R/plot_infection_histories.R
In seroanalytics/serosolver: Inference Framework for Serological Data
Defines functions
plot_attack_rates
plot_posteriors_infhist
Documented in
plot_attack_rates
plot_posteriors_infhist
#' Plot inferred posteriors infection histories
#'
#' Plots and calculates many summary statistics from the infection history MCMC chain
#' @param inf_chain the data table with infection history samples from \code{\link{serosolver}}
#' @param possible_exposure_times vector of the epochs of potential circulation
#' @param n_alive_group vector with the number of people alive in each year of circulation.
#' @param known_ar data frame of known attack rates, if known.
#' @param known_infection_history data frame of known infection histories.
#' @param burnin if not already discarded, discard burn in from chain (takes rows where samp_no > burnin)
#' @param samples how many samples from the chain to take
#' @param pad_chain if TRUE, pads the infection history MCMC chain with non-infection events
#' @return a list of ggplot objects and data frame of posterior estimates
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' ## Load in example data
#' data(example_inf_chain)
#' data(example_antigenic_map)
#' data(example_antibody_data)
#'
#' possible_exposure_times <- example_antigenic_map$inf_times
#' ## Setup known attack rates
#' n_alive <- get_n_alive(example_antibody_data, possible_exposure_times)
#' n_infs <- colSums(example_inf_hist)
#' known_ar <- n_infs/n_alive
#' known_ar <- data.frame("j"=possible_exposure_times,"AR"=known_ar,"population_group"=1)
#'
#' ## Setup known infection histories
#' known_inf_hist <- data.frame(example_inf_hist)
#' colnames(known_inf_hist) <- possible_exposure_times
#'
#' n_alive_group <- get_n_alive_group(example_antibody_data, possible_exposure_times,melt_dat = TRUE)
#' n_alive_group$j <- possible_exposure_times[n_alive_group$j]
#' all_plots <- plot_posteriors_infhist(example_inf_chain, possible_exposure_times, n_alive_group,
#' known_ar=known_ar,known_infection_history = known_inf_hist,
#' samples=100)
#' }
#' @export
plot_posteriors_infhist <- function(inf_chain,
possible_exposure_times,
n_alive,
known_ar = NULL,
known_infection_history = NULL,
burnin = 0,
samples = 100,
pad_chain = TRUE) {
## Discard burn in period if necessary
inf_chain <- inf_chain[inf_chain$samp_no > burnin, ]
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
if(samples > length(unique(inf_chain$samp_no))) {
samples <- min(samples, length(unique(inf_chain$samp_no)))
message("Number of samples requested is greater than number of MCMC samples provided, reducing to maximum number of possible samples")
}
## Pads the chain with 0 entries (ie. non-infection events)
if (pad_chain) inf_chain <- pad_inf_chain(inf_chain)
## Thin the chain a bit to increase speed for plots
thin_chain <- inf_chain[inf_chain$samp_no %in% sample(unique(inf_chain$samp_no), samples, replace = FALSE), ]
## Trace plot by time
n_time_sample <- min(length(possible_exposure_times),10)
time_plot <- plot_infection_history_chains_time(thin_chain, 0, sample(1:length(possible_exposure_times), 10), n_alive=NULL, FALSE)
## Trace plot by indiv
n_indiv_sample <- min(length(unique(inf_chain$i)),10)
indiv_plot <- plot_infection_history_chains_indiv(thin_chain, 0, sample(unique(inf_chain$i),n_indiv_sample,replace=FALSE), FALSE)
## Pointrange plot for total number of infections
number_plot <- plot_individual_number_infections(thin_chain, FALSE)
## Posterior summaries and ESS
results <- calculate_infection_history_statistics(inf_chain, 0, possible_exposure_times,
n_alive,
known_ar=known_ar,
known_infection_history=known_infection_history
)
return(list(
"by_time_trace" = time_plot, "by_indiv_trace" = indiv_plot,
"indiv_infections" = number_plot, "estimates" = results
))
}
#' Plot historical attack rates
#'
#' Plots inferred historical attack rates from the MCMC output on infection histories.
#' @inheritParams plot_attack_rates_pointrange
#' @param ymax Numeric. the maximum y value to put on the axis. Default = 1.
#' @param resolution Integer. How many buckets of time is each year split into? ie. 12 for monthly data, 4 for quarterly etc. Default = 1 for annual.
#' @param cumulative if TRUE, plots the cumulative attack rate
#' @return a ggplot2 object with the inferred attack rates for each potential epoch of circulation
#' @export
plot_attack_rates <- function(infection_histories, antibody_data=NULL, possible_exposure_times=NULL,
n_alive = NULL, ymax = 1, resolution = 1,
pad_chain = TRUE, true_ar = NULL, by_group = FALSE, group_subset = NULL,
cumulative = FALSE,add_box=FALSE,settings=NULL) {
if (is.null(infection_histories$chain_no)) {
infection_histories$chain_no <- 1
}
if (is.null(infection_histories$population_group)) {
infection_histories$population_group <- 1
}
## If the list of serosolver settings was included, use these rather than passing each one by one
if(!is.null(settings)){
message("Using provided serosolver settings list")
if(is.null(possible_exposure_times)) possible_exposure_times <- settings$possible_exposure_times
if(is.null(antibody_data)) antibody_data <- settings$antibody_data
}
## Some year/sample combinations might have no infections there.
## Need to make sure that these get considered
if (pad_chain) infection_histories <- pad_inf_chain(infection_histories)
## Subset of groups to plot
if (is.null(group_subset)) {
group_subset <- unique(antibody_data$population_group)
}
if (!by_group) antibody_data$population_group <- 1
## Find inferred total number of infections from the MCMC output
## Scale by number of individuals that were alive in each epoch
## and generate quantiles
months <- 1:resolution
years <- possible_exposure_times
years <- range(floor(years / resolution))
years <- years[1]:years[2]
labels <- c(sapply(years, function(x) paste0(months, "/", x)))
labels1 <- labels[1:length(possible_exposure_times)]
labels1 <- labels1[seq(1, length(labels1), by = 1)]
year_break <- possible_exposure_times[seq(1, length(possible_exposure_times), by = 1)]
if (is.null(n_alive)) {
n_alive <- as.data.frame(get_n_alive_group(antibody_data, possible_exposure_times))
n_alive$population_group <- 1:nrow(n_alive)
}
n_alive_tmp <- reshape2::melt(n_alive, id.vars = "population_group")
n_alive_tmp$variable <- as.numeric(n_alive_tmp$variable)
colnames(n_alive_tmp) <- c("population_group", "j", "n_alive")
colnames(infection_histories)[1] <- "individual"
infection_histories <- merge(infection_histories, data.table(unique(antibody_data[, c("individual", "population_group")])), by = c("individual","population_group"))
## Sum infections per year for each MCMC sample
data.table::setkey(infection_histories, "samp_no", "j", "population_group", "chain_no")
tmp <- infection_histories[, list(V1 = sum(x)), by = key(infection_histories)]
if (cumulative & !pad_chain) message("Error - cannot calculate cumulative incidence without pad_chain = TRUE\n")
tmp <- merge(tmp, data.table(n_alive_tmp), by = c("population_group", "j"))
tmp$time <- possible_exposure_times[tmp$j]
tmp$V1 <- tmp$V1 / tmp$n_alive
tmp[is.nan(tmp$V1), "V1"] <- 0
if (cumulative && pad_chain) {
data.table::setkey(tmp, "samp_no", "population_group", "chain_no")
tmp[, V1 := cumsum(V1), by = key(tmp)]
}
quantiles <- ddply(tmp, .(time, population_group), function(x) quantile(x$V1, c(0.025,0.25, 0.5,0.75, 0.975)))
colnames(quantiles) <- c("time", "population_group", "lower","lower2", "median", "upper2","upper")
p <- ggplot(quantiles[quantiles$population_group %in% group_subset, ])
if(add_box){
x_box_min <- min(antibody_data$sample_time)
p <- p +geom_rect(xmin=x_box_min, xmax=max(quantiles$time),ymin=-1,ymax=2,fill="gray90",alpha=0.1)
}
p <- p +
geom_ribbon(aes(x = time, ymin = lower, ymax = upper), fill = "red", alpha = 0.2) +
geom_ribbon(aes(x = time, ymin = lower2, ymax = upper2), fill = "red", alpha = 0.4) +
geom_line(aes(x = time, y = median), col = "red")
if (!is.null(true_ar)) {
if(is.null(true_ar$population_group)) true_ar$population_group <- 1
p <- p +
geom_line(
data = true_ar[true_ar$population_group %in% group_subset, ], aes(x = time, y = AR),
col = "purple", size = 0.5
)
}
p <- p +
## geom_point(aes(x = year, y = median), col = "purple", size = 0.5) +
facet_wrap(~population_group, ncol = 2) +
scale_y_continuous(expand = c(0, 0)) +
# #scale_x_continuous(expand = c(0, 0), breaks = year_break, labels = labels1) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylab("Estimated per capita incidence") +
xlab("Date")
return(p)
}
#' Plot historical attack rates with pointrange plots
#'
#' Plots inferred historical attack rates from the MCMC output on infection histories, with pointrange plots for per-time incidence estimates
#' @param infection_histories the MCMC chain for infection histories
#' @param antibody_data the data frame of antibody data
#' @param possible_exposure_times vector of the epochs of potential infection
#' @param n_alive vector with the number of people alive in each year of possible infection Can be left as NULL, and the `birth` variable in `antibody_data` will be used to calculate the number alive
#' @param resolution divides `possible_exposure_times` by this number for x axis labels
#' @param pointsize graphics option, numeric - how big should each point be?
