Nothing
R/outputConnectivity.R
In MigConnectivity: Estimate Migratory Connectivity for Migratory Animals
Defines functions
map.estPsi
plot.estMigConnectivity
plot.intrinsicAssign
summary.intrinsicAssign
summary.estMigConnectivity
print.intrinsicAssign
print.estMigConnectivity
is.estMC
is.isoAssign
Documented in
plot.estMigConnectivity
plot.intrinsicAssign
# @export
is.isoAssign <- function(x) inherits(x, "isoAssign")
# @export
is.estMC <- function(x) inherits(x, "estMC")
# @export
#print <- function(x,...) UseMethod("print")
#' @export
print.estMigConnectivity <- function(x, digits = max(3L, getOption("digits") - 3L), ...)
{
cat("Migratory Connectivity Estimates\n")
if (inherits(x, "estPsi")) {
dimnames(x$psi$mean) <- dimnames(x$psi$se) <- list(x$input$originNames,
x$input$targetNames)
cat("\nTransition probability (psi) estimates (mean):\n")
print(x$psi$mean, digits = digits)
cat("+/- SE:\n")
print(x$psi$se, digits = digits)
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile):\n", sep = "")
print(array(paste(format(x$psi$simpleCI[1,,],digits = digits, trim = TRUE),
format(x$psi$simpleCI[2,,],digits = digits, trim = TRUE),
sep = ' - '), dim = dim(x$psi$mean),
dimnames = list(x$input$originNames, x$input$targetNames)),
quote = FALSE)
}
if (inherits(x, "estMC")) {
cat("\nMC estimate (mean):", format(x$MC$mean, digits = digits), "+/- (SE)",
format(x$MC$se, digits = digits), '\n')
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile): ",
paste(format(x$MC$simpleCI, digits = digits, trim = TRUE),
collapse = ' - '), '\n', sep = "")
}
if (inherits(x, "estMantel")) {
if (is.null(x$corr)) {
x$corr <- list(mean = x$meanCorr, se = x$seCorr,
simpleCI = x$simpleCICorr)
x$input <- list(alpha = x$alpha)
}
cat("\nrM estimate (mean):", format(x$corr$mean, digits = digits), "+/- (SE)",
format(x$corr$se, digits = digits), '\n')
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile): ",
paste(format(x$corr$simpleCI, digits = digits, trim = TRUE),
collapse = ' - '), '\n', sep = "")
# cat(" ", ifelse(is.null(x$alpha), "", 100 * (1 - x$alpha)),
# "% confidence interval (bias-corrected): ",
# paste(format(x$bcCICorr, digits = digits, trim = TRUE), collapse = ' - '),
# '\n', sep = "")
# cat(" median:", format(x$medianCorr, digits = digits), '\n')
# if (!is.na(x$pointCorr))
# cat(" point calculation (not considering error):",
# format(x$pointCorr, digits = digits), '\n')
}
if (inherits(x, "estGamma")) {
cat("\nReverse transition probability (gamma) estimates (mean):\n")
print(x$gamma$mean, digits = digits)
cat("+/- SE:\n")
print(x$gamma$se, digits = digits)
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile):\n", sep = "")
print(array(paste(format(x$gamma$simpleCI[1,,],digits = digits, trim = TRUE),
format(x$gamma$simpleCI[2,,],digits = digits, trim = TRUE),
sep = ' - '), dim = dim(x$gamma$mean),
dimnames = list(x$input$targetNames, x$input$originNames)),
quote = FALSE)
}
if (inherits(x, "estTargetRelAbund")) {
cat("\nTarget site relative abundance estimates (mean):\n")
print(x$targetRelAbund$mean, digits = digits)
cat("+/- SE:\n")
print(x$targetRelAbund$se, digits = digits)
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile):\n", sep = "")
print(array(paste(format(x$targetRelAbund$simpleCI[1,],digits = digits, trim = TRUE),
format(x$targetRelAbund$simpleCI[2,],digits = digits, trim = TRUE),
sep = ' - '), dim = dim(x$gamma$mean)[1],
dimnames = list(x$input$targetNames)),
quote = FALSE)
}
if (inherits(x, "estPi")) {
cat("\nOrigin/target site combination probability (pi) estimates (mean):\n")
print(x$pi$mean, digits = digits)
cat("+/- SE:\n")
print(x$pi$se, digits = digits)
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile):\n", sep = "")
print(array(paste(format(x$pi$simpleCI[1,,],digits = digits, trim = TRUE),
format(x$pi$simpleCI[2,,],digits = digits, trim = TRUE),
sep = ' - '), dim = dim(x$pi$mean),
dimnames = list(x$input$originNames, x$input$targetNames)),
quote = FALSE)
}
cat("\nThis is a subset of what's available inside this estMigConnectivity output.\n")
if (inherits(x, "estPi"))
cat("For more info, try ?reverseTransition or str(obj_name, max.level = 2).\n")
else if (inherits(x, "estPsi"))
cat("For more info, try ?estTransition or str(obj_name, max.level = 2).\n")
else if (inherits(x, "estMC"))
cat("For more info, try ?estStrength or ?estMC or str(obj_name, max.level = 2).\n")
else if (inherits(x, "estGamma"))
cat("For more info, try ?reverseTransition or str(obj_name, max.level = 2).\n")
else if (inherits(x, "estMantel"))
cat("For more info, try ?estMantel or str(obj_name, max.level = 2).\n")
else # In case we left something out...
cat("For more info, try str(obj_name, max.level = 2).\n")
}
#' @export
print.intrinsicAssign <- function(x, digits = max(3L, getOption("digits") - 3L), ...){
if(inherits(x,"isoAssign")){
cat("Individual Probability Surfaces \n")
print(x$probassign,...)
cat("\n Individual likely/unlikely Surfaces \n")
print(x$oddsassign,...)
cat("\n Population-level assignment Surface \n")
print(x$popassign,...)
cat("\n Individual Probability data frame* \n")
utils::str(x$probDF[,1:5])
cat("\n Individual likely/unlikely data frame* \n")
utils::str(x$oddsDF[,1:5])
cat("\n Individual single cell assignment \n")
utils::str(x$SingleCell)
cat("\n Target sites spatial layer \n")
print(x$targetSites)
cat("\n Random number seed set to: \n")
print(x$RandomSeed)
cat("\n * only first few columns are printed")
}
}
# @export
#summary <- function(x,...) UseMethod("summary")
#' @export
summary.estMigConnectivity <- function(object, ...)
{
print.estMigConnectivity(object, ...)
}
#' @export
summary.intrinsicAssign<-function(object, ...){
print.intrinsicAssign(object,...)
}
#' Basic plot function for the different isoAssign outputs
#'
#' Generates a basic plot of the isotope assignments. If
#' \code{map = 'population'} generates a single map. If
#' \code{map = 'probability' or map = 'odds'} generates a map for each
#' individual is generated. User is asked for input before each individual is
#' drawn.
#'
#' @param x an isoAssign object
#' @param map which \code{isoAssign} output to plot either 'probability', 'population' or 'odds'
#' @param ... additional arguments passed to plot function
#' @seealso{\code{isoAssign}}
#' @return No return value, called to generate plot(s).
#'
#' @examples
#' \donttest{
#' extensions <- c("shp", "shx", "dbf", "sbn", "sbx")
#' tmp <- tempdir()
#' for (ext in extensions) {
#' download.file(paste0(
#' "https://raw.githubusercontent.com/SMBC-NZP/MigConnectivity",
#' "/master/data-raw/Spatial_Layers/OVENdist.",
#' ext),
#' destfile = paste0(tmp, "/OVENdist.", ext), mode = "wb")
#' }
#' OVENdist <- sf::st_read(paste0(tmp, "/OVENdist.shp"))
#' OVENdist <- OVENdist[OVENdist$ORIGIN==2,] # only breeding
#' sf::st_crs(OVENdist) <- sf::st_crs(4326)
#'
#' download.file(paste0(
#' "https://raw.githubusercontent.com/SMBC-NZP/MigConnectivity",
#' "/master/data-raw/deltaDvalues.csv"),
#' destfile = paste0(tmp, "/deltaDvalues.csv"))
#' OVENvals <- read.csv(paste0(tmp, "/deltaDvalues.csv"))
#'
#' b <- isoAssign(isovalues = OVENvals[,2],
#' isoSTD = 12,
#' intercept = -10,
#' slope = 0.8,
#' odds = NULL,
#' restrict2Likely = TRUE,
#' nSamples = 1000,
#' sppShapefile = OVENdist,
#' assignExtent = c(-179,-60,15,89),
#' element = "Hydrogen",
#' period = "GrowingSeason") # setting for demonstration only
#'
#' plot(b, map = "population")
#' }
#'
#' @export
plot.intrinsicAssign <- function(x,map,...){
op <- graphics::par(no.readonly = TRUE)
on.exit(graphics::par(op))
if(inherits(x,"isoAssign")){
if(!(map %in% c("probability","population","odds"))){
stop("map must be either probability, population, or odds")}
if(map == "population"){
terra::plot(x$popassign,horiz = TRUE,...)
