R/plotresults.R
In FPcounts/FPEMglobal: Estimation of Contraceptive Use for All Women
Defines functions
PlotResults
##--------------------------------------------------------------------------
## plotresults.R
## Leontine Alkema, 2011
##--------------------------------------------------------------------------
## function called from main to plot all non-diag results...
PlotResults <- function(# Plot lots of results!
### Wrapper function to plot lots of results.
run.name = "test", ##<< Run name
output.dir = NULL, ##<< Directory where MCMC array and meta are stored.
## If NULL, it's \code{output/run.name}, default from \code{runMCMC}.
fig.dir = NULL, ##<< Directory to store overview plots. If NULL, folder "fig" in current working directory.
do.SS.run.first.pass = FALSE, ##<< do first pass of run with SS data? # change JR, 20140414
plot.ind.country.results = FALSE, ##<< Create zillion plots for all countries?
## If TRUE, plots are saved in subdirectory "country.plots" in fig.dir.
plot.prior.post = !all.women, ##<< If TRUE, plots of priors and posteriors are generated.
plot.estimates = TRUE, ##<< If TRUE, plots of estimates are generated.
plot.parameters = !all.women, ##<< If TRUE, plots of parameters are generated.
start.year = NULL, ##<< Which years are plotted? Defaults to estimation years used in \code{CI.Lg.Lcat.qt}, or 1990 otherwise.
end.year = NULL ##<< Which years are plotted? Defaults to estimation years used in \code{CI.Lg.Lcat.qt}, or 1990 otherwise.
## HOTFIX [MCW-2016-02-26-1] Need to be able to turn these off for datafiles
## with no, e.g., developing regions.
,make.any.aggregates = TRUE
,sources.pal = c("#E41A1C", "#377EB8", "#FF7F00", "#A65628", "#4DAF4A", "#984EA3", "#F781BF", "black")[c(1,2,5,3,8,6,4,7)] ## [MCW-2016-04-05-17]: added to allow selection of colour scheme for data sources. NB: there are 7 data sources but need 8 colours because 'subpopulation' also has a colour. Colours and their sources are:
## 1. DHS
## 2. MICS
## 3. Other international survey
## 4. Service statistics (SS)
## 5. Repeated national survey (RN)
## 6. PMA
## 7. Subpopulation
,add.info = TRUE ##[MCW-2016-04-08-3]: Added to allow user to pass this through to PlotDataAndEstiamtes().
,cex.symbols = list(SS = 1.5, add.info = 6, no.info = 4) ##[MCW-2016-04-12-3] Added to allow control of size of plotting symbols (passed through to PlotDataAndEstimates()).
,non.std.symbol = 22 ##[MCW-2016-04-12] Added to allow control of plotting symbol for non-standard observations.
,layout.style = "UNPD" ##[MCW-2017-01-30-2] :: 'layout.style' default now set to '"UNPD"', based on what was called '"new"'.
,UWRA = FALSE ##[MCW-2016-08-16-1] Added to allow modifications to plots for unmarried women.
,pdf.useDingbats = FALSE
,fp2020.69.only = FALSE
,all.women = FALSE
,all.womenize.fig.name = isTRUE(all.women) #Make sure figure names have 'aw' at front if 'isTRUE(all.women)'.
,hide.CP.tot.lt.1pc = FALSE
,select.c.csv = NULL #MCW-2018-03-23:: CSV file with ISO codes of countries to be included in plots
,verbose = TRUE ) {
stopifnot(layout.style %in% c("orig", "UNPD", "diagnostic"))#[MCW-2016-06-13-9]
if(identical(layout.style, "orig")) PlotDataAndEstimates <- PlotDataAndEstimatesORIG #[MCW-2017-01-30-3] :: 'layout.style == "orig"' now uses renamed function 'PlotDataAndEstimatesORIG'.
if(identical(layout.style, "diagnostic")) {
PlotDataAndEstimates <- PlotDataAndEstimatesDIAG #[MCW-2017-01-30-4] :: 'layout.style == "diag"' now uses renamed function 'PlotDataAndEstimatesDIAG()'.
## [MCW-2016-08-29-1] Aggregate plots not working for 'diagnostic' style but don't need anyway.
if(make.any.aggregates) {
warning("Aggregates plots requested but not available for 'diagnostic' layout style.")
make.any.aggregates <- FALSE
}
}
if (is.null(fig.dir)){
fig.dir <- file.path(getwd(), "fig/")
dir.create(fig.dir, showWarnings = FALSE)
}
if (plot.ind.country.results){
fig.dir.countries <- file.path(fig.dir, "country.plots/")
}
## put separate?
if (is.null(output.dir)){
output.dir <- file.path(getwd(), "output", run.name, "/")
}
filename.append <- ifelse(do.SS.run.first.pass, "_pre", "")
if (do.SS.run.first.pass & !file.exists(file.path(output.dir, paste0("par.ciq", filename.append, ".rda")))) {
par.ciq <- NULL
} else {
if (file.exists(file.path(output.dir, paste0("par.ciq", filename.append, ".rda"))))
load(file.path(output.dir, paste0("par.ciq", filename.append, ".rda"))) # change JR, 20140418
else {
warning("'", file.path(output.dir, paste0("par.ciq", filename.append, ".rda")),
"' was not found; parameter estimates cannot be plotted.")