#' @param fatten graphics option, numeric - fatten parameter for ggplot pointrange
#' @param pad_chain if TRUE, fills the infection history data table with entries for non-infection events (ie. 0s). Can be switched to FALSE for speed to get a rough idea of what the attack rates look like.
#' @param prior_pars if not NULL, a list of parameters for the attack rate prior, giving the assumed prior_version along with infection_model_prior_shape1 and infection_model_prior_shape2
#' @param plot_den if TRUE, produces a violin plot of attack rates rather than pointrange
#' @param true_ar data frame of true attack rates, with first column `time` matching `possible_exposure_times`, and second column `AR` giving the attack rate. Column names: population_group, time, AR
#' @param by_group if TRUE, facets the plot by population_group ID
#' @param group_subset if not NULL, plots only this subset of groups eg. 1:5
#' @param plot_residuals if TRUE, plots the residuals between inferred and true attack rate
#' @param colour_by_taken if TRUE, then colours the attack rates by whether or not titres against the circulating antigen at that time were measured
#' @param by_val frequency of x-axis labels
#' @param settings if not NULL, list of serosolver settings as returned from the main serosolver function
#' @return a ggplot2 object with the inferred attack rates for each potential epoch of circulation
#' @export
plot_attack_rates_pointrange <- function(infection_histories,
antibody_data=NULL,
demographics = NULL,
par_tab=NULL,
possible_exposure_times=NULL,
n_alive = NULL,
pointsize = 1, fatten = 1,
pad_chain = TRUE, prior_pars = NULL,
plot_den = FALSE,
true_ar = NULL, by_group = FALSE,
group_subset = NULL, plot_residuals = FALSE,
colour_by_taken = TRUE, by_val = 5,
min_time=min(possible_exposure_times),max_time=max(possible_exposure_times),
settings=NULL) {
## Some year/sample combinations might have no infections there.
## Need to make sure that these get considered
if (is.null(infection_histories$chain_no)) {
infection_histories$chain_no <- 1
}
## If the list of serosolver settings was included, use these rather than passing each one by one
if(!is.null(settings)){
message("Using provided serosolver settings list")
if(is.null(possible_exposure_times)){
possible_exposure_times <- settings$possible_exposure_times
min_time<-min(possible_exposure_times)
max_time<-max(possible_exposure_times)
}
if(is.null(antibody_data)) antibody_data <- settings$antibody_data
if(is.null(par_tab)) par_tab <- settings$par_tab
if(is.null(demographics)) demographics <- settings$demographics
}
## Add stratifying variables to antibody_data and demographics
## Setup data vectors and extract
tmp <- get_demographic_groups(par_tab,antibody_data,demographics, NULL)
use_demographic_groups <- tmp$use_demographic_groups
use_timevarying_groups <- tmp$timevarying_demographics
tmp <- add_stratifying_variables(antibody_data, demographics, par_tab, use_demographic_groups)
group_ids_vec <- tmp$indiv_pop_group_indices
antibody_data <- tmp$antibody_data
demographics <- tmp$timevarying_demographics
demographic_groups <- tmp$demographics
population_groups <- tmp$population_groups
if (pad_chain) infection_histories <- pad_inf_chain(infection_histories)
## Subset of groups to plot
if (is.null(group_subset)) {
group_subset <- unique(antibody_data$population_group)
}
if (!by_group) {
antibody_data$population_group <- 1
infection_histories$population_group <- 1
demographics$population_group <- 1
if(!is.null(true_ar)) true_ar$population_group <- 1
}
## Find inferred total number of infections from the MCMC output
## Scale by number of individuals that were alive in each epoch
## and generate quantiles
if (is.null(n_alive)) {
n_alive <- get_n_alive_group(antibody_data, possible_exposure_times,demographics)
}
n_alive <- as.data.frame(n_alive)
n_alive$population_group <- 1:nrow(n_alive)
unique_groups1 <- unique(antibody_data$population_group)
n_groups <- length(unique_groups1[!is.na(unique_groups1)])
n_alive_tot <- get_n_alive(antibody_data, possible_exposure_times)
infection_histories$j <- possible_exposure_times[infection_histories$j]
colnames(infection_histories)[1] <- "individual"
if (!by_group) {
infection_histories <- merge(infection_histories, data.table(unique(antibody_data[, c("individual", "population_group")])), by = c("individual","population_group"))
} else {
if(!is.null(demographics)){
infection_histories <- merge(infection_histories,
demographics %>% select(individual, time, population_group) %>% distinct() %>%
rename(j = time) %>%
data.table(), by = c("individual","j"))
} else {
infection_histories <- merge(infection_histories, data.table(unique(antibody_data[, c("individual", "population_group")])), by = c("individual"))
}
}
years <- c(possible_exposure_times, max(possible_exposure_times) + 2)
data.table::setkey(infection_histories, "samp_no", "j", "chain_no", "population_group")
tmp <- infection_histories[, list(V1 = sum(x)), by = key(infection_histories)]
tmp$taken <- tmp$j %in% unique(antibody_data$sample_time)
tmp$taken <- ifelse(tmp$taken, "Yes", "No")
prior_dens <- NULL
n_alive1 <- n_alive
if (!is.null(prior_pars)) {
n_alive$Prior <- 1
prior_ver <- prior_pars[["prior_version"]]
alpha1 <- prior_pars[["infection_model_prior_shape1"]]
beta1 <- prior_pars[["infection_model_prior_shape2"]]
if (prior_ver == 3) {
prior_dens <- rbinom(10000, size = max(n_alive_tot), p = alpha1 / (alpha1 + beta1)) / max(n_alive_tot)
} else {
prior_dens <- rbeta(10000, alpha1, beta1)
}
prior_dens <- data.frame(
samp_no = 1:length(prior_dens), j = max(tmp$j) + 1,
chain_no = 1, V1 = prior_dens, taken = "Prior", population_group = 1
)
prior_dens_all <- NULL
for (i in 1:nrow(n_alive)) {
prior_dens$population_group <- i
prior_dens_all <- rbind(prior_dens_all, prior_dens)
}
tmp <- rbind(tmp, prior_dens_all)
}
n_alive_tmp <- reshape2::melt(n_alive, id.vars = "population_group")
n_alive_tmp$variable <- as.numeric(n_alive_tmp$variable)
colnames(n_alive_tmp) <- c("population_group", "j", "n_alive")
n_alive_tmp$j <- possible_exposure_times[n_alive_tmp$j]
tmp <- merge(tmp, data.table(n_alive_tmp), by = c("population_group", "j"))
tmp <- tmp %>% filter(n_alive > 0)
tmp$V1 <- tmp$V1 / tmp$n_alive
year_breaks <- c(min_time, seq(by_val * round(min_time / by_val), max_time, by = by_val))
year_labels <- c(min_time, seq(by_val * round(min_time / by_val), max_time, by = by_val))
if (!is.null(prior_dens)) {
year_breaks <- c(year_breaks, max_time + 2)
year_labels <- c(year_labels, "Prior")
}
if (!plot_den) {
quantiles <- ddply(tmp, .(j, population_group), function(x) quantile(x$V1, c(0.025, 0.5, 0.975)))
colnames(quantiles) <- c("j", "population_group", "lower", "median", "upper")
# quantiles[c("lower", "median", "upper")] <- quantiles[c("lower", "median", "upper")]# / n_alive1[quantiles$j]
#quantiles$j <- years[quantiles$j]
quantiles$taken <- quantiles$j %in% unique(antibody_data$sample_time)
quantiles$taken <- ifelse(quantiles$taken, "Yes", "No")
quantiles$tested <- quantiles$j %in% unique(antibody_data$biomarker_id)
quantiles$tested <- ifelse(quantiles$tested, "Yes", "No")
if (!is.null(prior_dens)) {
quantiles[quantiles$j == max(years), "taken"] <- "Prior"
}
colnames(quantiles)[which(colnames(quantiles) == "taken")] <- "Sample taken"
colnames(quantiles)[which(colnames(quantiles) == "tested")] <- "Biomarker tested"
p <- ggplot(quantiles[quantiles$population_group %in% group_subset, ]) +
coord_cartesian(ylim=c(0,1)) +
theme_classic() +
ylab("Estimated attack rate") +
xlab("Year")
## Colour depending on whether or not titres were taken in each year
if (colour_by_taken == TRUE) {
p <- p + geom_pointrange(aes(
x = j, y = median, ymin = lower, ymax = upper,
col = `Sample taken`
),
size = pointsize,
fatten = fatten
) +
scale_fill_manual(name="Samples taken",values=c("No"="darkorange","Yes"="blue","Prior"="grey40"))+
scale_color_manual(name="Samples taken",values=c("No"="darkorange","Yes"="blue","Prior"="grey40"))
} else {
p <- p + geom_pointrange(aes(
x = j, y = median, ymin = lower, ymax = upper,
col = `Biomarker tested`
),
size = pointsize,
fatten = fatten
)+
scale_fill_manual(name="Biomarker tested",values=c("No"="darkorange","Yes"="blue","Prior"="grey40"))+
scale_color_manual(name="Biomarker tested",values=c("No"="darkorange","Yes"="blue","Prior"="grey40"))
}
} else {
#tmp$j <- years[tmp$j]
colnames(tmp)[which(colnames(tmp) == "j")] <- "time"
if (plot_residuals) {
true_ar <- true_ar[, c("population_group", "time", "AR")]
if (!is.null(prior_pars)) {
true_ar <- rbind(true_ar, data.frame(