}
if(map == "probability"){
for(i in 1:terra::nlyr(x$probassign)){
terra::plot(x$probassign[[i]],horiz = TRUE,...)
graphics::par(ask = TRUE)
}
}
if(map == "odds"){
for(i in 1:terra::nlyr(x$probassign)){
terra::plot(x$oddsassign[[i]],horiz = TRUE,...)
graphics::par(ask = TRUE)
}
}
}
if(inherits(x,"weightAssign")){
graphics::par(bty = "L")
graphics::plot((x$performance$area/max(x$performance$area))~x$performance$error,
las = 1, ylab = "Assignment Area",
xlab = "Error",pch = 19, cex = 1.25, col = "gray")
graphics::points((x$frontier$area/max(x$performance$area))~x$frontier$error, col = "red", pch = 19, cex = 1.25)
graphics::points((x$top$area/max(x$performance$area))~x$top$error, col = "blue", pch = 19, cex = 1.25)
}
}
#plot <- function(x,...) UseMethod("plot")
#' Basic plot function for estMigConnectivity objects
#'
#' @param x an estMigConnectivity object (output of estTransition, estStrength,
#' estMC, or estMantel)
#' @param plot.which which parameter (psi, MC, rM, or r) to graph. Defaults to
#' psi for estMC objects, to rM (Mantel correlation) otherwise
#' @param point points on graph can represent mean, median, or point estimates
#' (not considering error). Defaults to mean, the standard estimate from
#' resampling
#' @param range lines / error bars drawn around points can represent simple
#' quantile-based confidence intervals (simpleCI), bias-corrected quantile-
#' based confidence intervals (bcCI), or +- standard error (se). Defaults to
#' simpleCI
#' @param xlab label for the x-axis. Defaults to "Origin" for psi, otherwise ""
#' @param ylab label for the y-axis. Defaults to the parameter being plotted
#' @param originNames names of the origin sites (for plotting psi). If left
#' NULL, the function attempts to get these from the estimate
#' @param targetNames names of the target sites (for plotting psi or r). If left
#' NULL, the function attempts to get these from the estimate
#' @param ageNames names of the age classes (for plotting r with more than one
#' age). If left NULL, the function uses 1:[number of ages]
#' @param col colors to use for labeling transition probabilities for
#' different target sites. If left NULL, defaults to 1:[number of target sites]
#' @param pch symbols to use for labeling transition probabilities for
#' different target sites. If left NULL, defaults to 21:25, then
#' 0:([number of target sites]-5)
#' @param las style of axis labels (0-3). We set the default at 1 (always
#' horizontal) here, but if you prefer your labels parallel to the axis, set
#' at 0
#' @param gap space left between the center of the error bar and the lines
#' marking the error bar in units of the height (width) of the letter "O".
#' Defaults to 0
#' @param sfrac width of "crossbar" at the end of error bar as a fraction of the
#' x plotting region. Defaults to 0, unless range is set to "se", in which
#' case it defaults to 0.01
#' @param legend leave as FALSE to not print a legend. Otherwise the position
#' of the legend (for psi or r (multi-age) only; one of "bottomright",
#' "bottom", "bottomleft", "left", "topleft", "top", "topright", "right", or
#' "center")
#' @param map placeholder for eventually allowing users to plot psi estimates
#' on a map
#' @param ... Additional parameters passed to \code{\link{plotCI}}
#'
#' @return No return value, called to generate plot.
#'
#' @seealso \code{\link{estMC}}, \code{\link{estMantel}}
#'
#' @export
plot.estMigConnectivity <- function(x,
plot.which = ifelse(inherits(x, "estPsi"),
"psi",
ifelse(inherits(x, "estMC"),
"MC",
ifelse(inherits(x, "estGamma"),
"gamma",
"rM"))),
point = c("mean", "median", "point"),
range = c("simpleCI", "bcCI", "se"),
xlab = NULL, ylab = plot.which,
originNames = NULL, targetNames = NULL,
ageNames = NULL,
col = NULL, pch = NULL, las = 1,
gap = 0,
sfrac = ifelse(range=="se", 0.01, 0),
legend = FALSE, map = FALSE, ...) {
if ((plot.which %in% c("corr", "Mantel")))
plot.which <- "rM"
if ((plot.which %in% c("transition", "Transition")))
plot.which <- "psi"
if ((plot.which %in% c("strength", "Strength")))
plot.which <- "MC"
if ((plot.which %in% c("abund", "abundance", "relAbund")))
plot.which <- "targetRelAbund"
if (!(plot.which %in% c("psi", "MC", "rM", "r", "gamma", "pi",
"targetRelAbund")))
stop("Set plot.which to psi, MC, rM, gamma, pi, targetRelAbund, or r")
point <- match.arg(point)
range <- match.arg(range)
if (inherits(x, "estMC")) {
if (is.null(x$psi) && !is.null(x$samplePsi)) {
bcCIPsi <- array(NA, dim = c(2, dim(x$samplePsi)[2], dim(x$samplePsi)[3]))
for (i in 1:dim(x$samplePsi)[2]) {
for (j in 1:dim(x$samplePsi)[3]) {
psi.z0 <- qnorm(sum(x$samplePsi[, i, j] < mean(x$samplePsi[, i, j],
na.rm = TRUE)) /
length(which(!is.na(x$samplePsi[, i, j]))))
bcCIPsi[ , i, j] <- quantile(x$samplePsi[, i, j],
pnorm(2 * psi.z0 +
qnorm(c(x$alpha/2,1-x$alpha/2))),
na.rm=TRUE, names = FALSE)
}
}
x$psi <- list(sample = x$samplePsi,
mean = apply(x$samplePsi, 2:3, mean),
se = apply(x$samplePsi, 2:3, sd),
simpleCI = apply(x$samplePsi, 2:3, quantile,
probs = c(x$alpha/2, 1-x$alpha/2),
na.rm=TRUE, names = FALSE),
bcCI = bcCIPsi,
median = apply(x$samplePsi, 2:3, median),
point = x$pointPsi)
x$MC <- list(mean = x$meanMC, se = x$seMC, simpleCI = x$simpleCI,
bcCI = x$bcCI, median = x$medianMC, point = x$pointMC)
x$input <- list(originNames = dimnames(x$samplePsi)[[2]],
targetNames = dimnames(x$samplePsi)[[3]])
}
}
else if (plot.which %in% c("psi", "MC") && !inherits(x, "estPsi"))
stop("This estimate does not include psi or MC - try setting plot.which to rM")
if (plot.which == "gamma" && !inherits(x, "estGamma"))
stop("This estimate does not include gamma - try setting plot.which to something else")
if (plot.which == "pi" && !inherits(x, "estPi"))
stop("This estimate does not include pi - try setting plot.which to something else")
if (plot.which == "targetRelAbund" && !inherits(x, "estTargetRelAbund"))
stop("This estimate does not include targetRelAbund - try setting plot.which to something else")
if (inherits(x, "estMantel")) {
if (is.null(x$corr)) {
x$corr <- list(sample = x$sampleCorr,
mean = x$meanCorr,
se = x$seCorr,
simpleCI = x$simpleCICorr,
bcCI = x$bcCICorr,
median = x$medianCorr,
point = x$pointCorr)
x$input <- list(alpha = x$alpha)
}
}
else if (plot.which == "rM")
stop("This estimate does not include rM - try setting plot.which to MC or psi")
if (plot.which=="MC") {
y <- ifelse(point == "mean", x$MC$mean,
ifelse(point == "median", x$MC$median, x$MC$point))
ests.df <- data.frame(y = y,
lower = ifelse(range == "bcCI", x$MC$bcCI[1],
ifelse(range == "simpleCI",
x$MC$simpleCI[1],
y - x$MC$se)),
upper = ifelse(range == "bcCI", x$MC$bcCI[2],
ifelse(range == "simpleCI",
x$MC$simpleCI[2],
y + x$MC$se)))
}
else if (plot.which == "rM") {
y <- ifelse(point == "mean", x$corr$mean,
ifelse(point == "median", x$corr$median, x$corr$point))
ests.df <- data.frame(y = y,
lower = ifelse(range == "bcCI", x$corr$bcCI[1],
ifelse(range == "simpleCI",
x$corr$simpleCI[1],
y - x$corr$se)),
upper = ifelse(range == "bcCI", x$corr$bcCI[2],
ifelse(range == "simpleCI",
x$corr$simpleCI[2],
y + x$corr$se)))
}
else if (plot.which %in% c("psi", "gamma", "pi")) {
if (is.null(originNames)) {
if (is.null(x$input$originNames)) {
if (is.null(dimnames(x$psi$sample)[2])) {
originNames <- LETTERS[1:dim(x$psi$sample)[2]]
