != 1) stop("prior$pi.birth[1] must be equal to 1.")
     if (prior$pi.split[prior.pari["kmax"]] != 0) stop("prior$pi.split[kmax] must be equal to 0.")
     if (prior$pi.birth[prior.pari["kmax"]] != 0) stop("prior$pi.birth[kmax] must be equal to 0.")      
     if (length(prior$pi.split) != prior.pari["kmax"]) stop("Incorrect length of a vector prior$pi.split.")
     if (length(prior$pi.birth) != prior.pari["kmax"]) stop("Incorrect length of a vector prior$pi.birth.")
   }  
   prior.pard[paste("pi.split", 1:prior.pari["kmax"], sep = "")] <- prior$pi.split
   prior.pard[paste("pi.birth", 1:prior.pari["kmax"], sep = "")] <- prior$pi.birth
   
   priordi <- list(integer = prior.pari, double = prior.pard)
   attr(priordi, "init") <- init
   attr(priordi, "prior") <- prior
   
   return(priordi)
   
 }

Try the bayesSurv package in your browser

Any scripts or data that you put into this service are public.

bayesSurv documentation built on May 2, 2019, 3:26 a.m.