population_group = 1:n_groups,
time = max(possible_exposure_times) + 3,
AR = median(prior_dens$V1)
))
}
tmp <- merge(tmp, true_ar, by = c("population_group", "time"))
tmp$V1 <- tmp$V1 - tmp$AR
}
p <- ggplot(tmp[tmp$population_group %in% group_subset, ]) +
geom_violin(aes(x = time, y = V1, fill = taken, group = time),
alpha=0.25,
draw_quantiles = c(0.025,0.5,0.975), scale = "width",
adjust=2
)
}
if (!is.null(true_ar) & !plot_residuals) {
p <- p +
geom_point(
data = true_ar[true_ar$population_group %in% group_subset, ], aes(x = time, y = AR,shape="True attack rate"),stroke=1.25,
col = "black", size = 2.5
) +
scale_shape_manual(name="",values=c("True attack rate"=1))
}
if (!plot_residuals) {
p <- p +
scale_y_continuous(expand = c(0, 0))
}
if (by_group) {
p <- p + facet_wrap(~population_group, ncol = 1)
}
if (!is.null(prior_dens)) {
max_time <- max_time + 2.5
}
p <- p +
scale_x_continuous(breaks = year_breaks, labels = year_labels,limits=c(min_time-0.5,max_time)) +
theme_classic() +
theme(legend.position = "bottom") +
ylab("Estimated attack rate") +
xlab("Date")
if (plot_residuals) {
p <- p +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_y_continuous(limits = c(-1, 1), expand = c(0, 0))
p_res <- ggplot(tmp) + geom_density(aes(x = V1), fill = "grey40",alpha=0.5) +
facet_wrap(~population_group) +
geom_vline(xintercept = 0, linetype = "dashed", colour = "blue") +
scale_x_continuous(limits = c(-1, 1)) +
theme_classic() + xlab("Estimated AR - true AR") + ylab("Density")
return(list(p, p_res))
}
return(p)
}
#' Plot MCMC trace for infections per year
#'
#' @param inf_chain the data table with infection history samples from \code{\link{serosolver}}
#' @param burnin optionally remove all samp_no < burnin from the chain
#' @param times vector of integers, if not NULL, only plots a subset of years (where 1 is the first year eg. 1968)
#' @param n_alive if not NULL, then divides number of infections per year by number alive to give attack rates rather than total infections
#' @param pad_chain if TRUE, pads the infection history MCMC chain to have entries for non-infection events
#' @return a list of two ggplot objects - the MCMC trace and MCMC densities
#' @seealso \code{\link{plot_infection_history_chains_indiv}}
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' data(example_inf_chain)
#' data(example_antibody_data)
#' data(example_antigenic_map)
#' times <- example_antigenic_map$inf_times
#' n_alive_group <- get_n_alive_group(example_antibody_data, possible_exposure_times,melt_dat = TRUE)
#' n_alive_group$j <- possible_exposure_times[n_alive_group$j]
#' plot_infection_history_chains_time(example_inf_chain, 0, sample(1:length(times),10),n_alive,FALSE)
#' }
#' @export
plot_infection_history_chains_time <- function(inf_chain, burnin = 0, times = NULL,
n_alive = NULL, pad_chain = TRUE) {
inf_chain <- inf_chain[inf_chain$samp_no > burnin, ]
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
if (pad_chain) inf_chain <- pad_inf_chain(inf_chain)
data.table::setkey(inf_chain, "j", "samp_no", "chain_no")
n_inf_chain <- inf_chain[, list(V1 = sum(x)), by = key(inf_chain)]
if (!is.null(n_alive)) {
n_inf_chain$V1 <- n_inf_chain$V1 / n_alive[match(n_inf_chain$j, n_alive$j),"n_alive"]
}
if (!is.null(times)) {
use_times<- intersect(unique(n_inf_chain$j), times)
n_inf_chain <- n_inf_chain[n_inf_chain$j %in% use_times, ]
}
n_inf_chain$chain_no <- as.factor(n_inf_chain$chain_no)
inf_chain_p <- ggplot(n_inf_chain) + geom_line(aes(x = samp_no, y = V1, col = chain_no)) +
ylab("Estimated attack rate") +
xlab("MCMC sample") +
theme_bw() +
facet_wrap(~j, scales = "free_y")
inf_chain_den <- ggplot(n_inf_chain) + geom_density(aes(x = V1, fill = chain_no),alpha=0.5) +
xlab("Estimated attack rate") +
ylab("Posterior density") +
theme_bw() +
facet_wrap(~j, scales = "free_x")
return(list(inf_chain_p, inf_chain_den))
}
#' Plot MCMC trace for infections per individual
#'
#' @inheritParams plot_infection_history_chains_time
#' @param indivs vector of integers, if not NULL, only plots a subset of individuals (where 1 is the first individual)
#' @return a list of two ggplot objects - the MCMC trace and MCMC densities
#' @seealso \code{\link{plot_infection_history_chains_indiv}}
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' data(example_inf_chain)
#' plot_infection_history_chains_indiv(example_inf_chain, 0, 1:10, FALSE)
#' }
#' @export
plot_infection_history_chains_indiv <- function(inf_chain, burnin = 0, indivs = NULL, pad_chain = TRUE) {
inf_chain <- inf_chain[inf_chain$samp_no > burnin, ]
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
if (pad_chain) inf_chain <- pad_inf_chain(inf_chain)
data.table::setkey(inf_chain, "i", "samp_no", "chain_no")
n_inf_chain_i <- inf_chain[, list(V1 = sum(x)), by = key(inf_chain)]
if (!is.null(indivs)) {
use_indivs <- intersect(unique(n_inf_chain_i$i), indivs)
n_inf_chain_i <- n_inf_chain_i[n_inf_chain_i$i %in% use_indivs, ]
}
n_inf_chain_i$chain_no <- as.factor(n_inf_chain_i$chain_no)
inf_chain_p_i <- ggplot(n_inf_chain_i) + geom_line(aes(x = samp_no, y = V1, col = chain_no)) +
ylab("Estimated total number of infections") +
xlab("MCMC sample") +
theme_bw() +
facet_wrap(~i)
inf_chain_den_i <- ggplot(n_inf_chain_i) + geom_histogram(aes(x = V1, fill = chain_no), binwidth = 1) +
xlab("Estimated total number of infections") +
ylab("Posterior density") +
theme_bw() +
facet_wrap(~i)
return(list(inf_chain_p_i, inf_chain_den_i))
}
#' Total number of infections
#'
#' Plots the total number of inferred infections in the MCMC chain as a trace plot and density plot
#' @inheritParams plot_infection_history_chains_time
#' @return two ggplot2 objects
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' data(example_inf_chain)
#' plot_total_number_infections(example_inf_chain)
#' }
#' @export
plot_total_number_infections <- function(inf_chain, pad_chain = TRUE) {
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
n_inf_chain <- get_total_number_infections(inf_chain, pad_chain)
n_inf_chain$chain_no <- as.factor(n_inf_chain$chain_no)
inf_chain_p <- ggplot(n_inf_chain) + geom_line(aes(x = samp_no, y = total_infections, col = chain_no)) +
ylab("Estimated total number of infections") +
xlab("MCMC sample") +
theme_bw()
inf_chain_den <- ggplot(n_inf_chain) + geom_density(aes(x = total_infections, fill = chain_no),alpha=0.5) +
xlab("Estimated total number of infections") +
ylab("Posterior density") +
theme_bw()
return(list(inf_chain_p, inf_chain_den))
}
#' Plot point range number infections per individual
#'
#' @inheritParams plot_infection_history_chains_time
#' @return a ggplot object
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' data(example_inf_chain)
#' plot_individual_number_infections(example_inf_chain)
#' }
#' @export
plot_individual_number_infections <- function(inf_chain, pad_chain = TRUE) {
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
if (pad_chain) inf_chain <- pad_inf_chain(inf_chain)
n_strain <- max(inf_chain$j)
data.table::setkey(inf_chain, "i", "samp_no", "chain_no")
## For each individual, how many infections did they have in each sample in total?
n_inf_chain <- inf_chain[, list(V1 = sum(x)), by = key(inf_chain)]
## Get quantiles on total number of infections per indiv across all samples
indiv_hist <- plyr::ddply(n_inf_chain, .(i), function(x) quantile(x$V1, c(0.025, 0.5, 0.975)))
colnames(indiv_hist) <- c("individual", "lower", "median", "upper")
indiv_hist <- indiv_hist[order(indiv_hist$median), ]
indiv_hist$individual <- 1:nrow(indiv_hist)
p <- ggplot(indiv_hist) +
geom_pointrange(aes(x = individual + 1, y = median, ymin = lower, ymax = upper),
linewidth = 0.1, shape = 21, fatten = 0.1
) +
scale_y_continuous(expand = c(0, 0)) +
xlab("Individual (ordered)") +
ylab("Estimated number of infections") +
theme_bw()
return(p)
}
#' Plot cumulative and per time posterior infection probability densities
#'
#' For each individual requested, plots the median and 95% quantiles on a) the cumulative number of infections over a lifetime and b) the posterior probability that an infection occured in a given time point
#' @param inf_chain the infection history chain
#' @param burnin only plot samples where samp_no > burnin
#' @param indivs vector of individual ids to plot
#' @param real_inf_hist if not NULL, adds lines to the plots showing the known true infection times
#' @param start_inf if not NULL, adds lines to show where the MCMC chain started
#' @param possible_exposure_times vector of times at which individuals could have been infected
#' @param nsamp how many samples from the MCMC chain to take?