}
else {
originNames <- dimnames(x$psi$sample)[[2]]
}
}
else
originNames <- x$input$originNames
}
if (is.null(targetNames)) {
if (is.null(x$input$targetNames)) {
if (is.null(dimnames(x$psi$sample)[3])) {
targetNames <- 1:dim(x$psi$sample)[3]
}
else {
targetNames <- dimnames(x$psi$sample)[[3]]
}
}
else
targetNames <- x$input$targetNames
}
nTargetSites <- ifelse(is.null(x$psi), dim(x$gamma$sample)[2], dim(x$psi$sample)[3])
nOriginSites <- ifelse(is.null(x$psi), dim(x$gamma$sample)[3], dim(x$psi$sample)[2])
if (length(originNames)==1) {
originNames <- rep(originNames, nOriginSites)
originNamesHidden <- 1:nOriginSites
}
else {
originNamesHidden <- originNames
}
if (length(targetNames)==1) {
targetNames <- rep(targetNames, nTargetSites)
targetNamesHidden <- 1:nTargetSites
}
else {
targetNamesHidden <- targetNames
}
if (plot.which == "psi") {
y <- switch(point,
mean = x$psi$mean,
median = x$psi$median,
point = x$psi$point)
yrange <- switch(range,
bcCI = x$psi$bcCI,
simpleCI = x$psi$simpleCI,
se = aperm(array(c(y - x$psi$se, y + x$psi$se),
c(dim(y), 2)), c(3, 1, 2)))
ests.df <- data.frame(y = c(y),
To = factor(rep(targetNamesHidden, each = nOriginSites),
levels = targetNamesHidden),
FromTo = rep(1:nOriginSites, nTargetSites) +
rep(1:nTargetSites, each = nOriginSites) /
(nTargetSites * 2) - 0.3,
lower = c(yrange[1,,]),
upper = c(yrange[2,,]))
}
else if (plot.which == "pi") {
y <- switch(point,
mean = x$pi$mean,
median = x$pi$median,
point = x$pi$point)
yrange <- switch(range,
bcCI = x$pi$bcCI,
simpleCI = x$pi$simpleCI,
se = aperm(array(c(y - x$pi$se, y + x$pi$se),
c(dim(y), 2)), c(3, 1, 2)))
ests.df <- data.frame(y = c(y),
To = factor(rep(targetNamesHidden, each = nOriginSites),
levels = targetNamesHidden),
FromTo = rep(1:nOriginSites, nTargetSites) +
rep(1:nTargetSites, each = nOriginSites) /
(nTargetSites * 2) - 0.3,
lower = c(yrange[1,,]),
upper = c(yrange[2,,]))
}
else {
y <- switch(point,
mean = x$gamma$mean,
median = x$gamma$median,
point = x$gamma$point)
yrange <- switch(range,
bcCI = x$gamma$bcCI,
simpleCI = x$gamma$simpleCI,
se = aperm(array(c(y - x$gamma$se, y + x$gamma$se),
c(dim(y), 2)), c(3, 1, 2)))
ests.df <- data.frame(y = c(y),
To = factor(rep(originNamesHidden, each = nTargetSites),
levels = originNamesHidden),
FromTo = rep(1:nTargetSites, nOriginSites) +
rep(1:nOriginSites, each = nTargetSites) /
(nOriginSites * 2) - 0.3,
lower = c(yrange[1,,]),
upper = c(yrange[2,,]))
}
}
else if (plot.which == "r" || plot.which == "targetRelAbund") {
if (is.null(targetNames)) {
if (is.null(x$input$targetNames)) {
if (is.null(dimnames(x$psi$sample)[3])) {
targetNames <- 1:dim(x$psi$sample)[3]
}
else {
targetNames <- dimnames(x$psi$sample)[[3]]
}
}
else
targetNames <- x$input$targetNames
}
nTargetSites <- length(targetNames)
if (plot.which == "r") {
y <- switch(point,
mean = x$r$mean,
median = x$r$median)
if (length(dim(y)) < 2){
nAges <- 1
yrange <- switch(range,
bcCI = x$r$bcCI,
simpleCI = x$r$simpleCI,
se = aperm(array(c(y - x$r$se, y + x$r$se),
c(length(y), 2)), c(2, 1)))
ests.df <- data.frame(y = c(y),
To = 1:nTargetSites,
lower = c(yrange[1,]),
upper = c(yrange[2,]))
}
else{
nAges <- dim(y)[1]
if (is.null(ageNames))
ageNames <- 1:nAges
yrange <- switch(range,
bcCI = x$r$bcCI,
simpleCI = x$r$simpleCI,
se = aperm(array(c(y - x$r$se, y + x$r$se),
c(dim(y), 2)), c(3, 1, 2)))
ests.df <- data.frame(y = c(y),
Age = rep(1:nAges, nTargetSites),
To = rep(1:nTargetSites, each = nAges),
AgeTo = rep(1:nAges, nTargetSites) +
rep(1:nTargetSites, each = nAges) /
(nTargetSites * 2) - 0.3,
lower = c(yrange[1,,]),
upper = c(yrange[2,,]))
}
}
else { # plotting targetRelAbund
y <- switch(point,
mean = x$targetRelAbund$mean,
median = x$targetRelAbund$median)
yrange <- switch(range,
bcCI = x$targetRelAbund$bcCI,
simpleCI = x$targetRelAbund$simpleCI,
se = aperm(array(c(y - x$targetRelAbund$se,
y + x$targetRelAbund$se),
c(length(y), 2)), c(2, 1)))
ests.df <- data.frame(y = c(y),
To = 1:nTargetSites,
lower = c(yrange[1,]),
upper = c(yrange[2,]))
nAges <- 1
}
}
if (map) {
warning("Map plotting not yet available")
}
if (plot.which %in% c("psi", "pi")) {
if (is.null(col)) {
col <- 1:nTargetSites
}
else if (length(col) < nTargetSites)
col <- rep_len(col, nTargetSites)
if (is.null(pch)) {
if (nTargetSites > 5)
pch <- c(21:25, 0:(nTargetSites - 6))
else
pch <- 20 + 1:nTargetSites
}
else if (length(pch) < nTargetSites)
pch <- rep_len(pch, nTargetSites)
if (is.null(xlab))
xlab <- "Origin"
gplots::plotCI(ests.df$FromTo, ests.df$y, li = ests.df$lower,
ui = ests.df$upper,
pch = pch[as.integer(ests.df$To)],
col = col[as.integer(ests.df$To)],
pt.bg = col[as.integer(ests.df$To)],
ylab = ylab, xaxt = "n", xlab = xlab, gap = gap,
sfrac = sfrac, las = las, ...)
graphics::axis(1, at = seq(from = 1, by = 1, length.out = nOriginSites),
labels = originNames, ...)
if (!isFALSE(legend))
legend(legend, legend = targetNames, col = col, pch = pch,
pt.bg = col)
}
else if (plot.which=="gamma") {
if (is.null(col)) {
col <- 1:nOriginSites
}
else if (length(col) < nOriginSites)
col <- rep_len(col, nOriginSites)
if (is.null(pch)) {
if (nOriginSites > 5)
pch <- c(21:25, 0:(nOriginSites - 6))
else
pch <- 20 + 1:nOriginSites
}
else if (length(pch) < nOriginSites)
pch <- rep_len(pch, nOriginSites)
if (is.null(xlab))
xlab <- "Target"
gplots::plotCI(ests.df$FromTo, ests.df$y, li = ests.df$lower,
ui = ests.df$upper,
pch = pch[as.integer(ests.df$To)],
col = col[as.integer(ests.df$To)],
pt.bg = col[as.integer(ests.df$To)],
ylab = ylab, xaxt = "n", xlab = xlab, gap = gap,
sfrac = sfrac, las = las, ...)
graphics::axis(1, at = seq(from = 1, by = 1, length.out = nTargetSites),
labels = targetNames, ...)
if (!isFALSE(legend))
legend(legend, legend = originNames, col = col, pch = pch,
pt.bg = col)
}
else if (plot.which=="r" || plot.which=="targetRelAbund") {
if (nAges == 1) {
if (is.null(col)) {
col <- "black"
}
if (is.null(pch)) {
pch <- 20
}
if (is.null(xlab))
xlab <- "Target"
gplots::plotCI(ests.df$To, ests.df$y, li = ests.df$lower,
ui = ests.df$upper,
pch = pch,
col = col,
pt.bg = col,
ylab = ylab, xlab = xlab, gap = gap, xaxt = "n",
sfrac = sfrac, las = las, ...)
graphics::axis(1, at = seq(from = 1, by = 1, length.out = nTargetSites),
labels = targetNames, ...)
}
else {
if (is.null(col)) {
col <- 1:nTargetSites
}
else if (length(col) < nTargetSites)
col <- rep_len(col, nTargetSites)
if (is.null(pch)) {
if (nTargetSites > 5)
pch <- c(21:25, 0:(nTargetSites - 6))
else
pch <- 20 + 1:nTargetSites
}
else if (length(pch) < nTargetSites)
pch <- rep_len(pch, nTargetSites)
if (is.null(xlab))
xlab <- "Age"
gplots::plotCI(ests.df$AgeTo, ests.df$y, li = ests.df$lower,
ui = ests.df$upper,
pch = pch[as.integer(ests.df$To)],
col = col[as.integer(ests.df$To)],
pt.bg = col[as.integer(ests.df$To)],
ylab = ylab, xaxt = "n", xlab = xlab, gap = gap,
sfrac = sfrac, las = las, ...)
graphics::axis(1, at = 1:nAges, labels = ageNames, ...)
if (!isFALSE(legend))
legend(legend, legend = targetNames, col = col, pch = pch,
pt.bg = col)
}
}
else {
if (is.null(col)) {
col <- "black"
}
if (is.null(pch)) {
pch <- 20
}
if (is.null(xlab))
xlab <- ""
gplots::plotCI(0, ests.df$y, li = ests.df$lower,
ui = ests.df$upper,
pch = pch[1],
col = col[1], gap = gap, sfrac = sfrac,
ylab = ylab, xaxt = "n", xlab = xlab, las = las, ...)