par.ciq <- NULL
plot.parameters <- FALSE
}
}
load(file.path(output.dir, paste0("mcmc.meta", filename.append, ".rda"))) # change JR, 20140418
do.country.specific.run <- mcmc.meta$general$do.country.specific.run # change JR, 20131104
age.group <- mcmc.meta$general$age.group
## [MCW-2017-08-14-13] :: Load the appropriate '.rda' files if 'all.women = TRUE'.
if(all.women) {
load(file.path(output.dir, paste0("res.country.all.women", filename.append, ".rda")))
res.country <- res.country.all.women
}
else load(file.path(output.dir, paste0("res.country", filename.append, ".rda"))) # change JR, 20140418
if(make.any.aggregates) { #HOTFIX [MCW-2016-02-26-2] (explained above)
if (!do.country.specific.run) { # change JR, 20131104
if(all.women) {
load(file.path(output.dir, "res.aggregate.all.women.rda")) # change JR, 20140418
res.aggregate <- res.aggregate.all.women
}
else load(file.path(output.dir, "res.aggregate.rda")) # changedFiles JR, 20140418
}
} #HOTFIX [MCW-2016-02-26-3] (explained above)
validation <- !is.null(mcmc.meta$validation.list)
## >>>>> RATE MODEL [MCW-2018-01-03]
rate.model <- ModelFunctionRateModel(mcmc.meta$general$write.model.fun)
## <<<<< RATE MODEL
data <- mcmc.meta$data.raw$data
country.info <- mcmc.meta$data.raw$country.info
region.info <- mcmc.meta$data.raw$region.info
winbugs.data <- mcmc.meta$winbugs.data
## [MCW-2016-08-26-2] Plot countries with no data as well if not
## 'diagnostic' plots. Note that 'data' is not sorted in the same order as
## 'country.info' (and 'data' has one row per observation anyway,
## 'country.info' has one row per country.)
if(!identical(layout.style, "diagnostic") && mcmc.meta$general$include.c.no.data) {
country.info <- rbind(mcmc.meta$data.raw$country.info
,mcmc.meta$data.raw$country.info.no.data
)
## Need this as well because PlotDataAndEstimates() does not take argument 'country.info'.
mcmc.meta$data.raw$country.info <- country.info
} else country.info <- mcmc.meta$data.raw$country.info
region.info <- mcmc.meta$data.raw$region.info
#This is already the union of regions for countries with and without data
winbugs.data <- mcmc.meta$winbugs.data
##details<< For validation run you have to use different function
##\code{\link{PlotValidationResults}}. Otherwise the following plots are
##made:
if (validation){
print("Use PlotValidationResults!")
return(invisible())
}# end validation results
##details<< Plot priors and posteriors using \code{\link{PlotPriorPost}}.
if (plot.prior.post && !identical(layout.style, "diagnostic"))
PlotPriorPost(run.name = run.name
,output.dir = output.dir, fig.dir = fig.dir #[MCW-2016-04-07-1] Added these
,disagg.RN.PMA = TRUE #[MCW-2016-05-16-3] Added so that variances are plotted
)
fig.run.name <- run.name
## [MCW-2017-08-14-18] :: For 'all.women = TRUE', Ensure '.pdf' filenames have "aw" in them, even
## if ~run.name~ does not have "umw" or "mw" in it.
if(all.women && all.womenize.fig.name) {
fig.run.name <- makeAWFileName(run.name)
}
##------------------------------------------------------------------------------------------
if (plot.estimates) {
##details<< Plot country overview plots for proportions with and without details using
##\code{\link{PlotDataAndEstimates}}.
fig.name.years <- ifelse(!is.null(start.year) | !is.null(end.year), # change JR, 20140407
paste0("_",
ifelse(!is.null(start.year), paste0("from", floor(start.year)), ""),
ifelse(!is.null(end.year), paste0("to", floor(end.year)), "")),
"")
if(fp2020.69.only) fp2020 <- "_fp2020_69"
else fp2020 <- ""
if(identical(layout.style, "diagnostic")) diag <- "_diag"
else diag <- ""
if(all.women) {
plotDE.data.raw <- NULL
plotDE.country.info <- country.info
plotDE.par.ciq <- NULL
if(verbose) cat("\n'CIs_nopar")
PlotDataAndEstimates(data.raw = plotDE.data.raw,
country.info = plotDE.country.info,
par.ciq = plotDE.par.ciq,
## select.c=1,
CI.Lg.Lcat.qt = res.country$CIprop.Lg.Lcat.qt,
CIstar.Lg.Lcat.qt = res.country$CIstar.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.country$CIratio.Lg.Lcat.qt,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir
, paste0(fig.run.name, filename.append
, "CIs", fig.name.years, fp2020, diag, ".pdf"))# change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info #[MCW-2016-04-08-4] Control
# 'add.info' and size of
# plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,fp2020.69.only = fp2020.69.only
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = select.c.csv
,age.group = age.group
)
} else {
plotDE.data.raw <- mcmc.meta$data.raw
plotDE.country.info <- NULL
plotDE.par.ciq <- par.ciq
## if(!rate.model) { ##[MCW-2016-10-07-2] :: Don't do plots with logistic curve parameters if rate model.
if(verbose) cat("\n'CIs")
PlotDataAndEstimates(data.raw = plotDE.data.raw,
country.info = plotDE.country.info,
par.ciq = plotDE.par.ciq,
## select.c=1,
CI.Lg.Lcat.qt = res.country$CIprop.Lg.Lcat.qt,
CIstar.Lg.Lcat.qt = res.country$CIstar.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.country$CIratio.Lg.Lcat.qt,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir
, paste0(fig.run.name, filename.append
, "CIs", fig.name.years, fp2020, diag, ".pdf"))# change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info # [MCW-2016-04-08-4] Control
# 'add.info' and size of
# plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,fp2020.69.only = fp2020.69.only
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = select.c.csv
,age.group = age.group
)
## }
if(verbose) cat("\n'CIs_nopar")
PlotDataAndEstimates(data.raw = plotDE.data.raw,
CI.Lg.Lcat.qt = res.country$CIprop.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.country$CIratio.Lg.Lcat.qt,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir
, paste0(fig.run.name, filename.append
, "CIs_nopar", fig.name.years, fp2020, diag, ".pdf"))# change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info # [MCW-2016-04-08-5] Control
# 'add.info' and size of
# plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,fp2020.69.only = fp2020.69.only
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = select.c.csv
,age.group = age.group
)
}
## to plot individual country results
if (plot.ind.country.results && !identical(layout.style, "diagnostic")) {
PlotDataAndEstimates(data.raw = mcmc.meta$data.raw,
CI.Lg.Lcat.qt = res.country$CIprop.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.country$CIratio.Lg.Lcat.qt,
ind.country.overviewplot = TRUE,
start.year = start.year,
end.year = end.year,
run.name = run.name
,sources.pal = sources.pal
,add.info = add.info # [MCW-2016-04-08-6] Control
# 'add.info' and size of
# plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,fp2020.69.only = fp2020.69.only
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = NULL #This is for country plots only.