#' @param ages if not NULL, adds lines to show when an individual was born
#' @param number_col how many columns to use for the cumulative infection history plot
#' @param pad_chain if TRUE, pads the infection history MCMC chain to have entries for non-infection events
#' @param subset_times if not NULL, pass a vector of indices to only take a subset of indices from possible_exposure_times
#' @param return_data if TRUE, returns the infection history posterior densities used to generate the plots
#' @return two ggplot objects
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' data(example_inf_chain)
#' data(example_antigenic_map)
#' data(example_inf_hist)
#' data(example_antibody_data)
#'
#' ages <- unique(example_antibody_data[,c("individual","birth")])
#' times <- example_antigenic_map$inf_times
#' indivs <- 1:10
#' plot_cumulative_infection_histories(example_inf_chain, 0, indivs, example_inf_hist, NULL, times,
#' ages=ages, number_col=2,pad_chain=FALSE, return_data=TRUE)
#' }
#' @export
plot_cumulative_infection_histories <- function(inf_chain, burnin = 0, indivs, real_inf_hist = NULL, start_inf = NULL,
possible_exposure_times, nsamp = 100, ages = NULL, number_col = 1,
pad_chain = TRUE, subset_times = NULL, return_data = FALSE) {
inf_chain <- inf_chain[inf_chain$samp_no > burnin, ]
inf_chain <- inf_chain[inf_chain$i %in% indivs,]
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
if (pad_chain) inf_chain <- pad_inf_chain(inf_chain)
samps <- sample(unique(inf_chain$samp_no), nsamp)
inf_chain <- inf_chain[inf_chain$samp_no %in% samps, ]
## Get number of probability that infected in a given time point per individual and year
inf_chain1 <- inf_chain[inf_chain$i %in% indivs, ]
if (!is.null(subset_times)) inf_chain1 <- inf_chain1[inf_chain1$j %in% subset_times, ]
data.table::setkey(inf_chain1, "i", "j", "chain_no")
# max_samp_no <- length(unique(inf_chain1$samp_no))
max_samp_no <- nsamp
## Number of samples with a 1 divided by total samples
densities <- inf_chain1[, list(V1 = sum(x) / max_samp_no), by = key(inf_chain1)]
## Convert to real time points
densities$j <- as.numeric(possible_exposure_times[densities$j])
densities$i <- as.numeric(densities$i)
## If someone wasn't infected in a given year at all, then need a 0
possible_exposure_times1 <- possible_exposure_times
if (!is.null(subset_times)) possible_exposure_times1 <- possible_exposure_times[subset_times]
all_combos <- data.table(expand.grid(i = indivs, j = possible_exposure_times1, chain_no = unique(inf_chain$chain_no)))
all_combos$j <- as.numeric(all_combos$j)
all_combos$i <- as.numeric(all_combos$i)
all_combos <- data.table::fsetdiff(all_combos[, c("i", "j", "chain_no")], densities[, c("i", "j", "chain_no")])
all_combos$V1 <- 0
densities <- rbind(all_combos, densities)
infection_history1 <- NULL
if (!is.null(real_inf_hist)) {
infection_history1 <- as.data.frame(real_inf_hist)
infection_history1 <- infection_history1[indivs, ]
infection_history1$individual <- indivs
colnames(infection_history1) <- c(possible_exposure_times, "i")
infection_history1 <- reshape2::melt(infection_history1, id.vars = "i")
infection_history1$variable <- as.numeric(as.character(infection_history1$variable))
infection_history1 <- infection_history1[infection_history1$value == 1, ]
}
densities$chain_no <- as.factor(densities$chain_no)
density_plot <- ggplot() +
geom_line(data = densities, aes(x = j, y = V1, col = chain_no))
if (!is.null(real_inf_hist)) {
density_plot <- density_plot +
geom_vline(data = infection_history1, aes(xintercept = variable), col = "red")
}
density_plot <- density_plot +
facet_wrap(~i) +
xlab("Time") +
ylab("Density") +
theme(legend.position = "bottom") +
theme_bw()
## Generate lower, upper and median cumulative infection histories from the
## MCMC chain
tmp_inf_chain <- inf_chain[inf_chain$i %in% indivs, ]
hist_profiles <- ddply(tmp_inf_chain, .(i, samp_no, chain_no), function(x) {
empty <- numeric(length(possible_exposure_times))
empty[x[x$x == 1, "j"]] <- 1
cumsum(empty)
})
hist_profiles <- hist_profiles[, colnames(hist_profiles) != "samp_no"]
colnames(hist_profiles) <- c("i", "chain_no", possible_exposure_times)
hist_profiles_lower <- ddply(hist_profiles, .(i, chain_no), function(x) apply(x, 2, function(y) quantile(y, c(0.025))))
hist_profiles_lower <- reshape2::melt(hist_profiles_lower, id.vars = c("i", "chain_no"))
colnames(hist_profiles_lower) <- c("individual", "chain_no", "variable", "lower")
hist_profiles_upper <- ddply(hist_profiles, .(i, chain_no), function(x) apply(x, 2, function(y) quantile(y, c(0.975))))
hist_profiles_upper <- reshape2::melt(hist_profiles_upper, id.vars = c("i", "chain_no"))
colnames(hist_profiles_upper) <- c("individual", "chain_no", "variable", "upper")
hist_profiles_median <- ddply(hist_profiles, .(i, chain_no), function(x) apply(x, 2, function(y) quantile(y, c(0.5))))
hist_profiles_median <- reshape2::melt(hist_profiles_median, id.vars = c("i", "chain_no"))
colnames(hist_profiles_median) <- c("individual", "chain_no", "variable", "median")
## Merge these quantiles into a data frame for plotting
quant_hist <- merge(hist_profiles_lower, hist_profiles_upper, by = c("individual", "chain_no", "variable"))
quant_hist <- merge(quant_hist, hist_profiles_median, by = c("individual", "chain_no", "variable"))
## If available, process the real infection history matrix for plotting
real_hist_profiles <- NULL
if (!is.null(real_inf_hist)) {
real_hist_profiles <- as.data.frame(t(apply(real_inf_hist, 1, cumsum)))
colnames(real_hist_profiles) <- possible_exposure_times
real_hist_profiles <- real_hist_profiles[indivs, ]
real_hist_profiles$individual <- indivs
real_hist_profiles <- reshape2::melt(real_hist_profiles, id.vars = "individual")
}
## Process starting point from MCMC chain
if (!is.null(start_inf)) {
start_hist_profiles <- as.data.frame(t(apply(start_inf, 1, cumsum)))
colnames(start_hist_profiles) <- possible_exposure_times
start_hist_profiles <- start_hist_profiles[indivs, ]
start_hist_profiles$individual <- indivs
start_hist_profiles <- reshape2::melt(start_hist_profiles, id.vars = "individual")
}
quant_hist$chain_no <- as.factor(quant_hist$chain_no)
p1 <- ggplot(quant_hist[quant_hist$individual %in% indivs, ]) +
geom_line(aes(x = as.integer(as.character(variable)), y = median, col = chain_no)) +
geom_ribbon(aes(x = as.integer(as.character(variable)), ymin = lower, ymax = upper, fill = chain_no), alpha = 0.2)
if (!is.null(real_inf_hist)) {
p1 <- p1 +
geom_line(data = real_hist_profiles[real_hist_profiles$individual %in% indivs, ], aes(x = as.integer(as.character(variable)), y = value), col = "blue")
}
if (!is.null(start_inf)) {
p1 <- p1 +
geom_line(data = start_hist_profiles[start_hist_profiles$individual %in% indivs, ], aes(x = as.integer(as.character(variable)), y = value), col = "red")
}
if (!is.null(ages)) {
tmp_age <- ages[ages$individual %in% indivs, ]
age_mask <- create_age_mask(tmp_age[, 2], possible_exposure_times)
age_dat <- data.frame(j = age_mask, individual = indivs[order(indivs)])
p1 <- p1 + geom_vline(data = age_dat, aes(xintercept = possible_exposure_times[j]), col = "purple", linetype = "dashed")
}
p1 <- p1 + facet_wrap(~individual, ncol = number_col) +
theme_bw() +
theme(legend.position = "bottom") +
ylab("Cumulative infections") +
xlab("Circulation time")
return_list <- NULL
if (return_data) {
return_list <- list(
"cumu_infections" = p1, "density_plot" = density_plot,
"density_data" = densities, "real_hist" = real_hist_profiles,
"cumu_data" = quant_hist
)
} else {
return_list <- list("cumu_infections" = p1, "density_plot" = density_plot)
}
return(return_list)
}
seroanalytics/serosolver documentation built on Aug. 18, 2024, 12:46 p.m.
