Nothing
R/plot.stlgcppm.R
In stopp: Spatio-Temporal Point Pattern Methods, Model Fitting, Diagnostics, Simulation, Local Tests
Defines functions
plot.stlgcppm
Documented in
plot.stlgcppm
#' Plot of the fitted intensity of a LGCP model
#'
#' The function plots the fitted intensity, displayed both in space and in space and time.
#' In the case of local covariance parameters, the function returns the
#' mean of the random intensity, displayed both in space and in space and time.
#'
#' @param x An object of class \code{stlgcppm}
#' @param scaler Optional. Controls the value for a scalar representation of the
#' spatial scale of the data.
#' Either a character string, \code{"silverman"} (default), \code{"IQR"},
#' \code{"sd"}, or \code{"var"};
#' or positive numeric value(s). See \link{OS}.
#' @param do.points Add points to plot
#' @param print.bw It prints the estimated oversmoothing (\link{OS}) bandwidth selector
#' @param zap Noise threshold factor (default to 0.00001). A numerical value greater than or equal to 1.
#' If the range of pixel values is less than \code{zap * .Machine$double.eps},
#' the image will be treated as constant. This avoids displaying images which
#' should be constant but contain small numerical errors.
#' @param par Default to \code{TRUE}.
#' @param ... additional unused argument
#'
#' @export
#'
#' @author Nicoletta D'Angelo and Giada Adelfio
#'
#' @seealso
#' \link{stlgcppm}, \link{print.stlgcppm}, \link{summary.stlgcppm},
#' \link{localsummary}, \link{localplot}
#'
#'
#'
#' @examples
#' \donttest{
#' catsub <- stp(greececatalog$df[1:200, ])
#'
#' lgcp_loc <- stlgcppm(catsub, formula = ~ x, first = "local")
#'
#' plot(lgcp_loc)
#'
#'}
#'
#'
#'
#' @references
#' D'Angelo, N., Adelfio, G., and Mateu, J. (2023). Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes. Computational Statistics & Data Analysis, 180, 107679.
#'
#' Davies, T.M. and Hazelton, M.L. (2010), Adaptive kernel estimation of spatial relative risk, Statistics in Medicine, 29(23) 2423-2437.
#'
#' Siino, M., Adelfio, G., and Mateu, J. (2018). Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes. Stochastic environmental research and risk assessment, 32(12), 3525-3539.
#'
#' Terrell, G.R. (1990). The maximal smoothing principle in density estimation, Journal of the American Statistical Association, 85, 470-477.
#'
#'
#'
plot.stlgcppm <- function(x,
scaler = c("silverman", "IQR", "sd", "var"),
do.points = TRUE,
print.bw = FALSE,
zap = 0.00001,
par = TRUE,
...){
if(inherits(x$CovCoefs, "numeric") & inherits(x$IntCoefs, "numeric") & length(x$IntCoefs) == 1){
stop("Constant intensity, no plot to show")
}
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar))
mark_int <- x$l
mark_all <- x$mu
ppx_int <- suppressWarnings(spatstat.geom::ppp(x$X$df$x, x$X$df$y, marks = mark_int,
window = spatstat.geom::owin(range(x$X$df$x), range(x$X$df$y))))
ppx_all <- suppressWarnings(spatstat.geom::ppp(x$X$df$x, x$X$df$y, marks = mark_all,
window = spatstat.geom::owin(range(x$X$df$x), range(x$X$df$y))))
sig <- sparr::OS(unmark(ppx_int), scaler = scaler)
if(inherits(x$CovCoefs, "numeric")){
if(par == T){
par(mfrow = c(1, 2))
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, 1))
plot(spatstat.explore::Smooth(ppx_int, sigma = sig), zap = zap,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
main = c("First-order Intensity in space \n Density Kernel Smoothing"))
if(do.points == T){plot(spatstat.geom::unmark(ppx_int), add = T)}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, -0.5))
plot3D::scatter3D(x$X$df$x, x$X$df$y, x$X$df$t,
theta = - 45, phi = 20,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
ticktype = "detailed", pch = 20,
colvar = mark_int,
xlab="x",ylab="y",zlab="t",
