STdata
/STmodel
/predCVSTmodel
objectsqqnorm
method for classes
STdata
/STmodel
/predCVSTmodel
.
Used for data and residual analysis of the cross validation.
# S3 method for predCVSTmodel
qqnorm(y, ID = "all",
main = "Q-Q plot for CV residuals", group = NULL, col = 1,
norm = FALSE, line = 0, org.scale = TRUE, ...)# S3 method for STdata
qqnorm(y, ID = "all", main = "Q-Q plot for observations",
group = NULL, col = 1, line = 0, ...)
# S3 method for STmodel
qqnorm(y, ID = "all", main = "Q-Q plot for observations",
group = NULL, col = 1, line = 0, ...)
STdata
/STmodel
/predCVSTmodel
object for the
qqnorm.
The location for which we want to norm-plot observations/residuals
or "all"
to plot for all locations.
Title of the plot
Do the norm-plot both for all data and then for each subset defined by the factor/levels in group variable.
Colour of points in the plot, either a scalar or a vector with length matching the number of observations/residuals.
TRUE
/FALSE
, plot normalised (mean=0, sd=1) or raw
cross-validation residuals. If norm=TRUE
a 0-1 line is added, to
indicate what normalised residuals should look like.
If non-zero add a qqline
with
lty=line
, to the plot; if 0 do not add a line.
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
)
Arguments passed on to the plotting function,
qqnorm
.
Nothing
Other predCVSTmodel methods: estimateCV.STmodel
,
plot.predCVSTmodel
,
print.predCVSTmodel
,
print.summary.predCVSTmodel
,
scatterPlot.predCVSTmodel
,
summary.predCVSTmodel
Other STdata methods: createSTdata
,
plot.STdata
, print.STdata
,
print.summary.STdata
,
scatterPlot.predCVSTmodel
,
summary.STdata
Other STmodel methods: MCMC.STmodel
,
c.STmodel
, createSTmodel
,
estimate.STmodel
,
estimateCV.STmodel
,
plot.STdata
, predict.STmodel
,
print.STmodel
,
print.summary.STmodel
,
scatterPlot.predCVSTmodel
,
simulate.STmodel
,
summary.STmodel
# NOT RUN {
################################
## Example for STdata/STmodel ##
################################
##load data
data(mesa.model)
##standard plot
qqnorm(mesa.model)
##add a line, and group (and colour) by AQS/FIXED
par(mfrow=c(2,2))
obs.type <- mesa.model$locations$type[match(mesa.model$obs$ID,
mesa.model$locations$ID)]
qqnorm(mesa.model, line=1, group=obs.type, col=obs.type)
##colour code by season and split by type
##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")
par(mfrow=c(2,2))
qqnorm(mesa.model, line=1, col=I.season, group=obs.type)
legend("bottomright", legend=as.character(levels(I.season)),
pch=1, col=1:nlevels(I.season))
###############################
## Example for predCVSTmodel ##
###############################
##load data
data(pred.cv.mesa)
##standard plot
par(mfrow=c(1,1))
qqnorm(pred.cv.mesa, line=3)
##or for the normalised residuals
qqnorm(pred.cv.mesa, line=3, norm=TRUE)
##add a line, and group by AQS/FIXED
par(mfrow=c(2,2))
qqnorm(pred.cv.mesa, line=1, group=obs.type)
##and for normalised residuals, colour-coded by season
par(mfrow=c(2,2))
qqnorm(pred.cv.mesa, line=2, norm=TRUE,
group=obs.type, col=I.season)
legend("bottomright", legend=as.character(levels(I.season)),
pch=1, col=1:nlevels(I.season))
# }
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