R Under development (unstable) (2024-12-04 r87420 ucrt) -- "Unsuffered Consequences"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(bayesLife)
Loading required package: bayesTFR
> 
> start.test <- function(name, wpp.year = NULL) cat('\n<=== Starting test of', name, if(!is.null(wpp.year)) paste0('(WPP ', wpp.year, ')') else '', '====\n')
> test.ok <- function(name) cat('\n==== Test of', name, 'OK.===>\n')
> 
> test.get.wpp.data <- function(wpp.year=2010) {
+ 	test.name <- 'getting WPP data'
+ 	start.test(test.name, wpp.year)
+ 	ncountries <- list('2008'=158, '2010'=159, '2012'=162, '2015'=178)
+ 	data <- bayesLife:::get.wpp.e0.data(wpp.year=wpp.year, present.year=if(wpp.year>2012) 2015 else 2010)
+ 	stopifnot(length(dim(data$e0.matrix))==2)
+ 	stopifnot(ncol(data$e0.matrix)==ncountries[[as.character(wpp.year)]])
+ 	stopifnot(nrow(data$e0.matrix)==if(wpp.year>2012) 13 else 12)
+ 	test.ok(test.name)
+ }
> 
> test.estimate.mcmc <- function(compression='None', wpp.year = 2019) {
+ 	sim.dir <- tempfile()
+     # run MCMC
+     test.name <- 'estimating MCMC'
+ 	start.test(test.name, wpp.year)
+     m <- run.e0.mcmc(nr.chains = 1, iter = 10, thin = 1, output.dir = sim.dir, 
+                      compression.type = compression, wpp.year = wpp.year,
+                      mcmc.options = list(buffer.size = 5))
+     stopifnot(m$mcmc.list[[1]]$finished.iter == 10)
+ 	stopifnot(get.total.iterations(m$mcmc.list, 0) == 10)
+ 	stopifnot(m$meta$mcmc.options$buffer.size == 5)
+ 	ncountries <- nrow(get.countries.table(m))
+ 	if(wpp.year == 2019)
+ 	    stopifnot(ncountries == 180) # in include_2019 there are 180 default countries for e0 
+ 	test.ok(test.name)
+ 	
+ 	# continue MCMC
+ 	test.name <- 'continuing MCMC'
+ 	start.test(test.name, wpp.year)
+ 	m <- continue.e0.mcmc(iter=10, output.dir=sim.dir)
+ 	stopifnot(m$mcmc.list[[1]]$finished.iter == 20)
+ 	stopifnot(get.total.iterations(m$mcmc.list, 0) == 20)
+ 	stopifnot(!is.element(900, m$meta$regions$country_code)) # 'World' should not be included
+ 	test.ok(test.name)
+ 	
+ 	# run MCMC for an aggregation
+ 	test.name <- 'estimating MCMC for extra areas'
+ 	start.test(test.name, wpp.year)
+ 	data.dir <- file.path(find.package("bayesLife"), 'extdata')
+ 	m <- run.e0.mcmc.extra(sim.dir=sim.dir, 
+ 					my.e0.file=file.path(data.dir, 'my_e0_template.txt'), burnin=0)
+ 	stopifnot(is.element(900, m$meta$regions$country_code)) # 'World' should be included
+ 	test.ok(test.name)
+ 	
+ 	# run prediction
+ 	test.name <- 'running projections'
+ 	start.test(test.name, wpp.year)
+ 	pred <- e0.predict(m, burnin=0, verbose=FALSE)
+ 	spred <- summary(pred)
+ 	stopifnot(spred$nr.traj == 20)
+ 	stopifnot(!is.element(903, pred$mcmc.set$regions$country_code))
+ 	stopifnot(all(dim(pred$joint.male$quantiles) == dim(pred$quantiles)))
+ 	stopifnot(dim(pred$joint.male$quantiles)[3] == 17)
+ 	npred <- dim(pred$e0.matrix.reconstructed)[2]
+ 	t <- e0.trajectories.table(pred, "Australia", pi=80, both.sexes=TRUE)
+ 	stopifnot(all(dim(t) == c(30, 3)))
+ 	smpred <- summary(get.e0.jmale.prediction(pred))
+ 	stopifnot(smpred$nr.traj == 20)
+ 	test.ok(test.name)
+ 	
+ 	# run MCMC for another aggregation
+ 	test.name <- 'running projections on extra areas'
+ 	start.test(test.name, wpp.year)
+ 	m <- run.e0.mcmc.extra(sim.dir=sim.dir, countries=903, burnin=0)
+ 	# run prediction only for the area 903
+ 	pred <- e0.predict.extra(sim.dir=sim.dir, verbose=FALSE)
+ 	stopifnot(dim(pred$e0.matrix.reconstructed)[2] == npred+1)
+ 	stopifnot(is.element(903, pred$mcmc.set$meta$regions$country_code))
+ 	stopifnot(!is.null(bayesTFR:::get.trajectories(pred, 903)$trajectories))
+ 	stopifnot(all(dim(pred$joint.male$quantiles) == dim(pred$quantiles)))
+ 	stopifnot(dim(pred$joint.male$quantiles)[1] == (ncountries + 2))
+ 	test.ok(test.name)
+     
+ 	test.name <- 'shifting the median'
+ 	start.test(test.name, wpp.year)
+     country <- 'Netherlands'
+     country.idx <- get.country.object(country, m$meta)$index
+     projs <- summary(pred, country=country)$projections
+     e0.median.shift(sim.dir, country=country, shift=5.3, from=2051, to=2080)
+ 	shifted.pred <- get.e0.prediction(sim.dir)
+ 	shifted.projs <- summary(shifted.pred, country=country)$projections
+ 	stopifnot(all(projs[8:13,c(1,3:dim(projs)[2])]+5.3 == shifted.projs[8:13,c(1,3:dim(projs)[2])]))
+ 	stopifnot(all(projs[c(1:7, 14:17),c(1,3:dim(projs)[2])] == shifted.projs[c(1:7, 14:17),
+ 								c(1,3:dim(projs)[2])]))
