R/modelexamples.R

Defines functions Gamma_Frailty_Interval_Censoring

Documented in Gamma_Frailty_Interval_Censoring

globalVariables("udderquarterinfection")


#' @importFrom stats nlm
#' @importFrom utils data
NULL


#'@export
#'@title Gamma Frailty Interval Censoring
#'@description Application of the Gamma Frailty Interval Censoring Model on the Udder Quarter Infection Data Set. For more information see Details.
#'@param print.level Parameter of \code{\link[stats]{nlm}} (default=2): this argument determines the level of printing which is done during the minimization process. The default value of 0 means that no printing occurs, a value of 1 means that initial and final details are printed and a value of 2 means that full tracing information is printed.
#'
#'@details This function fits a parametric Weibull baseline hazard frailty model with gamma distributed frailties for the udder quarter infection data taking into consideration the interval censored nature of the data. Further theoretical details can be found in the paper in the reference
#'
#'@author Klara Goethals
#'@author Luc Duchateau
#'@references Goethals, K., Ampe, B., Berkvens, D., Laevens, H., Janssen, P. and Duchateau, L. (2009). Modeling interval-censored, clustered cow udder quarter infection times through the shared gamma frailty model. Journal of Agricultural Biological and Environmental Statistics 14, 1-14.
#'
#'@return Returns a list with the NLM result in \code{nlm} and the covariance matrix in \code{covmat}.
#'
#'@section R Code for Model :
#'The source R code for this model can found:
#'\itemize{
#'\item in the \code{doc/Models_R_Code.R} file in the package installation folder.
#'\item by accessing the function by calling \code{Gamma_Frailty_Interval_Censoring} (without brackets) or \code{getAnywhere("Gamma_Frailty_Interval_Censoring")}.
#'}
#'
#' @examples
#' \dontrun{
#' library(UdderQuarterInfectionData)
#' data("udderquarterinfection")
#'
#' Gamma_Frailty_Interval_Censoring()
#' # $nlm
#' # $nlm$minimum
#' # [1] 5670.491
#' #
#' # $nlm$estimate
#' # [1] 3.7967246 0.1201593 1.9672298 0.8590531
#' #
#' # $nlm$gradient
#' # [1]  0.0002924871  0.0017653292 -0.0005460029  0.0003265086
#' #
#' # $nlm$hessian
#' # [,1]       [,2]      [,3]       [,4]
#' # [1,]   23.22965  -117.7682 -39.93813  -10.10561
#' # [2,] -117.76825 15471.4753 567.24283 1228.87332
#' # [3,]  -39.93813   567.2428 664.76359   24.63047
#' # [4,]  -10.10561  1228.8733  24.63047  147.76479
#' #
#' # $nlm$code
#' # [1] 1
#' #
#' # $nlm$iterations
#' # [1] 22
#' #
#' #
#' # $covmat
#' # [,1]          [,2]          [,3]         [,4]
#' # [1,] 0.049281911  0.0001242730  0.0027853686  0.001872592
#' # [2,] 0.000124273  0.0001982213 -0.0001015391 -0.001623066
#' # [3,] 0.002785369 -0.0001015391  0.0017306214  0.000746460
#' # [4,] 0.001872592 -0.0016230660  0.0007464600  0.020269244
#' }
Gamma_Frailty_Interval_Censoring <- function(print.level=2){
  data("udderquarterinfection",envir=environment())

  upper=udderquarterinfection$right/91.31 # Divide by 91.31 to avoid small lambdas
  lower=udderquarterinfection$left/91.31
  fail=udderquarterinfection$status
  cluster=udderquarterinfection$cowid
  X=udderquarterinfection$lactation>1


  #Calculate the number of clusters.
  clusternames <- levels(as.factor(cluster))
  ncluster <- length(clusternames)

  # Create a udderquarterinfection with the variables cluster, the lower bound, the upper bound,
  # the censoring indicator and the covariate.
  udderquarterinfectionint <- as.matrix(cbind(cluster,lower,upper,fail,X))

  # create subsets for right-censored and interval-censored observations
  cendata <- udderquarterinfectionint[udderquarterinfectionint[,4]==0,]
  intdata <- udderquarterinfectionint[udderquarterinfectionint[,4]==1,]

  # Create a list of signs that corresponds to the n_ik (here restricted to 4 events)
  signs <- list(1,c(1,-1))
  for(i in 3:5) signs[[i]] <- kronecker(signs[[i-1]],c(1,-1))

  # Function to calculate the loglikelihood per cluster
  CalcLogLikClust <- function(i,x)
  {
    theta <- x[1]; lambda <- x[2]; gamma <- x[3]; beta <- x[4]
    cenX <- cendata[cendata[,1]==clusternames[i],5]
    intL <- intdata[intdata[,1]==clusternames[i],2]
    nevents <- length(intL)
    crossprod <- 1
    if(nevents>0){ #comment: if there are no events, crosspod=1
      intX <- intdata[intdata[,1]==clusternames[i],5]
      # Calculate R*_ij and L*_ij
      intRster <- lambda*(intdata[intdata[,1]==clusternames[i],3]^gamma)*exp(intX*beta)
      intLster <- lambda*(intL^gamma)*exp(intX*beta)
      # Calculate the vector p_i
      crossprod <- c(exp(intLster[1]),exp(intRster[1]))
      if(nevents>1){
        for(ik in 2:nevents) crossprod<-kronecker(crossprod,c(exp(intLster[ik]),exp(intRster[ik])))
      }
    }
    # Loglikelihood for 1 cluster
    return(
      log(1/(theta^(1/theta))*sum((1/
                                     ((sum(lambda*(as.vector(cendata[cendata[,1]==clusternames[i],3])^gamma)
                                           *exp(cenX*beta))+1/theta+log(crossprod))^(1/theta)))*signs[[nevents+1]]))
    )
  }

  # Calculate full marginal loglikelihood (formula 5)
  CalcLogLik <- function(x)
  {
    -sum(sapply(1:ncluster,CalcLogLikClust,x=x))
  }

  # Maximising the full marginal loglikelihood to obtain parameter estimates
  init <- c(1,1,1,1)
  results <- nlm(CalcLogLik,init,print.level=print.level, hessian=TRUE) # Can take a while!
  # $minimum
  # [1] 5670.491
  #
  # $estimate
  # [1] 3.7967246 0.1201593 1.9672298 0.8590531
  #
  # $gradient
  # [1]  0.0002924871  0.0017653292 -0.0005460029  0.0003265086
  #
  # $hessian
  # [,1]       [,2]      [,3]       [,4]
  # [1,]   23.22965  -117.7682 -39.93813  -10.10561
  # [2,] -117.76825 15471.4753 567.24283 1228.87332
  # [3,]  -39.93813   567.2428 664.76359   24.63047
  # [4,]  -10.10561  1228.8733  24.63047  147.76479
  #
  # $code
  # [1] 1
  #
  # $iterations
  # [1] 22

  # Calculate covariance matrix
  covmatr <- solve(results$hessian)
  #             [,1]          [,2]          [,3]         [,4]
  # [1,] 0.049281911  0.0001242730  0.0027853686  0.001872592
  # [2,] 0.000124273  0.0001982213 -0.0001015391 -0.001623066
  # [3,] 0.002785369 -0.0001015391  0.0017306214  0.000746460
  # [4,] 0.001872592 -0.0016230660  0.0007464600  0.020269244


  return(list(
    nlm=results,
    covmat=covmatr
  ))
}

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UdderQuarterInfectionData documentation built on Sept. 6, 2017, 5:03 p.m.