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plots_paper_contrast_10000B.R
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rm(list = ls())
library(loo)
library(dplyr)
library(reshape2)
library(tidyverse)
library(cowplot)
library(ggpubr)
library(grid)
library(rstan)
library(tidyverse)
library(reshape2)
library(bayesplot)
library(loo)
library(pracma)
library(cowplot)
library(grid)
library(gridExtra)
source('fun/additional_functions.R')
plot_my_results <- function(res,
max_lambda=0.3 ,
max_prev=1 ,
horizontal = TRUE,
include_labels =TRUE){
dat <- res$dat
mod1 <- res$mod1
mod2 <- res$mod2
virus <- dat$virus[1]
PlaceName <- dat$PlaceName[1]
if (PlaceName== 'Pi-Pi') {PlaceName = 'Pirre 1-2 & Pijibasal'}
if (PlaceName== 'Tamar') {PlaceName = 'Tamarindo'}
if (PlaceName== 'Merca') {PlaceName = 'Mercadeo'}
if (PlaceName== 'Real') {PlaceName = 'El Real'}
tsur <- dat$tsur[1]
extract_foi_summary <- function (mod, dat)
{
fit <- mod$fit
N <- sum(dat$total)
RealYexpo <- mod$RealYexpo
foi_chain <- data.frame(rstan::extract(fit, 'foi')[[1]])
foi_summary <- data.frame(sapply(foi_chain, function(i) c(quantile(i, c(0.5, 0.025, 0.975)))))
foi_summary_yexpo <- matrix(NA, ncol = 50, nrow = 3)
foi_summary_yexpo[, 1:10] <- foi_summary[,1]
foi_summary_yexpo[, 11:20] <- foi_summary[,2]
foi_summary_yexpo[, 21:30] <- foi_summary[,3]
foi_summary_yexpo[, 31:40] <- foi_summary[,4]
foi_summary_yexpo[, 41:50] <- foi_summary[,5]
foi_summary_y <- as.data.frame(foi_summary_yexpo)
names(foi_summary_y) <- (tsur-49):tsur
foi_summary_y$metric <- c('median', 'lower', 'upper')
foi_summary_yo <- melt(foi_summary_y, id='metric', variable.name = 'year') %>% spread(metric, value)
foi_summary_yo$year <- (as.numeric(as.character(foi_summary_yo$year)))
return(foi_summary_yo)
}
foi_years1 <- extract_foi_summary(mod1, dat) %>% mutate(model = 'constant')
foi_years2 <- extract_foi_summary(mod2, dat) %>% mutate(model = 'time-varying')
foi_overt <- rbind(foi_years1, foi_years2)
size_text <- 25
space_years <- 10
breaks_x <- round(seq(1960, max(foi_overt$year), by = space_years),0)
g1 <-
foi_overt %>% filter(year > 1960) %>%
ggplot(aes(x=year, y=median)) +
geom_ribbon(aes(ymin = lower, ymax = upper, fill = model), alpha = .4) +
geom_line(aes(y = median, colour = model)) +
scale_fill_brewer("FOI", palette = "Set1") +
scale_color_brewer("FOI", palette = "Set1") +
coord_cartesian(ylim = c(0, max_lambda), xlim = c(1960, 2017)) +
theme_bw() +
theme(legend.position = 'none') +
ylab ('FOI') +
theme(
legend.position="none",
text = element_text(size=size_text),
plot.title = element_text(size = size_text*.8, face = "bold"),
plot.subtitle = element_text(size = size_text*.8, face = "bold")) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black")) +
theme(axis.text.x = element_text(angle=90, vjust=0.5)) +
scale_x_continuous(breaks = breaks_x) +
ggtitle (paste(PlaceName))+
theme(plot.title = element_text(hjust = 0.5))
get_lambda_yexpo_expanded <- function (fit,
model,