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'resetting the median'
+ 	start.test(test.name, wpp.year)
+ 	shifted.pred <- e0.median.shift(sim.dir, country=country, reset = TRUE)
+ 	shifted.projs <- summary(shifted.pred, country=country)$projections
+ 	stopifnot(all(projs[,c(1,3:dim(projs)[2])] == shifted.projs[,c(1,3:dim(projs)[2])]))
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'setting the median'
+ 	start.test(test.name, wpp.year)
+ 	expert.values <- c(90.5, 91, 93.8)
+     shift <- expert.values - pred$quantiles[country.idx, '0.5',2:4] # Netherlands has index 106
+ 	mod.pred <- e0.median.set(sim.dir, country=country, values=expert.values, years=2024)
+ 	mod.projs <- summary(mod.pred, country=country)$projections
+ 	stopifnot(all(mod.projs[2:4, c(1,3:dim(projs)[2])]==projs[2:4, c(1,3:dim(projs)[2])]+shift))
+ 	stopifnot(all(mod.projs[c(1,5:17), c(1,3:dim(projs)[2])]==projs[c(1,5:17), c(1,3:dim(projs)[2])]))
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'converting trajectories'
+ 	start.test(test.name, wpp.year)
+ 	convert.e0.trajectories(sim.dir, n=10)
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'shifting medians to WPP'
+ 	e0.shift.prediction.to.wpp(sim.dir)
+ 	e0.shift.prediction.to.wpp(sim.dir, joint.male = TRUE)
+ 	shifted.predF <- get.e0.prediction(sim.dir)
+ 	shifted.predM <- get.e0.prediction(sim.dir, joint.male = TRUE)
+ 	cntry <- 'Angola'
+ 	shifted.projsF <- summary(shifted.predF, country = cntry)$projections
+ 	shifted.projsM <- summary(shifted.predM, country = cntry)$projections
+ 	shifted.projsF <- data.table::data.table(shifted.projsF)[, year := as.integer(rownames(shifted.projsF))]
+ 	shifted.projsM <- data.table::data.table(shifted.projsM)[, year := as.integer(rownames(shifted.projsM))]
+ 	e <- new.env()
+ 	data("e0Fproj", "e0Mproj", package = paste0("wpp", wpp.year), envir = e)
+ 	wppl <- merge(data.table::melt(data.table::data.table(e$e0Fproj), id.vars = c("country_code", "name"), 
+ 	                            variable.name = "period", value.name = "e0F"),
+ 	              data.table::melt(data.table::data.table(e$e0Mproj), id.vars = c("country_code", "name"), 
+ 	                               variable.name = "period", value.name = "e0M"),
+ 	              by = c("country_code", "name", "period"))
+ 	wppl <- wppl[, year := as.integer(substr(period, 1,4)) + 3][name == cntry]
+ 	datF <- merge(wppl, shifted.projsF[, c("year", "50%"), with = FALSE], by = "year")
+ 	datM <- merge(wppl, shifted.projsM[, c("year", "50%"), with = FALSE], by = "year")
+ 	stopifnot(all.equal(datF$e0F, datF[, `50%`]))
+ 	stopifnot(all.equal(datM$e0M, datM[, `50%`]))
+ 	stopifnot(!is.null(shifted.predF$median.shift) && !is.null(shifted.predM$median.shift))
+ 	stopifnot(length(shifted.predF$median.shift) == nrow(get.countries.table(shifted.predF)) && 
+ 	              length(shifted.predM$median.shift) == nrow(get.countries.table(shifted.predM)) )
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'resetting all countries'
+ 	e0.median.reset(sim.dir)
+ 	new.predF <- get.e0.prediction(sim.dir)
+ 	new.predM <- get.e0.prediction(sim.dir, joint.male = TRUE)
+ 	stopifnot(is.null(new.predF$median.shift) && !is.null(new.predM$median.shift))
+ 	e0.median.reset(sim.dir, joint.male = TRUE)
+ 	new.predM2 <- get.e0.prediction(sim.dir, joint.male = TRUE)
+ 	stopifnot(is.null(new.predM2$median.shift))
+ 	test.ok(test.name)
+ 	
+ 	unlink(sim.dir, recursive=TRUE)
+ }
> 
> 
> test.estimate.mcmc.with.suppl.data <- function(compression='None') {
+ 	sim.dir <- tempfile()
+     # run MCMC
+     test.name <- 'estimating MCMC using supplemental data'
+ 	start.test(test.name)
+     m <- run.e0.mcmc(nr.chains = 1, iter = 30, thin = 1, output.dir = sim.dir, 
+                      start.year = 1750, seed = 1, compression.type = compression,
+                      mcmc.options = list(buffer.size = 10))
+     stopifnot(length(m$meta$suppl.data$regions$country_code) == 29)
+ 	stopifnot(all(dim(m$meta$suppl.data$e0.matrix) == c(40, 29)))
+ 	test.ok(test.name)
+ 	
+ 	# continue MCMC
+ 	test.name <- 'continuing MCMC with supplemental data'
+ 	start.test(test.name)
+ 	m <- continue.e0.mcmc(iter=10, output.dir=sim.dir)
+ 	stopifnot(m$mcmc.list[[1]]$finished.iter == 40)
+ 	stopifnot(get.total.iterations(m$mcmc.list, 0) == 40)
+ 	stopifnot(!is.element(900, m$meta$regions$country_code)) # 'World' should not be included
+ 	test.ok(test.name)
+ 	
+ 	# run MCMC for an aggregation