,age.group = age.group
)
}
##details<< Plot country overview plots for counts using
##\code{\link{PlotDataAndEstimates}}.
if(verbose) cat("\n'CIscountcountry")
PlotDataAndEstimates(
CI.Lg.Lcat.qt = res.country$CIcount.Lg.Lcat.qt,
plot.prop = FALSE,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir
, paste0(fig.run.name, filename.append
, "CIscountcountry", fig.name.years, fp2020, diag, ".pdf")) # change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info #[MCW-2016-04-08-7] Control 'add.info' and size
#of plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,country.info = country.info
,fp2020.69.only = fp2020.69.only
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = select.c.csv
,age.group = age.group
)
if(make.any.aggregates) { #HOTFIX [MCW-2016-02-26-4] (explained above)
##details<< Plot UNDP aggregates overview plots for proportions and counts using
##\code{\link{PlotDataAndEstimates}}.
if (!do.country.specific.run) {
if(verbose) cat("\n'CIsaggregate")
PlotDataAndEstimates(CI.Lg.Lcat.qt = res.aggregate$CIprop.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.aggregate$CIratio.Lg.Lcat.qt,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir, paste0(fig.run.name, filename.append
, "CIsaggregate", fig.name.years, diag, ".pdf")) # change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info #[MCW-2016-04-08-8]
#Control 'add.info' and
#size of plotting
#characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,country.info = country.info
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = NULL #This is for country plots only.
,age.group = age.group
)
if(verbose) cat("\n'CIscountaggregate")
PlotDataAndEstimates(
CI.Lg.Lcat.qt = res.aggregate$CIcount.Lg.Lcat.qt,
plot.prop = FALSE,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir
, paste0(fig.run.name, "CIscountaggregate"
, fig.name.years, diag, ".pdf")) # change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info #[MCW-2016-04-08-9] Control 'add.info'
#and size of plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,country.info = country.info
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = NULL #This is for country plots only.
,age.group = age.group
)
if(!identical(layout.style, "diagnostic")) {
if (plot.ind.country.results){
PlotDataAndEstimates(CI.Lg.Lcat.qt = res.aggregate$CIprop.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.aggregate$CIratio.Lg.Lcat.qt,
ind.country.overviewplot = TRUE,
start.year = start.year,
end.year = end.year,
run.name = run.name
,sources.pal = sources.pal #[MCW-2016-04-05-14]
#Control
#disaggregation
#of PMA and
#repeated
#national
#data sources
,add.info = add.info #[MCW-2016-04-08-10]
#Control 'add.info'
#and size of
#plotting
#characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,country.info = country.info
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = NULL #This is for country plots only.
,age.group = age.group
)
}
PlotCountryEstimatesForAggregate(
CI.Lg.Lcat.qt = res.country$CIprop.Lg.Lcat.qt,
country.info = country.info,
fig.name = file.path(fig.dir
, paste0(fig.run.name, "CIscountryestsinaggregates"
, fig.name.years, ".pdf")) # change JR, 20140418
,mcmc.meta = mcmc.meta
,all.women = all.women
)
}
}
} #HOTFIX [MCW-2016-02-26-5] (explained above)
}
##------------------------------------------------------------------------------------------
if(!identical(layout.style, "diagnostic")) {
if(verbose) cat("\n'Logistic parameters")
if (plot.parameters && !all.women) {
##details<< Plot model parameters using \code{\link{PlotLogisticParameters}}.
if (is.null(par.ciq)) {
stop("par.ciq is NULL. Model parameters cannot be plotted.")
} else {
PlotLogisticParameters(par.ciq = par.ciq, country.info = country.info,
region.info = region.info,
fig.name = file.path(fig.dir, paste0(fig.run.name, filename.append, "modelpar.pdf"))
,UWRA = UWRA ##[MCW-2016-08-16-2] Added to pass this argument through.
,write.model.fun = mcmc.meta$general$write.model.fun
) # change JR, 20140418
}
##------------------------------------------------------------------------------------------
##details<< Plot info about data parameters (variance by source, biases and perturbation multipliers).