}
}
map.estPsi <- function(x, originSites, targetSites, xOffset = NULL,
yOffset = NULL, col = NULL, maxWidth = 100000,
alpha.range = 0.2, alpha.point = 0,
subsetOrigin = NULL, subsetTarget = NULL,
doubled = FALSE) {
# #
# # data(OVENdata) # Ovenbird
# #
# M<-estMC(isGL=OVENdata$isGL, # Logical vector: light-level geolocator(T)/GPS(F)
# geoBias = OVENdata$geo.bias, # Light-level geolocator location bias
# geoVCov = OVENdata$geo.vcov, #Light-level geolocator covariance matrix
# targetDist = OVENdata$targetDist, # Target location distance matrix
# originDist = OVENdata$originDist, # Origin location distance matrix
# targetSites = OVENdata$targetSites, # Non-breeding / target sites
# originSites = OVENdata$originSites, # Breeding / origin sites
# originPoints = OVENdata$originPoints, # Capture Locations
# targetPoints = OVENdata$targetPoints, # Target locations from devices
# originRelAbund = OVENdata$originRelAbund, # Origin relative abundances
# resampleProjection = terra::crs(OVENdata$targetPoints),
# verbose = 0, # output options - see help ??estMC
# nSamples = 10000) # This is set low for example
#
nTargetSites <- ncol(x$psi$mean)
nOriginSites <- nrow(x$psi$mean)
if (is.null(subsetOrigin))
subsetOrigin <- 1:nOriginSites
if (is.null(subsetTarget))
subsetTarget <- 1:nTargetSites
originNames <- x$input$originNames
targetNames <- x$input$targetNames
if (is.null(xOffset))
xOffset <- matrix(0, nOriginSites, nTargetSites)
if (is.null(yOffset))
yOffset <- matrix(0, nOriginSites, nTargetSites)
allSites <- rbind(originSites[, "geometry"], targetSites[, "geometry"])
if (is.null(col)) {
col <- 1:nTargetSites
}
# png('psi_plot1.png', width = 6, height = 6, units = 'in', res = 1200)
op <- graphics::par(no.readonly = TRUE)
on.exit(graphics::par(op))
graphics::par(mar=c(0,0,0,0))
extent <- sf::st_bbox(allSites)
plot(allSites, xlim=c(extent[1],extent[3]),
ylim=c(extent[2], extent[4]), lwd = 1.5)
# plot(OVENdata$originSites[1],add=TRUE,lwd=1.75)
# plot(OVENdata$originSites[2],add=TRUE,lwd=1.75)
# plot(OVENdata$targetSites,add=TRUE,lwd=1.5,col=c("gray70","gray35","gray10"))
# legend("topleft",legend=paste("MC =",round(M$meanMC,2), "\u00b1", round(M$seMC,2)),bty="n",cex=1.8,bg="white",xjust=0)
for (i in subsetOrigin) {
xO <- sf::st_coordinates(sf::st_centroid(originSites[i,]))[,1]
yO <- sf::st_coordinates(sf::st_centroid(originSites[i,]))[,2]
for (j in subsetTarget) {
if (x$psi$simpleCI[2,i,j] > 0) {
xT <- sf::st_coordinates(sf::st_centroid(targetSites[j,]))[,1] + xOffset[i, j]
yT <- sf::st_coordinates(sf::st_centroid(targetSites[j,]))[,2] + yOffset[i, j]
angle <- atan((yT - yO)/(xT - xO))
if (is.nan(angle))
angle <- 0
if (xT < xO)
angle <- angle + pi
cosa <- cos(angle)
sina <- sin(angle)
if (doubled) {
graphics::polygon(c(xO + x$psi$simpleCI[2,i,j] * sina * maxWidth,
xT + x$psi$simpleCI[2,i,j] * sina * maxWidth,
xT + x$psi$simpleCI[1,i,j] * sina * maxWidth,
xO + x$psi$simpleCI[1,i,j] * sina * maxWidth),
c(yO - x$psi$simpleCI[2,i,j] * cosa * maxWidth,
yT - x$psi$simpleCI[2,i,j] * cosa * maxWidth,
yT - x$psi$simpleCI[1,i,j] * cosa * maxWidth,
yO - x$psi$simpleCI[1,i,j] * cosa * maxWidth),
col = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=alpha.range), border = NA)
graphics::polygon(c(xO - x$psi$simpleCI[2,i,j] * sina * maxWidth,
xT - x$psi$simpleCI[2,i,j] * sina * maxWidth,
xT - x$psi$simpleCI[1,i,j] * sina * maxWidth,
xO - x$psi$simpleCI[1,i,j] * sina * maxWidth),
c(yO + x$psi$simpleCI[2,i,j] * cosa * maxWidth,
yT + x$psi$simpleCI[2,i,j] * cosa * maxWidth,
yT + x$psi$simpleCI[1,i,j] * cosa * maxWidth,
yO + x$psi$simpleCI[1,i,j] * cosa * maxWidth),
col = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=alpha.range), border = NA)
graphics::polygon(c(xO - x$psi$mean[i,j] * sina * maxWidth,
xT - x$psi$mean[i,j] * sina * maxWidth,
xT + x$psi$mean[i,j] * sina * maxWidth,
xO + x$psi$mean[i,j] * sina * maxWidth),
c(yO + x$psi$mean[i,j] * cosa * maxWidth,
yT + x$psi$mean[i,j] * cosa * maxWidth,
yT - x$psi$mean[i,j] * cosa * maxWidth,
yO - x$psi$mean[i,j] * cosa * maxWidth),
col = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=alpha.point),
border = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=1))
}
else {
graphics::polygon(c(xO - x$psi$mean[i,j] * sina * maxWidth / 2,
xT - x$psi$mean[i,j] * sina * maxWidth / 2,
xT + x$psi$mean[i,j] * sina * maxWidth / 2,
xO + x$psi$mean[i,j] * sina * maxWidth / 2),
c(yO + x$psi$mean[i,j] * cosa * maxWidth / 2,
yT + x$psi$mean[i,j] * cosa * maxWidth / 2,
yT - x$psi$mean[i,j] * cosa * maxWidth / 2,
yO - x$psi$mean[i,j] * cosa * maxWidth / 2),
col = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=alpha.point),
border = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=1))
graphics::polygon(c(xO - sina * maxWidth * (x$psi$simpleCI[2,i,j] -
x$psi$mean[i,j] / 2),
xT - (x$psi$simpleCI[2,i,j] - x$psi$mean[i,j] / 2) *
sina * maxWidth,
xT - (x$psi$simpleCI[1,i,j] - x$psi$mean[i,j] / 2) *
sina * maxWidth,
xO - (x$psi$simpleCI[1,i,j] - x$psi$mean[i,j] / 2) *
sina * maxWidth),
c(yO+(x$psi$simpleCI[2,i,j]-x$psi$mean[i,j]/2)*cosa*maxWidth,
yT+(x$psi$simpleCI[2,i,j]-x$psi$mean[i,j]/2)*cosa*maxWidth,
yT+(x$psi$simpleCI[1,i,j]-x$psi$mean[i,j]/2)*cosa*maxWidth,
yO+(x$psi$simpleCI[1,i,j]-x$psi$mean[i,j]/2)*cosa*maxWidth),
col = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=alpha.range), border = NA)
}
shape::Arrowhead(xT, yT, angle / pi * 180, arr.width = x$psi$mean[i,j], arr.length = 1/8,
arr.type = 'curved', npoint = 15,
lcol = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=1), arr.adj = 0)
}
}
}
# dev.off()
# shape::Arrows(gCentroid(OVENdata$originSites[1])@coords[,1],
# gCentroid(OVENdata$originSites[1])@coords[,2],
# gCentroid(OVENdata$targetSites[2])@coords[,1]+80000,
# extent(OVENdata$targetSites[2])[4]+150000,
# arr.length = 0.3,
# arr.adj = 0.5,
# arr.lwd = 1,
# arr.width = 0.4,
# arr.type = "triangle",
# lwd=(apply(M$samplePsi[,1,],2,mean)[2]*10),
# lty=1)
#
# shape::Arrows(gCentroid(OVENdata$originSites[1])@coords[,1],
# gCentroid(OVENdata$originSites[1])@coords[,2],
# gCentroid(OVENdata$targetSites[3])@coords[,1],
# extent(OVENdata$targetSites[3])[4]+150000,
# arr.length = 0.3,
# arr.adj = 0.5,
# arr.lwd = 1,
# arr.width = 0.4,
# arr.type = "triangle",
# lwd=(apply(M$samplePsi[,1,],2,mean)[3]*10),
# lty=1)
#
# shape::Arrows(gCentroid(OVENdata$originSites[2])@coords[,1],
# gCentroid(OVENdata$originSites[2])@coords[,2],
# gCentroid(OVENdata$targetSites[1])@coords[,1],
# extent(OVENdata$targetSites[1])[4]+150000,
# arr.length = 0.3,
# arr.adj = 0.5,
# arr.lwd = 1,
# arr.width = 0.4,
# arr.type = "triangle",
# lwd=(apply(M$samplePsi[,2,],2,mean)[1]*10),
# lty=1)
#
# shape::Arrows(gCentroid(OVENdata$originSites[2])@coords[,1],
# gCentroid(OVENdata$originSites[2])@coords[,2],
# (gCentroid(OVENdata$targetSites[2])@coords[,1]-80000),
# extent(OVENdata$targetSites[2])[4]+150000,
# arr.length = 0.3,
# arr.adj = 0.5,
# arr.lwd = 1,
# arr.width = 0.4,
# arr.type = "triangle",
# lwd=(apply(M$samplePsi[,2,],2,mean)[2]*10))
#
# box(which="plot")
# #
}
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MigConnectivity documentation built on May 29, 2024, 8:37 a.m.