+ 	test.name <- 'estimating MCMC for extra areas with supplemental data'
+ 	start.test(test.name)
+ 	data.dir <- file.path(find.package("bayesLife"), 'extdata')
+ 	m <- run.e0.mcmc.extra(sim.dir = sim.dir, 
+ 					my.e0.file = file.path(data.dir, 'my_e0_template.txt'), 
+ 					burnin = 0)
+ 	stopifnot(is.element(900, m$meta$regions$country_code)) # 'World' should be included
+ 	stopifnot(m$meta$mcmc.options$buffer.size == 10) # inherited from main run
+ 	test.ok(test.name)
+ 	
+ 	# run prediction
+ 	test.name <- 'running projections for simulation with supplemental data'
+ 	start.test(test.name)
+ 	pred <- e0.predict(m, burnin=10, verbose=FALSE, save.as.ascii=0)
+ 	spred <- summary(pred)
+ 	stopifnot(spred$nr.traj == 30)
+ 	stopifnot(!is.element(903, pred$mcmc.set$regions$country_code))
+ 	npred <- dim(pred$e0.matrix.reconstructed)[2]
+ 	test.ok(test.name)
+ 	unlink(sim.dir, recursive=TRUE)
+ }
> 
> 
> test.existing.simulation <- function() {
+ 	test.name <- 'retrieving MCMC results'
+ 	start.test(test.name)
+ 	sim.dir <- file.path(find.package("bayesLife"), "ex-data", 'bayesLife.output')
+ 	m <- get.e0.mcmc(sim.dir, low.memory=FALSE)
+ 	stopifnot(length(m$mcmc.list)==1)
+ 	stopifnot(dim(m$mcmc.list[[1]]$traces)[1]==60) 
+ 	m <- get.e0.mcmc(sim.dir)
+ 	#summary(m)
+ 	#summary(e0.mcmc(m, 1), par.names.cs=NULL)
+ 	stopifnot(bayesTFR:::get.total.iterations(m$mcmc.list) == 60)
+ 	stopifnot(bayesTFR:::get.stored.mcmc.length(m$mcmc.list, burnin=35) == 25)
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'retrieving projection results'
+ 	start.test(test.name)
+ 	pred <- get.e0.prediction(sim.dir)
+ 	s <- summary(pred, country='Japan')
+ 	stopifnot(s$nr.traj == 30)
+ 	stopifnot(all(dim(s$projections)==c(17,11)))
+ 	# comment out if thinned mcmcs are not included in the package
+ 	#mb <- get.thinned.e0.mcmc(m, thin=2, burnin=30)
+ 	#s <- summary(mb, meta.only=TRUE)
+ 	#stopifnot(s$iters == 30)
+ 	test.ok(test.name)
+ }
> 
> test.DLcurve <- function() {
+ 	test.name <- 'plotting DL curves'
+ 	start.test(test.name)
+ 	sim.dir <- file.path(find.package("bayesLife"), "ex-data", 'bayesLife.output')
+ 	m <- get.e0.mcmc(sim.dir)
+ 	filename <- tempfile()
+ 	png(filename=filename)
+ 	e0.DLcurve.plot(m, 'Slovenia')
+ 	dev.off()
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	test.ok(test.name)
+ 	test.name <- 'obtaining DL curves'
+ 	start.test(test.name)
+ 	e0 <- seq(40, 90, length=100)
+ 	dl <- e0.country.dlcurves(e0, m, "Slovenia", burnin=10)
+ 	stopifnot(all(dim(dl)==c(50,100)))
+ 	# world distribution
+ 	dlw <- e0.world.dlcurves(e0, m, burnin=10)
+ 	stopifnot(all(dim(dlw)==c(50,100)))
+ 	# median of the world DL in the e0 range of 50-60 is larger than the country-specific median in that range
+ 	# check visually with:
+ 	# e0.DLcurve.plot(m, 'Slovenia'); lines(e0, apply(dlw, 2, median), col="blue")
+ 	stopifnot(all(apply(dlw[, e0 > 50 & e0 < 60], 2, median) > apply(dl[, e0 > 50 & e0 < 60], 2, median)))
+ 	test.ok(test.name)
+ }
> 
> test.e0trajectories <- function() {
+ 	test.name <- 'plotting e0 trajectories'
+ 	start.test(test.name)
+ 	sim.dir <- file.path(find.package("bayesLife"), "ex-data", 'bayesLife.output')
+ 	pred <- get.e0.prediction(sim.dir=sim.dir)
+ 	filename <- tempfile()
+ 	png(filename=filename)
+ 	e0.trajectories.plot(pred, 'Australia')
+ 	dev.off()
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'tabulating e0 trajectories'
+ 	start.test(test.name)
+ 	t <- e0.trajectories.table(pred, "Australia", pi=c(90, 80, 70, 52))
+ 	stopifnot(all(dim(t) == c(30, 9)))
+ 	test.ok(test.name)
+ }
> 
> test.plot.all <- function() {
+ 	test.name <- 'plotting e0 trajectories and DL curves for all countries'
+ 	start.test(test.name)
+ 	sim.dir <- file.path(find.package("bayesLife"), "ex-data", 'bayesLife.output')
+ 	pred <- get.e0.prediction(sim.dir=sim.dir)
+ 	mc <- get.e0.mcmc(sim.dir)
+ 	dir <- tempfile()
+ 	dir.create(dir)
+ 	e0.trajectories.plot.all(pred, output.dir=dir, main='XXX trajs')
+ 	trajf <- length(list.files(dir, pattern='png$', full.names=FALSE))
+ 	e0.DLcurve.plot.all(mc, output.dir=dir, main='DL XXX', output.type='jpeg')
+ 	dlf <- length(list.files(dir, pattern='jpeg$', full.names=FALSE))
+ 	unlink(dir, recursive=TRUE)
+ 	stopifnot(trajf == get.nr.countries(mc$meta))
+ 	stopifnot(dlf == get.nr.countries(mc$meta))
+ 	test.ok(test.name)
+ }
> 
> 
> test.plot.density <- function() {
+ 	test.name <- 'plotting parameter density'
+ 	start.test(test.name)
