R/plot-am.R
In shannong19/catalyst:
Defines functions
am_plot_mean_var
plot_epidemic_summary
summarize_epidemic
plot_epidemic_df
## SKG
## March 26, 2019
## Plot object from am-sir.R
#' Plot the output of am_sir()
#'
#' @param X X output of result of am_sir()
#' @param cols vector of colors
#' @param title title to give ggplot
#' @param subtitle subtitle to give ggplot
#' @param obs data frame of observations with column names (t, S, I, R) . Default is NULL, and so no observations are plotted
#' @param plot_var logical. Do we plot CI? Default is TRUE
#' @param labs model type labels. Default is NULL
#' @param color_guide_name Default is "Model Avg."
#' @return ggplot and the plotting df
am_plot_mean_var <- function(X, obs = NULL,
cols = c("blue", "red", "yellow3"),
title = "Hagelloch AM Simulations",
subtitle = "",
plot_var = TRUE,
labs = NULL,
color_guide_name = "Model Avg."){
if(!("model" %in% colnames(X))){
X <- as.data.frame(X)
X$model <- 1
n_models <- 1
} else{
n_models <- length(unique(X$model))
}
if(is.null(labs)){
labs <- 1:length(cols)
}
N <- X$S[1] + X$I[1] + X$R[1]
Xm <- reshape2::melt(X, id.vars = c("t", "ll", "model"))
Xm <- Xm %>% dplyr::group_by(variable, t, model) %>%
dplyr::summarize(mean = mean(value), var = var(value))
g <- ggplot2::ggplot() +
ggplot2::geom_line(data = Xm,
ggplot2::aes(x = t, y = mean / N * 100, group = factor(model),
col = factor(model)),
size = 1) +
ggplot2::scale_color_manual(values = cols[1:n_models],
name = color_guide_name, labels = labs) +
ggplot2::scale_fill_manual(values = cols[1:n_models], guide = FALSE) +
my_theme() +
ggplot2::facet_wrap(~variable, nrow = 3, scales = "free_y")
if(plot_var){
g <- g +
ggplot2::geom_ribbon(data = Xm, ggplot2::aes(x = t, group = factor(model),
ymin = mean / N * 100 - 2 * sqrt(var)/ N * 100,
ymax = mean / N * 100 + 2 * sqrt(var)/ N * 100,
fill = factor(model)),
col = NA, alpha = .3)
}
g <- g +
ggplot2::labs(x = "Time",
y = "% of individuals",
title = title,
subtitle = subtitle,
color = "Model Avg.")
if(!is.null(obs)){
cn <- c("t", "S", "I", "R")
obs_m <- reshape2::melt(obs[, cn], id.vars = "t")
g <- g + ggplot2::geom_point(data = obs_m,
ggplot2::aes(x = t, y = value / N * 100, group = variable))
}
print(g)
return(list(g = g, plot_df = df))
}
#' Plot epidemic summary of SIR AM
#'
#' @param sims_list list of the "X" output from am_sir() function
#' @param N number of total agents
#' @param title character passed on to ggplot
#' @param subtitle character passed on to ggplot
#' @param summary_fxn either "mean" or "median". How we summarize the summary statistics
#' @param many_groups logical. Default is FALSE. If TRUE, we will do different colors on them. Corresponds to the "Type" column
#' @return list with two ggplots and summarized, plottable df
plot_epidemic_summary <- function(sims_list, N,
title,
subtitle,
summary_fxn = "mean",
many_groups = FALSE,
cols = "black",
type_name = "Type",
labs = NULL,
xmin = 0,
xmax = 80,
y1min = -25,
y1max = 75,
y2min = -50,
y2max = 150,
y3min = -5,
y3max = 120,
lty = 0,
show_labels = FALSE,
alpha = .3,
do_facet = FALSE,
do_color_ramp = FALSE,
pal = "Greys",
legend_name = latex2exp::TeX("$\\rho$"),
bg = FALSE){
df <- summarize_epidemic(sims_list, N, summary_fxn)
