inst/app/server/non-shiny/non-shiny-calibration-test.R
In jackolney/CascadeDashboard: A Shiny App for Investigating The Current Trajectory of HIV Care
# This is a calibration test script that will run a non-shiny version of the cascade model.
# For calibration testing, in the absence of all the bells and whistles that come with the shiny app.
# This script will be a template for future shiny versions of the model.
# Ideally, will just be a case of drag and drop of functions.
# Create all functions external to this script.
rm(list=ls())
setwd("~/git/CascadeDashboard")
# dir()
graphics.off()
quartz.options(w = 10, h = 8)
figFont <- "Avenir Next"
# -------- #
# WORKFLOW #
# -------- #
# --------- #
# STAGE ONE #
# --------- #
# source all the relevant files
require(RColorBrewer)
source("server/calibration/master.R", local = FALSE)
source("server/calibration/initial.R", local = FALSE)
source("server/calibration/model.R", local = FALSE)
source("server/calibration/error.R", local = FALSE)
# This contains simple function calls for the models in various permutations
source("server/calibration/calibration.R", local = FALSE)
source("server/calibration/assumptions.R", local = FALSE)
source("server/calibration/calibration-data.R", local = FALSE)
source("server/calibration/marrakech-data.R", local = FALSE)
source("server/calibration/misc-functions.R", local = FALSE)
source("server/misc-functions.R", local = FALSE)
source("server/calibration/plot-functions.R", local = FALSE)
source("server/non-shiny/non-shiny-calibration.R", local = FALSE)
source("server/country/misc-functions.R", local = FALSE)
source("server/non-shiny/thesis/thesis-figures.R", local = FALSE)
# load 'cascade' package and ensure it is the latest build.
devtools::load_all(pkg = "~/git/cascade")
# Unit tests
devtools::test(pkg = "~/git/cascade")
testthat::test_dir("tests")
## Run baseline model (nothing fancy)
# Kenya
KenyaData <- GetMasterDataSet("Kenya")
RunNSCalibration(country = "Kenya", data = KenyaData, maxIterations = 1e4, maxError = 3, limit = 1000)
BuildCalibrationPlotDetail(data = CalibOut, originalData = KenyaData, limit = 1000)
# Tanzania
TanzaniaData <- GetMasterDataSet("Tanzania")
RunNSCalibration(country = "Tanzania", data = TanzaniaData, maxIterations = 1e4, maxError = 2, limit = 100)
BuildCalibrationPlotDetail(data = CalibOut, originalData = TanzaniaData, limit = 100)
# Zimbabwe
ZimbabweData <- GetMasterDataSet("Zimbabwe")
RunNSCalibration(country = "Zimbabwe", data = ZimbabweData, maxIterations = 1e4, maxError = 3, limit = 1000)
BuildCalibrationPlotDetail(data = CalibOut, originalData = ZimbabweData, limit = 1000)
# Nigeria
data <- GetMasterDataSet("Nigeria")
data$calib
####################################################################################################
####################################################################################################
