funGp (version 0.2.1)

plotSims: Plot for simulations from a funGp model

Description

This method displays the simulated output values delivered by a funGp Gaussian process model.

Usage

# S4 method for fgpm
plotSims(model, sims, detail = "full", ...)

Arguments

model

a '>fgpm object for which the plot is to be made.

sims

a list containing the simulated output values. In funGp, this argument is just the data structure delivered by the simulate method.

detail

an optional character string specifying the data elements that should be included in the plot, to be chosen between "light" and "full". A light plot will include only include the simulated values, while a a full plot will also include the predicted mean and confidence bands at the simulation points. This argument will only be used if full simulations (including the mean and confidence bands) are provided, otherwise it will be dropped. See simulate for more details on the generation of light and full simulations.

...

additional arguments affecting the display. The following typical graphics parameters are valid entries: xlim, ylim, xlab, ylab, main. The boolean argument legends can also be included in any of the two lists in order to control the display of legends in the corresponding plot.

Value

None.

See Also

* fgpm for the construction of funGp models;

* plotLOO for model diagnostic plots;

* predict for predictions based on a funGp model;

* plotPreds for prediction plots.

Examples

# NOT RUN {
# plotting light simulations_______________________________________________________________
# building the model
set.seed(100)
n.tr <- 25
sIn <- expand.grid(x1 = seq(0,1,length = sqrt(n.tr)), x2 = seq(0,1,length = sqrt(n.tr)))
fIn <- list(f1 = matrix(runif(n.tr*10), ncol = 10), f2 = matrix(runif(n.tr*22), ncol = 22))
sOut <- fgp_BB3(sIn, fIn, n.tr)
m1 <- fgpm(sIn = sIn, fIn = fIn, sOut = sOut)

# making light simulations
n.sm <- 100
sIn.sm <- as.matrix(expand.grid(x1 = seq(0,1,length = sqrt(n.sm)),
                                x2 = seq(0,1,length = sqrt(n.sm))))
fIn.sm <- list(f1 = matrix(runif(n.sm*10), ncol = 10), matrix(runif(n.sm*22), ncol = 22))
m1.sims <- simulate(m1, nsim = 10, sIn.sm = sIn.sm, fIn.sm = fIn.sm)

# plotting light simulations
plotSims(m1, m1.sims)


# plotting full simulations________________________________________________________________
# building the model
set.seed(100)
n.tr <- 25
sIn <- expand.grid(x1 = seq(0,1,length = sqrt(n.tr)), x2 = seq(0,1,length = sqrt(n.tr)))
fIn <- list(f1 = matrix(runif(n.tr*10), ncol = 10), f2 = matrix(runif(n.tr*22), ncol = 22))
sOut <- fgp_BB3(sIn, fIn, n.tr)
m1 <- fgpm(sIn = sIn, fIn = fIn, sOut = sOut)

# making full simulations
n.sm <- 100
sIn.sm <- as.matrix(expand.grid(x1 = seq(0,1,length = sqrt(n.sm)),
                                x2 = seq(0,1,length = sqrt(n.sm))))
fIn.sm <- list(f1 = matrix(runif(n.sm*10), ncol = 10), matrix(runif(n.sm*22), ncol = 22))
m1.sims <- simulate(m1, nsim = 10, sIn.sm = sIn.sm, fIn.sm = fIn.sm, detail = "full")

# plotting full simulations in full mode
plotSims(m1, m1.sims)

# plotting full simulations in light mode
plotSims(m1, m1.sims, detail = "light")

# }