## Plot max t vs max I
plot_list <- plot_epidemic_df(df, N, many_groups = many_groups,
cols = cols,
title,
subtitle,
type_name,
labs,
xmin = xmin,
xmax = xmax,
y1min = y1min,
y1max = y1max,
y2min = y2min,
y2max = y2max,
y3min = y3min,
y3max = y3max,
lty = lty,
show_labels = show_labels,
alpha = alpha,
do_facet = do_facet,
do_color_ramp = do_color_ramp,
pal = pal,
legend_name = legend_name,
bg = bg)
return(list(g1 = plot_list$g1, g2 = plot_list$g2,
g3 = plot_list$g3,
df = df))
}
#' Summarize list of AM simulations
#'
#' @param sims_list list of the "X" output from am_sir() function
#' @param N total number of agents
#' @return summary data frame with columns mean_* and var_* for ("max_I", "sum_I", and "max_t", "len_I") and 95% CIs for each of these vars around the mean
summarize_epidemic <- function(sims_list, N, summary_fxn = "mean"){
if(summary_fxn == "median"){
sum_fxn <- median
} else{
sum_fxn = mean
}
X <- do.call('rbind', sims_list)
if("mult" %in% colnames(X)){
X$model <- X$mult
df <- X[, - which(colnames(X) == "mult")]
} else {
df <- X
}
if(!("color_ramp" %in% colnames(X))){
df$color_ramp <- 1
}
if(!("Type" %in% colnames(X))){
df$Type <- "Type"
} else {
if(class(df$Type) != "factor"){
df$Type <- factor(df$Type)
}
}
vars <- c("model", "ll", "color_ramp", "Type")
vars2 <- c("model", "color_ramp", "Type")
## Make into a function
sum_df <- plyr::ddply(df, .var = vars,
.fun = function(df){
c(max_I = max(df$I),
sum_I = max(df$R),
max_t = df$t[which.max(df$I)],
len_I = max(which(df$I >= 1))
)
})
ave_sum_df <- plyr::ddply(sum_df, .var = vars2,
.fun = function(df){
c(mean_max_I = sum_fxn(df$max_I),
var_max_I = var(df$max_I),
mean_sum_I = sum_fxn(df$sum_I),
var_sum_I = var(df$sum_I),
mean_max_t = sum_fxn(df$max_t),
var_max_t = var(df$max_t),
mean_len_I = sum_fxn(df$len_I),
var_len_I = var(df$len_I)
)
})
ave_sum_df$tmin <- (ave_sum_df$mean_max_t -
2 * sqrt(ave_sum_df$var_max_t))
ave_sum_df$tmax <- (ave_sum_df$mean_max_t +
2 * sqrt(ave_sum_df$var_max_t))
ave_sum_df$Imin <- (ave_sum_df$mean_max_I / N * 100 -
2 * sqrt(ave_sum_df$var_max_I) / N * 100)
ave_sum_df$Imax <- (ave_sum_df$mean_max_I / N * 100 +
2 * sqrt(ave_sum_df$var_max_I) / N * 100)
ave_sum_df$sumImin <- (ave_sum_df$mean_sum_I / N * 100 -
2 * sqrt(ave_sum_df$var_sum_I) / N * 100)
ave_sum_df$sumImax <- (ave_sum_df$mean_sum_I / N * 100 +
2 * sqrt(ave_sum_df$var_sum_I) / N * 100)
ave_sum_df$lenImin <- (ave_sum_df$mean_len_I / N * 100 -
2 * sqrt(ave_sum_df$var_len_I) / N * 100)
ave_sum_df$lenImax <- (ave_sum_df$mean_len_I +
2 * sqrt(ave_sum_df$var_len_I) )
return(ave_sum_df)
}
plot_epidemic_df <- function(df, N, many_groups, cols = "black",
title,
subtitle,
type_name = "Type",
labs = NULL,
xmin = 0,
xmax = 80,
y1min = -50,
y1max = 100,
y2min = -25,
y2max = 150,
y3min = -5,
y3max = 120,
lty = 1,
show_labels = FALSE,
alpha = .3,
do_facet = FALSE,
do_color_ramp = FALSE,
pal = "Greys",
legend_name = latex2exp::TeX("$\\rho$"),
bg = FALSE
){
col_guide <- NULL
if(!many_groups){
df$Type <- "Model"
col_guide <- FALSE
if(!("color_ramp" %in%colnames(df))){
df$color_ramp <- df$Type
}
} else{
if(!("color_ramp" %in% colnames(df))){
df$color_ramp <- df$Type
}
}
if(is.null(labs)){
labs <- unique(df$Type)
}
if(do_color_ramp){
df$color_ramp <- as.numeric(as.character(df$model))
