plot.influenceSSN: Plotting Method for influenceSSN Objects

In SSN: Spatial Modeling on Stream Networks

Plotting Method for influenceSSN Objects

Description

plot.influenceSSN is a generic plot function that has been adapted for influenceSSN-class objects that have been created from the residuals function.

Usage

## S3 method for class 'influenceSSN'
plot(x, color.palette = NULL, nclasses = NULL, inflcol = "_resid_",
breaktype = "quantile", brks=NULL, pch = 19, ...)

Arguments

x	an object of class influenceSSN.
color.palette	a color palette for plotting points. The default is rainbow(nclasses, start = .66, end = .99). The number of colors should equal the number of classes. See palette for many ways to create palettes.
nclasses	the number of classes for coloring the predictions (or standard errors) according to their value. The default is 10.
inflcol	an influence diagnostic or cross-validation variable name in the influenceSSN object. If NULL (default), just locations are plotted. If a variable is specified, it will be colored according to its value.
breaktype	The method for breaking the response values into classes for coloring while plotting. A character argument that must be one of "quantile" (default), "even", or "user".
brks	if breaktype = "user", the break values must be specified here as a vector or matrix using c(...) or cbind(...). The sorted unique values are used as break points (together with the min and max of the variable being plotted if required)
pch	either an integer specifying a symbol or a single character to be used as the default in plotting points. See link{points} for possible values and their interpretation. Note that only integers and single-character strings can be set as a graphics parameter (and not NA nor NULL).
...	arguments to be passed to methods, such as graphical parameters (see par).

Details

The plot.influenceSSN function creates a map showing data locations that can be color-coded according to the values of the diagnostic or influence variables.

Value

Maps of stream networks, with the spatial distribution of the influence or cross-validation variables shown.

Author(s)

Jay Ver Hoef [e-mail]

See Also

influenceSSN-class, residuals, plot.SpatialStreamNetwork

Examples

# get some model fits stored as data objects
data(modelFits)
#NOT RUN use this one
#fitSp <- glmssn(Summer_mn ~ ELEV_DEM + netID,
#    ssn.object = mf04p, EstMeth = "REML", family = "Gaussian",
#    CorModels = c("Exponential.tailup","Exponential.taildown",
#    "Exponential.Euclid"), addfunccol = "afvArea")
#for examples only, make sure fitSp has the correct path
#if you use importSSN(), path will be correct
fitSp$ssn.object <- updatePath(fitSp$ssn.object, 
	paste0(tempdir(),'/MiddleFork04.ssn'))

resids <- residuals(fitSp,cross.validation = TRUE)
plot(resids)

## plot using user-defined breakpoints
brks <- seq(-3,2,by=1)
plot(resids, nclasses = 6, inflcol = "_resid_",
    breaktype = "user", brks = brks, pch = 3)

## plot crossvalidation residuals
plot(resids, nclasses = 6, inflcol = "_resid.crossv_")

SSN documentation built on March 7, 2023, 5:30 p.m.