+ 	sim.dir <- file.path(find.package("bayesLife"), "ex-data", 'bayesLife.output')
+ 	pred <- get.e0.prediction(sim.dir=sim.dir)
+ 	filename <- tempfile()
+ 	png(filename=filename)
+ 	e0.pardensity.cs.plot('Ireland', pred)
+ 	dev.off()
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	test.ok(test.name)
+ }
> 
> test.plot.map <- function() {
+ 	test.name <- 'creating e0 maps via rworldmap'
+ 	start.test(test.name)
+ 	sim.dir <- file.path(find.package("bayesLife"), "ex-data", 'bayesLife.output')
+ 	pred <- get.e0.prediction(sim.dir=sim.dir)
+ 	filename <- tempfile()
+ 	e0.map(pred, year=2098, device='png', device.args=list(filename=filename))
+ 	dev.off()
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'creating e0 maps of observed data'
+ 	start.test(test.name)
+ 	sim.dir <- file.path(find.package("bayesLife"), "ex-data", 'bayesLife.output')
+ 	pred <- get.e0.prediction(sim.dir=sim.dir)
+ 	filename <- tempfile()
+ 	e0.map(pred, year=1974, device='png', device.args=list(filename=filename))
+ 	dev.off()
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'creating parameter maps'
+ 	start.test(test.name)
+ 	sim.dir <- file.path(find.package("bayesLife"), "ex-data", 'bayesLife.output')
+ 	pred <- get.e0.prediction(sim.dir=sim.dir)
+ 	filename <- tempfile()
+ 	e0.map(pred, par.name='z.c', device='png', device.args=list(filename=filename))
+ 	dev.off()
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'creating e0 maps using ggplot2'
+ 	filename <- paste0(tempfile(), ".png")
+ 	e0.ggmap(pred, file.name = filename)
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	e0.ggmap(pred, same.scale = TRUE, file.name = filename)
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	e0.ggmap(pred, same.scale = TRUE, year = 2100, file.name = filename)
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	test.ok(test.name)
+ }
> 	
> test.get.parameter.traces <- function() {
+ 	test.name <- 'getting parameter traces'
+ 	start.test(test.name)
+ 	sim.dir <- file.path(find.package("bayesLife"), "ex-data", 'bayesLife.output')
+ 	m <- get.e0.mcmc(sim.dir, low.memory=TRUE)
+ 	traces <- get.e0.parameter.traces(m$mcmc.list, burnin=20, 
+ 					thinning.index=c(4, 21, 39))
+ 	stopifnot(nrow(traces)==3)
+ 	m.check <- get.e0.mcmc(sim.dir, low.memory=FALSE, burnin=20)
+ 	stopifnot(traces[1,'omega']==m.check$mcmc.list[[1]]$traces[4,'omega'])
+ 	# the following is from the time when there were two chains in the example data
+ 	# indices 21 and 39 in the collapsed traces correspond to indices 1 and 19, respectively, in chain 2
+ 	# stopifnot(all(traces[c(2,3),'omega']==m.check$mcmc.list[[2]]$traces[c(1,19),'omega']))
+ 	
+ 	traces <- get.e0.parameter.traces(m$mcmc.list, burnin=20, thin=8) 
+ 	stopifnot(nrow(traces)==5)
+ 	#stopifnot(traces[2,'z']==m.check$mcmc.list[[1]]$traces[5,'z']) #(4+1)
+ 	#stopifnot(traces[9,'z']==m.check$mcmc.list[[2]]$traces[13,'z']) #(3*4 + 1)
+ 	test.ok(test.name)
+ }
> 
> test.run.mcmc.simulation.auto <- function(compression='None') {
+ 	sim.dir <- tempfile()
+ 	# run MCMC
+ 	test.name <- 'running auto MCMC'
+ 	start.test(test.name)
+ 	m <- run.e0.mcmc(iter = 'auto', output.dir = sim.dir, thin = 1, compression.type = compression,
+ 					mcmc.options = list(auto.conf = list(iter=10, iter.incr=5, max.loops=3, nr.chains=2, thin=1, burnin=5)))
+ 	stopifnot(get.total.iterations(m$mcmc.list, 0) == 40)
+ 	test.ok(test.name)
+ 
+ 	test.name <- 'continuing auto MCMC'
+ 	start.test(test.name)
+ 	m <- continue.e0.mcmc(iter='auto', output.dir=sim.dir, 
+ 	                      auto.conf=list(max.loops=2))
+ 	stopifnot(get.total.iterations(m$mcmc.list, 0) == 60)
+ 	test.ok(test.name)
+ 
+ 	unlink(sim.dir, recursive=TRUE)
+ }
> 
> test.estimate.mcmc.with.overwrites <- function() {
+ 	sim.dir <- tempfile()
+ 	test.name <- 'estimating MCMC with country overwrites'
+ 	start.test(test.name)
+ 	overwrites <- data.frame(country_code=c(562, 686), #Niger, Senegal (index 17, 18)
+ 							k.c.prior.up=c(5, 7),
+ 							Triangle_3.c.prior.low=c(NA, 0),
+ 							Triangle_3.c.prior.up=c(0, NA)
+ 						)
+ 	# run MCMC
+     m <- run.e0.mcmc(nr.chains = 1, iter = 50, thin = 1, output.dir = sim.dir, 
+                      mcmc.options = list(Triangle.c = list(prior.low =c(0, 0, -20, 0)),
+                                          country.overwrites = overwrites),
+     				seed = 10)
+     iNiger <- get.country.object(562, m$meta)
+     iSene <- get.country.object(686, m$meta)