## Thesis figures
# Make sure that you set the seed nice and early.
set.seed(100)
ZimbabweData <- GetMasterDataSet("Zimbabwe")
RunNSCalibration(country = "Zimbabwe", data = ZimbabweData, maxIterations = 1e4, maxError = 0.4, limit = 1000)
graphics.off()
quartz.options(w = 10, h = 4)
BuildCalibPlot_Thesis(data = CalibOut, originalData = ZimbabweData, limit = 1000)
graphics.off()
quartz.options(w = 10, h = 8)
BuildCalibDetailPlot_Thesis(data = CalibOut, originalData = ZimbabweData, limit = 1000)
graphics.off()
quartz.options(w = 6, h = 3)
BuildCalibrationHistogram_Thesis(runError, maxError = 0.4)
graphics.off()
quartz.options(w = 10, h = 4)
BuildCalibrationParameterHistGroup_Thesis()
round(colMeans(CalibParamOut), 4)
DefineParmRange()
a <- paste0(round(Quantile_95(CalibParamOut[["rho"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["rho"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["rho"]])[["upper"]], 4), "]")
b <- paste0(round(Quantile_95(CalibParamOut[["q"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["q"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["q"]])[["upper"]], 4), "]")
c <- paste0(round(Quantile_95(CalibParamOut[["epsilon"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["epsilon"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["epsilon"]])[["upper"]], 4), "]")
d <- paste0(round(Quantile_95(CalibParamOut[["kappa"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["kappa"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["kappa"]])[["upper"]], 4), "]")
e <- paste0(round(Quantile_95(CalibParamOut[["gamma"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["gamma"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["gamma"]])[["upper"]], 4), "]")
f <- paste0(round(Quantile_95(CalibParamOut[["theta"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["theta"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["theta"]])[["upper"]], 4), "]")
g <- paste0(round(Quantile_95(CalibParamOut[["p"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["p"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["p"]])[["upper"]], 4), "]")
h <- paste0(round(Quantile_95(CalibParamOut[["omega"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["omega"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["omega"]])[["upper"]], 4), "]")
list(a,b,c,d,e,f,g,h)
Quantile_95(CalibParamOut[["q"]])
Quantile_95(CalibParamOut[["epsilon"]])
Quantile_95(CalibParamOut[["kappa"]])
Quantile_95(CalibParamOut[["gamma"]])
Quantile_95(CalibParamOut[["theta"]])
Quantile_95(CalibParamOut[["p"]])
Quantile_95(CalibParamOut[["omega"]])
####################################################################################################
####################################################################################################
### TESTING OF WEIGHTS ###
KenyaData <- GetMasterDataSet("Kenya")
# Basic Setup
error = 2
limit = 1000
iterations = 1e4
KenyaData$calib$weight <- "green"
RunNSCalibration(country = "Kenya", data = KenyaData, maxIterations = iterations, maxError = error, limit = limit)
BuildCalibrationTestPlot(data = CalibOut, originalData = KenyaData, limit = limit, runError = runError, maxError = error)
quartz.save(file = "captures/weight-test/kenya-green.pdf", type = 'pdf')
KenyaData$calib$weight <- "amber"
RunNSCalibration(country = "Kenya", data = KenyaData, maxIterations = iterations, maxError = error, limit = limit)
BuildCalibrationTestPlot(data = CalibOut, originalData = KenyaData, limit = limit, runError = runError, maxError = error)
quartz.save(file = "captures/weight-test/kenya-amber.pdf", type = 'pdf')
KenyaData$calib$weight <- "red"
RunNSCalibration(country = "Kenya", data = KenyaData, maxIterations = iterations, maxError = error, limit = limit)
BuildCalibrationTestPlot(data = CalibOut, originalData = KenyaData, limit = limit, runError = runError, maxError = error)
quartz.save(file = "captures/weight-test/kenya-red.pdf", type = 'pdf')
## I would like to see:
# 1. Histogram of errors.
# 2. Mean simulation error (does it decrease when all data are RED?)
# 6. Do the above for all "RED", "AMBER" and "GREEN"
####################################################################################################
####################################################################################################
# Parameter Histograms (why don't I wrap all this crap in a function?)
test <- as.data.frame(CalibParamOut)
ggOne <- ggplot(test, aes(rho)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggTwo <- ggplot(test, aes(epsilon)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggThree <- ggplot(test, aes(kappa)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggFour <- ggplot(test, aes(gamma)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggFive <- ggplot(test, aes(theta)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggSix <- ggplot(test, aes(omega)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggSeven <- ggplot(test, aes(p)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggEight <- ggplot(test, aes(q)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
gridExtra::grid.arrange(ggOne, ggTwo, ggThree, ggFour, ggFive, ggSix, ggSeven, ggEight, ncol = 4, nrow = 2)
# BuildCalibrationPlotDetail(data = CalibOut, originalData = KenyaData, limit = 500)
BuildCalibrationPlot(data = CalibOut, originalData = KenyaData)
BuildCalibrationPlotComplex(data = CalibOut, originalData = KenyaData)
run <- 1:length(runError)
theError <- data.frame(run, runError)
ggplot(theError, aes(runError)) + geom_histogram(aes(fill = ..count..), bins = 30)
# Need to compare 2015 estimate of cascade WITH projection model in 2015.
# Should be identical...
BuildCalibrationPlot(data = CalibOut, originalData = KenyaData)
CalibOut[CalibOut$year == 2015 & CalibOut$source == "model",][1:5,]