cat("\n'Source variances")
load(file.path(output.dir, paste0("mcmc.array", filename.append, ".rda"))) # change JR, 20140418
if (!do.country.specific.run) { # change JR, 20131105
## variance by source
figname_ss <- paste0(fig.run.name, "sigmasource.pdf")
if(hide.CP.tot.lt.1pc) {
figname_ss <- paste0(fig.run.name, "sigmasource_no_CPTotLT1pc.pdf")
}
pdf(file.path(fig.dir, figname_ss), width = 7, height = 5
,useDingbats = pdf.useDingbats) # change JR, 20140418
PlotSourceSigmas(mcmc.array = mcmc.array,
,write.model.fun = mcmc.meta$general$write.model.fun
,UWRA = UWRA
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
)
PlotSourceSigmasUnmet(mcmc.array = mcmc.array
)
dev.off()
figname_ss <- paste0(fig.run.name, "sigmasource_ylim_0_4.pdf")
if(hide.CP.tot.lt.1pc) {
figname_ss <- paste0(fig.run.name, "sigmasource_no_CPTotLT1pc_ylim_0_4.pdf")
}
pdf(file.path(fig.dir, figname_ss), width = 7, height = 5
,useDingbats = pdf.useDingbats) # change JR, 20140418
PlotSourceSigmas(mcmc.array = mcmc.array,
,write.model.fun = mcmc.meta$general$write.model.fun
,UWRA = UWRA, ylim = c(0, 4)
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
)
PlotSourceSigmasUnmet(mcmc.array = mcmc.array,
ylim = c(0, 1.5)
)
dev.off()
figname_ss <- paste0(fig.run.name, "sigmasource_ylim_1-5.pdf")
if(hide.CP.tot.lt.1pc) {
figname_ss <- paste0(fig.run.name, "sigmasource_no_CPTotLT1pc_ylim_1-5.pdf")
}
pdf(file.path(fig.dir, figname_ss), width = 7, height = 5
,useDingbats = pdf.useDingbats) # change JR, 20140418
PlotSourceSigmas(mcmc.array = mcmc.array,
,write.model.fun = mcmc.meta$general$write.model.fun
,UWRA = UWRA, ylim = c(0, 1.5)
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
)
PlotSourceSigmasUnmet(mcmc.array = mcmc.array,
ylim = c(0, 1.5)
)
dev.off()
## biases,
pdf(file.path(fig.dir, paste0(fig.run.name, "Biases.pdf"))
,width = 7, height = 5, useDingbats = pdf.useDingbats
) # change JR, 20140418
PlotBiases(mcmc.array = mcmc.array)
dev.off()
if("v.mics" %in% dimnames(mcmc.array)[[3]]) {
parnames.biases <- c("v.mneg", "v.mpos", "v.folk", "v.mics")
} else {
parnames.biases <- c("v.mneg", "v.mpos", "v.folk", "v.abs.probe.q")
}
percentiles <- c(0.025,0.5, 0.975)
biases.i3 <- NULL
parnames.biases <-
parnames.biases[parnames.biases %in% dimnames(mcmc.array)[[3]]]
for (parname in (parnames.biases)) {
biases.i3 <-
rbind(biases.i3, quantile(mcmc.array[,,parname],percentiles))
}
rownames(biases.i3) <- parnames.biases
write.csv(biases.i3, row.names = TRUE
, file = file.path(fig.dir, paste0(fig.run.name, "Biases_summary.csv"))
)
## If survey-specific SEs are used, plot total measurement error
## variance for each source by averaging over all surveys within type
## (e.g., average over all DHSs).
if(ModelFunctionSurveySEs(mcmc.meta$general$write.model.fun)) {
pdf(file.path(fig.dir, paste0(fig.run.name, "sigmasource_total.pdf"))
, width = 7, height = 5, useDingbats = pdf.useDingbats)
PlotDataModelSEs(run.name = run.name, output.dir = output.dir, mcmc.array = mcmc.array)
dev.off()
pdf(file.path(fig.dir, paste0(fig.run.name, "sigmasource_total_no_CPTotLT1pc.pdf"))
, width = 7, height = 5, useDingbats = pdf.useDingbats)
PlotDataModelSEs(run.name = run.name, output.dir = output.dir, mcmc.array = mcmc.array,
hide.CP.tot.lt.1pc = TRUE)
dev.off()
pdf(file.path(fig.dir, paste0(fig.run.name, "sigmasource_total_ylim_0_1-5.pdf"))
, width = 7, height = 5, useDingbats = pdf.useDingbats)
PlotDataModelSEs(run.name = run.name, output.dir = output.dir, mcmc.array = mcmc.array,
ylim = c(0, 1.5))
dev.off()
pdf(file.path(fig.dir, paste0(fig.run.name, "sigmasource_total_no_CPTotLT1pc_ylim_0_1-5.pdf"))
, width = 7, height = 5, useDingbats = pdf.useDingbats)
PlotDataModelSEs(run.name = run.name, output.dir = output.dir, mcmc.array = mcmc.array,
hide.CP.tot.lt.1pc = TRUE, ylim = c(0, 1.5))
dev.off()
}
}
## (long!) list of names of unique multipliers
par.V <- mcmc.meta$par.V
##InternalGetParnamesV(winbugs.data = winbugs.data,
## name.short.j = InternalMakeCountryNamesShort(mcmc.meta$data.raw$data$name.j))
##par.V$parnames.V.in.bugs
##par.V$parnames.V.nice
##data.frame(par.V$parnames.V.in.bugs, par.V$parnames.V.nice)
pdf(file.path(fig.dir, paste0(fig.run.name, filename.append, "Multipliers.pdf")))#, height = 15, width = 20) # change JR, 20140418
PlotGeoEtc(par.V = par.V, mcmc.array = mcmc.array)
dev.off()
## To check data dummies:
## number of observations with bias (folk/mics/M+/M-):
## summary.biases <-SummarizeBiases(winbugs.data) # 237 biases
## number of unique multipliers included (per composition):
## summary.multipliers <- SummarizeMultipliers(winbugs.data, data) # 214 sets
}
}
cat("\nAll results plotted and saved to ", fig.dir, "\n")
##value<< NULL
return(invisible(NULL))
} ## end function
##----------------------------------------------------------------------
## The End!
FPcounts/FPEMglobal documentation built on July 20, 2024, 2:35 a.m.
R Package Documentation
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Defines functions PlotResults
##--------------------------------------------------------------------------
## plotresults.R
## Leontine Alkema, 2011
##--------------------------------------------------------------------------
## function called from main to plot all non-diag results...
PlotResults <- function(# Plot lots of results!