min_age = 1,
max_age = 60)
{
foi_fit <- data.frame(rstan::extract(fit, 'foi')[[1]])
lambdaYexpo <- as.matrix(foi_fit)
iterf = NROW(foi_fit)
lambdaYexpoExpanded <- matrix(NA, nrow = iterf, ncol= max_age)
Ymax <- max_age
if(model == 'time_varying') {
lambdaYexpoExpanded[,(Ymax-9):Ymax] <- lambdaYexpo[,5]
lambdaYexpoExpanded[,(Ymax-19):(Ymax-10)] <- lambdaYexpo[,4]
lambdaYexpoExpanded[,(Ymax-29):(Ymax-20)] <- lambdaYexpo[,3]
lambdaYexpoExpanded[,(Ymax-39):(Ymax-30)] <- lambdaYexpo[,2]
lambdaYexpoExpanded[,1:(Ymax-40)] <- lambdaYexpo[,1]
} else {
lambdaYexpoExpanded[,1:Ymax] <- lambdaYexpo[,1]
}
return(lambdaYexpoExpanded)
}
min_age <-1
max_age <-60
yexpo_expanded <- min_age:max_age
lambdaYexpoExpanded1 <- get_lambda_yexpo_expanded(mod1$fit, 'constant')
lambdaYexpoExpanded2 <- get_lambda_yexpo_expanded(mod2$fit, 'time_varying')
ExposureMatrixFit <- get_exposure_matrix_expanded(yexpo_expanded, min_age ,max_age)
iterf <- NROW(lambdaYexpoExpanded2)
PrevP1 <- matrix(NA, nrow = iterf, ncol = max(yexpo_expanded))
PrevP2 <- matrix(NA, nrow = iterf, ncol = max(yexpo_expanded))
for (i in 1:iterf)
{
PrevP1[i,] <- 1 - exp(- ExposureMatrixFit %*% lambdaYexpoExpanded1[i,])
PrevP2[i,] <- 1 - exp(- ExposureMatrixFit %*% lambdaYexpoExpanded2[i,])
}
PPP1 <- matrix(NA, ncol = 3, nrow = max_age)
PPP2 <- PPP1
for (j in seq_along(min_age:max_age))
{
PPP1[j,] <- quantile(PrevP1[,j], c(.025, .5, .975))
PPP2[j,] <- quantile(PrevP2[,j], c(.025, .5, .975))
}
PPP1 <- as.data.frame(PPP1) %>% mutate(age = min_age:max_age,
model = 'constant')
PPP2 <- as.data.frame(PPP2) %>% mutate(age = min_age:max_age,
model = 'time_varying')
PPP_both <- rbind(PPP1, PPP2)
colnames(PPP_both) <- c("L", "M", "U", "age", 'model')
g2 <-
ggplot() +
geom_errorbar(data=dat,
aes(x=age_mean_f, ymin=prev_obs_lower, ymax = prev_obs_upper),
color = 'grey', width = .1) +
geom_ribbon(data=PPP_both, aes(x=age, ymin = L, ymax = U, fill = model), alpha = 0.3) +
geom_line (data=PPP_both, aes(x=age, y= M, color = model)) +
scale_fill_brewer(palette = "Set1") +
scale_color_brewer(palette = "Set1") +
geom_point (data=dat, aes(x=age_mean_f, y=prev_obs, size = total),
pch = 21,fill = 'black', color = 'black') +
theme_bw() + coord_cartesian(ylim = c(0,max_prev), xlim = c(1,60)) +
theme(axis.text.x = element_text(angle= 0)) +
theme(
legend.position="none",
text = element_text(size=size_text),
plot.title = element_text(size = size_text*.8, face = "bold"),
plot.subtitle = element_text(size = size_text*.8, face = "bold")) +
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))
if (include_labels == TRUE) {
g2 <- g2 + xlab("Age (years)") + ylab("Seropositivity") + labs(subtitle = 'B')
g1 <- g1 + xlab("Years") + ylab("FOI") + labs(subtitle = 'A')
} else {
g2 <- g2 + xlab("") + ylab("")
g1 <- g1 + xlab("") + ylab("")
}
if(horizontal == TRUE) {num_rows = 1} else {num_rows = 2}
p_res <- plot_grid(g1, g2,
nrow = num_rows,
align="hv",
rel_heights = c(0.9, 1))
return(p_res)
}
VP1_05 <- readRDS('res_final_10000/VEEV 2012 Pi-Pi 005.RDS')