+     
+     stopifnot((m$meta$country.bounds$k.c.prior.up[iNiger$index] == 5) && (m$meta$country.bounds$k.c.prior.up[iSene$index] == 7) && 
+     			all(m$meta$country.bounds$k.c.prior.up[-c(iNiger$index,iSene$index)]==10))
+     stopifnot((m$meta$country.bounds$Triangle_3.c.prior.low[iSene$index] == 0) &&  
+     			all(m$meta$country.bounds$Triangle_3.c.prior.low[-iSene$index] == -20))
+     #check traces		
+     traces.Niger <- get.e0.parameter.traces.cs(m$mcmc.list, get.country.object('Niger', m$meta), 
+     					par.names=c('Triangle.c', 'k.c'))
+ 	stopifnot(all(traces.Niger[,'k.c_c562'] <= 5) && all(traces.Niger[,'Triangle.c_3_c562'] <= 0))
+ 	traces.Sen <- get.e0.parameter.traces.cs(m$mcmc.list, get.country.object('Senegal', m$meta), 
+     					par.names=c('Triangle.c', 'k.c'))
+ 	stopifnot(all(traces.Sen[,'k.c_c686'] < 7) && all(traces.Sen[,'Triangle.c_3_c686'] >= 0))
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'estimating MCMC for extra countries with country overwrites'
+ 	start.test(test.name)
+ 	overwrites <- data.frame(country_code = c(800, 900), #Uganda, World
+ 							k.c.prior.up = c(3, 8),
+ 							k.c.prior.low = c(2, NA))
+ 	m <- run.e0.mcmc.extra(sim.dir = sim.dir, countries = c(800, 900), burnin = 0, 
+ 	                       country.overwrites = overwrites)
+ 	Ug <- get.country.object('Uganda', m$meta)
+ 	Wrld <- get.country.object(900, m$meta)
+ 	stopifnot((m$meta$country.bounds$k.c.prior.up[Ug$index] == 3) && (m$meta$country.bounds$k.c.prior.up[iSene$index] == 7) && 
+     			(m$meta$country.bounds$k.c.prior.up[Wrld$index] == 8) && all(m$meta$country.bounds$k.c.prior.up[-c(iNiger$index, iSene$index, Ug$index,Wrld$index)]==10))
+     stopifnot((m$meta$country.bounds$k.c.prior.low[Ug$index] == 2) && all(m$meta$country.bounds$k.c.prior.low[-Ug$index]==0))
+     traces.Ug <- get.e0.parameter.traces.cs(m$mcmc.list, Ug, par.names=c('k.c'))
+ 	stopifnot(all(traces.Ug[,'k.c_c800'] <= 3) && all(traces.Ug[,'k.c_c800'] > 2))
+ 	traces.world <- get.e0.parameter.traces.cs(m$mcmc.list, Wrld, par.names=c('k.c'))
+ 	stopifnot(all(traces.world[,'k.c_c900'] <= 8))
+ 	test.ok(test.name)
+ 	unlink(sim.dir, recursive=TRUE)
+ }
> 
> test.run.mcmc.simulation.auto.parallel <- function() {
+ 	sim.dir <- tempfile()
+ 	# run MCMC
+ 	test.name <- 'running auto MCMC in parallel'
+ 	start.test(test.name)
+ 	m <- run.e0.mcmc(iter='auto', output.dir=sim.dir, parallel=TRUE, cltype='SOCK', thin=1,
+ 					mcmc.options = list(auto.conf=list(iter=10, iter.incr=5, max.loops=3, nr.chains=2, thin=1, burnin=5)))
+ 	stopifnot(get.total.iterations(m$mcmc.list, 0) == 40)
+ 	test.ok(test.name)
+ 
+ 	test.name <- 'continuing auto MCMC in parallel'
+ 	start.test(test.name)
+ 	m <- continue.e0.mcmc(iter='auto', output.dir=sim.dir, 
+ 	                      auto.conf=list(max.loops=2), 
+ 	                      parallel=TRUE, cltype='SOCK')
+ 	stopifnot(get.total.iterations(m$mcmc.list, 0) == 60)
+ 	test.ok(test.name)
+ 	unlink(sim.dir, recursive=TRUE)
+ }
> 
> test.imputation <- function() {
+ 	sim.dir <- tempfile()
+ 
+ 	# run MCMC
+ 	test.name <- 'running MCMC with missing values'
+ 	start.test(test.name)
+ 	my.e0.data <- data.frame(country_code=4, last.observed=1990)
+ 	my.e0.file <- tempfile()
+ 	write.table(my.e0.data, my.e0.file, sep='\t', row.names=FALSE)
+ 	m <- run.e0.mcmc(iter=5, nr.chains=1, output.dir=sim.dir, my.e0.file=my.e0.file, thin=1)
+ 	unlink(my.e0.file)
+ 	cindex <- get.country.object(4, m$meta)$index
+ 	stopifnot(m$mcmc.list[[1]]$finished.iter == 5)
+ 	stopifnot(get.total.iterations(m$mcmc.list, 0) == 5)
+ 	stopifnot(length(m$meta$Tc.index[[cindex]]) == 8)
+ 	test.name <- 'running projections with imputation'
+ 	start.test(test.name)
+ 	pred <- e0.predict(m, burnin=0, verbose=FALSE)
+ 	stopifnot(length(pred$joint.male$meta.changes$Tc.index[[cindex]])==14)
+ 	spred <- summary(pred)
+ 	stopifnot(spred$nr.traj == 5)	
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'plotting imputed e0 trajectories'
+ 	start.test(test.name)
+ 	filename <- tempfile()
+ 	png(filename=filename)
+ 	e0.trajectories.plot(pred, 4, pi=c(90, 54))
+ 	e0.trajectories.plot(pred, 4, pi=c(90, 54), both.sexes=TRUE)
+ 	dev.off()
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'running projections with male imputation'
+ 	start.test(test.name)
+ 	my.e0.data <- data.frame(country_code=4, last.observed=1979)
+ 	my.e0.file <- tempfile()
+ 	write.table(my.e0.data, my.e0.file, sep='\t', row.names=FALSE)