# CALIBRATION <- out[out$year == 2015,][1:5,]
CALIBRATION
# NOW FROM CALIBOUT.
# PROJECTION <- df
PROJECTION[["res"]]
dim(MasterOut)
names(result)
names(MasterOut[[1]])
MasterOut[[100]]
test <- c()
for(i in 1:100) {
test[i] <- MasterOut[[i]]$N[1]
}
test
sum(test)
sum(unlist(lapply(result, function(x) sum(x$N[year]))))
## MIN ERROR RUN CHECK ##
BuildCalibrationPlotDetail(data = CalibOut, originalData = KenyaData, limit = 100)
BuildCalibrationPlotDetail_Best(data = CalibOut, originalData = KenyaData, limit = 500, minErrorRun = minErrorRun)
theMean <- CallMeanModel()
dim(theMean)
meanRun <- AssembleComparisonDataFrame(country = "Kenya", model = theMean, data = KenyaData)
head(output, 100)
head(test,100)
### END ###
# dim(CalibOut)
p <- parameters(
prop_preART_500 = MasterData$cd4_2015[1,"prop.Off.ART.500"][[1]],
prop_preART_350500 = MasterData$cd4_2015[1,"prop.Off.ART.350500"][[1]],
prop_preART_250350 = MasterData$cd4_2015[1,"prop.Off.ART.250350"][[1]],
prop_preART_200250 = MasterData$cd4_2015[1,"prop.Off.ART.200250"][[1]],
prop_preART_100200 = MasterData$cd4_2015[1,"prop.Off.ART.100200"][[1]],
prop_preART_50100 = MasterData$cd4_2015[1,"prop.Off.ART.50100"][[1]],
prop_preART_50 = MasterData$cd4_2015[1,"prop.Off.ART.50"][[1]],
t_1 = ConvertYear2015(MasterData[["treatment_guidelines"]][["more500"]]),
t_2 = ConvertYear2015(MasterData[["treatment_guidelines"]][["less500"]]),
t_3 = ConvertYear2015(MasterData[["treatment_guidelines"]][["less350"]]),
t_4 = ConvertYear2015(MasterData[["treatment_guidelines"]][["less250"]]),
t_5 = ConvertYear2015(MasterData[["treatment_guidelines"]][["less200"]]),
Rho = CalibParamOut[minErrorRun,"rho"],
Epsilon = CalibParamOut[minErrorRun,"epsilon"],
Kappa = CalibParamOut[minErrorRun,"kappa"],
Gamma = CalibParamOut[minErrorRun,"gamma"],
Theta = CalibParamOut[minErrorRun,"theta"],
Omega = CalibParamOut[minErrorRun,"omega"],
p = CalibParamOut[minErrorRun,"p"],
q = CalibParamOut[minErrorRun,"q"]
)
p[["Mu"]]
p[["Rho"]]
# # Now we need the initials.
# y <- GetInitial(
# p = p,
# iterationResult = GetBestCalibOut(calibOut = CalibOut, minErrorRun = minErrorRun),
# masterCD4 = MasterData$cd4_2015
# )
# p[["beta"]] <- GetBeta(y = y, p = p, iterationInc = CalibIncOut[minErrorRun,])
zPLHIV <- CalibOut[CalibOut$indicator == "PLHIV" & CalibOut$year == 2015 & CalibOut$source == "model", "value"]
zDx <- CalibOut[CalibOut$indicator == "PLHIV Diagnosed" & CalibOut$year == 2015 & CalibOut$source == "model", "value"]
zCx <- CalibOut[CalibOut$indicator == "PLHIV in Care" & CalibOut$year == 2015 & CalibOut$source == "model", "value"]
zTx <- CalibOut[CalibOut$indicator == "PLHIV on ART" & CalibOut$year == 2015 & CalibOut$source == "model", "value"]
zVs <- CalibOut[CalibOut$indicator == "PLHIV Suppressed" & CalibOut$year == 2015 & CalibOut$source == "model", "value"]
Quantile_95(zPLHIV)
Quantile_95(zDx)
Quantile_95(zCx)
Quantile_95(zTx)
Quantile_95(zVs)
jackolney/CascadeDashboard documentation built on May 18, 2019, 7:56 a.m.
R Package Documentation
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# This is a calibration test script that will run a non-shiny version of the cascade model.