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Defines functions map.estPsi plot.estMigConnectivity plot.intrinsicAssign summary.intrinsicAssign summary.estMigConnectivity print.intrinsicAssign print.estMigConnectivity is.estMC is.isoAssign
Documented in plot.estMigConnectivity plot.intrinsicAssign
# @export
is.isoAssign <- function(x) inherits(x, "isoAssign")
# @export
is.estMC <- function(x) inherits(x, "estMC")
# @export
#print <- function(x,...) UseMethod("print")
#' @export
print.estMigConnectivity <- function(x, digits = max(3L, getOption("digits") - 3L), ...)
{
cat("Migratory Connectivity Estimates\n")
if (inherits(x, "estPsi")) {
dimnames(x$psi$mean) <- dimnames(x$psi$se) <- list(x$input$originNames,
x$input$targetNames)
cat("\nTransition probability (psi) estimates (mean):\n")
print(x$psi$mean, digits = digits)
cat("+/- SE:\n")
print(x$psi$se, digits = digits)
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile):\n", sep = "")
print(array(paste(format(x$psi$simpleCI[1,,],digits = digits, trim = TRUE),
format(x$psi$simpleCI[2,,],digits = digits, trim = TRUE),
sep = ' - '), dim = dim(x$psi$mean),
dimnames = list(x$input$originNames, x$input$targetNames)),
quote = FALSE)
}
if (inherits(x, "estMC")) {
cat("\nMC estimate (mean):", format(x$MC$mean, digits = digits), "+/- (SE)",
format(x$MC$se, digits = digits), '\n')
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile): ",
paste(format(x$MC$simpleCI, digits = digits, trim = TRUE),
collapse = ' - '), '\n', sep = "")
}
if (inherits(x, "estMantel")) {
if (is.null(x$corr)) {
x$corr <- list(mean = x$meanCorr, se = x$seCorr,
simpleCI = x$simpleCICorr)
x$input <- list(alpha = x$alpha)
}
cat("\nrM estimate (mean):", format(x$corr$mean, digits = digits), "+/- (SE)",
format(x$corr$se, digits = digits), '\n')
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile): ",
paste(format(x$corr$simpleCI, digits = digits, trim = TRUE),
collapse = ' - '), '\n', sep = "")
# cat(" ", ifelse(is.null(x$alpha), "", 100 * (1 - x$alpha)),
# "% confidence interval (bias-corrected): ",
# paste(format(x$bcCICorr, digits = digits, trim = TRUE), collapse = ' - '),
# '\n', sep = "")
# cat(" median:", format(x$medianCorr, digits = digits), '\n')
# if (!is.na(x$pointCorr))
# cat(" point calculation (not considering error):",
# format(x$pointCorr, digits = digits), '\n')
}
if (inherits(x, "estGamma")) {
cat("\nReverse transition probability (gamma) estimates (mean):\n")
print(x$gamma$mean, digits = digits)
cat("+/- SE:\n")
print(x$gamma$se, digits = digits)
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile):\n", sep = "")
print(array(paste(format(x$gamma$simpleCI[1,,],digits = digits, trim = TRUE),
format(x$gamma$simpleCI[2,,],digits = digits, trim = TRUE),
sep = ' - '), dim = dim(x$gamma$mean),
dimnames = list(x$input$targetNames, x$input$originNames)),
quote = FALSE)
}
if (inherits(x, "estTargetRelAbund")) {
cat("\nTarget site relative abundance estimates (mean):\n")
print(x$targetRelAbund$mean, digits = digits)
cat("+/- SE:\n")
print(x$targetRelAbund$se, digits = digits)
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile):\n", sep = "")
print(array(paste(format(x$targetRelAbund$simpleCI[1,],digits = digits, trim = TRUE),
format(x$targetRelAbund$simpleCI[2,],digits = digits, trim = TRUE),
sep = ' - '), dim = dim(x$gamma$mean)[1],
dimnames = list(x$input$targetNames)),
quote = FALSE)
}
if (inherits(x, "estPi")) {
cat("\nOrigin/target site combination probability (pi) estimates (mean):\n")
print(x$pi$mean, digits = digits)
cat("+/- SE:\n")
print(x$pi$se, digits = digits)
cat(ifelse(is.null(x$input$alpha), "", 100 * (1 - x$input$alpha)),
"% confidence interval (simple quantile):\n", sep = "")
print(array(paste(format(x$pi$simpleCI[1,,],digits = digits, trim = TRUE),
format(x$pi$simpleCI[2,,],digits = digits, trim = TRUE),
sep = ' - '), dim = dim(x$pi$mean),
dimnames = list(x$input$originNames, x$input$targetNames)),
quote = FALSE)
}
cat("\nThis is a subset of what's available inside this estMigConnectivity output.\n")
if (inherits(x, "estPi"))
cat("For more info, try ?reverseTransition or str(obj_name, max.level = 2).\n")
else if (inherits(x, "estPsi"))
cat("For more info, try ?estTransition or str(obj_name, max.level = 2).\n")
else if (inherits(x, "estMC"))
cat("For more info, try ?estStrength or ?estMC or str(obj_name, max.level = 2).\n")
else if (inherits(x, "estGamma"))
cat("For more info, try ?reverseTransition or str(obj_name, max.level = 2).\n")
else if (inherits(x, "estMantel"))
cat("For more info, try ?estMantel or str(obj_name, max.level = 2).\n")
else # In case we left something out...
cat("For more info, try str(obj_name, max.level = 2).\n")
}
#' @export
print.intrinsicAssign <- function(x, digits = max(3L, getOption("digits") - 3L), ...){
if(inherits(x,"isoAssign")){
cat("Individual Probability Surfaces \n")
print(x$probassign,...)
cat("\n Individual likely/unlikely Surfaces \n")
print(x$oddsassign,...)
cat("\n Population-level assignment Surface \n")
print(x$popassign,...)
cat("\n Individual Probability data frame* \n")
utils::str(x$probDF[,1:5])
cat("\n Individual likely/unlikely data frame* \n")
utils::str(x$oddsDF[,1:5])
cat("\n Individual single cell assignment \n")
utils::str(x$SingleCell)
cat("\n Target sites spatial layer \n")
print(x$targetSites)
cat("\n Random number seed set to: \n")
print(x$RandomSeed)
cat("\n * only first few columns are printed")
}
}
# @export
#summary <- function(x,...) UseMethod("summary")
#' @export
summary.estMigConnectivity <- function(object, ...)
{
print.estMigConnectivity(object, ...)
}
#' @export
summary.intrinsicAssign<-function(object, ...){
print.intrinsicAssign(object,...)
}
#' Basic plot function for the different isoAssign outputs
#'
#' Generates a basic plot of the isotope assignments. If
#' \code{map = 'population'} generates a single map. If
#' \code{map = 'probability' or map = 'odds'} generates a map for each
#' individual is generated. User is asked for input before each individual is
#' drawn.
#'
#' @param x an isoAssign object
#' @param map which \code{isoAssign} output to plot either 'probability', 'population' or 'odds'
#' @param ... additional arguments passed to plot function
#' @seealso{\code{isoAssign}}
#' @return No return value, called to generate plot(s).
#'
#' @examples
#' \donttest{
#' extensions <- c("shp", "shx", "dbf", "sbn", "sbx")
#' tmp <- tempdir()
#' for (ext in extensions) {
#' download.file(paste0(
#' "https://raw.githubusercontent.com/SMBC-NZP/MigConnectivity",
#' "/master/data-raw/Spatial_Layers/OVENdist.",
#' ext),
#' destfile = paste0(tmp, "/OVENdist.", ext), mode = "wb")
#' }
#' OVENdist <- sf::st_read(paste0(tmp, "/OVENdist.shp"))
#' OVENdist <- OVENdist[OVENdist$ORIGIN==2,] # only breeding
#' sf::st_crs(OVENdist) <- sf::st_crs(4326)
#'
#' download.file(paste0(
#' "https://raw.githubusercontent.com/SMBC-NZP/MigConnectivity",
#' "/master/data-raw/deltaDvalues.csv"),
#' destfile = paste0(tmp, "/deltaDvalues.csv"))
#' OVENvals <- read.csv(paste0(tmp, "/deltaDvalues.csv"))
#'
#' b <- isoAssign(isovalues = OVENvals[,2],
#' isoSTD = 12,
#' intercept = -10,
#' slope = 0.8,
#' odds = NULL,
#' restrict2Likely = TRUE,
#' nSamples = 1000,
#' sppShapefile = OVENdist,
#' assignExtent = c(-179,-60,15,89),
#' element = "Hydrogen",
#' period = "GrowingSeason") # setting for demonstration only
#'
#' plot(b, map = "population")
#' }
#'
#' @export
plot.intrinsicAssign <- function(x,map,...){
op <- graphics::par(no.readonly = TRUE)
on.exit(graphics::par(op))
if(inherits(x,"isoAssign")){
if(!(map %in% c("probability","population","odds"))){
stop("map must be either probability, population, or odds")}
if(map == "population"){
terra::plot(x$popassign,horiz = TRUE,...)