+ 	pred <- e0.predict(m, burnin=0, my.e0.file=my.e0.file, verbose=FALSE, replace.output=TRUE)
+ 	unlink(my.e0.file)
+ 	stopifnot(length(pred$joint.male$meta.changes$Tc.index[[cindex]])==6)
+ 	spred <- summary(pred)
+ 	stopifnot(spred$nr.traj == 5)
+ 	test.ok(test.name)
+ 
+ 	test.name <- 'plotting imputed F&M e0 trajectories'
+ 	start.test(test.name)
+ 	filename <- tempfile()
+ 	png(filename=filename)
+ 	e0.trajectories.plot(pred, 4, pi=c(90, 54), both.sexes=TRUE)
+ 	dev.off()
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'running MCMC and prediction with missing values for extra country'
+ 	start.test(test.name)
+ 	my.e0.data <- data.frame(country_code=900, last.observed=1996)
+ 	my.e0.file <- tempfile()
+ 	write.table(my.e0.data, my.e0.file, sep='\t', row.names=FALSE)
+ 	m <- run.e0.mcmc.extra(sim.dir=sim.dir, countries=900, my.e0.file=my.e0.file, burnin=0)
+ 	my.e0M.data <- data.frame(country_code=900, last.observed=1989)
+ 	write.table(my.e0M.data, my.e0.file, sep='\t', row.names=FALSE)
+ 	pred <- e0.predict.extra(sim.dir, my.e0.file=my.e0.file, verbose=FALSE)
+ 	unlink(my.e0.file)
+ 	stopifnot(length(pred$joint.male$meta.changes$Tc.index[[cindex]])==6)
+ 	stopifnot(length(pred$joint.male$meta.changes$Tc.index[[get.country.object(900, m$meta)$index]])==8)
+ 	stopifnot(length(m$meta$Tc.index[[get.country.object(900, m$meta)$index]]) == 9)
+ 	test.ok(test.name)
+ 	
+ 	test.name <- 'plotting imputed F&M e0 trajectories for extra countries'
+ 	start.test(test.name)
+ 	filename <- tempfile()
+ 	png(filename=filename)
+ 	e0.trajectories.plot(pred, 4, pi=c(90, 54), both.sexes=TRUE)
+ 	e0.trajectories.plot(pred, 900, pi=c(90, 54), both.sexes=TRUE)
+ 	dev.off()
+ 	size <- file.info(filename)['size']
+ 	unlink(filename)
+ 	stopifnot(size > 0)
+ 	test.ok(test.name)
+ 	
+ 	unlink(sim.dir, recursive=TRUE)
+ }
> 
> test.my.locations.extra <- function() {
+ 	sim.dir <- tempfile()
+     # run MCMC
+     test.name <- 'using my.locations.file in extra estimation'
+ 	start.test(test.name)
+     m <- run.e0.mcmc(nr.chains=1, iter=10, thin=1, output.dir=sim.dir)
+     data.dim <- dim(m$meta$e0.matrix)
+     # prepare data and locations
+     my.e0.file <- file.path(find.package("bayesLife"), 'extdata', 'my_e0_template.txt')
+     e0 <- bayesTFR:::read.tfr.file(file=my.e0.file)
+     e0['country_code'] <- 9999
+     fdata <- tempfile()
+     write.table(e0, file=fdata, sep='\t', row.names=FALSE)
+     new.locations <- data.frame(country_code=9999, name='my location', reg_code=9999, area_code=8888, reg_name='my region', area_name='my area', location_type=4)
+     flocs <- tempfile()
+     write.table(new.locations, file=flocs, sep='\t', row.names=FALSE)
+     m <- run.e0.mcmc.extra(sim.dir=sim.dir, my.e0.file=fdata, my.locations.file=flocs, burnin=0)
+     stopifnot(all(dim(m$meta$e0.matrix) == c(data.dim[1], data.dim[2]+1)))
+ 	stopifnot(m$meta$regions$country_code[m$meta$nr.countries] == 9999)
+ 	unlink(flocs)
+ 	unlink(fdata)
+ 	unlink(sim.dir, recursive=TRUE)
+ 	test.ok(test.name)
+ }
> 
> test.reproduce.simulation <- function() {
+     sim.dir <- tempfile()
+     test.name <- 'reproducing simulation'
+     start.test(test.name)
+     seed <- 1234
+     
+     m1 <- run.e0.mcmc(iter=5, nr.chains=2, thin = 1, output.dir=sim.dir, start.year=1950, seed = seed, verbose = FALSE)
+     res1 <- summary(m1)$results$statistics
+     m2 <- run.e0.mcmc(iter=5, nr.chains=2, thin = 1, output.dir=sim.dir, start.year=1950, seed = seed, replace.output = TRUE, verbose = FALSE)
+     res2 <- summary(m2)$results$statistics
+     stopifnot(all(res1 == res2))
+     
+     m3 <- run.e0.mcmc(iter=5, nr.chains=2, thin = 1, output.dir=sim.dir, start.year=1950, replace.output = TRUE) # no seed
+     res3 <- summary(m3)$results$statistics
+     stopifnot(!all(res3 == res2))
+     
+     # parallel
+     m1p <- run.e0.mcmc(iter=5, nr.chains=2, thin = 1, output.dir=sim.dir, start.year=1950, seed = seed, replace.output = TRUE, parallel = TRUE, ft_verbose = FALSE)
+     res1p <- summary(m1p)$results$statistics
+     m2p <- run.e0.mcmc(iter=5, nr.chains=2, thin = 1, output.dir=sim.dir, start.year=1950, seed = seed, replace.output = TRUE, parallel = TRUE, ft_verbose = FALSE)
+     res2p <- summary(m2p)$results$statistics
+     stopifnot(all(res1p == res2p))
+     
+     m3p <- run.e0.mcmc(iter=5, nr.chains=2, thin = 1, output.dir=sim.dir, start.year=1950, replace.output = TRUE, parallel = TRUE, ft_verbose = TRUE) # no seed