} else {
df$color_ramp <- factor(df$Type)
}
g1 <- ggplot2::ggplot(data = df,
ggplot2::aes(x = mean_max_I / N * 100,
y = mean_max_t,
group = color_ramp,
col = color_ramp,
## shape = Type,
x0 = mean_max_I / N * 100,
y0 = mean_max_t,
a = 2 * sqrt(var_max_I) / N * 100,
b = 2 * sqrt(var_max_t),
angle = 0,
fill = color_ramp)) +
ggplot2::geom_point(size = 2) + my_theme() +
ggplot2::labs(x = "Peak % infectious over all days",
y = "Day of peak % infectious",
title = title,
subtitle = subtitle) +
ggplot2::guides(color = col_guide, shape = col_guide, fill = col_guide) +
ggforce::geom_ellipse(alpha = alpha, size =1, linetype = lty) +
ggplot2::ylim(y1min, y1max) +
ggplot2::xlim(xmin, xmax)
if(show_labels){
g1 <- g1 + ggplot2::geom_text(ggplot2::aes(label = model), nudge_x = -3 / N * 100,
nudge_y = 5, show_legend = FALSE)
}
if(do_facet){
g1 <- g1 + ggplot2::facet_wrap(~Type)
}
if(do_color_ramp){
g1 <- g1 + ggplot2::scale_color_distiller(legend_name,
palette = pal, direction =1 ) +
ggplot2::scale_fill_distiller(legend_name, palette = pal, direction = 1 )
} else{
g1 <- g1 + ggplot2::scale_color_manual(values = cols, name = type_name, labels = labs) +
ggplot2::scale_fill_manual(values = cols, name = type_name, labels = labs)
}
## ggplot2::geom_errorbar(ggplot2::aes(ymin = tmin,
## ymax = tmax)) +
## ggplot2::geom_errorbarh(ggplot2::aes(xmin = Imin,
## xmax = Imax)) +
## Plot max total I vs max I
g2 <- ggplot2::ggplot(data = df,
ggplot2::aes(x = mean_max_I / N * 100,
y = mean_sum_I / N * 100,
group = color_ramp,
col = color_ramp,
# shape = Type,
x0 = mean_max_I / N * 100,
y0 = mean_sum_I / N * 100,
a = 2 * sqrt(var_max_I) / N * 100,
b = 2 * sqrt(var_sum_I) / N * 100,
angle = 0,
fill = color_ramp))
if(bg){
g2 <- g2 + ggplot2::geom_point(size = 3, col = "black")
}
g2 <- g2 +
ggplot2::geom_point(size = 2) + my_theme() +
ggplot2::labs(x = "Peak % infectious over all days",
y = "Final size (%)",
title = title,
subtitle = subtitle) +
ggplot2::ylim(y2min, y2max) +
ggplot2::xlim(xmin, xmax) +
## ggplot2::geom_errorbar(ggplot2::aes(ymin = sumImin,
## ymax = sumImax)) +
## ggplot2::geom_errorbarh(ggplot2::aes(xmin = Imin,
## xmax = Imax)) +
ggplot2::guides(color = col_guide, shape = col_guide, fill = col_guide)
if(bg){
g2 <- g2 + ggforce::geom_ellipse(alpha = alpha, size =2, linetype = lty, col = "black")
}
g2 <- g2 +
ggforce::geom_ellipse(alpha = alpha, size =1, linetype = lty)
if(show_labels){
g2 <- g2 + ggplot2::geom_text(ggplot2::aes(label = model), nudge_x = -3/ N * 100,
nudge_y = 5)
}
if(do_facet){
g2 <- g2 + ggplot2::facet_wrap(~Type)
}
if(do_color_ramp){
g2 <- g2 + ggplot2::scale_color_distiller(legend_name, palette = pal, direction = 1) +
ggplot2::scale_fill_distiller(legend_name, palette = pal, direction = 1)
} else{
g2 <- g2 + ggplot2::scale_color_manual(values = cols, name = type_name, labels = labs) +
ggplot2::scale_fill_manual(values = cols, name = type_name, labels = labs)
}
################################################################3
## Plot length I vs max I
g3 <- ggplot2::ggplot(data = df,
ggplot2::aes(x = mean_max_I / N * 100,
y = mean_len_I ,
group = color_ramp,
col = color_ramp,
# shape = Type,
x0 = mean_max_I / N * 100,
y0 = mean_len_I ,
a = 2 * sqrt(var_max_I) / N * 100,
b = 2 * sqrt(var_len_I),
