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# IGraph R package
# Copyright (C) 2014 Gabor Csardi <csardi.gabor@gmail.com>
# 334 Harvard street, Cambridge, MA 02139 USA
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
# 02110-1301 USA
#
###################################################################
#' @export
time_bins <- function(x, middle=TRUE)
UseMethod("time_bins")
#' @method time_bins sir
#' @rdname sir
#' @export
#' @importFrom stats IQR
time_bins.sir <- function(x, middle=TRUE) {
sir <- x
if (!inherits(sir, "sir")) {
stop("This is not an SIR model output")
}
big.time <- unlist(sapply(sir, "[[", "times"))
medlen <- median(sapply(lapply(sir, "[[", "times"), length))
## Adhoc use of Freedman-Diaconis binwidth; rescale time accordingly.
w <- 2 * IQR(big.time) / (medlen^(1/3))
minbt <- min(big.time) ; maxbt <- max(big.time)
res <- seq(minbt, maxbt, length.out=ceiling((maxbt - minbt)/w))
if (middle) { res <- (res[-1] + res[-length(res)]) / 2 }
res
}
#' @importFrom stats median
#' @method median sir
#' @rdname sir
#' @export
median.sir <- function(x, na.rm=FALSE, ...) {
sir <- x
if (!inherits(sir, "sir")) {
stop("This is not an SIR model output")
}
times <- unlist(sapply(sir, "[[", "times"))
big.N.NS <- unlist(sapply(sir, "[[", "NS"))
big.N.NI <- unlist(sapply(sir, "[[", "NI"))
big.N.NR <- unlist(sapply(sir, "[[", "NR"))
time.bin <- cut(times, time_bins(sir, middle=FALSE), include.lowest=TRUE)
NS <- tapply(big.N.NS, time.bin, median, na.rm=na.rm)
NI <- tapply(big.N.NI, time.bin, median, na.rm=na.rm)
NR <- tapply(big.N.NR, time.bin, median, na.rm=na.rm)
list(NS=NS, NI=NI, NR=NR)
}
#' @importFrom stats quantile
#' @method quantile sir
#' @rdname sir
#' @export
quantile.sir <- function(x, comp=c("NI", "NS", "NR"), prob, ...) {
sir <- x
if (!inherits(sir, "sir")) {
stop("This is not an SIR model output")
}
comp <- toupper(igraph.match.arg(comp))
times <- unlist(sapply(sir, "[[", "times"))
big.N <- unlist(sapply(sir, function(x) { x[[comp]] }))
time.bin <- cut(times, time_bins(sir, middle=FALSE), include.lowest=TRUE)
res <- lapply(prob, function(pp) {
tapply(big.N, time.bin, function(x) { quantile(x, prob=pp) })
})
if (length(res) == 1) {
res <- res[[1]]
}
res
}
# R function to plot compartment total curves from simul.net.epi .
# Inputs: sim.res := list of simulated network SIR processes
# comp := compartment (i.e., "NS", "NI", or "NR")
# q := vector of lower and upper quantiles, resp
# cols := char vector of colors for lines, median, and quantiles, resp.
# Outputs: None. Just produces the plot of all compartment curves,
# with median and quantiles.
#' Plotting the results on multiple SIR model runs
#'
#' This function can conveniently plot the results of multiple SIR model
#' simulations.
#'
#' The number of susceptible/infected/recovered individuals is plotted over
#' time, for multiple simulations.
#'
#' @param x The output of the SIR simulation, coming from the \code{\link{sir}}
#' function.
#' @param comp Character scalar, which component to plot. Either \sQuote{NI}
#' (infected, default), \sQuote{NS} (susceptible) or \sQuote{NR} (recovered).
#' @param median Logical scalar, whether to plot the (binned) median.
#' @param quantiles A vector of (binned) quantiles to plot.
#' @param color Color of the individual simulation curves.
#' @param median_color Color of the median curve.
