Description Usage Arguments Value Author(s) See Also Examples
plot
method for classes predictSTmodel
and predCVSTmodel
. Provides several different plots of the
data.
1 2 3 4 5 6 7 8 9 10 11 | ## S3 method for class 'predCVSTmodel'
plot(x, y = "time", ID = colnames(x$pred.all$EX)[1],
col = c("black", "red", "grey"), pch = c(NA, NA), cex = c(1, 1),
lty = c(1, 1), lwd = c(1, 1), p = 0.95, pred.type = "EX",
pred.var = TRUE, add = FALSE, ...)
## S3 method for class 'predictSTmodel'
plot(x, y = "time", STmodel = NULL,
ID = x$I$ID[1], col = c("black", "red", "grey"), pch = c(NA, NA),
cex = c(1, 1), lty = c(1, 1), lwd = c(1, 1), p = 0.95,
pred.type = "EX", pred.var = FALSE, add = FALSE, ...)
|
x |
|
y |
Plot predictions as a function of either |
ID |
The location for which we want to plot predictions. A
string matching names in |
col |
A vector of three colours: The first is the colour of the
predictions, second for the observations and third for the polygon
illustrating the confidence bands. |
pch, cex, lty, lwd |
Vectors with two elements giving the point type,
size, line type and line width to use when plotting the predictions and
observations respectively. Setting a value to |
p |
Width of the plotted confidence bands (as coverage percentage, used to find appropriate two-sided normal quantiles). |
pred.type |
Which type of prediction to plot, one of
|
pred.var |
Should we plot confidence bands based on prediction (TRUE)
or confidence intrevalls (FALSE), see |
add |
Add to existing plot? |
... |
Additional parameters passed to
|
STmodel |
|
Nothing
Johan Lindstrom
Other predCVSTmodel methods: estimateCV.STmodel
,
print.predCVSTmodel
,
print.summary.predCVSTmodel
,
qqnorm.predCVSTmodel
,
scatterPlot.predCVSTmodel
,
summary.predCVSTmodel
Other predictSTmodel methods: predict.STmodel
,
print.predictSTmodel
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 | #######################################
## plot predictions for a given site ##
#######################################
##load data
data(mesa.model)
##load predictions
data(pred.mesa.model)
par(mfrow=c(2,1))
plot(pred.mesa.model)
##different site with data and prediction variances
plot(pred.mesa.model, STmodel=mesa.model, ID="L001",
pred.var=TRUE)
##compare the different contributions to the predictions
plot(pred.mesa.model)
plot(pred.mesa.model, pred.type="EX.mu", col="red", add=TRUE)
plot(pred.mesa.model, pred.type="EX.mu.beta", col="green", add=TRUE)
##compare the two confidence and prediction intervalls
plot(pred.mesa.model, ID=3, pred.var=TRUE, col=c(0,0,"darkgrey"))
plot(pred.mesa.model, ID=3, STmodel=mesa.model,
col=c("black","red","lightgrey"), add=TRUE)
##plot predictions as function of observations
par(mfrow=c(2,2))
plot(pred.mesa.model, y="obs", STmodel=mesa.model, pred.var=TRUE)
##all data, using points and colour coded by site
plot(pred.mesa.model, y="obs", STmodel=mesa.model, ID="all",
lty=c(NA,1), pch=c(19,NA), col=c("ID", "red", "grey"),
cex=.25, pred.var=TRUE)
##compare prediction methods, for one site only
plot(pred.mesa.model, y="obs", STmodel=mesa.model,
lty=c(NA,1), pch=c(19,NA), cex=.25, pred.var=TRUE)
plot(pred.mesa.model, y="obs", STmodel=mesa.model, col="red",
lty=NA, pch=c(19,NA), cex=.25, pred.type="EX.mu",
add=TRUE)
plot(pred.mesa.model, y="obs", STmodel=mesa.model, col="green",
lty=NA, pch=c(19,NA), cex=.25, pred.type="EX.mu.beta",
add=TRUE)
####################################
## plot CV-pred. for a given site ##
####################################
##load CV-predictions
data(pred.cv.mesa)
par(mfcol=c(3,1),mar=c(2.5,2.5,2,.5))
plot(pred.cv.mesa, ID=1)
plot(pred.cv.mesa, ID=1, pred.type="EX.mu", col="green", add=TRUE)
plot(pred.cv.mesa, ID=1, pred.type="EX.mu.beta", col="blue", add=TRUE)
##different colours
plot(pred.cv.mesa, ID=10, col=c("blue","magenta","light blue"))
##points and lines for the observations
plot(pred.cv.mesa, ID=17, lty=c(1,NA), pch=c(NA,19), cex=.5)
##plot predictions as function of observations
par(mfrow=c(2,2))
plot(pred.cv.mesa, y="obs")
##all data, using points and colour coded by site
plot(pred.cv.mesa, y="obs", ID="all", lty=c(NA,1),
pch=c(19,NA), cex=.25, col=c("ID", "red", "grey"))
##compare prediction methods, for one site only
plot(pred.cv.mesa, y="obs", lty=c(NA,1), pch=c(19,NA), cex=.25)
plot(pred.cv.mesa, y="obs", col="red", lty=NA, pch=c(19,NA),
cex=.25, pred.type="EX.mu", add=TRUE)
plot(pred.cv.mesa, y="obs", col="green", lty=NA, pch=c(19,NA),
cex=.25, pred.type="EX.mu.beta", add=TRUE)
|
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