+     res3p <- summary(m3p)$results$statistics
+     stopifnot(!all(res3p == res2p))
+ 
+     test.ok(test.name)
+     unlink(sim.dir, recursive=TRUE)
+ }
> 
> test.subnational.predictions <- function() {
+     sim.dir <- tempfile()
+     test.name <- 'predicting subnational e0'
+     start.test(test.name)
+     
+     my.sub.file <- file.path(find.package("bayesLife"), 'extdata', 'subnational_e0_template.txt')
+     nat.dir <- file.path(find.package("bayesLife"), "ex-data", "bayesLife.output")
+     
+     # Subnational female projections for Australia and Canada; start 1 time period before national projections
+     preds <- e0.predict.subnat(c(36, 124), my.e0.file=my.sub.file,
+                                 sim.dir=nat.dir, output.dir=sim.dir, start.year=2018)
+     stopifnot(all(names(preds) %in% c("36", "124")))
+     stopifnot(nrow(get.countries.table(preds[["36"]]))==8)
+     stopifnot(identical(preds[["124"]]$quantiles, get.rege0.prediction(sim.dir, 124)$quantiles))
+     filename <- tempfile()
+     pdf(file=filename)
+     e0.trajectories.plot(preds[["36"]], "Queensland")
+     e0.trajectories.plot(preds[["124"]], "Quebec")
+     dev.off()
+     size <- file.info(filename)['size']
+     unlink(filename)
+     stopifnot(size > 0)
+     
+     # generate predictions with different method
+     preds.const <- e0.predict.subnat(c(36, 360), my.e0.file = my.sub.file, method = "shift",
+                                sim.dir=nat.dir, output.dir=sim.dir, predict.jmale = TRUE, 
+                                my.e0M.file = my.sub.file) # using the same e0 for male just for testing purposes
+     stopifnot(identical(preds.const[["360"]]$quantiles, get.rege0.prediction(sim.dir, 360, method = "shift")$quantiles))
+     stopifnot(!identical(preds.const[["360"]]$quantiles, preds[["360"]]$quantiles))
+     
+     stopifnot(!has.e0.jmale.prediction(preds[["36"]])) # default AR1 prediction does not have male predictions
+     
+     prF <- get.rege0.prediction(sim.dir, 360, method = "shift")
+     prM1 <- get.e0.jmale.prediction(prF)
+     prM2 <- get.rege0.prediction(sim.dir, 360, method = "shift", joint.male = TRUE)
+     stopifnot(identical(prM1$quantiles, prM2$quantiles))
+     stopifnot(!identical(prM1$quantiles, prF$quantiles))
+     
+     unlink(sim.dir, recursive=TRUE)
+     test.ok(test.name)
+     
+     test.name <- 'predicting subnational e0 with imputation'
+     start.test(test.name)
+     sim.dir <- tempfile()
+     preds <- e0.predict.subnat("Canada", my.e0.file = my.sub.file,
+                                 sim.dir = nat.dir, output.dir = sim.dir, start.year = 2021)
+     stopifnot(all(names(preds) %in% c("124")))
+     spred <- summary(preds[["124"]], "Quebec")
+     stopifnot(min(spred$projection.years) == 2023)
+     stopifnot(!has.e0.jmale.prediction(get.rege0.prediction(sim.dir, 124)))
+     
+     # generate male prediction
+     predCan <- e0.jmale.predict.subnat(preds[["124"]], my.e0.file = my.sub.file)
+     stopifnot(has.e0.jmale.prediction(get.rege0.prediction(sim.dir, 124)))
+     
+     filename <- tempfile()
+     pdf(file=filename)
+     e0.trajectories.plot(predCan, "Ontario", both.sexes = TRUE)
+     dev.off()
+     size <- file.info(filename)['size']
+     unlink(filename)
+     stopifnot(size > 0)
+     
+     unlink(sim.dir, recursive=TRUE)
+     
+     # Subnational projections for Canada with one region (Alberta) having imputed values
+     sim.dir <- tempfile()
+     mye0 <- read.delim(my.sub.file, check.names = FALSE)
+     mye0[mye0$country_code == 124 & mye0$reg_code == 658, "last.observed"] <- 1999
+     my.sub.file.mis <- tempfile()
+     write.table(mye0, file = my.sub.file.mis, row.names = FALSE, quote = FALSE, sep = "\t")
+     preds <- e0.predict.subnat("Canada", my.e0.file=my.sub.file.mis,
+                                 sim.dir=nat.dir, output.dir=sim.dir, start.year=2018, 
+                                predict.jmale = TRUE, my.e0M.file = my.sub.file)
+     obs <- preds[["124"]]$mcmc.set$meta$e0.matrix[,"658"]
+     recon <- preds[["124"]]$e0.matrix.reconstructed[,"658"]
+     stopifnot(all(is.na(obs[c("2003", "2008")]))) # observed is NA
+     stopifnot(all(!is.na(obs["1998"]))) # last observed non-NA 
+     stopifnot(all(!is.na(recon[c("2003", "2008")]))) # missing values were reconstructed
+     unlink(sim.dir, recursive=TRUE)
+     unlink(my.sub.file.mis)
+     