### Wrapper function to plot lots of results.
run.name = "test", ##<< Run name
output.dir = NULL, ##<< Directory where MCMC array and meta are stored.
## If NULL, it's \code{output/run.name}, default from \code{runMCMC}.
fig.dir = NULL, ##<< Directory to store overview plots. If NULL, folder "fig" in current working directory.
do.SS.run.first.pass = FALSE, ##<< do first pass of run with SS data? # change JR, 20140414
plot.ind.country.results = FALSE, ##<< Create zillion plots for all countries?
## If TRUE, plots are saved in subdirectory "country.plots" in fig.dir.
plot.prior.post = !all.women, ##<< If TRUE, plots of priors and posteriors are generated.
plot.estimates = TRUE, ##<< If TRUE, plots of estimates are generated.
plot.parameters = !all.women, ##<< If TRUE, plots of parameters are generated.
start.year = NULL, ##<< Which years are plotted? Defaults to estimation years used in \code{CI.Lg.Lcat.qt}, or 1990 otherwise.
end.year = NULL ##<< Which years are plotted? Defaults to estimation years used in \code{CI.Lg.Lcat.qt}, or 1990 otherwise.
## HOTFIX [MCW-2016-02-26-1] Need to be able to turn these off for datafiles
## with no, e.g., developing regions.
,make.any.aggregates = TRUE
,sources.pal = c("#E41A1C", "#377EB8", "#FF7F00", "#A65628", "#4DAF4A", "#984EA3", "#F781BF", "black")[c(1,2,5,3,8,6,4,7)] ## [MCW-2016-04-05-17]: added to allow selection of colour scheme for data sources. NB: there are 7 data sources but need 8 colours because 'subpopulation' also has a colour. Colours and their sources are:
## 1. DHS
## 2. MICS
## 3. Other international survey
## 4. Service statistics (SS)
## 5. Repeated national survey (RN)
## 6. PMA
## 7. Subpopulation
,add.info = TRUE ##[MCW-2016-04-08-3]: Added to allow user to pass this through to PlotDataAndEstiamtes().
,cex.symbols = list(SS = 1.5, add.info = 6, no.info = 4) ##[MCW-2016-04-12-3] Added to allow control of size of plotting symbols (passed through to PlotDataAndEstimates()).
,non.std.symbol = 22 ##[MCW-2016-04-12] Added to allow control of plotting symbol for non-standard observations.
,layout.style = "UNPD" ##[MCW-2017-01-30-2] :: 'layout.style' default now set to '"UNPD"', based on what was called '"new"'.
,UWRA = FALSE ##[MCW-2016-08-16-1] Added to allow modifications to plots for unmarried women.
,pdf.useDingbats = FALSE
,fp2020.69.only = FALSE
,all.women = FALSE
,all.womenize.fig.name = isTRUE(all.women) #Make sure figure names have 'aw' at front if 'isTRUE(all.women)'.
,hide.CP.tot.lt.1pc = FALSE
,select.c.csv = NULL #MCW-2018-03-23:: CSV file with ISO codes of countries to be included in plots
,verbose = TRUE ) {
stopifnot(layout.style %in% c("orig", "UNPD", "diagnostic"))#[MCW-2016-06-13-9]
if(identical(layout.style, "orig")) PlotDataAndEstimates <- PlotDataAndEstimatesORIG #[MCW-2017-01-30-3] :: 'layout.style == "orig"' now uses renamed function 'PlotDataAndEstimatesORIG'.
if(identical(layout.style, "diagnostic")) {
PlotDataAndEstimates <- PlotDataAndEstimatesDIAG #[MCW-2017-01-30-4] :: 'layout.style == "diag"' now uses renamed function 'PlotDataAndEstimatesDIAG()'.
## [MCW-2016-08-29-1] Aggregate plots not working for 'diagnostic' style but don't need anyway.
if(make.any.aggregates) {
warning("Aggregates plots requested but not available for 'diagnostic' layout style.")
make.any.aggregates <- FALSE
}
}
if (is.null(fig.dir)){
fig.dir <- file.path(getwd(), "fig/")
dir.create(fig.dir, showWarnings = FALSE)
}
if (plot.ind.country.results){
fig.dir.countries <- file.path(fig.dir, "country.plots/")
}
## put separate?
if (is.null(output.dir)){
output.dir <- file.path(getwd(), "output", run.name, "/")
}
filename.append <- ifelse(do.SS.run.first.pass, "_pre", "")
if (do.SS.run.first.pass & !file.exists(file.path(output.dir, paste0("par.ciq", filename.append, ".rda")))) {
par.ciq <- NULL
} else {
if (file.exists(file.path(output.dir, paste0("par.ciq", filename.append, ".rda"))))
load(file.path(output.dir, paste0("par.ciq", filename.append, ".rda"))) # change JR, 20140418
else {
warning("'", file.path(output.dir, paste0("par.ciq", filename.append, ".rda")),
"' was not found; parameter estimates cannot be plotted.")