VP2_05 <- readRDS('res_final_10000/VEEV 2012 Merca 005.RDS')
VP3_05 <- readRDS('res_final_10000/VEEV 2012 Tamar 005.RDS')
VP4_05 <- readRDS('res_final_10000/VEEV 2012 Real 005.RDS')
VP5_05 <- readRDS('res_final_10000/VEEV 2012 Aruza 005.RDS')
VP6_05 <- readRDS('res_final_10000/VEEV 2017 Mogue 005.RDS')
MP1_05 <- readRDS('res_final_10000/MADV 2012 Pi-Pi 005.RDS')
MP2_05 <- readRDS('res_final_10000/MADV 2012 Merca 005.RDS')
MP3_05 <- readRDS('res_final_10000/MADV 2012 Tamar 005.RDS')
MP4_05 <- readRDS('res_final_10000/MADV 2012 Real 005.RDS')
MP5_05 <- readRDS('res_final_10000/MADV 2012 Aruza 005.RDS')
MP6_05 <- readRDS('res_final_10000/MADV 2017 Mogue 005.RDS')
UP6_05 <- readRDS('res_final_10000/UNAV 2017 Mogue 005.RDS')
tiff("res_final_10000/foi_UNAV_final.tiff",
width = 350 *1, height = 350 *2)
# png("res_final_10000/foi_UNAV_5y_horizontal_final.png",
# width = 350 *1, height = 350 *2)
plot_my_results(UP6_05, max_lambda = 0.1, max_prev = 1, horizontal = FALSE)
dev.off()
title1=textGrob("VEEV", gp=gpar(fontface="bold"))
title2=textGrob("MADV", gp=gpar(fontface="bold"))
tiff("res_final_10000/foi_VEEV_final.tiff",
width = 350 *6, height = 350 *2)
# png("res_final_10000/foi_VEEV_5y_horizontal_final.png",
# width = 350 *6, height = 350 *2)
gridExtra::grid.arrange(
plot_my_results(VP1_05, max_lambda = 0.2, max_prev = 1, horizontal = FALSE),
plot_my_results(VP2_05, max_lambda = 0.2, max_prev = 1, horizontal = FALSE, include_labels = FALSE) ,
plot_my_results(VP3_05, max_lambda = 0.2, max_prev = 1, horizontal = FALSE, include_labels = FALSE) ,
plot_my_results(VP4_05, max_lambda = 0.2, max_prev = 1, horizontal = FALSE, include_labels = FALSE) ,
plot_my_results(VP5_05, max_lambda = 0.2, max_prev = 1, horizontal = FALSE, include_labels = FALSE) ,
plot_my_results(VP6_05, max_lambda = 0.2, max_prev = 1, horizontal = FALSE, include_labels = FALSE) ,
nrow = 1
)
dev.off()
tiff("res_final_10000/foi_MADV_final.tiff",
width = 350 *6, height = 350 *2)
# png("res_final_10000/foi_MADV_5y_horizontal_final.png",
# width = 350 *6, height = 350 *2)
gridExtra::grid.arrange(
plot_my_results(MP1_05, max_lambda = 0.1, max_prev = 1, horizontal = FALSE),
plot_my_results(MP2_05, max_lambda = 0.1, max_prev = 1, horizontal = FALSE, include_labels = FALSE) ,
plot_my_results(MP3_05, max_lambda = 0.1, max_prev = 1, horizontal = FALSE, include_labels = FALSE) ,
plot_my_results(MP4_05, max_lambda = 0.1, max_prev = 1, horizontal = FALSE, include_labels = FALSE) ,
plot_my_results(MP5_05, max_lambda = 0.1, max_prev = 1, horizontal = FALSE, include_labels = FALSE) ,
plot_my_results(MP6_05, max_lambda = 0.1, max_prev = 1, horizontal = FALSE, include_labels = FALSE) ,
nrow = 1)
dev.off()