angle = 0,
fill = color_ramp)) +
ggplot2::geom_point(size = 2) + my_theme() +
ggplot2::labs(x = "Peak % infectious over all days",
y = "Infection duration",
title = title,
subtitle = subtitle) +
ggplot2::ylim(y3min, y3max) +
ggplot2::xlim(xmin, xmax) +
## ggplot2::geom_errorbar(ggplot2::aes(ymin = sumImin,
## ymax = sumImax)) +
## ggplot2::geom_errorbarh(ggplot2::aes(xmin = Imin,
## xmax = Imax)) +
ggplot2::guides(color = col_guide, shape = col_guide, fill = col_guide) +
ggforce::geom_ellipse(alpha = alpha, size =1, linetype = lty)
if(show_labels){
g3 <- g3 + ggplot2::geom_text(ggplot2::aes(label = model), nudge_x = -3/ N * 100,
nudge_y = 5)
}
if(do_facet){
g3 <- g3 + ggplot2::facet_wrap(~Type)
}
if(do_color_ramp){
g3 <- g3 + ggplot2::scale_color_distiller(legend_name, palette = pal, direction = 1) +
ggplot2::scale_fill_distiller(legend_name, palette = pal, direction = 1)
} else{
g3 <- g3 + ggplot2::scale_color_manual(values = cols, name = type_name, labels = labs) +
ggplot2::scale_fill_manual(values = cols, name = type_name, labels = labs)
}
gridExtra::grid.arrange(g1, g2, g3, nrow = 3)
return(list(g1 = g1, g2 = g2, g3 = g3))
}
shannong19/catalyst documentation built on July 9, 2019, 8:43 p.m.
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Defines functions am_plot_mean_var plot_epidemic_summary summarize_epidemic plot_epidemic_df
## SKG
## March 26, 2019
## Plot object from am-sir.R
#' Plot the output of am_sir()
#'
#' @param X X output of result of am_sir()
#' @param cols vector of colors
#' @param title title to give ggplot
#' @param subtitle subtitle to give ggplot
#' @param obs data frame of observations with column names (t, S, I, R) . Default is NULL, and so no observations are plotted
#' @param plot_var logical. Do we plot CI? Default is TRUE
#' @param labs model type labels. Default is NULL
#' @param color_guide_name Default is "Model Avg."
#' @return ggplot and the plotting df
am_plot_mean_var <- function(X, obs = NULL,
cols = c("blue", "red", "yellow3"),
title = "Hagelloch AM Simulations",
subtitle = "",
plot_var = TRUE,
labs = NULL,
color_guide_name = "Model Avg."){
if(!("model" %in% colnames(X))){
X <- as.data.frame(X)
X$model <- 1
n_models <- 1
} else{
n_models <- length(unique(X$model))
}
if(is.null(labs)){
labs <- 1:length(cols)
}
N <- X$S[1] + X$I[1] + X$R[1]
Xm <- reshape2::melt(X, id.vars = c("t", "ll", "model"))
Xm <- Xm %>% dplyr::group_by(variable, t, model) %>%
dplyr::summarize(mean = mean(value), var = var(value))
g <- ggplot2::ggplot() +
ggplot2::geom_line(data = Xm,
ggplot2::aes(x = t, y = mean / N * 100, group = factor(model),
col = factor(model)),
size = 1) +
ggplot2::scale_color_manual(values = cols[1:n_models],
name = color_guide_name, labels = labs) +
ggplot2::scale_fill_manual(values = cols[1:n_models], guide = FALSE) +
my_theme() +
ggplot2::facet_wrap(~variable, nrow = 3, scales = "free_y")
if(plot_var){
g <- g +
ggplot2::geom_ribbon(data = Xm, ggplot2::aes(x = t, group = factor(model),
ymin = mean / N * 100 - 2 * sqrt(var)/ N * 100,
ymax = mean / N * 100 + 2 * sqrt(var)/ N * 100,
fill = factor(model)),
col = NA, alpha = .3)
}
g <- g +
ggplot2::labs(x = "Time",
y = "% of individuals",
title = title,
subtitle = subtitle,
color = "Model Avg.")