# For calibration testing, in the absence of all the bells and whistles that come with the shiny app.
# This script will be a template for future shiny versions of the model.
# Ideally, will just be a case of drag and drop of functions.
# Create all functions external to this script.
rm(list=ls())
setwd("~/git/CascadeDashboard")
# dir()
graphics.off()
quartz.options(w = 10, h = 8)
figFont <- "Avenir Next"
# -------- #
# WORKFLOW #
# -------- #
# --------- #
# STAGE ONE #
# --------- #
# source all the relevant files
require(RColorBrewer)
source("server/calibration/master.R", local = FALSE)
source("server/calibration/initial.R", local = FALSE)
source("server/calibration/model.R", local = FALSE)
source("server/calibration/error.R", local = FALSE)
# This contains simple function calls for the models in various permutations
source("server/calibration/calibration.R", local = FALSE)
source("server/calibration/assumptions.R", local = FALSE)
source("server/calibration/calibration-data.R", local = FALSE)
source("server/calibration/marrakech-data.R", local = FALSE)
source("server/calibration/misc-functions.R", local = FALSE)
source("server/misc-functions.R", local = FALSE)
source("server/calibration/plot-functions.R", local = FALSE)
source("server/non-shiny/non-shiny-calibration.R", local = FALSE)
source("server/country/misc-functions.R", local = FALSE)
source("server/non-shiny/thesis/thesis-figures.R", local = FALSE)
# load 'cascade' package and ensure it is the latest build.
devtools::load_all(pkg = "~/git/cascade")
# Unit tests
devtools::test(pkg = "~/git/cascade")
testthat::test_dir("tests")
## Run baseline model (nothing fancy)
# Kenya
KenyaData <- GetMasterDataSet("Kenya")
RunNSCalibration(country = "Kenya", data = KenyaData, maxIterations = 1e4, maxError = 3, limit = 1000)
BuildCalibrationPlotDetail(data = CalibOut, originalData = KenyaData, limit = 1000)
# Tanzania
TanzaniaData <- GetMasterDataSet("Tanzania")
RunNSCalibration(country = "Tanzania", data = TanzaniaData, maxIterations = 1e4, maxError = 2, limit = 100)
BuildCalibrationPlotDetail(data = CalibOut, originalData = TanzaniaData, limit = 100)
# Zimbabwe
ZimbabweData <- GetMasterDataSet("Zimbabwe")
RunNSCalibration(country = "Zimbabwe", data = ZimbabweData, maxIterations = 1e4, maxError = 3, limit = 1000)
BuildCalibrationPlotDetail(data = CalibOut, originalData = ZimbabweData, limit = 1000)
# Nigeria
data <- GetMasterDataSet("Nigeria")
data$calib
####################################################################################################
####################################################################################################
## Thesis figures
# Make sure that you set the seed nice and early.
set.seed(100)
ZimbabweData <- GetMasterDataSet("Zimbabwe")
RunNSCalibration(country = "Zimbabwe", data = ZimbabweData, maxIterations = 1e4, maxError = 0.4, limit = 1000)
graphics.off()
quartz.options(w = 10, h = 4)
BuildCalibPlot_Thesis(data = CalibOut, originalData = ZimbabweData, limit = 1000)
graphics.off()
quartz.options(w = 10, h = 8)
BuildCalibDetailPlot_Thesis(data = CalibOut, originalData = ZimbabweData, limit = 1000)
graphics.off()
quartz.options(w = 6, h = 3)
BuildCalibrationHistogram_Thesis(runError, maxError = 0.4)
graphics.off()
quartz.options(w = 10, h = 4)
BuildCalibrationParameterHistGroup_Thesis()
round(colMeans(CalibParamOut), 4)
DefineParmRange()
a <- paste0(round(Quantile_95(CalibParamOut[["rho"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["rho"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["rho"]])[["upper"]], 4), "]")
b <- paste0(round(Quantile_95(CalibParamOut[["q"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["q"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["q"]])[["upper"]], 4), "]")
c <- paste0(round(Quantile_95(CalibParamOut[["epsilon"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["epsilon"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["epsilon"]])[["upper"]], 4), "]")
d <- paste0(round(Quantile_95(CalibParamOut[["kappa"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["kappa"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["kappa"]])[["upper"]], 4), "]")
