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STdata
/STmodel
/predCVSTmodel
objectsDoes a scatterPlot of observations/residuals against covariates (either
geographic or temporal trends), adding a spline fit (similar to
scatter.smooth
.
# S3 method for 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 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 STmodel
scatterPlot(x, covar = NULL, trend = NULL, pch = 1,
col = 1, cex = 1, lty = 1, subset = NULL, group = NULL,
add = FALSE, smooth.args = NULL, ...)
STdata
/STmodel
/predCVSTmodel
object to plot.
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).
Point and point size for the plot, a single value or
nlevels(group)
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.
A subset of locations for which to plot observations as a function of covariates.
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 to existing plot
List of arguments for
loess.smooth
.
STdata
or STmodel
containing covariates and
trend against which to plot.
What to use in the scatter plot, valid options are "obs"
for observations, "res"
residuals, and "res.norm"
for
normalised residuals.
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
.
Nothing
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
# NOT RUN {
################################
## 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))
# }
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