}
if(map == "probability"){
for(i in 1:terra::nlyr(x$probassign)){
terra::plot(x$probassign[[i]],horiz = TRUE,...)
graphics::par(ask = TRUE)
}
}
if(map == "odds"){
for(i in 1:terra::nlyr(x$probassign)){
terra::plot(x$oddsassign[[i]],horiz = TRUE,...)
graphics::par(ask = TRUE)
}
}
}
if(inherits(x,"weightAssign")){
graphics::par(bty = "L")
graphics::plot((x$performance$area/max(x$performance$area))~x$performance$error,
las = 1, ylab = "Assignment Area",
xlab = "Error",pch = 19, cex = 1.25, col = "gray")
graphics::points((x$frontier$area/max(x$performance$area))~x$frontier$error, col = "red", pch = 19, cex = 1.25)
graphics::points((x$top$area/max(x$performance$area))~x$top$error, col = "blue", pch = 19, cex = 1.25)
}
}
#plot <- function(x,...) UseMethod("plot")
#' Basic plot function for estMigConnectivity objects
#'
#' @param x an estMigConnectivity object (output of estTransition, estStrength,
#' estMC, or estMantel)
#' @param plot.which which parameter (psi, MC, rM, or r) to graph. Defaults to
#' psi for estMC objects, to rM (Mantel correlation) otherwise
#' @param point points on graph can represent mean, median, or point estimates
#' (not considering error). Defaults to mean, the standard estimate from
#' resampling
#' @param range lines / error bars drawn around points can represent simple
#' quantile-based confidence intervals (simpleCI), bias-corrected quantile-
#' based confidence intervals (bcCI), or +- standard error (se). Defaults to
#' simpleCI
#' @param xlab label for the x-axis. Defaults to "Origin" for psi, otherwise ""
#' @param ylab label for the y-axis. Defaults to the parameter being plotted
#' @param originNames names of the origin sites (for plotting psi). If left
#' NULL, the function attempts to get these from the estimate
#' @param targetNames names of the target sites (for plotting psi or r). If left
#' NULL, the function attempts to get these from the estimate
#' @param ageNames names of the age classes (for plotting r with more than one
#' age). If left NULL, the function uses 1:[number of ages]
#' @param col colors to use for labeling transition probabilities for
#' different target sites. If left NULL, defaults to 1:[number of target sites]
#' @param pch symbols to use for labeling transition probabilities for
#' different target sites. If left NULL, defaults to 21:25, then
#' 0:([number of target sites]-5)
#' @param las style of axis labels (0-3). We set the default at 1 (always
#' horizontal) here, but if you prefer your labels parallel to the axis, set
#' at 0
#' @param gap space left between the center of the error bar and the lines
#' marking the error bar in units of the height (width) of the letter "O".
#' Defaults to 0
#' @param sfrac width of "crossbar" at the end of error bar as a fraction of the
#' x plotting region. Defaults to 0, unless range is set to "se", in which
#' case it defaults to 0.01
#' @param legend leave as FALSE to not print a legend. Otherwise the position
#' of the legend (for psi or r (multi-age) only; one of "bottomright",
#' "bottom", "bottomleft", "left", "topleft", "top", "topright", "right", or
#' "center")
#' @param map placeholder for eventually allowing users to plot psi estimates
#' on a map
#' @param ... Additional parameters passed to \code{\link{plotCI}}
#'
#' @return No return value, called to generate plot.
#'
#' @seealso \code{\link{estMC}}, \code{\link{estMantel}}
#'
#' @export
plot.estMigConnectivity <- function(x,
plot.which = ifelse(inherits(x, "estPsi"),
"psi",
ifelse(inherits(x, "estMC"),
"MC",
ifelse(inherits(x, "estGamma"),
"gamma",
"rM"))),
point = c("mean", "median", "point"),
range = c("simpleCI", "bcCI", "se"),
xlab = NULL, ylab = plot.which,
originNames = NULL, targetNames = NULL,
ageNames = NULL,
col = NULL, pch = NULL, las = 1,
gap = 0,
sfrac = ifelse(range=="se", 0.01, 0),
legend = FALSE, map = FALSE, ...) {
if ((plot.which %in% c("corr", "Mantel")))
plot.which <- "rM"
if ((plot.which %in% c("transition", "Transition")))
plot.which <- "psi"
if ((plot.which %in% c("strength", "Strength")))
plot.which <- "MC"
if ((plot.which %in% c("abund", "abundance", "relAbund")))
plot.which <- "targetRelAbund"
if (!(plot.which %in% c("psi", "MC", "rM", "r", "gamma", "pi",
"targetRelAbund")))
stop("Set plot.which to psi, MC, rM, gamma, pi, targetRelAbund, or r")
point <- match.arg(point)
range <- match.arg(range)
if (inherits(x, "estMC")) {
if (is.null(x$psi) && !is.null(x$samplePsi)) {
bcCIPsi <- array(NA, dim = c(2, dim(x$samplePsi)[2], dim(x$samplePsi)[3]))
for (i in 1:dim(x$samplePsi)[2]) {
for (j in 1:dim(x$samplePsi)[3]) {
psi.z0 <- qnorm(sum(x$samplePsi[, i, j] < mean(x$samplePsi[, i, j],
na.rm = TRUE)) /
length(which(!is.na(x$samplePsi[, i, j]))))
bcCIPsi[ , i, j] <- quantile(x$samplePsi[, i, j],
pnorm(2 * psi.z0 +
qnorm(c(x$alpha/2,1-x$alpha/2))),
na.rm=TRUE, names = FALSE)
}
}
x$psi <- list(sample = x$samplePsi,
mean = apply(x$samplePsi, 2:3, mean),
se = apply(x$samplePsi, 2:3, sd),
simpleCI = apply(x$samplePsi, 2:3, quantile,
probs = c(x$alpha/2, 1-x$alpha/2),
na.rm=TRUE, names = FALSE),
bcCI = bcCIPsi,
median = apply(x$samplePsi, 2:3, median),
point = x$pointPsi)
x$MC <- list(mean = x$meanMC, se = x$seMC, simpleCI = x$simpleCI,
bcCI = x$bcCI, median = x$medianMC, point = x$pointMC)
x$input <- list(originNames = dimnames(x$samplePsi)[[2]],
targetNames = dimnames(x$samplePsi)[[3]])
}
}
else if (plot.which %in% c("psi", "MC") && !inherits(x, "estPsi"))
stop("This estimate does not include psi or MC - try setting plot.which to rM")
if (plot.which == "gamma" && !inherits(x, "estGamma"))
stop("This estimate does not include gamma - try setting plot.which to something else")
if (plot.which == "pi" && !inherits(x, "estPi"))
stop("This estimate does not include pi - try setting plot.which to something else")
if (plot.which == "targetRelAbund" && !inherits(x, "estTargetRelAbund"))
stop("This estimate does not include targetRelAbund - try setting plot.which to something else")
if (inherits(x, "estMantel")) {
if (is.null(x$corr)) {
x$corr <- list(sample = x$sampleCorr,
mean = x$meanCorr,
se = x$seCorr,
simpleCI = x$simpleCICorr,
bcCI = x$bcCICorr,
median = x$medianCorr,
point = x$pointCorr)
x$input <- list(alpha = x$alpha)
}
}
else if (plot.which == "rM")
stop("This estimate does not include rM - try setting plot.which to MC or psi")
if (plot.which=="MC") {
y <- ifelse(point == "mean", x$MC$mean,
ifelse(point == "median", x$MC$median, x$MC$point))
ests.df <- data.frame(y = y,
lower = ifelse(range == "bcCI", x$MC$bcCI[1],
ifelse(range == "simpleCI",
x$MC$simpleCI[1],
y - x$MC$se)),
upper = ifelse(range == "bcCI", x$MC$bcCI[2],
ifelse(range == "simpleCI",
x$MC$simpleCI[2],
y + x$MC$se)))
}
else if (plot.which == "rM") {
y <- ifelse(point == "mean", x$corr$mean,
ifelse(point == "median", x$corr$median, x$corr$point))
ests.df <- data.frame(y = y,
lower = ifelse(range == "bcCI", x$corr$bcCI[1],
ifelse(range == "simpleCI",
x$corr$simpleCI[1],
y - x$corr$se)),
upper = ifelse(range == "bcCI", x$corr$bcCI[2],
ifelse(range == "simpleCI",
x$corr$simpleCI[2],
y + x$corr$se)))
}
else if (plot.which %in% c("psi", "gamma", "pi")) {
if (is.null(originNames)) {
if (is.null(x$input$originNames)) {
if (is.null(dimnames(x$psi$sample)[2])) {
originNames <- LETTERS[1:dim(x$psi$sample)[2]]