par.ciq <- NULL
plot.parameters <- FALSE
}
}
load(file.path(output.dir, paste0("mcmc.meta", filename.append, ".rda"))) # change JR, 20140418
do.country.specific.run <- mcmc.meta$general$do.country.specific.run # change JR, 20131104
age.group <- mcmc.meta$general$age.group
## [MCW-2017-08-14-13] :: Load the appropriate '.rda' files if 'all.women = TRUE'.
if(all.women) {
load(file.path(output.dir, paste0("res.country.all.women", filename.append, ".rda")))
res.country <- res.country.all.women
}
else load(file.path(output.dir, paste0("res.country", filename.append, ".rda"))) # change JR, 20140418
if(make.any.aggregates) { #HOTFIX [MCW-2016-02-26-2] (explained above)
if (!do.country.specific.run) { # change JR, 20131104
if(all.women) {
load(file.path(output.dir, "res.aggregate.all.women.rda")) # change JR, 20140418
res.aggregate <- res.aggregate.all.women
}
else load(file.path(output.dir, "res.aggregate.rda")) # changedFiles JR, 20140418
}
} #HOTFIX [MCW-2016-02-26-3] (explained above)
validation <- !is.null(mcmc.meta$validation.list)
## >>>>> RATE MODEL [MCW-2018-01-03]
rate.model <- ModelFunctionRateModel(mcmc.meta$general$write.model.fun)
## <<<<< RATE MODEL
data <- mcmc.meta$data.raw$data
country.info <- mcmc.meta$data.raw$country.info
region.info <- mcmc.meta$data.raw$region.info
winbugs.data <- mcmc.meta$winbugs.data
## [MCW-2016-08-26-2] Plot countries with no data as well if not
## 'diagnostic' plots. Note that 'data' is not sorted in the same order as
## 'country.info' (and 'data' has one row per observation anyway,
## 'country.info' has one row per country.)
if(!identical(layout.style, "diagnostic") && mcmc.meta$general$include.c.no.data) {
country.info <- rbind(mcmc.meta$data.raw$country.info
,mcmc.meta$data.raw$country.info.no.data
)
## Need this as well because PlotDataAndEstimates() does not take argument 'country.info'.
mcmc.meta$data.raw$country.info <- country.info
} else country.info <- mcmc.meta$data.raw$country.info
region.info <- mcmc.meta$data.raw$region.info
#This is already the union of regions for countries with and without data
winbugs.data <- mcmc.meta$winbugs.data
##details<< For validation run you have to use different function
##\code{\link{PlotValidationResults}}. Otherwise the following plots are
##made:
if (validation){
print("Use PlotValidationResults!")
return(invisible())
}# end validation results
##details<< Plot priors and posteriors using \code{\link{PlotPriorPost}}.
if (plot.prior.post && !identical(layout.style, "diagnostic"))
PlotPriorPost(run.name = run.name
,output.dir = output.dir, fig.dir = fig.dir #[MCW-2016-04-07-1] Added these
,disagg.RN.PMA = TRUE #[MCW-2016-05-16-3] Added so that variances are plotted
)
fig.run.name <- run.name
## [MCW-2017-08-14-18] :: For 'all.women = TRUE', Ensure '.pdf' filenames have "aw" in them, even
## if ~run.name~ does not have "umw" or "mw" in it.
if(all.women && all.womenize.fig.name) {
fig.run.name <- makeAWFileName(run.name)
}
##------------------------------------------------------------------------------------------
if (plot.estimates) {
##details<< Plot country overview plots for proportions with and without details using
##\code{\link{PlotDataAndEstimates}}.
fig.name.years <- ifelse(!is.null(start.year) | !is.null(end.year), # change JR, 20140407
paste0("_",
ifelse(!is.null(start.year), paste0("from", floor(start.year)), ""),
ifelse(!is.null(end.year), paste0("to", floor(end.year)), "")),
"")
if(fp2020.69.only) fp2020 <- "_fp2020_69"
else fp2020 <- ""
if(identical(layout.style, "diagnostic")) diag <- "_diag"
else diag <- ""
if(all.women) {
plotDE.data.raw <- NULL
plotDE.country.info <- country.info
plotDE.par.ciq <- NULL
if(verbose) cat("\n'CIs_nopar")
PlotDataAndEstimates(data.raw = plotDE.data.raw,
country.info = plotDE.country.info,
par.ciq = plotDE.par.ciq,
## select.c=1,
CI.Lg.Lcat.qt = res.country$CIprop.Lg.Lcat.qt,
CIstar.Lg.Lcat.qt = res.country$CIstar.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.country$CIratio.Lg.Lcat.qt,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir
, paste0(fig.run.name, filename.append
, "CIs", fig.name.years, fp2020, diag, ".pdf"))# change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info #[MCW-2016-04-08-4] Control
# 'add.info' and size of
# plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,fp2020.69.only = fp2020.69.only
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = select.c.csv
,age.group = age.group
)
} else {
plotDE.data.raw <- mcmc.meta$data.raw
plotDE.country.info <- NULL
plotDE.par.ciq <- par.ciq
## if(!rate.model) { ##[MCW-2016-10-07-2] :: Don't do plots with logistic curve parameters if rate model.
if(verbose) cat("\n'CIs")
PlotDataAndEstimates(data.raw = plotDE.data.raw,
country.info = plotDE.country.info,
par.ciq = plotDE.par.ciq,
## select.c=1,
CI.Lg.Lcat.qt = res.country$CIprop.Lg.Lcat.qt,
CIstar.Lg.Lcat.qt = res.country$CIstar.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.country$CIratio.Lg.Lcat.qt,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir
, paste0(fig.run.name, filename.append
, "CIs", fig.name.years, fp2020, diag, ".pdf"))# change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info # [MCW-2016-04-08-4] Control
# 'add.info' and size of
# plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,fp2020.69.only = fp2020.69.only
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = select.c.csv
,age.group = age.group
)
## }
if(verbose) cat("\n'CIs_nopar")
PlotDataAndEstimates(data.raw = plotDE.data.raw,
CI.Lg.Lcat.qt = res.country$CIprop.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.country$CIratio.Lg.Lcat.qt,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir
, paste0(fig.run.name, filename.append
, "CIs_nopar", fig.name.years, fp2020, diag, ".pdf"))# change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info # [MCW-2016-04-08-5] Control
# 'add.info' and size of
# plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,fp2020.69.only = fp2020.69.only
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = select.c.csv
,age.group = age.group
)
}
## to plot individual country results
if (plot.ind.country.results && !identical(layout.style, "diagnostic")) {
PlotDataAndEstimates(data.raw = mcmc.meta$data.raw,
CI.Lg.Lcat.qt = res.country$CIprop.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.country$CIratio.Lg.Lcat.qt,
ind.country.overviewplot = TRUE,
start.year = start.year,
end.year = end.year,
run.name = run.name
,sources.pal = sources.pal
,add.info = add.info # [MCW-2016-04-08-6] Control
# 'add.info' and size of
# plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,fp2020.69.only = fp2020.69.only
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = NULL #This is for country plots only.