e <- paste0(round(Quantile_95(CalibParamOut[["gamma"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["gamma"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["gamma"]])[["upper"]], 4), "]")
f <- paste0(round(Quantile_95(CalibParamOut[["theta"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["theta"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["theta"]])[["upper"]], 4), "]")
g <- paste0(round(Quantile_95(CalibParamOut[["p"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["p"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["p"]])[["upper"]], 4), "]")
h <- paste0(round(Quantile_95(CalibParamOut[["omega"]])[["mean"]], 4), " [", round(Quantile_95(CalibParamOut[["omega"]])[["lower"]], 4), " to ", round(Quantile_95(CalibParamOut[["omega"]])[["upper"]], 4), "]")
list(a,b,c,d,e,f,g,h)
Quantile_95(CalibParamOut[["q"]])
Quantile_95(CalibParamOut[["epsilon"]])
Quantile_95(CalibParamOut[["kappa"]])
Quantile_95(CalibParamOut[["gamma"]])
Quantile_95(CalibParamOut[["theta"]])
Quantile_95(CalibParamOut[["p"]])
Quantile_95(CalibParamOut[["omega"]])
####################################################################################################
####################################################################################################
### TESTING OF WEIGHTS ###
KenyaData <- GetMasterDataSet("Kenya")
# Basic Setup
error = 2
limit = 1000
iterations = 1e4
KenyaData$calib$weight <- "green"
RunNSCalibration(country = "Kenya", data = KenyaData, maxIterations = iterations, maxError = error, limit = limit)
BuildCalibrationTestPlot(data = CalibOut, originalData = KenyaData, limit = limit, runError = runError, maxError = error)
quartz.save(file = "captures/weight-test/kenya-green.pdf", type = 'pdf')
KenyaData$calib$weight <- "amber"
RunNSCalibration(country = "Kenya", data = KenyaData, maxIterations = iterations, maxError = error, limit = limit)
BuildCalibrationTestPlot(data = CalibOut, originalData = KenyaData, limit = limit, runError = runError, maxError = error)
quartz.save(file = "captures/weight-test/kenya-amber.pdf", type = 'pdf')
KenyaData$calib$weight <- "red"
RunNSCalibration(country = "Kenya", data = KenyaData, maxIterations = iterations, maxError = error, limit = limit)
BuildCalibrationTestPlot(data = CalibOut, originalData = KenyaData, limit = limit, runError = runError, maxError = error)
quartz.save(file = "captures/weight-test/kenya-red.pdf", type = 'pdf')
## I would like to see:
# 1. Histogram of errors.
# 2. Mean simulation error (does it decrease when all data are RED?)
# 6. Do the above for all "RED", "AMBER" and "GREEN"
####################################################################################################
####################################################################################################
# Parameter Histograms (why don't I wrap all this crap in a function?)
test <- as.data.frame(CalibParamOut)
ggOne <- ggplot(test, aes(rho)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggTwo <- ggplot(test, aes(epsilon)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggThree <- ggplot(test, aes(kappa)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggFour <- ggplot(test, aes(gamma)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggFive <- ggplot(test, aes(theta)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggSix <- ggplot(test, aes(omega)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggSeven <- ggplot(test, aes(p)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
ggEight <- ggplot(test, aes(q)) + geom_histogram(aes(fill = ..count..), bins = 10) + theme_classic() + theme(legend.position = "none", text = element_text(family = figFont), axis.line.x = element_line(), axis.line.y = element_line()) + scale_y_continuous(expand = c(0,0))
gridExtra::grid.arrange(ggOne, ggTwo, ggThree, ggFour, ggFive, ggSix, ggSeven, ggEight, ncol = 4, nrow = 2)
# BuildCalibrationPlotDetail(data = CalibOut, originalData = KenyaData, limit = 500)
BuildCalibrationPlot(data = CalibOut, originalData = KenyaData)
BuildCalibrationPlotComplex(data = CalibOut, originalData = KenyaData)
run <- 1:length(runError)
theError <- data.frame(run, runError)
ggplot(theError, aes(runError)) + geom_histogram(aes(fill = ..count..), bins = 30)