}
else {
originNames <- dimnames(x$psi$sample)[[2]]
}
}
else
originNames <- x$input$originNames
}
if (is.null(targetNames)) {
if (is.null(x$input$targetNames)) {
if (is.null(dimnames(x$psi$sample)[3])) {
targetNames <- 1:dim(x$psi$sample)[3]
}
else {
targetNames <- dimnames(x$psi$sample)[[3]]
}
}
else
targetNames <- x$input$targetNames
}
nTargetSites <- ifelse(is.null(x$psi), dim(x$gamma$sample)[2], dim(x$psi$sample)[3])
nOriginSites <- ifelse(is.null(x$psi), dim(x$gamma$sample)[3], dim(x$psi$sample)[2])
if (length(originNames)==1) {
originNames <- rep(originNames, nOriginSites)
originNamesHidden <- 1:nOriginSites
}
else {
originNamesHidden <- originNames
}
if (length(targetNames)==1) {
targetNames <- rep(targetNames, nTargetSites)
targetNamesHidden <- 1:nTargetSites
}
else {
targetNamesHidden <- targetNames
}
if (plot.which == "psi") {
y <- switch(point,
mean = x$psi$mean,
median = x$psi$median,
point = x$psi$point)
yrange <- switch(range,
bcCI = x$psi$bcCI,
simpleCI = x$psi$simpleCI,
se = aperm(array(c(y - x$psi$se, y + x$psi$se),
c(dim(y), 2)), c(3, 1, 2)))
ests.df <- data.frame(y = c(y),
To = factor(rep(targetNamesHidden, each = nOriginSites),
levels = targetNamesHidden),
FromTo = rep(1:nOriginSites, nTargetSites) +
rep(1:nTargetSites, each = nOriginSites) /
(nTargetSites * 2) - 0.3,
lower = c(yrange[1,,]),
upper = c(yrange[2,,]))
}
else if (plot.which == "pi") {
y <- switch(point,
mean = x$pi$mean,
median = x$pi$median,
point = x$pi$point)
yrange <- switch(range,
bcCI = x$pi$bcCI,
simpleCI = x$pi$simpleCI,
se = aperm(array(c(y - x$pi$se, y + x$pi$se),
c(dim(y), 2)), c(3, 1, 2)))
ests.df <- data.frame(y = c(y),
To = factor(rep(targetNamesHidden, each = nOriginSites),
levels = targetNamesHidden),
FromTo = rep(1:nOriginSites, nTargetSites) +
rep(1:nTargetSites, each = nOriginSites) /
(nTargetSites * 2) - 0.3,
lower = c(yrange[1,,]),
upper = c(yrange[2,,]))
}
else {
y <- switch(point,
mean = x$gamma$mean,
median = x$gamma$median,
point = x$gamma$point)
yrange <- switch(range,
bcCI = x$gamma$bcCI,
simpleCI = x$gamma$simpleCI,
se = aperm(array(c(y - x$gamma$se, y + x$gamma$se),
c(dim(y), 2)), c(3, 1, 2)))
ests.df <- data.frame(y = c(y),
To = factor(rep(originNamesHidden, each = nTargetSites),
levels = originNamesHidden),
FromTo = rep(1:nTargetSites, nOriginSites) +
rep(1:nOriginSites, each = nTargetSites) /
(nOriginSites * 2) - 0.3,
lower = c(yrange[1,,]),
upper = c(yrange[2,,]))
}
}
else if (plot.which == "r" || plot.which == "targetRelAbund") {
if (is.null(targetNames)) {
if (is.null(x$input$targetNames)) {
if (is.null(dimnames(x$psi$sample)[3])) {
targetNames <- 1:dim(x$psi$sample)[3]
}
else {
targetNames <- dimnames(x$psi$sample)[[3]]
}
}
else
targetNames <- x$input$targetNames
}
nTargetSites <- length(targetNames)
if (plot.which == "r") {
y <- switch(point,
mean = x$r$mean,
median = x$r$median)
if (length(dim(y)) < 2){
nAges <- 1
yrange <- switch(range,
bcCI = x$r$bcCI,
simpleCI = x$r$simpleCI,
se = aperm(array(c(y - x$r$se, y + x$r$se),
c(length(y), 2)), c(2, 1)))
ests.df <- data.frame(y = c(y),
To = 1:nTargetSites,
lower = c(yrange[1,]),
upper = c(yrange[2,]))
}
else{
nAges <- dim(y)[1]
if (is.null(ageNames))
ageNames <- 1:nAges
yrange <- switch(range,
bcCI = x$r$bcCI,
simpleCI = x$r$simpleCI,
se = aperm(array(c(y - x$r$se, y + x$r$se),
c(dim(y), 2)), c(3, 1, 2)))
ests.df <- data.frame(y = c(y),
Age = rep(1:nAges, nTargetSites),
To = rep(1:nTargetSites, each = nAges),
AgeTo = rep(1:nAges, nTargetSites) +
rep(1:nTargetSites, each = nAges) /
(nTargetSites * 2) - 0.3,
lower = c(yrange[1,,]),
upper = c(yrange[2,,]))
}
}
else { # plotting targetRelAbund
y <- switch(point,
mean = x$targetRelAbund$mean,
median = x$targetRelAbund$median)
yrange <- switch(range,
bcCI = x$targetRelAbund$bcCI,
simpleCI = x$targetRelAbund$simpleCI,
se = aperm(array(c(y - x$targetRelAbund$se,
y + x$targetRelAbund$se),
c(length(y), 2)), c(2, 1)))
ests.df <- data.frame(y = c(y),
To = 1:nTargetSites,
lower = c(yrange[1,]),
upper = c(yrange[2,]))
nAges <- 1
}
}
if (map) {
warning("Map plotting not yet available")
}
if (plot.which %in% c("psi", "pi")) {
if (is.null(col)) {
col <- 1:nTargetSites
}
else if (length(col) < nTargetSites)
col <- rep_len(col, nTargetSites)
if (is.null(pch)) {
if (nTargetSites > 5)
pch <- c(21:25, 0:(nTargetSites - 6))
else
pch <- 20 + 1:nTargetSites
}
else if (length(pch) < nTargetSites)
pch <- rep_len(pch, nTargetSites)
if (is.null(xlab))
xlab <- "Origin"
gplots::plotCI(ests.df$FromTo, ests.df$y, li = ests.df$lower,
ui = ests.df$upper,
pch = pch[as.integer(ests.df$To)],
col = col[as.integer(ests.df$To)],
pt.bg = col[as.integer(ests.df$To)],
ylab = ylab, xaxt = "n", xlab = xlab, gap = gap,
sfrac = sfrac, las = las, ...)
graphics::axis(1, at = seq(from = 1, by = 1, length.out = nOriginSites),
labels = originNames, ...)
if (!isFALSE(legend))
legend(legend, legend = targetNames, col = col, pch = pch,
pt.bg = col)
}
else if (plot.which=="gamma") {
if (is.null(col)) {
col <- 1:nOriginSites
}
else if (length(col) < nOriginSites)
col <- rep_len(col, nOriginSites)
if (is.null(pch)) {
if (nOriginSites > 5)
pch <- c(21:25, 0:(nOriginSites - 6))
else
pch <- 20 + 1:nOriginSites
}
else if (length(pch) < nOriginSites)
pch <- rep_len(pch, nOriginSites)
if (is.null(xlab))
xlab <- "Target"
gplots::plotCI(ests.df$FromTo, ests.df$y, li = ests.df$lower,
ui = ests.df$upper,
pch = pch[as.integer(ests.df$To)],
col = col[as.integer(ests.df$To)],
pt.bg = col[as.integer(ests.df$To)],
ylab = ylab, xaxt = "n", xlab = xlab, gap = gap,
sfrac = sfrac, las = las, ...)
graphics::axis(1, at = seq(from = 1, by = 1, length.out = nTargetSites),
labels = targetNames, ...)
if (!isFALSE(legend))
legend(legend, legend = originNames, col = col, pch = pch,
pt.bg = col)
}
else if (plot.which=="r" || plot.which=="targetRelAbund") {
if (nAges == 1) {
if (is.null(col)) {
col <- "black"
}
if (is.null(pch)) {
pch <- 20
}
if (is.null(xlab))
xlab <- "Target"
gplots::plotCI(ests.df$To, ests.df$y, li = ests.df$lower,
ui = ests.df$upper,
pch = pch,
col = col,
pt.bg = col,
ylab = ylab, xlab = xlab, gap = gap, xaxt = "n",
sfrac = sfrac, las = las, ...)
graphics::axis(1, at = seq(from = 1, by = 1, length.out = nTargetSites),
labels = targetNames, ...)
}
else {
if (is.null(col)) {
col <- 1:nTargetSites
}
else if (length(col) < nTargetSites)
col <- rep_len(col, nTargetSites)
if (is.null(pch)) {
if (nTargetSites > 5)
pch <- c(21:25, 0:(nTargetSites - 6))
else
pch <- 20 + 1:nTargetSites
}
else if (length(pch) < nTargetSites)
pch <- rep_len(pch, nTargetSites)
if (is.null(xlab))
xlab <- "Age"
gplots::plotCI(ests.df$AgeTo, ests.df$y, li = ests.df$lower,
ui = ests.df$upper,
pch = pch[as.integer(ests.df$To)],
col = col[as.integer(ests.df$To)],
pt.bg = col[as.integer(ests.df$To)],
ylab = ylab, xaxt = "n", xlab = xlab, gap = gap,
sfrac = sfrac, las = las, ...)
graphics::axis(1, at = 1:nAges, labels = ageNames, ...)
if (!isFALSE(legend))
legend(legend, legend = targetNames, col = col, pch = pch,
pt.bg = col)
}
}
else {
if (is.null(col)) {
col <- "black"
}
if (is.null(pch)) {
pch <- 20
}
if (is.null(xlab))
xlab <- ""
gplots::plotCI(0, ests.df$y, li = ests.df$lower,
ui = ests.df$upper,
pch = pch[1],
col = col[1], gap = gap, sfrac = sfrac,
ylab = ylab, xaxt = "n", xlab = xlab, las = las, ...)