#
#
#
#
#
#
# get_DIC <- function(mod,model_name, dat)
# {
# fit <- mod$fit
# loo_fit <- loo(fit, save_psis = TRUE, 'logLikelihood')
# yexpo <- make_yexpo(dat)
# yexpo <- yexpo[-length(yexpo)]
# ExposureMatrixFit <- get_exposure_matrix(dat, yexpo)
# foi <- data.frame(rstan::extract(fit, 'foi')[[1]])
# # lambdaTM <- (as.numeric(sapply(foi, function(i) c(quantile(i, 0.5)))))
# lambdaTM <- (as.numeric(sapply(foi, function(i) c(mean(i)))))
#
# if(model_name == 'time_varying'){
# lambdaTM_expanded <- matrix(NA, nrow = 1, ncol=max(yexpo))
# Ymax <- max(yexpo)
# lambdaTM_expanded[(Ymax-9):Ymax] <- lambdaTM[5]
# lambdaTM_expanded[(Ymax-19):(Ymax-10)] <- lambdaTM[4]
# lambdaTM_expanded[(Ymax-29):(Ymax-20)] <- lambdaTM[3]
# lambdaTM_expanded[(Ymax-39):(Ymax-30)] <- lambdaTM[2]
# lambdaTM_expanded[1:(Ymax-40)] <- lambdaTM[1]
#
# lambdaTM <- lambdaTM_expanded
#
# }
#
#
# PrevM <- 1-(exp(- ExposureMatrixFit %*% as.numeric(lambdaTM)))
# ll_best_params <- sum(dbinom(as.matrix(dat$pos), as.matrix(dat$total), PrevM, log = TRUE))
#
# ll_mean <- loo_fit$estimates[1,1]
# Dbar = -2 * ll_mean #------> Dbar is the posterior mean of the deviance
# Dhat = -2 * ll_best_params #------> Dhat point estimate of the deviance obtained by substituting in the posterior means
# pD = Dbar - Dhat #------> Effective number of parameters
# DIC = Dbar + pD
#
# PlaceName <- dat$PlaceName[1]
# if (PlaceName== 'Pi-Pi') {PlaceName = 'Pirries & Pijivasal'}
# if (PlaceName== 'Tamar') {PlaceName = 'Tamarindo'}
# if (PlaceName== 'Merca') {PlaceName = 'Mercadeo'}
# if (PlaceName== 'Real') {PlaceName = 'El Real'}
#
# virus <- dat$virus[1]
# res_DIC <- data.frame(Place = PlaceName,
# virus = dat$virus[1],
# sample_size = sum(dat$total),
# log_lik = ll_mean, DIC = DIC,
# n_eff = pD,
# model = model_name)
#
# return(res_DIC)
#
# }
#
#
#
#
# results <-
# rbind(
# get_DIC(VP1_05$mod1, model_name ='constant', VP1_05$dat),
# get_DIC(VP1_05$mod2, model_name ='time_varying', VP1_05$dat),
# get_DIC(VP2_05$mod1, model_name ='constant', VP2_05$dat),
# get_DIC(VP2_05$mod2, model_name ='time_varying', VP2_05$dat),
# get_DIC(VP3_05$mod1, model_name ='constant', VP3_05$dat),
# get_DIC(VP3_05$mod2, model_name ='time_varying', VP3_05$dat),
# get_DIC(VP4_05$mod1, model_name ='constant', VP4_05$dat),
# get_DIC(VP4_05$mod2, model_name ='time_varying', VP4_05$dat),
# get_DIC(VP5_05$mod1, model_name ='constant', VP5_05$dat),
# get_DIC(VP5_05$mod2, model_name ='time_varying', VP5_05$dat),
# get_DIC(VP6_05$mod1, model_name ='constant', VP6_05$dat),
# get_DIC(VP6_05$mod2, model_name ='time_varying', VP6_05$dat),
#
#
#
# get_DIC(MP1_05$mod1, model_name ='constant', MP1_05$dat),
# get_DIC(MP1_05$mod2, model_name ='time_varying', MP1_05$dat),
# get_DIC(MP2_05$mod1, model_name ='constant', MP2_05$dat),
# get_DIC(MP2_05$mod2, model_name ='time_varying', MP2_05$dat),
# get_DIC(MP3_05$mod1, model_name ='constant', MP3_05$dat),
# get_DIC(MP3_05$mod2, model_name ='time_varying', MP3_05$dat),
# get_DIC(MP4_05$mod1, model_name ='constant', MP4_05$dat),
# get_DIC(MP4_05$mod2, model_name ='time_varying', MP4_05$dat),
# get_DIC(MP5_05$mod1, model_name ='constant', MP5_05$dat),
# get_DIC(MP5_05$mod2, model_name ='time_varying', MP5_05$dat),
# get_DIC(MP6_05$mod1, model_name ='constant', MP6_05$dat),
# get_DIC(MP6_05$mod2, model_name ='time_varying', MP6_05$dat)
#
# )
#
#
# write_csv(results, 'res_final_10000/DIC.csv')
#
#
#
# fit <- MP5_05$mod2$fit
# traceplot(fit, 'lp__')
#
#
# diagnostic_plots <- function (mod, virus)
# {
#
# dat <- mod$dat
#
# PlaceName <- dat$PlaceName[1]
# if (PlaceName== 'Pi-Pi') {PlaceName = 'Pirries & Pijivasal'}
# if (PlaceName== 'Tamar') {PlaceName = 'Tamarindo'}
# if (PlaceName== 'Merca') {PlaceName = 'Mercadeo'}
# if (PlaceName== 'Real') {PlaceName = 'El Real'}
#
# fit1 <- mod$mod1$fit
# fit2 <- mod$mod2$fit
#
#
# title_plot1 <- paste(virus, PlaceName, '(constant model)')
# title_plot2 <- paste(virus, PlaceName, '(time_varying model)')
#
# p1 <- traceplot(fit1, 'lp__', inc_warmup = TRUE, size = 0.3) + ggtitle(title_plot1)
# p2 <- traceplot(fit2, 'lp__', inc_warmup = TRUE, size = 0.3) + ggtitle(title_plot2)
#
# pars1 <- traceplot(fit1, 'foi[1]', inc_warmup = TRUE, size = 0.3, nrow = 5) +
# ggtitle('params') + ylab ('foi')
# pars2 <- traceplot(fit2, 'foi' , inc_warmup = TRUE, size = 0.3, nrow = 2) +
# ggtitle('params')
#
# rhats1 <- rhat(fit1, 'foi[1]')
# rhats2 <- rhat(fit2, 'foi')
#
# color_scheme_set("viridisC")
# rh1 <- mcmc_rhat(rhats1) + ggtitle('R^')
# rh2 <- mcmc_rhat(rhats2) + ggtitle('R^')
#
# grid.arrange(p1, pars1, rh1,
# p2, pars2, rh2,
# nrow = 2, widths = c(1, 1.5, 0.5))
#
#
#
# }
#
# pdf('res_final_10000/diagnostics_VEEV.pdf', height = 8, width = 20)
# diagnostic_plots(VP1_05, 'VEEV')
# diagnostic_plots(VP2_05, 'VEEV')
# diagnostic_plots(VP3_05, 'VEEV')
# diagnostic_plots(VP4_05, 'VEEV')
# diagnostic_plots(VP5_05, 'VEEV')
# diagnostic_plots(VP6_05, 'VEEV')
#
# dev.off()
#
#
# pdf('res_final_10000/diagnostics_MADV.pdf', height = 8, width = 20)
# diagnostic_plots(MP1_05, 'MADV')
# diagnostic_plots(MP2_05, 'MADV')
# diagnostic_plots(MP3_05, 'MADV')
# diagnostic_plots(MP4_05, 'MADV')
# diagnostic_plots(MP5_05, 'MADV')
# diagnostic_plots(MP6_05, 'MADV')
#
# dev.off()
pirries_veev_mod <- VP1_05$mod1
mercadeo_veev_mod <- VP2_05$mod2
aruza_madv_mod <- MP5_05$mod2
mogue_madv_mod <- MP6_05$mod1
mogue_unav_mod <- UP6_05$mod1
extract_foi_values <- function (mod)
{
fit <- mod$fit
RealYexpo <- mod$RealYexpo
foi_chain <- data.frame(rstan::extract(fit, 'foi')[[1]])
foi_summary <- data.frame(sapply(foi_chain, function(i) c(quantile(i, c(0.5, 0.025, 0.975)))))
return(foi_summary)
}
extract_prev_values <- function (mod)
{
fit <- mod$fit
RealYexpo <- mod$RealYexpo
foi_chain <- data.frame(rstan::extract(fit, 'P')[[1]])
foi_summary <- data.frame(sapply(foi_chain, function(i) c(quantile(i, c(0.5, 0.025, 0.975)))))
return(foi_summary)
}
extract_foi_values(pirries_veev_mod)
extract_prev_values(pirries_veev_mod)
extract_foi_values(mercadeo_veev_mod)
extract_foi_values(aruza_madv_mod)
extract_prev_values(aruza_madv_mod)
extract_foi_values(mogue_madv_mod)
extract_prev_values(mogue_madv_mod)
extract_foi_values(mogue_unav_mod)
extract_prev_values(mogue_unav_mod)