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Defines functions plot_attack_rates plot_posteriors_infhist
Documented in plot_attack_rates plot_posteriors_infhist
#' Plot inferred posteriors infection histories
#'
#' Plots and calculates many summary statistics from the infection history MCMC chain
#' @param inf_chain the data table with infection history samples from \code{\link{serosolver}}
#' @param possible_exposure_times vector of the epochs of potential circulation
#' @param n_alive_group vector with the number of people alive in each year of circulation.
#' @param known_ar data frame of known attack rates, if known.
#' @param known_infection_history data frame of known infection histories.
#' @param burnin if not already discarded, discard burn in from chain (takes rows where samp_no > burnin)
#' @param samples how many samples from the chain to take
#' @param pad_chain if TRUE, pads the infection history MCMC chain with non-infection events
#' @return a list of ggplot objects and data frame of posterior estimates
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' ## Load in example data
#' data(example_inf_chain)
#' data(example_antigenic_map)
#' data(example_antibody_data)
#'
#' possible_exposure_times <- example_antigenic_map$inf_times
#' ## Setup known attack rates
#' n_alive <- get_n_alive(example_antibody_data, possible_exposure_times)
#' n_infs <- colSums(example_inf_hist)
#' known_ar <- n_infs/n_alive
#' known_ar <- data.frame("j"=possible_exposure_times,"AR"=known_ar,"population_group"=1)
#'
#' ## Setup known infection histories
#' known_inf_hist <- data.frame(example_inf_hist)
#' colnames(known_inf_hist) <- possible_exposure_times
#'
#' n_alive_group <- get_n_alive_group(example_antibody_data, possible_exposure_times,melt_dat = TRUE)
#' n_alive_group$j <- possible_exposure_times[n_alive_group$j]
#' all_plots <- plot_posteriors_infhist(example_inf_chain, possible_exposure_times, n_alive_group,
#' known_ar=known_ar,known_infection_history = known_inf_hist,
#' samples=100)
#' }
#' @export
plot_posteriors_infhist <- function(inf_chain,
possible_exposure_times,
n_alive,
known_ar = NULL,
known_infection_history = NULL,
burnin = 0,
samples = 100,
pad_chain = TRUE) {
## Discard burn in period if necessary
inf_chain <- inf_chain[inf_chain$samp_no > burnin, ]
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
if(samples > length(unique(inf_chain$samp_no))) {
samples <- min(samples, length(unique(inf_chain$samp_no)))
message("Number of samples requested is greater than number of MCMC samples provided, reducing to maximum number of possible samples")
}
## Pads the chain with 0 entries (ie. non-infection events)
if (pad_chain) inf_chain <- pad_inf_chain(inf_chain)
## Thin the chain a bit to increase speed for plots
thin_chain <- inf_chain[inf_chain$samp_no %in% sample(unique(inf_chain$samp_no), samples, replace = FALSE), ]
## Trace plot by time
n_time_sample <- min(length(possible_exposure_times),10)
time_plot <- plot_infection_history_chains_time(thin_chain, 0, sample(1:length(possible_exposure_times), 10), n_alive=NULL, FALSE)
## Trace plot by indiv
n_indiv_sample <- min(length(unique(inf_chain$i)),10)
indiv_plot <- plot_infection_history_chains_indiv(thin_chain, 0, sample(unique(inf_chain$i),n_indiv_sample,replace=FALSE), FALSE)
## Pointrange plot for total number of infections
number_plot <- plot_individual_number_infections(thin_chain, FALSE)
## Posterior summaries and ESS
results <- calculate_infection_history_statistics(inf_chain, 0, possible_exposure_times,
n_alive,
known_ar=known_ar,
known_infection_history=known_infection_history
)
return(list(
"by_time_trace" = time_plot, "by_indiv_trace" = indiv_plot,
"indiv_infections" = number_plot, "estimates" = results
))
}
#' Plot historical attack rates
#'
#' Plots inferred historical attack rates from the MCMC output on infection histories.
#' @inheritParams plot_attack_rates_pointrange
#' @param ymax Numeric. the maximum y value to put on the axis. Default = 1.
#' @param resolution Integer. How many buckets of time is each year split into? ie. 12 for monthly data, 4 for quarterly etc. Default = 1 for annual.
#' @param cumulative if TRUE, plots the cumulative attack rate
#' @return a ggplot2 object with the inferred attack rates for each potential epoch of circulation
#' @export
plot_attack_rates <- function(infection_histories, antibody_data=NULL, possible_exposure_times=NULL,
n_alive = NULL, ymax = 1, resolution = 1,
pad_chain = TRUE, true_ar = NULL, by_group = FALSE, group_subset = NULL,
cumulative = FALSE,add_box=FALSE,settings=NULL) {
if (is.null(infection_histories$chain_no)) {
infection_histories$chain_no <- 1
}
if (is.null(infection_histories$population_group)) {
infection_histories$population_group <- 1
}
## If the list of serosolver settings was included, use these rather than passing each one by one
if(!is.null(settings)){
message("Using provided serosolver settings list")
if(is.null(possible_exposure_times)) possible_exposure_times <- settings$possible_exposure_times
if(is.null(antibody_data)) antibody_data <- settings$antibody_data
}
## Some year/sample combinations might have no infections there.
## Need to make sure that these get considered
if (pad_chain) infection_histories <- pad_inf_chain(infection_histories)
## Subset of groups to plot
if (is.null(group_subset)) {
group_subset <- unique(antibody_data$population_group)
}
if (!by_group) antibody_data$population_group <- 1
## Find inferred total number of infections from the MCMC output
## Scale by number of individuals that were alive in each epoch
## and generate quantiles
months <- 1:resolution
years <- possible_exposure_times
years <- range(floor(years / resolution))
years <- years[1]:years[2]
labels <- c(sapply(years, function(x) paste0(months, "/", x)))
labels1 <- labels[1:length(possible_exposure_times)]
labels1 <- labels1[seq(1, length(labels1), by = 1)]
year_break <- possible_exposure_times[seq(1, length(possible_exposure_times), by = 1)]
if (is.null(n_alive)) {
n_alive <- as.data.frame(get_n_alive_group(antibody_data, possible_exposure_times))
n_alive$population_group <- 1:nrow(n_alive)
}
n_alive_tmp <- reshape2::melt(n_alive, id.vars = "population_group")
n_alive_tmp$variable <- as.numeric(n_alive_tmp$variable)
colnames(n_alive_tmp) <- c("population_group", "j", "n_alive")
colnames(infection_histories)[1] <- "individual"
infection_histories <- merge(infection_histories, data.table(unique(antibody_data[, c("individual", "population_group")])), by = c("individual","population_group"))
## Sum infections per year for each MCMC sample
data.table::setkey(infection_histories, "samp_no", "j", "population_group", "chain_no")
tmp <- infection_histories[, list(V1 = sum(x)), by = key(infection_histories)]
if (cumulative & !pad_chain) message("Error - cannot calculate cumulative incidence without pad_chain = TRUE\n")
tmp <- merge(tmp, data.table(n_alive_tmp), by = c("population_group", "j"))
tmp$time <- possible_exposure_times[tmp$j]
tmp$V1 <- tmp$V1 / tmp$n_alive
tmp[is.nan(tmp$V1), "V1"] <- 0
if (cumulative && pad_chain) {
data.table::setkey(tmp, "samp_no", "population_group", "chain_no")
tmp[, V1 := cumsum(V1), by = key(tmp)]
}
quantiles <- ddply(tmp, .(time, population_group), function(x) quantile(x$V1, c(0.025,0.25, 0.5,0.75, 0.975)))
colnames(quantiles) <- c("time", "population_group", "lower","lower2", "median", "upper2","upper")
p <- ggplot(quantiles[quantiles$population_group %in% group_subset, ])
if(add_box){
x_box_min <- min(antibody_data$sample_time)
p <- p +geom_rect(xmin=x_box_min, xmax=max(quantiles$time),ymin=-1,ymax=2,fill="gray90",alpha=0.1)
}
p <- p +
geom_ribbon(aes(x = time, ymin = lower, ymax = upper), fill = "red", alpha = 0.2) +
geom_ribbon(aes(x = time, ymin = lower2, ymax = upper2), fill = "red", alpha = 0.4) +
geom_line(aes(x = time, y = median), col = "red")
if (!is.null(true_ar)) {
if(is.null(true_ar$population_group)) true_ar$population_group <- 1
p <- p +
geom_line(
data = true_ar[true_ar$population_group %in% group_subset, ], aes(x = time, y = AR),
col = "purple", size = 0.5
)
}
p <- p +
## geom_point(aes(x = year, y = median), col = "purple", size = 0.5) +
facet_wrap(~population_group, ncol = 2) +
scale_y_continuous(expand = c(0, 0)) +
# #scale_x_continuous(expand = c(0, 0), breaks = year_break, labels = labels1) +
theme_bw() +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
ylab("Estimated per capita incidence") +
xlab("Date")
return(p)
}
#' Plot historical attack rates with pointrange plots
#'
#' Plots inferred historical attack rates from the MCMC output on infection histories, with pointrange plots for per-time incidence estimates
#' @param infection_histories the MCMC chain for infection histories
#' @param antibody_data the data frame of antibody data
#' @param possible_exposure_times vector of the epochs of potential infection
#' @param n_alive vector with the number of people alive in each year of possible infection Can be left as NULL, and the `birth` variable in `antibody_data` will be used to calculate the number alive
#' @param resolution divides `possible_exposure_times` by this number for x axis labels
#' @param pointsize graphics option, numeric - how big should each point be?