}
}
map.estPsi <- function(x, originSites, targetSites, xOffset = NULL,
yOffset = NULL, col = NULL, maxWidth = 100000,
alpha.range = 0.2, alpha.point = 0,
subsetOrigin = NULL, subsetTarget = NULL,
doubled = FALSE) {
# #
# # data(OVENdata) # Ovenbird
# #
# M<-estMC(isGL=OVENdata$isGL, # Logical vector: light-level geolocator(T)/GPS(F)
# geoBias = OVENdata$geo.bias, # Light-level geolocator location bias
# geoVCov = OVENdata$geo.vcov, #Light-level geolocator covariance matrix
# targetDist = OVENdata$targetDist, # Target location distance matrix
# originDist = OVENdata$originDist, # Origin location distance matrix
# targetSites = OVENdata$targetSites, # Non-breeding / target sites
# originSites = OVENdata$originSites, # Breeding / origin sites
# originPoints = OVENdata$originPoints, # Capture Locations
# targetPoints = OVENdata$targetPoints, # Target locations from devices
# originRelAbund = OVENdata$originRelAbund, # Origin relative abundances
# resampleProjection = terra::crs(OVENdata$targetPoints),
# verbose = 0, # output options - see help ??estMC
# nSamples = 10000) # This is set low for example
#
nTargetSites <- ncol(x$psi$mean)
nOriginSites <- nrow(x$psi$mean)
if (is.null(subsetOrigin))
subsetOrigin <- 1:nOriginSites
if (is.null(subsetTarget))
subsetTarget <- 1:nTargetSites
originNames <- x$input$originNames
targetNames <- x$input$targetNames
if (is.null(xOffset))
xOffset <- matrix(0, nOriginSites, nTargetSites)
if (is.null(yOffset))
yOffset <- matrix(0, nOriginSites, nTargetSites)
allSites <- rbind(originSites[, "geometry"], targetSites[, "geometry"])
if (is.null(col)) {
col <- 1:nTargetSites
}
# png('psi_plot1.png', width = 6, height = 6, units = 'in', res = 1200)
op <- graphics::par(no.readonly = TRUE)
on.exit(graphics::par(op))
graphics::par(mar=c(0,0,0,0))
extent <- sf::st_bbox(allSites)
plot(allSites, xlim=c(extent[1],extent[3]),
ylim=c(extent[2], extent[4]), lwd = 1.5)
# plot(OVENdata$originSites[1],add=TRUE,lwd=1.75)
# plot(OVENdata$originSites[2],add=TRUE,lwd=1.75)
# plot(OVENdata$targetSites,add=TRUE,lwd=1.5,col=c("gray70","gray35","gray10"))
# legend("topleft",legend=paste("MC =",round(M$meanMC,2), "\u00b1", round(M$seMC,2)),bty="n",cex=1.8,bg="white",xjust=0)
for (i in subsetOrigin) {
xO <- sf::st_coordinates(sf::st_centroid(originSites[i,]))[,1]
yO <- sf::st_coordinates(sf::st_centroid(originSites[i,]))[,2]
for (j in subsetTarget) {
if (x$psi$simpleCI[2,i,j] > 0) {
xT <- sf::st_coordinates(sf::st_centroid(targetSites[j,]))[,1] + xOffset[i, j]
yT <- sf::st_coordinates(sf::st_centroid(targetSites[j,]))[,2] + yOffset[i, j]
angle <- atan((yT - yO)/(xT - xO))
if (is.nan(angle))
angle <- 0
if (xT < xO)
angle <- angle + pi
cosa <- cos(angle)
sina <- sin(angle)
if (doubled) {
graphics::polygon(c(xO + x$psi$simpleCI[2,i,j] * sina * maxWidth,
xT + x$psi$simpleCI[2,i,j] * sina * maxWidth,
xT + x$psi$simpleCI[1,i,j] * sina * maxWidth,
xO + x$psi$simpleCI[1,i,j] * sina * maxWidth),
c(yO - x$psi$simpleCI[2,i,j] * cosa * maxWidth,
yT - x$psi$simpleCI[2,i,j] * cosa * maxWidth,
yT - x$psi$simpleCI[1,i,j] * cosa * maxWidth,
yO - x$psi$simpleCI[1,i,j] * cosa * maxWidth),
col = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=alpha.range), border = NA)
graphics::polygon(c(xO - x$psi$simpleCI[2,i,j] * sina * maxWidth,
xT - x$psi$simpleCI[2,i,j] * sina * maxWidth,
xT - x$psi$simpleCI[1,i,j] * sina * maxWidth,
xO - x$psi$simpleCI[1,i,j] * sina * maxWidth),
c(yO + x$psi$simpleCI[2,i,j] * cosa * maxWidth,
yT + x$psi$simpleCI[2,i,j] * cosa * maxWidth,
yT + x$psi$simpleCI[1,i,j] * cosa * maxWidth,
yO + x$psi$simpleCI[1,i,j] * cosa * maxWidth),
col = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=alpha.range), border = NA)
graphics::polygon(c(xO - x$psi$mean[i,j] * sina * maxWidth,
xT - x$psi$mean[i,j] * sina * maxWidth,
xT + x$psi$mean[i,j] * sina * maxWidth,
xO + x$psi$mean[i,j] * sina * maxWidth),
c(yO + x$psi$mean[i,j] * cosa * maxWidth,
yT + x$psi$mean[i,j] * cosa * maxWidth,
yT - x$psi$mean[i,j] * cosa * maxWidth,
yO - x$psi$mean[i,j] * cosa * maxWidth),
col = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=alpha.point),
border = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=1))
}
else {
graphics::polygon(c(xO - x$psi$mean[i,j] * sina * maxWidth / 2,
xT - x$psi$mean[i,j] * sina * maxWidth / 2,
xT + x$psi$mean[i,j] * sina * maxWidth / 2,
xO + x$psi$mean[i,j] * sina * maxWidth / 2),
c(yO + x$psi$mean[i,j] * cosa * maxWidth / 2,
yT + x$psi$mean[i,j] * cosa * maxWidth / 2,
yT - x$psi$mean[i,j] * cosa * maxWidth / 2,
yO - x$psi$mean[i,j] * cosa * maxWidth / 2),
col = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=alpha.point),
border = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=1))
graphics::polygon(c(xO - sina * maxWidth * (x$psi$simpleCI[2,i,j] -
x$psi$mean[i,j] / 2),
xT - (x$psi$simpleCI[2,i,j] - x$psi$mean[i,j] / 2) *
sina * maxWidth,
xT - (x$psi$simpleCI[1,i,j] - x$psi$mean[i,j] / 2) *
sina * maxWidth,
xO - (x$psi$simpleCI[1,i,j] - x$psi$mean[i,j] / 2) *
sina * maxWidth),
c(yO+(x$psi$simpleCI[2,i,j]-x$psi$mean[i,j]/2)*cosa*maxWidth,
yT+(x$psi$simpleCI[2,i,j]-x$psi$mean[i,j]/2)*cosa*maxWidth,
yT+(x$psi$simpleCI[1,i,j]-x$psi$mean[i,j]/2)*cosa*maxWidth,
yO+(x$psi$simpleCI[1,i,j]-x$psi$mean[i,j]/2)*cosa*maxWidth),
col = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=alpha.range), border = NA)
}
shape::Arrowhead(xT, yT, angle / pi * 180, arr.width = x$psi$mean[i,j], arr.length = 1/8,
arr.type = 'curved', npoint = 15,
lcol = grDevices::rgb(i/nOriginSites, j/nTargetSites,
1 - (i + j) / (nOriginSites + nTargetSites),
alpha=1), arr.adj = 0)
}
}
}
# dev.off()
# shape::Arrows(gCentroid(OVENdata$originSites[1])@coords[,1],
# gCentroid(OVENdata$originSites[1])@coords[,2],
# gCentroid(OVENdata$targetSites[2])@coords[,1]+80000,
# extent(OVENdata$targetSites[2])[4]+150000,
# arr.length = 0.3,
# arr.adj = 0.5,
# arr.lwd = 1,
# arr.width = 0.4,
# arr.type = "triangle",
# lwd=(apply(M$samplePsi[,1,],2,mean)[2]*10),
# lty=1)
#
# shape::Arrows(gCentroid(OVENdata$originSites[1])@coords[,1],
# gCentroid(OVENdata$originSites[1])@coords[,2],
# gCentroid(OVENdata$targetSites[3])@coords[,1],
# extent(OVENdata$targetSites[3])[4]+150000,
# arr.length = 0.3,
# arr.adj = 0.5,
# arr.lwd = 1,
# arr.width = 0.4,
# arr.type = "triangle",
# lwd=(apply(M$samplePsi[,1,],2,mean)[3]*10),
# lty=1)
#
# shape::Arrows(gCentroid(OVENdata$originSites[2])@coords[,1],
# gCentroid(OVENdata$originSites[2])@coords[,2],
# gCentroid(OVENdata$targetSites[1])@coords[,1],
# extent(OVENdata$targetSites[1])[4]+150000,
# arr.length = 0.3,
# arr.adj = 0.5,
# arr.lwd = 1,
# arr.width = 0.4,
# arr.type = "triangle",
# lwd=(apply(M$samplePsi[,2,],2,mean)[1]*10),
# lty=1)
#
# shape::Arrows(gCentroid(OVENdata$originSites[2])@coords[,1],
# gCentroid(OVENdata$originSites[2])@coords[,2],
# (gCentroid(OVENdata$targetSites[2])@coords[,1]-80000),
# extent(OVENdata$targetSites[2])[4]+150000,
# arr.length = 0.3,
# arr.adj = 0.5,
# arr.lwd = 1,
# arr.width = 0.4,
# arr.type = "triangle",
# lwd=(apply(M$samplePsi[,2,],2,mean)[2]*10))
#
# box(which="plot")
# #
}
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