+     # Subnational projections for all countries in the file
+     sim.dir <- tempfile()
+     preds <- e0.predict.subnat(c(36, 360, 124), my.e0.file=my.sub.file, sim.dir=nat.dir, 
+                                 output.dir=sim.dir, end.year=2050)
+     stopifnot(length(preds) == 3)
+     
+     # Retrieve results for all countries
+     preds <- get.rege0.prediction(sim.dir)
+     stopifnot(length(preds) == 3)
+     t <- e0.trajectories.table(preds[["360"]], "Maluku")
+     stopifnot(all(dim(t) == c(21, 5)))
+     
+     # Retrieve results for one country
+     pred <- get.rege0.prediction(sim.dir, 124)
+     spred <- summary(pred, "British Columbia")
+     stopifnot(max(spred$projection.years) == 2048)
+     
+     # Retrieve trajectories
+     trajs <- get.e0.trajectories(preds[["36"]], "Victoria")
+     stopifnot(all(dim(trajs) == c(7, 30)))
+     
+     unlink(sim.dir, recursive=TRUE)
+     
+     # Annual subnational projections
+     my.sub.file.annual.F <- tempfile()
+     my.sub.file.annual.M <- tempfile()
+     # female data
+     datF <- data.table::fread(my.sub.file) # load the data and save only the last time period, pretending this is a single year data point
+     datF <- datF[, .(country_code, country_name, reg_code, name, `2013` = `2010-2015`)]
+     data.table::fwrite(datF, file = my.sub.file.annual.F, sep = "\t") # save it
+     # male data
+     datM <- data.table::copy(datF)[, `2013` := `2013`*0.95]
+     data.table::fwrite(datM, file = my.sub.file.annual.M, sep = "\t")
+     
+     sim.dir <- tempfile()
+     preds <- e0.predict.subnat(c(36, 124, 360), my.e0.file=my.sub.file.annual.F, sim.dir=nat.dir, 
+                                 output.dir=sim.dir, start.year = 2016, # will impute 2 years
+                                end.year=2030, annual = TRUE, 
+                                predict.jmale = TRUE, my.e0M.file = my.sub.file.annual.M)
+     # Retrieve trajectories
+     trajs <- get.e0.trajectories(preds[["124"]], "Alberta")
+     stopifnot(all(dim(trajs) == c(16, 30))) # from 2015 to 2030
+     stopifnot(all(c(2015, 2030) %in% as.integer(rownames(trajs))))
+     
+     pred <- get.rege0.prediction(sim.dir, 360)
+     spred <- summary(pred, "Papua")
+     stopifnot(max(spred$projection.years) == 2030)
+     
+     predM <- get.e0.jmale.prediction(pred)
+     t <- tfr.trajectories.table(predM, "Bali")
+     stopifnot(max(as.integer(rownames(t))) == 2030)
+     stopifnot(all(c(2015, 2024, 2029) %in% as.integer(rownames(t))))
+     
+     unlink(sim.dir, recursive=TRUE)
+     unlink(my.sub.file.annual.F)
+     unlink(my.sub.file.annual.M)
+     
+     test.ok(test.name)
+     
+ }
> 
> test.run.annual.simulation <- function(wpp.year = 2019) {
+     sim.dir <- tempfile()
+     
+     test.name <- 'running MCMC with annual data'
+     start.test(test.name, wpp.year)
+     m <- run.e0.mcmc(iter = 5, nr.chains = 2, thin = 1, output.dir = sim.dir, 
+                      annual = TRUE, present.year = 2018, wpp.year = wpp.year)
+     stopifnot(get.total.iterations(m$mcmc.list, 0) == 10)
+     stopifnot(all(1953:2018 %in% rownames(m$meta$e0.matrix)))
+     test.ok(test.name)
+     
+     test.name <- 'running annual MCMC for extra country'
+     start.test(test.name, wpp.year)
+     countries <- c(900, 908)
+     m <- run.e0.mcmc.extra(sim.dir = sim.dir, countries = countries, burnin = 0)
+     stopifnot(countries %in% m$meta$regions$country_code)
+     test.ok(test.name)
+     
+     test.name <- 'running annual projections'
+     start.test(test.name, wpp.year)
+     pred <- e0.predict(m, burnin=1, verbose = FALSE)
+     spred <- summary(pred)
+     tbl <- e0.trajectories.table(pred, "Japan")
+     stopifnot(spred$nr.traj == 8)
+     stopifnot(all(2019:2100 %in% spred$projection.years))
+     stopifnot(all(c(908, 900) %in% get.countries.table(pred)$code))
+     if(wpp.year > 2019)
+         stopifnot('1900' %in% rownames(tbl)) # checks that supplemental data is used
+     
+     mpred <- get.e0.jmale.prediction(pred)
+     smpred <- summary(mpred)
+     mtbl <- e0.trajectories.table(mpred, "Japan")
+     stopifnot(all(2019:2100 %in% smpred$projection.years))
+     stopifnot(all(c(908, 900) %in% get.countries.table(mpred)$code))
+     if(wpp.year > 2019)
+         stopifnot('1900' %in% rownames(mtbl))
+     
+     test.ok(test.name)
+     
+     unlink(sim.dir, recursive=TRUE)
+ }
> 
> 
> proc.time()
   user  system elapsed 
   0.46    0.12    0.57