#' @param fatten graphics option, numeric - fatten parameter for ggplot pointrange
#' @param pad_chain if TRUE, fills the infection history data table with entries for non-infection events (ie. 0s). Can be switched to FALSE for speed to get a rough idea of what the attack rates look like.
#' @param prior_pars if not NULL, a list of parameters for the attack rate prior, giving the assumed prior_version along with infection_model_prior_shape1 and infection_model_prior_shape2
#' @param plot_den if TRUE, produces a violin plot of attack rates rather than pointrange
#' @param true_ar data frame of true attack rates, with first column `time` matching `possible_exposure_times`, and second column `AR` giving the attack rate. Column names: population_group, time, AR
#' @param by_group if TRUE, facets the plot by population_group ID
#' @param group_subset if not NULL, plots only this subset of groups eg. 1:5
#' @param plot_residuals if TRUE, plots the residuals between inferred and true attack rate
#' @param colour_by_taken if TRUE, then colours the attack rates by whether or not titres against the circulating antigen at that time were measured
#' @param by_val frequency of x-axis labels
#' @param settings if not NULL, list of serosolver settings as returned from the main serosolver function
#' @return a ggplot2 object with the inferred attack rates for each potential epoch of circulation
#' @export
plot_attack_rates_pointrange <- function(infection_histories,
antibody_data=NULL,
demographics = NULL,
par_tab=NULL,
possible_exposure_times=NULL,
n_alive = NULL,
pointsize = 1, fatten = 1,
pad_chain = TRUE, prior_pars = NULL,
plot_den = FALSE,
true_ar = NULL, by_group = FALSE,
group_subset = NULL, plot_residuals = FALSE,
colour_by_taken = TRUE, by_val = 5,
min_time=min(possible_exposure_times),max_time=max(possible_exposure_times),
settings=NULL) {
## Some year/sample combinations might have no infections there.
## Need to make sure that these get considered
if (is.null(infection_histories$chain_no)) {
infection_histories$chain_no <- 1
}
## If the list of serosolver settings was included, use these rather than passing each one by one
if(!is.null(settings)){
message("Using provided serosolver settings list")
if(is.null(possible_exposure_times)){
possible_exposure_times <- settings$possible_exposure_times
min_time<-min(possible_exposure_times)
max_time<-max(possible_exposure_times)
}
if(is.null(antibody_data)) antibody_data <- settings$antibody_data
if(is.null(par_tab)) par_tab <- settings$par_tab
if(is.null(demographics)) demographics <- settings$demographics
}
## Add stratifying variables to antibody_data and demographics
## Setup data vectors and extract
tmp <- get_demographic_groups(par_tab,antibody_data,demographics, NULL)
use_demographic_groups <- tmp$use_demographic_groups
use_timevarying_groups <- tmp$timevarying_demographics
tmp <- add_stratifying_variables(antibody_data, demographics, par_tab, use_demographic_groups)
group_ids_vec <- tmp$indiv_pop_group_indices
antibody_data <- tmp$antibody_data
demographics <- tmp$timevarying_demographics
demographic_groups <- tmp$demographics
population_groups <- tmp$population_groups
if (pad_chain) infection_histories <- pad_inf_chain(infection_histories)
## Subset of groups to plot
if (is.null(group_subset)) {
group_subset <- unique(antibody_data$population_group)
}
if (!by_group) {
antibody_data$population_group <- 1
infection_histories$population_group <- 1
demographics$population_group <- 1
if(!is.null(true_ar)) true_ar$population_group <- 1
}
## Find inferred total number of infections from the MCMC output
## Scale by number of individuals that were alive in each epoch
## and generate quantiles
if (is.null(n_alive)) {
n_alive <- get_n_alive_group(antibody_data, possible_exposure_times,demographics)
}
n_alive <- as.data.frame(n_alive)
n_alive$population_group <- 1:nrow(n_alive)
unique_groups1 <- unique(antibody_data$population_group)
n_groups <- length(unique_groups1[!is.na(unique_groups1)])
n_alive_tot <- get_n_alive(antibody_data, possible_exposure_times)
infection_histories$j <- possible_exposure_times[infection_histories$j]
colnames(infection_histories)[1] <- "individual"
if (!by_group) {
infection_histories <- merge(infection_histories, data.table(unique(antibody_data[, c("individual", "population_group")])), by = c("individual","population_group"))
} else {
if(!is.null(demographics)){
infection_histories <- merge(infection_histories,
demographics %>% select(individual, time, population_group) %>% distinct() %>%
rename(j = time) %>%
data.table(), by = c("individual","j"))
} else {
infection_histories <- merge(infection_histories, data.table(unique(antibody_data[, c("individual", "population_group")])), by = c("individual"))
}
}
years <- c(possible_exposure_times, max(possible_exposure_times) + 2)
data.table::setkey(infection_histories, "samp_no", "j", "chain_no", "population_group")
tmp <- infection_histories[, list(V1 = sum(x)), by = key(infection_histories)]
tmp$taken <- tmp$j %in% unique(antibody_data$sample_time)
tmp$taken <- ifelse(tmp$taken, "Yes", "No")
prior_dens <- NULL
n_alive1 <- n_alive
if (!is.null(prior_pars)) {
n_alive$Prior <- 1
prior_ver <- prior_pars[["prior_version"]]
alpha1 <- prior_pars[["infection_model_prior_shape1"]]
beta1 <- prior_pars[["infection_model_prior_shape2"]]
if (prior_ver == 3) {
prior_dens <- rbinom(10000, size = max(n_alive_tot), p = alpha1 / (alpha1 + beta1)) / max(n_alive_tot)
} else {
prior_dens <- rbeta(10000, alpha1, beta1)
}
prior_dens <- data.frame(
samp_no = 1:length(prior_dens), j = max(tmp$j) + 1,
chain_no = 1, V1 = prior_dens, taken = "Prior", population_group = 1
)
prior_dens_all <- NULL
for (i in 1:nrow(n_alive)) {
prior_dens$population_group <- i
prior_dens_all <- rbind(prior_dens_all, prior_dens)
}
tmp <- rbind(tmp, prior_dens_all)
}
n_alive_tmp <- reshape2::melt(n_alive, id.vars = "population_group")
n_alive_tmp$variable <- as.numeric(n_alive_tmp$variable)
colnames(n_alive_tmp) <- c("population_group", "j", "n_alive")
n_alive_tmp$j <- possible_exposure_times[n_alive_tmp$j]
tmp <- merge(tmp, data.table(n_alive_tmp), by = c("population_group", "j"))
tmp <- tmp %>% filter(n_alive > 0)
tmp$V1 <- tmp$V1 / tmp$n_alive
year_breaks <- c(min_time, seq(by_val * round(min_time / by_val), max_time, by = by_val))
year_labels <- c(min_time, seq(by_val * round(min_time / by_val), max_time, by = by_val))
if (!is.null(prior_dens)) {
year_breaks <- c(year_breaks, max_time + 2)
year_labels <- c(year_labels, "Prior")
}
if (!plot_den) {
quantiles <- ddply(tmp, .(j, population_group), function(x) quantile(x$V1, c(0.025, 0.5, 0.975)))
colnames(quantiles) <- c("j", "population_group", "lower", "median", "upper")
# quantiles[c("lower", "median", "upper")] <- quantiles[c("lower", "median", "upper")]# / n_alive1[quantiles$j]
#quantiles$j <- years[quantiles$j]
quantiles$taken <- quantiles$j %in% unique(antibody_data$sample_time)
quantiles$taken <- ifelse(quantiles$taken, "Yes", "No")
quantiles$tested <- quantiles$j %in% unique(antibody_data$biomarker_id)
quantiles$tested <- ifelse(quantiles$tested, "Yes", "No")
if (!is.null(prior_dens)) {
quantiles[quantiles$j == max(years), "taken"] <- "Prior"
}
colnames(quantiles)[which(colnames(quantiles) == "taken")] <- "Sample taken"
colnames(quantiles)[which(colnames(quantiles) == "tested")] <- "Biomarker tested"
p <- ggplot(quantiles[quantiles$population_group %in% group_subset, ]) +
coord_cartesian(ylim=c(0,1)) +
theme_classic() +
ylab("Estimated attack rate") +
xlab("Year")
## Colour depending on whether or not titres were taken in each year
if (colour_by_taken == TRUE) {
p <- p + geom_pointrange(aes(
x = j, y = median, ymin = lower, ymax = upper,
col = `Sample taken`
),
size = pointsize,
fatten = fatten
) +
scale_fill_manual(name="Samples taken",values=c("No"="darkorange","Yes"="blue","Prior"="grey40"))+
scale_color_manual(name="Samples taken",values=c("No"="darkorange","Yes"="blue","Prior"="grey40"))
} else {
p <- p + geom_pointrange(aes(
x = j, y = median, ymin = lower, ymax = upper,
col = `Biomarker tested`
),
size = pointsize,
fatten = fatten
)+
scale_fill_manual(name="Biomarker tested",values=c("No"="darkorange","Yes"="blue","Prior"="grey40"))+
scale_color_manual(name="Biomarker tested",values=c("No"="darkorange","Yes"="blue","Prior"="grey40"))
}
} else {
#tmp$j <- years[tmp$j]
colnames(tmp)[which(colnames(tmp) == "j")] <- "time"
if (plot_residuals) {
true_ar <- true_ar[, c("population_group", "time", "AR")]
if (!is.null(prior_pars)) {
true_ar <- rbind(true_ar, data.frame(
population_group = 1:n_groups,
