scatterPlot.STdata: scatterPlot for 'STdata'/'STmodel'/'predCVSTmodel' objects

In SpatioTemporal: Spatio-Temporal Model Estimation

Description

Does a scatterPlot of observations/residuals against covariates (either geographic or temporal trends), adding a spline fit (similar to scatter.smooth.

Usage

## S3 method for class 'predCVSTmodel'
scatterPlot(x, covar = NULL, trend = NULL,
  pch = 1, col = 1, cex = 1, lty = 1, subset = NULL, group = NULL,
  add = FALSE, smooth.args = NULL, STdata, type = c("obs", "res",
  "res.norm"), org.scale = TRUE, ...)

## S3 method for class 'STdata'
scatterPlot(x, covar = NULL, trend = NULL, pch = 1,
  col = 1, cex = 1, lty = 1, subset = NULL, group = NULL,
  add = FALSE, smooth.args = NULL, ...)

## S3 method for class 'STmodel'
scatterPlot(x, covar = NULL, trend = NULL, pch = 1,
  col = 1, cex = 1, lty = 1, subset = NULL, group = NULL,
  add = FALSE, smooth.args = NULL, ...)

Arguments

x	STdata/STmodel/predCVSTmodel object to plot.
covar, trend	Plot observations as a function of? Only one of these should be not NULL. covar uses location covariates, and trend uses temporal trend (or dates); trend=0 uses a temporal intercept (i.e. a constant).
pch, cex	Point and point size for the plot, a single value or nlevels(group)
col, lty	Color of points and smooth lines. A single value or nlevels(group)+1 values; the last value is used for fitting a line to all data. Use lty=NA to supress smooth lines.
subset	A subset of locations for which to plot observations as a function of covariates.
group	A vector of factors of the same length as the number of observations (typically length(x$obs$obs), or length(x$pred.obs$obs)) used to group data and fit different smooths to each group.
add	Add to existing plot
smooth.args	List of arguments for loess.smooth.
STdata	STdata or STmodel containing covariates and trend against which to plot.
type	What to use in the scatter plot, valid options are "obs" for observations, "res" residuals, and "res.norm" for normalised residuals.
org.scale	TRUE/FALSE scatter plots on the original untransformed scale, or using exp(y). Only relevant if x was computed using transform in predictCV.STmodel (as pass through argument to predict.STmodel)
...	Additional parameters passed to plot.

Value

Nothing

Author(s)

Johan Lindstrom

See Also

Other predCVSTmodel methods: estimateCV.STmodel, plot.predCVSTmodel, print.predCVSTmodel, print.summary.predCVSTmodel, qqnorm.predCVSTmodel, summary.predCVSTmodel

Other STdata methods: createSTdata, plot.STdata, print.STdata, print.summary.STdata, qqnorm.predCVSTmodel, summary.STdata

Other STmodel methods: MCMC.STmodel, c.STmodel, createSTmodel, estimate.STmodel, estimateCV.STmodel, plot.STdata, predict.STmodel, print.STmodel, print.summary.STmodel, qqnorm.predCVSTmodel, simulate.STmodel, summary.STmodel

Examples

################################
## Example for STdata/STmodel ##
################################
##load data
data(mesa.model)

par(mfrow=c(2,2))
##plot observations as a function of longitude for an STmodel object
scatterPlot(mesa.model, covar="long")

##as a function of the first temporal trend, subset to only AQS sites
##and fit for each location
scatterPlot(mesa.model, trend=1, col=c(1:25,1), pch=19, cex=.1,
            group=mesa.model$obs$ID, lty=c(rep(2,25),1),
            subset=with(mesa.model$locations, ID[type=="AQS"]))

##if plotting against the distance to coast, we might have to change the
##smooting.
suppressWarnings( scatterPlot(mesa.model, covar="km.to.coast") )
##better
scatterPlot(mesa.model, covar="km.to.coast", col=c(NA,2), add=TRUE,
            smooth.args=list(span=4/5,degree=2))

##Lets group data by season
##First create a vector dividing data into four seasons
I.season <- as.factor(as.POSIXlt(mesa.model$obs$date)$mon+1)
levels(I.season) <- c(rep("Winter",2), rep("Spring",3), 
                      rep("Summer",3), rep("Fall",3), "Winter") 
scatterPlot(mesa.model, covar="log10.m.to.a1", col=c(2:5,1),
            group=I.season)
legend("bottomleft", c(levels(I.season),"All"), col=c(2:5,1), pch=1)


###############################
## Example for predCVSTmodel ##
###############################
##load data
data(pred.cv.mesa)

##simple case of residuals against temporal trends
par(mfrow=c(2,1))
scatterPlot(pred.cv.mesa, trend=1, STdata=mesa.model, type="res")

##colour coded by season
I.season <- as.factor(as.POSIXlt(pred.cv.mesa$pred.obs$date)$mon+1)
levels(I.season) <- c(rep("Winter",2), rep("Spring",3), 
                      rep("Summer",3), rep("Fall",3), "Winter") 

scatterPlot(pred.cv.mesa, trend=1, STdata=mesa.model, type="res",
            group=I.season, col=c(2:5,1), lty=c(1,1,1,1,2),
            smooth.args=list(span=.1,degree=2))
            
##or as function of covariates
par(mfcol=c(2,2))
scatterPlot(pred.cv.mesa, , type="res", covar="log10.m.to.a1",
            STdata=mesa.model, group=I.season, col=c(2:5,1))
scatterPlot(pred.cv.mesa, type="res", covar="km.to.coast",
            STdata=mesa.model, group=I.season, col=c(2:5,1),
            smooth.args=list(span=4/5,degree=1))

##let's compare to the original observations
scatterPlot(pred.cv.mesa, covar="log10.m.to.a1", STdata=mesa.model,
            group=I.season, col=c(2:5,1), type="obs")
scatterPlot(pred.cv.mesa, covar="km.to.coast", STdata=mesa.model,
            group=I.season, col=c(2:5,1), type="obs",
            smooth.args=list(span=4/5,degree=1))

SpatioTemporal documentation built on May 2, 2019, 8:49 a.m.