# Need to compare 2015 estimate of cascade WITH projection model in 2015.
# Should be identical...
BuildCalibrationPlot(data = CalibOut, originalData = KenyaData)
CalibOut[CalibOut$year == 2015 & CalibOut$source == "model",][1:5,]
# CALIBRATION <- out[out$year == 2015,][1:5,]
CALIBRATION
# NOW FROM CALIBOUT.
# PROJECTION <- df
PROJECTION[["res"]]
dim(MasterOut)
names(result)
names(MasterOut[[1]])
MasterOut[[100]]
test <- c()
for(i in 1:100) {
test[i] <- MasterOut[[i]]$N[1]
}
test
sum(test)
sum(unlist(lapply(result, function(x) sum(x$N[year]))))
## MIN ERROR RUN CHECK ##
BuildCalibrationPlotDetail(data = CalibOut, originalData = KenyaData, limit = 100)
BuildCalibrationPlotDetail_Best(data = CalibOut, originalData = KenyaData, limit = 500, minErrorRun = minErrorRun)
theMean <- CallMeanModel()
dim(theMean)
meanRun <- AssembleComparisonDataFrame(country = "Kenya", model = theMean, data = KenyaData)
head(output, 100)
head(test,100)
### END ###
# dim(CalibOut)
p <- parameters(
prop_preART_500 = MasterData$cd4_2015[1,"prop.Off.ART.500"][[1]],
prop_preART_350500 = MasterData$cd4_2015[1,"prop.Off.ART.350500"][[1]],
prop_preART_250350 = MasterData$cd4_2015[1,"prop.Off.ART.250350"][[1]],
prop_preART_200250 = MasterData$cd4_2015[1,"prop.Off.ART.200250"][[1]],
prop_preART_100200 = MasterData$cd4_2015[1,"prop.Off.ART.100200"][[1]],
prop_preART_50100 = MasterData$cd4_2015[1,"prop.Off.ART.50100"][[1]],
prop_preART_50 = MasterData$cd4_2015[1,"prop.Off.ART.50"][[1]],
t_1 = ConvertYear2015(MasterData[["treatment_guidelines"]][["more500"]]),
t_2 = ConvertYear2015(MasterData[["treatment_guidelines"]][["less500"]]),
t_3 = ConvertYear2015(MasterData[["treatment_guidelines"]][["less350"]]),
t_4 = ConvertYear2015(MasterData[["treatment_guidelines"]][["less250"]]),
t_5 = ConvertYear2015(MasterData[["treatment_guidelines"]][["less200"]]),
Rho = CalibParamOut[minErrorRun,"rho"],
Epsilon = CalibParamOut[minErrorRun,"epsilon"],
Kappa = CalibParamOut[minErrorRun,"kappa"],
Gamma = CalibParamOut[minErrorRun,"gamma"],
Theta = CalibParamOut[minErrorRun,"theta"],
Omega = CalibParamOut[minErrorRun,"omega"],
p = CalibParamOut[minErrorRun,"p"],
q = CalibParamOut[minErrorRun,"q"]
)
p[["Mu"]]
p[["Rho"]]
# # Now we need the initials.
# y <- GetInitial(
# p = p,
# iterationResult = GetBestCalibOut(calibOut = CalibOut, minErrorRun = minErrorRun),
# masterCD4 = MasterData$cd4_2015
# )
# p[["beta"]] <- GetBeta(y = y, p = p, iterationInc = CalibIncOut[minErrorRun,])
zPLHIV <- CalibOut[CalibOut$indicator == "PLHIV" & CalibOut$year == 2015 & CalibOut$source == "model", "value"]
zDx <- CalibOut[CalibOut$indicator == "PLHIV Diagnosed" & CalibOut$year == 2015 & CalibOut$source == "model", "value"]
zCx <- CalibOut[CalibOut$indicator == "PLHIV in Care" & CalibOut$year == 2015 & CalibOut$source == "model", "value"]
zTx <- CalibOut[CalibOut$indicator == "PLHIV on ART" & CalibOut$year == 2015 & CalibOut$source == "model", "value"]
zVs <- CalibOut[CalibOut$indicator == "PLHIV Suppressed" & CalibOut$year == 2015 & CalibOut$source == "model", "value"]
Quantile_95(zPLHIV)
Quantile_95(zDx)
Quantile_95(zCx)
Quantile_95(zTx)
Quantile_95(zVs)
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