time = max(possible_exposure_times) + 3,
AR = median(prior_dens$V1)
))
}
tmp <- merge(tmp, true_ar, by = c("population_group", "time"))
tmp$V1 <- tmp$V1 - tmp$AR
}
p <- ggplot(tmp[tmp$population_group %in% group_subset, ]) +
geom_violin(aes(x = time, y = V1, fill = taken, group = time),
alpha=0.25,
draw_quantiles = c(0.025,0.5,0.975), scale = "width",
adjust=2
)
}
if (!is.null(true_ar) & !plot_residuals) {
p <- p +
geom_point(
data = true_ar[true_ar$population_group %in% group_subset, ], aes(x = time, y = AR,shape="True attack rate"),stroke=1.25,
col = "black", size = 2.5
) +
scale_shape_manual(name="",values=c("True attack rate"=1))
}
if (!plot_residuals) {
p <- p +
scale_y_continuous(expand = c(0, 0))
}
if (by_group) {
p <- p + facet_wrap(~population_group, ncol = 1)
}
if (!is.null(prior_dens)) {
max_time <- max_time + 2.5
}
p <- p +
scale_x_continuous(breaks = year_breaks, labels = year_labels,limits=c(min_time-0.5,max_time)) +
theme_classic() +
theme(legend.position = "bottom") +
ylab("Estimated attack rate") +
xlab("Date")
if (plot_residuals) {
p <- p +
geom_hline(yintercept = 0, linetype = "dashed") +
scale_y_continuous(limits = c(-1, 1), expand = c(0, 0))
p_res <- ggplot(tmp) + geom_density(aes(x = V1), fill = "grey40",alpha=0.5) +
facet_wrap(~population_group) +
geom_vline(xintercept = 0, linetype = "dashed", colour = "blue") +
scale_x_continuous(limits = c(-1, 1)) +
theme_classic() + xlab("Estimated AR - true AR") + ylab("Density")
return(list(p, p_res))
}
return(p)
}
#' Plot MCMC trace for infections per year
#'
#' @param inf_chain the data table with infection history samples from \code{\link{serosolver}}
#' @param burnin optionally remove all samp_no < burnin from the chain
#' @param times vector of integers, if not NULL, only plots a subset of years (where 1 is the first year eg. 1968)
#' @param n_alive if not NULL, then divides number of infections per year by number alive to give attack rates rather than total infections
#' @param pad_chain if TRUE, pads the infection history MCMC chain to have entries for non-infection events
#' @return a list of two ggplot objects - the MCMC trace and MCMC densities
#' @seealso \code{\link{plot_infection_history_chains_indiv}}
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' data(example_inf_chain)
#' data(example_antibody_data)
#' data(example_antigenic_map)
#' times <- example_antigenic_map$inf_times
#' n_alive_group <- get_n_alive_group(example_antibody_data, possible_exposure_times,melt_dat = TRUE)
#' n_alive_group$j <- possible_exposure_times[n_alive_group$j]
#' plot_infection_history_chains_time(example_inf_chain, 0, sample(1:length(times),10),n_alive,FALSE)
#' }
#' @export
plot_infection_history_chains_time <- function(inf_chain, burnin = 0, times = NULL,
n_alive = NULL, pad_chain = TRUE) {
inf_chain <- inf_chain[inf_chain$samp_no > burnin, ]
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
if (pad_chain) inf_chain <- pad_inf_chain(inf_chain)
data.table::setkey(inf_chain, "j", "samp_no", "chain_no")
n_inf_chain <- inf_chain[, list(V1 = sum(x)), by = key(inf_chain)]
if (!is.null(n_alive)) {
n_inf_chain$V1 <- n_inf_chain$V1 / n_alive[match(n_inf_chain$j, n_alive$j),"n_alive"]
}
if (!is.null(times)) {
use_times<- intersect(unique(n_inf_chain$j), times)
n_inf_chain <- n_inf_chain[n_inf_chain$j %in% use_times, ]
}
n_inf_chain$chain_no <- as.factor(n_inf_chain$chain_no)
inf_chain_p <- ggplot(n_inf_chain) + geom_line(aes(x = samp_no, y = V1, col = chain_no)) +
ylab("Estimated attack rate") +
xlab("MCMC sample") +
theme_bw() +
facet_wrap(~j, scales = "free_y")
inf_chain_den <- ggplot(n_inf_chain) + geom_density(aes(x = V1, fill = chain_no),alpha=0.5) +
xlab("Estimated attack rate") +
ylab("Posterior density") +
theme_bw() +
facet_wrap(~j, scales = "free_x")
return(list(inf_chain_p, inf_chain_den))
}
#' Plot MCMC trace for infections per individual
#'
#' @inheritParams plot_infection_history_chains_time
#' @param indivs vector of integers, if not NULL, only plots a subset of individuals (where 1 is the first individual)
#' @return a list of two ggplot objects - the MCMC trace and MCMC densities
#' @seealso \code{\link{plot_infection_history_chains_indiv}}
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' data(example_inf_chain)
#' plot_infection_history_chains_indiv(example_inf_chain, 0, 1:10, FALSE)
#' }
#' @export
plot_infection_history_chains_indiv <- function(inf_chain, burnin = 0, indivs = NULL, pad_chain = TRUE) {
inf_chain <- inf_chain[inf_chain$samp_no > burnin, ]
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
if (pad_chain) inf_chain <- pad_inf_chain(inf_chain)
data.table::setkey(inf_chain, "i", "samp_no", "chain_no")
n_inf_chain_i <- inf_chain[, list(V1 = sum(x)), by = key(inf_chain)]
if (!is.null(indivs)) {
use_indivs <- intersect(unique(n_inf_chain_i$i), indivs)
n_inf_chain_i <- n_inf_chain_i[n_inf_chain_i$i %in% use_indivs, ]
}
n_inf_chain_i$chain_no <- as.factor(n_inf_chain_i$chain_no)
inf_chain_p_i <- ggplot(n_inf_chain_i) + geom_line(aes(x = samp_no, y = V1, col = chain_no)) +
ylab("Estimated total number of infections") +
xlab("MCMC sample") +
theme_bw() +
facet_wrap(~i)
inf_chain_den_i <- ggplot(n_inf_chain_i) + geom_histogram(aes(x = V1, fill = chain_no), binwidth = 1) +
xlab("Estimated total number of infections") +
ylab("Posterior density") +
theme_bw() +
facet_wrap(~i)
return(list(inf_chain_p_i, inf_chain_den_i))
}
#' Total number of infections
#'
#' Plots the total number of inferred infections in the MCMC chain as a trace plot and density plot
#' @inheritParams plot_infection_history_chains_time
#' @return two ggplot2 objects
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' data(example_inf_chain)
#' plot_total_number_infections(example_inf_chain)
#' }
#' @export
plot_total_number_infections <- function(inf_chain, pad_chain = TRUE) {
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
n_inf_chain <- get_total_number_infections(inf_chain, pad_chain)
n_inf_chain$chain_no <- as.factor(n_inf_chain$chain_no)
inf_chain_p <- ggplot(n_inf_chain) + geom_line(aes(x = samp_no, y = total_infections, col = chain_no)) +
ylab("Estimated total number of infections") +
xlab("MCMC sample") +
theme_bw()
inf_chain_den <- ggplot(n_inf_chain) + geom_density(aes(x = total_infections, fill = chain_no),alpha=0.5) +
xlab("Estimated total number of infections") +
ylab("Posterior density") +
theme_bw()
return(list(inf_chain_p, inf_chain_den))
}
#' Plot point range number infections per individual
#'
#' @inheritParams plot_infection_history_chains_time
#' @return a ggplot object
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' data(example_inf_chain)
#' plot_individual_number_infections(example_inf_chain)
#' }
#' @export
plot_individual_number_infections <- function(inf_chain, pad_chain = TRUE) {
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
if (pad_chain) inf_chain <- pad_inf_chain(inf_chain)
n_strain <- max(inf_chain$j)
data.table::setkey(inf_chain, "i", "samp_no", "chain_no")
## For each individual, how many infections did they have in each sample in total?
n_inf_chain <- inf_chain[, list(V1 = sum(x)), by = key(inf_chain)]
## Get quantiles on total number of infections per indiv across all samples
indiv_hist <- plyr::ddply(n_inf_chain, .(i), function(x) quantile(x$V1, c(0.025, 0.5, 0.975)))
colnames(indiv_hist) <- c("individual", "lower", "median", "upper")
indiv_hist <- indiv_hist[order(indiv_hist$median), ]
indiv_hist$individual <- 1:nrow(indiv_hist)
p <- ggplot(indiv_hist) +
geom_pointrange(aes(x = individual + 1, y = median, ymin = lower, ymax = upper),
linewidth = 0.1, shape = 21, fatten = 0.1
) +
scale_y_continuous(expand = c(0, 0)) +
xlab("Individual (ordered)") +
ylab("Estimated number of infections") +
theme_bw()
return(p)
}
#' Plot cumulative and per time posterior infection probability densities
#'
#' For each individual requested, plots the median and 95% quantiles on a) the cumulative number of infections over a lifetime and b) the posterior probability that an infection occured in a given time point
#' @param inf_chain the infection history chain
#' @param burnin only plot samples where samp_no > burnin
#' @param indivs vector of individual ids to plot
#' @param real_inf_hist if not NULL, adds lines to the plots showing the known true infection times
#' @param start_inf if not NULL, adds lines to show where the MCMC chain started
#' @param possible_exposure_times vector of times at which individuals could have been infected
#' @param nsamp how many samples from the MCMC chain to take?