main = c("First-order Intensity in space-time \n Pointwise computation"))
par(mar = c(5, 4, 4, 2) + 0.1)
} else {
par(mfrow = c(1, 1))
par(ask = TRUE)
plot(spatstat.explore::Smooth(ppx_int, sigma = sig), zap = zap,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
main = c("First-order Intensity in space \n Density Kernel Smoothing"))
if(do.points == T){plot(spatstat.geom::unmark(ppx_int), add = T)}
par(ask = TRUE)
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, -0.5))
plot3D::scatter3D(x$X$df$x, x$X$df$y, x$X$df$t,
theta = - 45, phi = 20,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
ticktype = "detailed", pch = 20,
colvar = mark_int,
xlab="x",ylab="y",zlab="t",
main = c("First-order Intensity in space-time \n Pointwise computation"))
par(mar = c(5, 4, 4, 2) + 0.1)
par(ask = FALSE)
}
} else {
if(inherits(x$IntCoefs, "numeric") & length(x$IntCoefs) == 1){
if(par == T){
par(mfrow = c(1, 2))
} else {
par(mfrow = c(1, 1))
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, 1))
plot(spatstat.explore::Smooth(ppx_all, sigma = sig), zap = zap,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_all)),
"stuff")$outputs,
main = c("Mean Function of Lambda in space \n Density Kernel Smoothing"))
if(do.points == T){plot(spatstat.geom::unmark(ppx_all), add = T)}
if(par != T){
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, -0.5))
plot3D::scatter3D(x$X$df$x, x$X$df$y, x$X$df$t,
theta = - 45, phi = 20,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_all)),
"stuff")$outputs,
ticktype = "detailed", pch = 20,
colvar = mark_all,
xlab="x",ylab="y",zlab="t",
main = c("Mean Function of Lambda in space-time \n Pointwise computation"))
par(mar = c(5, 4, 4, 2) + 0.1)
if(par != T){
par(ask = FALSE)
}
} else {
if(par == T){
par(mfrow = c(2, 2))
} else {
par(mfrow = c(1, 1))
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, 1))
plot(spatstat.explore::Smooth(ppx_int, sigma = sig), zap = zap,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
main = c("First-order Intensity in space \n Density Kernel Smoothing"))
if(do.points == T){plot(spatstat.geom::unmark(ppx_int), add = T)}
if(par != T){
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, -0.5))
plot3D::scatter3D(x$X$df$x, x$X$df$y, x$X$df$t,
theta = - 45, phi = 20,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
ticktype = "detailed", pch = 20,
colvar = mark_int,
xlab="x",ylab="y",zlab="t",
main = c("First-order Intensity in space-time \n Pointwise computation"))
if(par != T){
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, 1))
plot(spatstat.explore::Smooth(ppx_all, sigma = sig), zap = zap,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_all)),
"stuff")$outputs,
main = c("Mean Function of Lambda in space \n Density Kernel Smoothing"))
if(do.points == T){plot(spatstat.geom::unmark(ppx_all), add = T)}
if(par != T){
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, -0.5))
plot3D::scatter3D(x$X$df$x, x$X$df$y, x$X$df$t,
theta = - 45, phi = 20,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_all)),
"stuff")$outputs,
ticktype = "detailed", pch = 20,
colvar = mark_all,
xlab="x",ylab="y",zlab="t",
main = c("Mean Function of Lambda in space-time \n Pointwise computation"))
if(par != T){
par(ask = FALSE)
}
par(mar = c(5, 4, 4, 2) + 0.1)
}}
if(print.bw == T){print(sig)}
}
Try the stopp package in your browser
Any scripts or data that you put into this service are public.
stopp documentation built on May 29, 2024, 12:32 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions plot.stlgcppm
Documented in plot.stlgcppm
#' Plot of the fitted intensity of a LGCP model
#'
#' The function plots the fitted intensity, displayed both in space and in space and time.