#' @param quantile_color Color(s) of the quantile curves. (It is recycled if
#' needed and non-needed entries are ignored if too long.)
#' @param lwd.median Line width of the median.
#' @param lwd.quantile Line width of the quantile curves.
#' @param lty.quantile Line type of the quantile curves.
#' @param xlim The x limits, a two-element numeric vector. If \code{NULL}, then
#' it is calculated from the data.
#' @param ylim The y limits, a two-element numeric vector. If \code{NULL}, then
#' it is calculated from the data.
#' @param xlab The x label.
#' @param ylab The y label. If \code{NULL} then it is automatically added based
#' on the \code{comp} argument.
#' @param \dots Additional arguments are passed to \code{plot}, that is run
#' before any of the curves are added, to create the figure.
#' @return Nothing.
#' @author Eric Kolaczyk (\url{http://math.bu.edu/people/kolaczyk/}) and Gabor
#' Csardi \email{csardi.gabor@@gmail.com}.
#' @seealso \code{\link{sir}} for running the actual simulation.
#' @references Bailey, Norman T. J. (1975). The mathematical theory of
#' infectious diseases and its applications (2nd ed.). London: Griffin.
#' @method plot sir
#' @export
#' @importFrom graphics plot lines
#' @keywords graphs
#' @examples
#'
#' g <- sample_gnm(100, 100)
#' sm <- sir(g, beta=5, gamma=1)
#' plot(sm)
#'
plot.sir <- function(x, comp=c("NI", "NS", "NR"),
median=TRUE, quantiles=c(0.1, 0.9), color=NULL,
median_color=NULL, quantile_color=NULL,
lwd.median=2, lwd.quantile=2, lty.quantile=3,
xlim=NULL, ylim=NULL, xlab="Time", ylab=NULL, ...) {
sir <- x
if (!inherits(sir, "sir")) {
stop("This is not an SIR model output")
}
comp <- toupper(igraph.match.arg(comp))
if (!all(quantiles >= 0 & quantiles <= 1)) {
stop("Quantiles should be in [0,1]")
}
if (is.null(color)) {
color <- c(NI="skyblue", NS="pink", NR="palegoldenrod")[comp]
}
if (is.null(median_color)) {
median_color <- c(NI="blue", NS="red", NR="gold")[comp]
}
if (is.null(quantile_color)) {
quantile_color <- c(NI="blue", NS="red", NR="gold")[comp]
}
quantile_color <- rep(quantile_color, length.out=length(quantiles))
ns <- length(sir)
if (is.null(xlim)) {
xlim <- c(0, max(sapply(sir, function(x) max(x$times))))
}
if (is.null(ylim)) {
ylim <- c(0, max(sapply(sir, function(x) max(x[[comp]]))))
}
## Generate the plot, first with individual curves, and then
## adding median and quantile curves.
if (is.null(ylab)) {
if (comp == "NI") { ylab <- expression(N[I](t)) }
if (comp == "NR") { ylab <- expression(N[R](t)) }
if (comp == "NS") { ylab <- expression(N[S](t)) }
}
# Plot the stochastic curves individually.
plot(0, 0, type="n", xlim=xlim, ylim=ylim, xlab=xlab, ylab=ylab, ...)
lapply(seq_along(sir), function(i) {
lines(sir[[i]]$time, sir[[i]][[comp]], col=color[1])
})
# Plot the median and quantiles.
if (median || length(quantiles) > 0) {
time.bin <- time_bins(sir, middle=TRUE)
}
if (median) {
lines(time.bin, median(sir)[[comp]], type="l",
lwd=lwd.median, col=median_color)
}
for (i in seq_along(quantiles)) {
my.ql <- quantile(sir, comp, quantiles[i])
lines(time.bin, my.ql, type="l", lty=lty.quantile,
lwd=lwd.quantile, col=quantile_color[i])
}
invisible()
}
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