if(!is.null(obs)){
cn <- c("t", "S", "I", "R")
obs_m <- reshape2::melt(obs[, cn], id.vars = "t")
g <- g + ggplot2::geom_point(data = obs_m,
ggplot2::aes(x = t, y = value / N * 100, group = variable))
}
print(g)
return(list(g = g, plot_df = df))
}
#' Plot epidemic summary of SIR AM
#'
#' @param sims_list list of the "X" output from am_sir() function
#' @param N number of total agents
#' @param title character passed on to ggplot
#' @param subtitle character passed on to ggplot
#' @param summary_fxn either "mean" or "median". How we summarize the summary statistics
#' @param many_groups logical. Default is FALSE. If TRUE, we will do different colors on them. Corresponds to the "Type" column
#' @return list with two ggplots and summarized, plottable df
plot_epidemic_summary <- function(sims_list, N,
title,
subtitle,
summary_fxn = "mean",
many_groups = FALSE,
cols = "black",
type_name = "Type",
labs = NULL,
xmin = 0,
xmax = 80,
y1min = -25,
y1max = 75,
y2min = -50,
y2max = 150,
y3min = -5,
y3max = 120,
lty = 0,
show_labels = FALSE,
alpha = .3,
do_facet = FALSE,
do_color_ramp = FALSE,
pal = "Greys",
legend_name = latex2exp::TeX("$\\rho$"),
bg = FALSE){
df <- summarize_epidemic(sims_list, N, summary_fxn)
## Plot max t vs max I
plot_list <- plot_epidemic_df(df, N, many_groups = many_groups,
cols = cols,
title,
subtitle,
type_name,
labs,
xmin = xmin,
xmax = xmax,
y1min = y1min,
y1max = y1max,
y2min = y2min,
y2max = y2max,
y3min = y3min,
y3max = y3max,
lty = lty,
show_labels = show_labels,
alpha = alpha,
do_facet = do_facet,
do_color_ramp = do_color_ramp,
pal = pal,
legend_name = legend_name,
bg = bg)
return(list(g1 = plot_list$g1, g2 = plot_list$g2,
g3 = plot_list$g3,
df = df))
}
#' Summarize list of AM simulations
#'
#' @param sims_list list of the "X" output from am_sir() function
#' @param N total number of agents
#' @return summary data frame with columns mean_* and var_* for ("max_I", "sum_I", and "max_t", "len_I") and 95% CIs for each of these vars around the mean
summarize_epidemic <- function(sims_list, N, summary_fxn = "mean"){
if(summary_fxn == "median"){
sum_fxn <- median
} else{
sum_fxn = mean
}
X <- do.call('rbind', sims_list)
if("mult" %in% colnames(X)){
X$model <- X$mult
df <- X[, - which(colnames(X) == "mult")]
} else {
df <- X
}
if(!("color_ramp" %in% colnames(X))){
df$color_ramp <- 1
}
if(!("Type" %in% colnames(X))){
df$Type <- "Type"
} else {
if(class(df$Type) != "factor"){
df$Type <- factor(df$Type)
}
}
vars <- c("model", "ll", "color_ramp", "Type")
vars2 <- c("model", "color_ramp", "Type")
## Make into a function
sum_df <- plyr::ddply(df, .var = vars,
.fun = function(df){
c(max_I = max(df$I),
sum_I = max(df$R),
max_t = df$t[which.max(df$I)],
len_I = max(which(df$I >= 1))
)
})
ave_sum_df <- plyr::ddply(sum_df, .var = vars2,
.fun = function(df){
c(mean_max_I = sum_fxn(df$max_I),
var_max_I = var(df$max_I),
mean_sum_I = sum_fxn(df$sum_I),
var_sum_I = var(df$sum_I),
mean_max_t = sum_fxn(df$max_t),
var_max_t = var(df$max_t),