#' In the case of local covariance parameters, the function returns the
#' mean of the random intensity, displayed both in space and in space and time.
#'
#' @param x An object of class \code{stlgcppm}
#' @param scaler Optional. Controls the value for a scalar representation of the
#' spatial scale of the data.
#' Either a character string, \code{"silverman"} (default), \code{"IQR"},
#' \code{"sd"}, or \code{"var"};
#' or positive numeric value(s). See \link{OS}.
#' @param do.points Add points to plot
#' @param print.bw It prints the estimated oversmoothing (\link{OS}) bandwidth selector
#' @param zap Noise threshold factor (default to 0.00001). A numerical value greater than or equal to 1.
#' If the range of pixel values is less than \code{zap * .Machine$double.eps},
#' the image will be treated as constant. This avoids displaying images which
#' should be constant but contain small numerical errors.
#' @param par Default to \code{TRUE}.
#' @param ... additional unused argument
#'
#' @export
#'
#' @author Nicoletta D'Angelo and Giada Adelfio
#'
#' @seealso
#' \link{stlgcppm}, \link{print.stlgcppm}, \link{summary.stlgcppm},
#' \link{localsummary}, \link{localplot}
#'
#'
#'
#' @examples
#' \donttest{
#' catsub <- stp(greececatalog$df[1:200, ])
#'
#' lgcp_loc <- stlgcppm(catsub, formula = ~ x, first = "local")
#'
#' plot(lgcp_loc)
#'
#'}
#'
#'
#'
#' @references
#' D'Angelo, N., Adelfio, G., and Mateu, J. (2023). Locally weighted minimum contrast estimation for spatio-temporal log-Gaussian Cox processes. Computational Statistics & Data Analysis, 180, 107679.
#'
#' Davies, T.M. and Hazelton, M.L. (2010), Adaptive kernel estimation of spatial relative risk, Statistics in Medicine, 29(23) 2423-2437.
#'
#' Siino, M., Adelfio, G., and Mateu, J. (2018). Joint second-order parameter estimation for spatio-temporal log-Gaussian Cox processes. Stochastic environmental research and risk assessment, 32(12), 3525-3539.
#'
#' Terrell, G.R. (1990). The maximal smoothing principle in density estimation, Journal of the American Statistical Association, 85, 470-477.
#'
#'
#'
plot.stlgcppm <- function(x,
scaler = c("silverman", "IQR", "sd", "var"),
do.points = TRUE,
print.bw = FALSE,
zap = 0.00001,
par = TRUE,
...){
if(inherits(x$CovCoefs, "numeric") & inherits(x$IntCoefs, "numeric") & length(x$IntCoefs) == 1){
stop("Constant intensity, no plot to show")
}
oldpar <- par(no.readonly = TRUE)
on.exit(par(oldpar))
mark_int <- x$l
mark_all <- x$mu
ppx_int <- suppressWarnings(spatstat.geom::ppp(x$X$df$x, x$X$df$y, marks = mark_int,
window = spatstat.geom::owin(range(x$X$df$x), range(x$X$df$y))))
ppx_all <- suppressWarnings(spatstat.geom::ppp(x$X$df$x, x$X$df$y, marks = mark_all,
window = spatstat.geom::owin(range(x$X$df$x), range(x$X$df$y))))
sig <- sparr::OS(unmark(ppx_int), scaler = scaler)
if(inherits(x$CovCoefs, "numeric")){
if(par == T){
par(mfrow = c(1, 2))
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, 1))
plot(spatstat.explore::Smooth(ppx_int, sigma = sig), zap = zap,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
main = c("First-order Intensity in space \n Density Kernel Smoothing"))
if(do.points == T){plot(spatstat.geom::unmark(ppx_int), add = T)}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, -0.5))
plot3D::scatter3D(x$X$df$x, x$X$df$y, x$X$df$t,
theta = - 45, phi = 20,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
ticktype = "detailed", pch = 20,
colvar = mark_int,
xlab="x",ylab="y",zlab="t",
main = c("First-order Intensity in space-time \n Pointwise computation"))
