plot.predCVSTmodel {SpatioTemporal} | R Documentation

## Plots for `predictSTmodel` and `predCVSTmodel` Objects

### Description

`plot` method for classes `predictSTmodel` and `predCVSTmodel`. Provides several different plots of the data.

### Usage

    ## 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, ...)

### Arguments

`x` | `predictSTmodel` or `predCVSTmodel` object to plot.
`y` | Plot predictions as a function of either `"time"` or `"obs"`ervations.
`ID` | The location for which we want to plot predictions. A string matching names in `colnames(x$EX)` (or `x$I$ID`, number(s) which are used as `ID = colnames(x$EX)[ID]`, or `"all"` in which case all predictions are used. If several locations are given (or `"all"`) then `y` must be `"obs"`.
`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. For `y="obs"` the colours are 1) colour of the points, 2) colour of the 1-1 line, and 3) colour of the polygon. If `ID="all"`, picking `col[1]="ID"` will colour code the observations-prediction points by site ID.

`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 `NA` will give no points/lines for the predictions/observations.
When plotting predictions as a function of observations `lty[2]` is used for the addition of `abline(0,1, lty=lty[2], col=col[2], lwd=lwd[2])`; `pch[2]` and `cex[2]` are ignored.

`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 `"EX"`, `"EX.mu"`, `"EX.mu.beta"`, or `"EX.pred"`; see the output from `predict.STmodel`
`pred.var` | Should we plot confidence bands based on prediction (TRUE) or confidence intrevalls (FALSE), see `predict.STmodel`. Only relevant if `pred.type="EX"` or `pred.type="EX.pred"`. NOTE: The default differs for `plot.predictSTmodel` and `plot.predCVSTmodel`!
`add` | Add to existing plot?
`...` | Additional parameters passed to `plot`.
`STmodel` | `STdata`/`STmodel` object containing observations with which to compare the predictions (not used for `plot.predCVSTmodel`).

### Value

Nothing

### Author(s)

Johan Lindstrom

### See Also

Other predCVSTmodel methods: `estimateCV.STmodel`, `print.predCVSTmodel`, `print.summary.predCVSTmodel`, `qqnorm.predCVSTmodel`, `scatterPlot.predCVSTmodel`, `summary.predCVSTmodel`

Other predictSTmodel methods: `predict.STmodel`, `print.predictSTmodel`

### Examples

    #######################################
    ## 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)

[Package SpatioTemporal version 1.1.9.1 Index]