mean_len_I = sum_fxn(df$len_I),
var_len_I = var(df$len_I)
)
})
ave_sum_df$tmin <- (ave_sum_df$mean_max_t -
2 * sqrt(ave_sum_df$var_max_t))
ave_sum_df$tmax <- (ave_sum_df$mean_max_t +
2 * sqrt(ave_sum_df$var_max_t))
ave_sum_df$Imin <- (ave_sum_df$mean_max_I / N * 100 -
2 * sqrt(ave_sum_df$var_max_I) / N * 100)
ave_sum_df$Imax <- (ave_sum_df$mean_max_I / N * 100 +
2 * sqrt(ave_sum_df$var_max_I) / N * 100)
ave_sum_df$sumImin <- (ave_sum_df$mean_sum_I / N * 100 -
2 * sqrt(ave_sum_df$var_sum_I) / N * 100)
ave_sum_df$sumImax <- (ave_sum_df$mean_sum_I / N * 100 +
2 * sqrt(ave_sum_df$var_sum_I) / N * 100)
ave_sum_df$lenImin <- (ave_sum_df$mean_len_I / N * 100 -
2 * sqrt(ave_sum_df$var_len_I) / N * 100)
ave_sum_df$lenImax <- (ave_sum_df$mean_len_I +
2 * sqrt(ave_sum_df$var_len_I) )
return(ave_sum_df)
}
plot_epidemic_df <- function(df, N, many_groups, cols = "black",
title,
subtitle,
type_name = "Type",
labs = NULL,
xmin = 0,
xmax = 80,
y1min = -50,
y1max = 100,
y2min = -25,
y2max = 150,
y3min = -5,
y3max = 120,
lty = 1,
show_labels = FALSE,
alpha = .3,
do_facet = FALSE,
do_color_ramp = FALSE,
pal = "Greys",
legend_name = latex2exp::TeX("$\\rho$"),
bg = FALSE
){
col_guide <- NULL
if(!many_groups){
df$Type <- "Model"
col_guide <- FALSE
if(!("color_ramp" %in%colnames(df))){
df$color_ramp <- df$Type
}
} else{
if(!("color_ramp" %in% colnames(df))){
df$color_ramp <- df$Type
}
}
if(is.null(labs)){
labs <- unique(df$Type)
}
if(do_color_ramp){
df$color_ramp <- as.numeric(as.character(df$model))
} else {
df$color_ramp <- factor(df$Type)
}
g1 <- ggplot2::ggplot(data = df,
ggplot2::aes(x = mean_max_I / N * 100,
y = mean_max_t,
group = color_ramp,
col = color_ramp,
## shape = Type,
x0 = mean_max_I / N * 100,
y0 = mean_max_t,
a = 2 * sqrt(var_max_I) / N * 100,
b = 2 * sqrt(var_max_t),
angle = 0,
fill = color_ramp)) +
ggplot2::geom_point(size = 2) + my_theme() +
ggplot2::labs(x = "Peak % infectious over all days",
y = "Day of peak % infectious",
title = title,
subtitle = subtitle) +
ggplot2::guides(color = col_guide, shape = col_guide, fill = col_guide) +
ggforce::geom_ellipse(alpha = alpha, size =1, linetype = lty) +
ggplot2::ylim(y1min, y1max) +
ggplot2::xlim(xmin, xmax)
if(show_labels){
g1 <- g1 + ggplot2::geom_text(ggplot2::aes(label = model), nudge_x = -3 / N * 100,
nudge_y = 5, show_legend = FALSE)
}
if(do_facet){
g1 <- g1 + ggplot2::facet_wrap(~Type)
}
if(do_color_ramp){
g1 <- g1 + ggplot2::scale_color_distiller(legend_name,
palette = pal, direction =1 ) +
ggplot2::scale_fill_distiller(legend_name, palette = pal, direction = 1 )
} else{
g1 <- g1 + ggplot2::scale_color_manual(values = cols, name = type_name, labels = labs) +
ggplot2::scale_fill_manual(values = cols, name = type_name, labels = labs)
}
## ggplot2::geom_errorbar(ggplot2::aes(ymin = tmin,
## ymax = tmax)) +
## ggplot2::geom_errorbarh(ggplot2::aes(xmin = Imin,
## xmax = Imax)) +
## Plot max total I vs max I
g2 <- ggplot2::ggplot(data = df,
ggplot2::aes(x = mean_max_I / N * 100,