,age.group = age.group
)
}
##details<< Plot country overview plots for counts using
##\code{\link{PlotDataAndEstimates}}.
if(verbose) cat("\n'CIscountcountry")
PlotDataAndEstimates(
CI.Lg.Lcat.qt = res.country$CIcount.Lg.Lcat.qt,
plot.prop = FALSE,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir
, paste0(fig.run.name, filename.append
, "CIscountcountry", fig.name.years, fp2020, diag, ".pdf")) # change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info #[MCW-2016-04-08-7] Control 'add.info' and size
#of plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,country.info = country.info
,fp2020.69.only = fp2020.69.only
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = select.c.csv
,age.group = age.group
)
if(make.any.aggregates) { #HOTFIX [MCW-2016-02-26-4] (explained above)
##details<< Plot UNDP aggregates overview plots for proportions and counts using
##\code{\link{PlotDataAndEstimates}}.
if (!do.country.specific.run) {
if(verbose) cat("\n'CIsaggregate")
PlotDataAndEstimates(CI.Lg.Lcat.qt = res.aggregate$CIprop.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.aggregate$CIratio.Lg.Lcat.qt,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir, paste0(fig.run.name, filename.append
, "CIsaggregate", fig.name.years, diag, ".pdf")) # change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info #[MCW-2016-04-08-8]
#Control 'add.info' and
#size of plotting
#characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,country.info = country.info
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = NULL #This is for country plots only.
,age.group = age.group
)
if(verbose) cat("\n'CIscountaggregate")
PlotDataAndEstimates(
CI.Lg.Lcat.qt = res.aggregate$CIcount.Lg.Lcat.qt,
plot.prop = FALSE,
start.year = start.year,
end.year = end.year,
fig.name = file.path(fig.dir
, paste0(fig.run.name, "CIscountaggregate"
, fig.name.years, diag, ".pdf")) # change JR, 20140418
,sources.pal = sources.pal
,add.info = add.info #[MCW-2016-04-08-9] Control 'add.info'
#and size of plotting characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,country.info = country.info
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = NULL #This is for country plots only.
,age.group = age.group
)
if(!identical(layout.style, "diagnostic")) {
if (plot.ind.country.results){
PlotDataAndEstimates(CI.Lg.Lcat.qt = res.aggregate$CIprop.Lg.Lcat.qt,
CIratio.Lg.Lcat.qt = res.aggregate$CIratio.Lg.Lcat.qt,
ind.country.overviewplot = TRUE,
start.year = start.year,
end.year = end.year,
run.name = run.name
,sources.pal = sources.pal #[MCW-2016-04-05-14]
#Control
#disaggregation
#of PMA and
#repeated
#national
#data sources
,add.info = add.info #[MCW-2016-04-08-10]
#Control 'add.info'
#and size of
#plotting
#characters.
,cex.symbols = cex.symbols
,non.std.symbol = non.std.symbol
,UWRA = UWRA
,all.women = all.women
,all.womenize.fig.name = all.womenize.fig.name
,country.info = country.info
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
,select.c.csv = NULL #This is for country plots only.
,age.group = age.group
)
}
PlotCountryEstimatesForAggregate(
CI.Lg.Lcat.qt = res.country$CIprop.Lg.Lcat.qt,
country.info = country.info,
fig.name = file.path(fig.dir
, paste0(fig.run.name, "CIscountryestsinaggregates"
, fig.name.years, ".pdf")) # change JR, 20140418
,mcmc.meta = mcmc.meta
,all.women = all.women
)
}
}
} #HOTFIX [MCW-2016-02-26-5] (explained above)
}
##------------------------------------------------------------------------------------------
if(!identical(layout.style, "diagnostic")) {
if(verbose) cat("\n'Logistic parameters")
if (plot.parameters && !all.women) {
##details<< Plot model parameters using \code{\link{PlotLogisticParameters}}.
if (is.null(par.ciq)) {
stop("par.ciq is NULL. Model parameters cannot be plotted.")
} else {
PlotLogisticParameters(par.ciq = par.ciq, country.info = country.info,
region.info = region.info,
fig.name = file.path(fig.dir, paste0(fig.run.name, filename.append, "modelpar.pdf"))
,UWRA = UWRA ##[MCW-2016-08-16-2] Added to pass this argument through.
