R/IC_HdrLN.R

Defines functions IC_HdrLN

Documented in IC_HdrLN

#' Calculating IC 95 percent of  hdr estimated from log normal distribution, using non parametric bootstrap samples
#' @param edr10 A dataframe containing the edr10 for all the var_group  we want to calculate hdr th
#' @param th The desired probability (i.e. 0.05 for HDR5)
#' @param B TNumber of bootstrap samples
#' @examples
#' hdr5_LNA <- IC_HdrLN_act(ciprKP, Class, bm_mu, bm_sigma, 0.05,1000)
#' @return a data frame containing the hdr calculated from log normal distribution and the 95 percent confidence interval
#' @export


#edr10.df <- edr10_obs

IC_HdrLN <- function(edr10.df, th, B)
{

  library(dplyr)
  library(sampling)
  library(data.table)

  ##### Pre pocess
  #####

    dat <- edr10.df
    # set var_grp
    VarGrpName<-names(dat)[1]


    # arrange edr10 by Var Group alphabetic oredr
    dat<- arrange (dat, dat[,1])

    # put into list , 1 vargroup level by element
    dat.ls <- split(dat,droplevels(dat[,1]) )
    N <- length(dat.ls)

  ##### Calculating hdr of real data (not bootstraped samples)
  #####
    hdrR <- Hdr_LN(dat,th)
    hdrR[,2] <- round( hdrR[,2],2)

  ##### Calculating IC

  ## Bootstrap on edr10_obs

    #

    # on recupere la taille des échantillons par var_grp et doseType
    Nb <- table(dat[,1])
    # on initilise une list de taille B
    SampleList<-vector("list",B)

    for (i in 1 : B)
    {

      # des indices sont tirées au sort avec replacement en fonction de la varGroup (i.e. Class)
      ind<-data.table(strata(dat, VarGrpName,Nb , "srswr"))$ID_unit

      # ces indices sont utilisées pour créer l'échantillon
      SampleList[[i]]<-data.frame(dat[ind,])


      rm(ind)

    }


  ## Calculating Hdr th from LN distribution for all the bootstraped samples

    hdr_bt_list <- vector("list", B)
    hdr_bt_list <- lapply ( SampleList, Hdr_LN,th)


    ## putting in a list (1 element by var_grp level), all the B bootstrap sample hdr5
    hdr_tmp <- vector("list",N)
    for (i in 1 : N) {
       hdr_tmp[[i]] <-sapply(hdr_bt_list,"[", i,2 )
    }
    names(hdr_tmp) <-  hdr_bt_list[[1]][,1]



    binf <- vector(length=N)
    bsup <- vector(length=N)

    for (i in 1:N) {
      binf[i] <- 10^(quantile(log10(hdr_tmp[[i]]),0.025) - quantile(log10(hdr_tmp[[i]]),0.5) + log10(hdrR[i,2]))
      bsup[i] <- 10^(quantile(log10(hdr_tmp[[i]]),0.975) - quantile(log10(hdr_tmp[[i]]),0.5) + log10(hdrR[i,2]))
    }

  binf <- as.numeric(sprintf("%4.3e",binf))
  bsup <- as.numeric(sprintf("%4.3e",bsup))



  ## a build a dataframe of results
    res<- cbind(hdrR,binf,bsup)
    names(res) [3:4]<- c("q2.5%", "q97.5%")
    rownames(res) <- NULL
    res <- res [,-5]
    return(res)
  }
cdv04/ACTR documentation built on May 12, 2017, 1:15 a.m.