#' @param ages if not NULL, adds lines to show when an individual was born
#' @param number_col how many columns to use for the cumulative infection history plot
#' @param pad_chain if TRUE, pads the infection history MCMC chain to have entries for non-infection events
#' @param subset_times if not NULL, pass a vector of indices to only take a subset of indices from possible_exposure_times
#' @param return_data if TRUE, returns the infection history posterior densities used to generate the plots
#' @return two ggplot objects
#' @family infection_history_plots
#' @examples
#' \dontrun{
#' data(example_inf_chain)
#' data(example_antigenic_map)
#' data(example_inf_hist)
#' data(example_antibody_data)
#'
#' ages <- unique(example_antibody_data[,c("individual","birth")])
#' times <- example_antigenic_map$inf_times
#' indivs <- 1:10
#' plot_cumulative_infection_histories(example_inf_chain, 0, indivs, example_inf_hist, NULL, times,
#' ages=ages, number_col=2,pad_chain=FALSE, return_data=TRUE)
#' }
#' @export
plot_cumulative_infection_histories <- function(inf_chain, burnin = 0, indivs, real_inf_hist = NULL, start_inf = NULL,
possible_exposure_times, nsamp = 100, ages = NULL, number_col = 1,
pad_chain = TRUE, subset_times = NULL, return_data = FALSE) {
inf_chain <- inf_chain[inf_chain$samp_no > burnin, ]
inf_chain <- inf_chain[inf_chain$i %in% indivs,]
if (is.null(inf_chain$chain_no)) {
inf_chain$chain_no <- 1
}
if (pad_chain) inf_chain <- pad_inf_chain(inf_chain)
samps <- sample(unique(inf_chain$samp_no), nsamp)
inf_chain <- inf_chain[inf_chain$samp_no %in% samps, ]
## Get number of probability that infected in a given time point per individual and year
inf_chain1 <- inf_chain[inf_chain$i %in% indivs, ]
if (!is.null(subset_times)) inf_chain1 <- inf_chain1[inf_chain1$j %in% subset_times, ]
data.table::setkey(inf_chain1, "i", "j", "chain_no")
# max_samp_no <- length(unique(inf_chain1$samp_no))
max_samp_no <- nsamp
## Number of samples with a 1 divided by total samples
densities <- inf_chain1[, list(V1 = sum(x) / max_samp_no), by = key(inf_chain1)]
## Convert to real time points
densities$j <- as.numeric(possible_exposure_times[densities$j])
densities$i <- as.numeric(densities$i)
## If someone wasn't infected in a given year at all, then need a 0
possible_exposure_times1 <- possible_exposure_times
if (!is.null(subset_times)) possible_exposure_times1 <- possible_exposure_times[subset_times]
all_combos <- data.table(expand.grid(i = indivs, j = possible_exposure_times1, chain_no = unique(inf_chain$chain_no)))
all_combos$j <- as.numeric(all_combos$j)
all_combos$i <- as.numeric(all_combos$i)
all_combos <- data.table::fsetdiff(all_combos[, c("i", "j", "chain_no")], densities[, c("i", "j", "chain_no")])
all_combos$V1 <- 0
densities <- rbind(all_combos, densities)
infection_history1 <- NULL
if (!is.null(real_inf_hist)) {
infection_history1 <- as.data.frame(real_inf_hist)
infection_history1 <- infection_history1[indivs, ]
infection_history1$individual <- indivs
colnames(infection_history1) <- c(possible_exposure_times, "i")
infection_history1 <- reshape2::melt(infection_history1, id.vars = "i")
infection_history1$variable <- as.numeric(as.character(infection_history1$variable))
infection_history1 <- infection_history1[infection_history1$value == 1, ]
}
densities$chain_no <- as.factor(densities$chain_no)
density_plot <- ggplot() +
geom_line(data = densities, aes(x = j, y = V1, col = chain_no))
if (!is.null(real_inf_hist)) {
density_plot <- density_plot +
geom_vline(data = infection_history1, aes(xintercept = variable), col = "red")
}
density_plot <- density_plot +
facet_wrap(~i) +
xlab("Time") +
ylab("Density") +
theme(legend.position = "bottom") +
theme_bw()
## Generate lower, upper and median cumulative infection histories from the
## MCMC chain
tmp_inf_chain <- inf_chain[inf_chain$i %in% indivs, ]
hist_profiles <- ddply(tmp_inf_chain, .(i, samp_no, chain_no), function(x) {
empty <- numeric(length(possible_exposure_times))
empty[x[x$x == 1, "j"]] <- 1
cumsum(empty)
})
hist_profiles <- hist_profiles[, colnames(hist_profiles) != "samp_no"]
colnames(hist_profiles) <- c("i", "chain_no", possible_exposure_times)
hist_profiles_lower <- ddply(hist_profiles, .(i, chain_no), function(x) apply(x, 2, function(y) quantile(y, c(0.025))))
hist_profiles_lower <- reshape2::melt(hist_profiles_lower, id.vars = c("i", "chain_no"))
colnames(hist_profiles_lower) <- c("individual", "chain_no", "variable", "lower")
hist_profiles_upper <- ddply(hist_profiles, .(i, chain_no), function(x) apply(x, 2, function(y) quantile(y, c(0.975))))
hist_profiles_upper <- reshape2::melt(hist_profiles_upper, id.vars = c("i", "chain_no"))
colnames(hist_profiles_upper) <- c("individual", "chain_no", "variable", "upper")
hist_profiles_median <- ddply(hist_profiles, .(i, chain_no), function(x) apply(x, 2, function(y) quantile(y, c(0.5))))
hist_profiles_median <- reshape2::melt(hist_profiles_median, id.vars = c("i", "chain_no"))
colnames(hist_profiles_median) <- c("individual", "chain_no", "variable", "median")
## Merge these quantiles into a data frame for plotting
quant_hist <- merge(hist_profiles_lower, hist_profiles_upper, by = c("individual", "chain_no", "variable"))
quant_hist <- merge(quant_hist, hist_profiles_median, by = c("individual", "chain_no", "variable"))
## If available, process the real infection history matrix for plotting
real_hist_profiles <- NULL
if (!is.null(real_inf_hist)) {
real_hist_profiles <- as.data.frame(t(apply(real_inf_hist, 1, cumsum)))
colnames(real_hist_profiles) <- possible_exposure_times
real_hist_profiles <- real_hist_profiles[indivs, ]
real_hist_profiles$individual <- indivs
real_hist_profiles <- reshape2::melt(real_hist_profiles, id.vars = "individual")
}
## Process starting point from MCMC chain
if (!is.null(start_inf)) {
start_hist_profiles <- as.data.frame(t(apply(start_inf, 1, cumsum)))
colnames(start_hist_profiles) <- possible_exposure_times
start_hist_profiles <- start_hist_profiles[indivs, ]
start_hist_profiles$individual <- indivs
start_hist_profiles <- reshape2::melt(start_hist_profiles, id.vars = "individual")
}
quant_hist$chain_no <- as.factor(quant_hist$chain_no)
p1 <- ggplot(quant_hist[quant_hist$individual %in% indivs, ]) +
geom_line(aes(x = as.integer(as.character(variable)), y = median, col = chain_no)) +
geom_ribbon(aes(x = as.integer(as.character(variable)), ymin = lower, ymax = upper, fill = chain_no), alpha = 0.2)
if (!is.null(real_inf_hist)) {
p1 <- p1 +
geom_line(data = real_hist_profiles[real_hist_profiles$individual %in% indivs, ], aes(x = as.integer(as.character(variable)), y = value), col = "blue")
}
if (!is.null(start_inf)) {
p1 <- p1 +
geom_line(data = start_hist_profiles[start_hist_profiles$individual %in% indivs, ], aes(x = as.integer(as.character(variable)), y = value), col = "red")
}
if (!is.null(ages)) {
tmp_age <- ages[ages$individual %in% indivs, ]
age_mask <- create_age_mask(tmp_age[, 2], possible_exposure_times)
age_dat <- data.frame(j = age_mask, individual = indivs[order(indivs)])
p1 <- p1 + geom_vline(data = age_dat, aes(xintercept = possible_exposure_times[j]), col = "purple", linetype = "dashed")
}
p1 <- p1 + facet_wrap(~individual, ncol = number_col) +
theme_bw() +
theme(legend.position = "bottom") +
ylab("Cumulative infections") +
xlab("Circulation time")
return_list <- NULL
if (return_data) {
return_list <- list(
"cumu_infections" = p1, "density_plot" = density_plot,
"density_data" = densities, "real_hist" = real_hist_profiles,
"cumu_data" = quant_hist
)
} else {
return_list <- list("cumu_infections" = p1, "density_plot" = density_plot)
}
return(return_list)
}
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