y = mean_sum_I / N * 100,
group = color_ramp,
col = color_ramp,
# shape = Type,
x0 = mean_max_I / N * 100,
y0 = mean_sum_I / N * 100,
a = 2 * sqrt(var_max_I) / N * 100,
b = 2 * sqrt(var_sum_I) / N * 100,
angle = 0,
fill = color_ramp))
if(bg){
g2 <- g2 + ggplot2::geom_point(size = 3, col = "black")
}
g2 <- g2 +
ggplot2::geom_point(size = 2) + my_theme() +
ggplot2::labs(x = "Peak % infectious over all days",
y = "Final size (%)",
title = title,
subtitle = subtitle) +
ggplot2::ylim(y2min, y2max) +
ggplot2::xlim(xmin, xmax) +
## ggplot2::geom_errorbar(ggplot2::aes(ymin = sumImin,
## ymax = sumImax)) +
## ggplot2::geom_errorbarh(ggplot2::aes(xmin = Imin,
## xmax = Imax)) +
ggplot2::guides(color = col_guide, shape = col_guide, fill = col_guide)
if(bg){
g2 <- g2 + ggforce::geom_ellipse(alpha = alpha, size =2, linetype = lty, col = "black")
}
g2 <- g2 +
ggforce::geom_ellipse(alpha = alpha, size =1, linetype = lty)
if(show_labels){
g2 <- g2 + ggplot2::geom_text(ggplot2::aes(label = model), nudge_x = -3/ N * 100,
nudge_y = 5)
}
if(do_facet){
g2 <- g2 + ggplot2::facet_wrap(~Type)
}
if(do_color_ramp){
g2 <- g2 + ggplot2::scale_color_distiller(legend_name, palette = pal, direction = 1) +
ggplot2::scale_fill_distiller(legend_name, palette = pal, direction = 1)
} else{
g2 <- g2 + ggplot2::scale_color_manual(values = cols, name = type_name, labels = labs) +
ggplot2::scale_fill_manual(values = cols, name = type_name, labels = labs)
}
################################################################3
## Plot length I vs max I
g3 <- ggplot2::ggplot(data = df,
ggplot2::aes(x = mean_max_I / N * 100,
y = mean_len_I ,
group = color_ramp,
col = color_ramp,
# shape = Type,
x0 = mean_max_I / N * 100,
y0 = mean_len_I ,
a = 2 * sqrt(var_max_I) / N * 100,
b = 2 * sqrt(var_len_I),
angle = 0,
fill = color_ramp)) +
ggplot2::geom_point(size = 2) + my_theme() +
ggplot2::labs(x = "Peak % infectious over all days",
y = "Infection duration",
title = title,
subtitle = subtitle) +
ggplot2::ylim(y3min, y3max) +
ggplot2::xlim(xmin, xmax) +
## ggplot2::geom_errorbar(ggplot2::aes(ymin = sumImin,
## ymax = sumImax)) +
## ggplot2::geom_errorbarh(ggplot2::aes(xmin = Imin,
## xmax = Imax)) +
ggplot2::guides(color = col_guide, shape = col_guide, fill = col_guide) +
ggforce::geom_ellipse(alpha = alpha, size =1, linetype = lty)
if(show_labels){
g3 <- g3 + ggplot2::geom_text(ggplot2::aes(label = model), nudge_x = -3/ N * 100,
nudge_y = 5)
}
if(do_facet){
g3 <- g3 + ggplot2::facet_wrap(~Type)
}
if(do_color_ramp){
g3 <- g3 + ggplot2::scale_color_distiller(legend_name, palette = pal, direction = 1) +
ggplot2::scale_fill_distiller(legend_name, palette = pal, direction = 1)
} else{
g3 <- g3 + ggplot2::scale_color_manual(values = cols, name = type_name, labels = labs) +
ggplot2::scale_fill_manual(values = cols, name = type_name, labels = labs)
}
gridExtra::grid.arrange(g1, g2, g3, nrow = 3)
return(list(g1 = g1, g2 = g2, g3 = g3))
}
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