,write.model.fun = mcmc.meta$general$write.model.fun
) # change JR, 20140418
}
##------------------------------------------------------------------------------------------
##details<< Plot info about data parameters (variance by source, biases and perturbation multipliers).
cat("\n'Source variances")
load(file.path(output.dir, paste0("mcmc.array", filename.append, ".rda"))) # change JR, 20140418
if (!do.country.specific.run) { # change JR, 20131105
## variance by source
figname_ss <- paste0(fig.run.name, "sigmasource.pdf")
if(hide.CP.tot.lt.1pc) {
figname_ss <- paste0(fig.run.name, "sigmasource_no_CPTotLT1pc.pdf")
}
pdf(file.path(fig.dir, figname_ss), width = 7, height = 5
,useDingbats = pdf.useDingbats) # change JR, 20140418
PlotSourceSigmas(mcmc.array = mcmc.array,
,write.model.fun = mcmc.meta$general$write.model.fun
,UWRA = UWRA
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
)
PlotSourceSigmasUnmet(mcmc.array = mcmc.array
)
dev.off()
figname_ss <- paste0(fig.run.name, "sigmasource_ylim_0_4.pdf")
if(hide.CP.tot.lt.1pc) {
figname_ss <- paste0(fig.run.name, "sigmasource_no_CPTotLT1pc_ylim_0_4.pdf")
}
pdf(file.path(fig.dir, figname_ss), width = 7, height = 5
,useDingbats = pdf.useDingbats) # change JR, 20140418
PlotSourceSigmas(mcmc.array = mcmc.array,
,write.model.fun = mcmc.meta$general$write.model.fun
,UWRA = UWRA, ylim = c(0, 4)
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
)
PlotSourceSigmasUnmet(mcmc.array = mcmc.array,
ylim = c(0, 1.5)
)
dev.off()
figname_ss <- paste0(fig.run.name, "sigmasource_ylim_1-5.pdf")
if(hide.CP.tot.lt.1pc) {
figname_ss <- paste0(fig.run.name, "sigmasource_no_CPTotLT1pc_ylim_1-5.pdf")
}
pdf(file.path(fig.dir, figname_ss), width = 7, height = 5
,useDingbats = pdf.useDingbats) # change JR, 20140418
PlotSourceSigmas(mcmc.array = mcmc.array,
,write.model.fun = mcmc.meta$general$write.model.fun
,UWRA = UWRA, ylim = c(0, 1.5)
,hide.CP.tot.lt.1pc = hide.CP.tot.lt.1pc
)
PlotSourceSigmasUnmet(mcmc.array = mcmc.array,
ylim = c(0, 1.5)
)
dev.off()
## biases,
pdf(file.path(fig.dir, paste0(fig.run.name, "Biases.pdf"))
,width = 7, height = 5, useDingbats = pdf.useDingbats
) # change JR, 20140418
PlotBiases(mcmc.array = mcmc.array)
dev.off()
if("v.mics" %in% dimnames(mcmc.array)[[3]]) {
parnames.biases <- c("v.mneg", "v.mpos", "v.folk", "v.mics")
} else {
parnames.biases <- c("v.mneg", "v.mpos", "v.folk", "v.abs.probe.q")
}
percentiles <- c(0.025,0.5, 0.975)
biases.i3 <- NULL
parnames.biases <-
parnames.biases[parnames.biases %in% dimnames(mcmc.array)[[3]]]
for (parname in (parnames.biases)) {
biases.i3 <-
rbind(biases.i3, quantile(mcmc.array[,,parname],percentiles))
}
rownames(biases.i3) <- parnames.biases
write.csv(biases.i3, row.names = TRUE
, file = file.path(fig.dir, paste0(fig.run.name, "Biases_summary.csv"))
)
## If survey-specific SEs are used, plot total measurement error
## variance for each source by averaging over all surveys within type
## (e.g., average over all DHSs).
if(ModelFunctionSurveySEs(mcmc.meta$general$write.model.fun)) {
pdf(file.path(fig.dir, paste0(fig.run.name, "sigmasource_total.pdf"))
, width = 7, height = 5, useDingbats = pdf.useDingbats)
PlotDataModelSEs(run.name = run.name, output.dir = output.dir, mcmc.array = mcmc.array)
dev.off()
pdf(file.path(fig.dir, paste0(fig.run.name, "sigmasource_total_no_CPTotLT1pc.pdf"))
, width = 7, height = 5, useDingbats = pdf.useDingbats)
PlotDataModelSEs(run.name = run.name, output.dir = output.dir, mcmc.array = mcmc.array,
hide.CP.tot.lt.1pc = TRUE)
dev.off()
pdf(file.path(fig.dir, paste0(fig.run.name, "sigmasource_total_ylim_0_1-5.pdf"))
, width = 7, height = 5, useDingbats = pdf.useDingbats)
PlotDataModelSEs(run.name = run.name, output.dir = output.dir, mcmc.array = mcmc.array,
ylim = c(0, 1.5))
dev.off()
pdf(file.path(fig.dir, paste0(fig.run.name, "sigmasource_total_no_CPTotLT1pc_ylim_0_1-5.pdf"))
, width = 7, height = 5, useDingbats = pdf.useDingbats)
PlotDataModelSEs(run.name = run.name, output.dir = output.dir, mcmc.array = mcmc.array,
hide.CP.tot.lt.1pc = TRUE, ylim = c(0, 1.5))
dev.off()
}
}
## (long!) list of names of unique multipliers
par.V <- mcmc.meta$par.V
##InternalGetParnamesV(winbugs.data = winbugs.data,
## name.short.j = InternalMakeCountryNamesShort(mcmc.meta$data.raw$data$name.j))
##par.V$parnames.V.in.bugs
##par.V$parnames.V.nice
##data.frame(par.V$parnames.V.in.bugs, par.V$parnames.V.nice)
pdf(file.path(fig.dir, paste0(fig.run.name, filename.append, "Multipliers.pdf")))#, height = 15, width = 20) # change JR, 20140418
PlotGeoEtc(par.V = par.V, mcmc.array = mcmc.array)
dev.off()
## To check data dummies:
## number of observations with bias (folk/mics/M+/M-):
## summary.biases <-SummarizeBiases(winbugs.data) # 237 biases
## number of unique multipliers included (per composition):
## summary.multipliers <- SummarizeMultipliers(winbugs.data, data) # 214 sets
}
}
cat("\nAll results plotted and saved to ", fig.dir, "\n")
##value<< NULL
return(invisible(NULL))
} ## end function
##----------------------------------------------------------------------
## The End!
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