par(mar = c(5, 4, 4, 2) + 0.1)
} else {
par(mfrow = c(1, 1))
par(ask = TRUE)
plot(spatstat.explore::Smooth(ppx_int, sigma = sig), zap = zap,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
main = c("First-order Intensity in space \n Density Kernel Smoothing"))
if(do.points == T){plot(spatstat.geom::unmark(ppx_int), add = T)}
par(ask = TRUE)
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, -0.5))
plot3D::scatter3D(x$X$df$x, x$X$df$y, x$X$df$t,
theta = - 45, phi = 20,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
ticktype = "detailed", pch = 20,
colvar = mark_int,
xlab="x",ylab="y",zlab="t",
main = c("First-order Intensity in space-time \n Pointwise computation"))
par(mar = c(5, 4, 4, 2) + 0.1)
par(ask = FALSE)
}
} else {
if(inherits(x$IntCoefs, "numeric") & length(x$IntCoefs) == 1){
if(par == T){
par(mfrow = c(1, 2))
} else {
par(mfrow = c(1, 1))
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, 1))
plot(spatstat.explore::Smooth(ppx_all, sigma = sig), zap = zap,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_all)),
"stuff")$outputs,
main = c("Mean Function of Lambda in space \n Density Kernel Smoothing"))
if(do.points == T){plot(spatstat.geom::unmark(ppx_all), add = T)}
if(par != T){
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, -0.5))
plot3D::scatter3D(x$X$df$x, x$X$df$y, x$X$df$t,
theta = - 45, phi = 20,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_all)),
"stuff")$outputs,
ticktype = "detailed", pch = 20,
colvar = mark_all,
xlab="x",ylab="y",zlab="t",
main = c("Mean Function of Lambda in space-time \n Pointwise computation"))
par(mar = c(5, 4, 4, 2) + 0.1)
if(par != T){
par(ask = FALSE)
}
} else {
if(par == T){
par(mfrow = c(2, 2))
} else {
par(mfrow = c(1, 1))
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, 1))
plot(spatstat.explore::Smooth(ppx_int, sigma = sig), zap = zap,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
main = c("First-order Intensity in space \n Density Kernel Smoothing"))
if(do.points == T){plot(spatstat.geom::unmark(ppx_int), add = T)}
if(par != T){
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, -0.5))
plot3D::scatter3D(x$X$df$x, x$X$df$y, x$X$df$t,
theta = - 45, phi = 20,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_int)),
"stuff")$outputs,
ticktype = "detailed", pch = 20,
colvar = mark_int,
xlab="x",ylab="y",zlab="t",
main = c("First-order Intensity in space-time \n Pointwise computation"))
if(par != T){
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, 1))
plot(spatstat.explore::Smooth(ppx_all, sigma = sig), zap = zap,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_all)),
"stuff")$outputs,
main = c("Mean Function of Lambda in space \n Density Kernel Smoothing"))
if(do.points == T){plot(spatstat.geom::unmark(ppx_all), add = T)}
if(par != T){
par(ask = TRUE)
}
par(mar = c(5, 4, 4, 2) + 0.1 - c(4, 1 , 1, -0.5))
plot3D::scatter3D(x$X$df$x, x$X$df$y, x$X$df$t,
theta = - 45, phi = 20,
col = attr(spatstat.geom::colourmap(grDevices::hcl.colors(100, "YlOrRd", rev = TRUE),
range = range(mark_all)),
"stuff")$outputs,
ticktype = "detailed", pch = 20,
colvar = mark_all,
xlab="x",ylab="y",zlab="t",
main = c("Mean Function of Lambda in space-time \n Pointwise computation"))
if(par != T){
par(ask = FALSE)
}
par(mar = c(5, 4, 4, 2) + 0.1)
}}
if(print.bw == T){print(sig)}
}
Try the stopp package in your browser
Any scripts or data that you put into this service are public.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.