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incubationDistSens.R
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# incubationDistSens.R
require(tidyverse)
require(bbmle)
require(interval)
fname <- '~/Dropbox\ (Personal)/SACEMA/NICD/Listeria/Goulet2013.xlsx'
sname <- 'incDistGouletSens.Rdata'
if(sname %in% dir()){
load(sname)
}else{
incDat <- (
readxl::read_xlsx(fname)
)
incDat <- (
incDat
%>% filter(.,ref != 25) # remove data from outbreak where source is questionable
%>% filter(.,ref != 27) # remove data from outbreak without individual-level information
%>% select(.,-c(id,country,year,ref,food))
)
# Duration of the incubation period
perInc_min <- rep(NA,nrow(incDat))
perInc_max <- rep(NA,nrow(incDat))
# Add uncertainty for 'known' incubation periods
ipKnown <- which(incDat$treatment == 'known')
perInc_min[ipKnown] <- incDat$incPer[ipKnown] - .5
perInc_max[ipKnown] <- incDat$incPer[ipKnown] + .5
# Add uncertainty for incubation periods based on sampling times
ipSens <- which(incDat$treatment == 'sens')
perInc_min[ipSens] <- incDat$incPerMax[ipSens] - sampleDelay
perInc_min[perInc_min<0] <- 0
perInc_max[ipSens] <- incDat$incPerMax[ipSens]
# Add uncertainty for left censored data
ipLeft <- which(incDat$treatment == 'censored')
perInc_min[ipLeft] <- 0
perInc_max[ipLeft] <- incDat$incPerMax[ipLeft]
# Add uncertainty for interval censored data
ipOr <- which(incDat$treatment == 'or')
perInc_min[ipOr] <- incDat$incPerMin[ipOr]
perInc_max[ipOr] <- incDat$incPerMax[ipOr]
# Nonparametric fit
npmle_perInc <- icfit(perInc_min,perInc_max,conf.int = T,control=icfitControl(B=bsIterations))
# Parametric fit
initParams <- c(logmean = log(20), lograte = log(1))
estIncGamma <- mle(nllGammaIcens,start = as.list(initParams),method = )
ciIncGamma <- confint(estIncGamma)
ciIncGamma_Rate <- round(unname(exp(ciIncGamma[2,])),2)
meanIncGamma <- round(unname(exp(coef(estIncGamma)[1])),2)
rateIncGamma <- round(unname(exp(coef(estIncGamma)[2])),2)
ciIncGamma <- round(unname(exp(ciIncGamma[1,])),2)
save(npmle_perInc,meanIncGamma,rateIncGamma,ciIncGamma,file = sname)
}