R2BayesX (version 1.0-0)

plot.bayesx: Default BayesX Plotting

Description

Generic functions for plotting objects of class "bayesx" and model term classes "geo.bayesx", "linear.bayesx", "mrf.bayesx", "random.bayesx" and "sm.bayesx".

Usage

"plot"(x, model = NULL, term = NULL, which = 1L, ask = FALSE, ...)

Arguments

x

a fitted bayesx object.

model

for which model the plot should be provided, either an integer or a character, e.g. model = "mcmc.model".

term

the term that should be plotted, either an integer or a character, e.g. term = "sx(x)".

which

choose the type of plot that should be drawn, possible options are: "effect", "coef-samples", "var-samples", "intcpt-samples", "hist-resid", "qq-resid", "scatter-resid", "scale-resid", "max-acf". Argument which may also be specified as integer, e.g. which = 1. The first three arguments are all model term specific. For the residual model diagnostic plot options which may be set with which = 5:8.

ask

...

...

other graphical parameters passed to plotblock, plotmap, plot2d, plot3d, acf and density.

Details

Depending on the class of the term that should be plotted, function plot.bayesx calls one of the following plotting functions in the end:

• plotblock,
• plotsamples,
• plotmap,
• plot2d,
• plot3d,
• acf,
• density,

For details on argument specifications, please see the help sites for the corresponding function. If argument x contains of more than one model and e.g. term = 2, the second terms of all models will be plotted

See Also

plotblock, plotsamples, plotmap, plot2d, plot3d, bayesx, read.bayesx.output.

Examples

## Not run: 
# ## generate some data
# set.seed(111)
# n <- 500
# 
# ## regressors
# dat <- data.frame(x = runif(n, -3, 3), z = runif(n, -3, 3),
#    w = runif(n, 0, 6), fac = factor(rep(1:10, n/10)))
# 
# ## response
# dat$y <- with(dat, 1.5 + sin(x) + cos(z) * sin(w) +
#    c(2.67, 5, 6, 3, 4, 2, 6, 7, 9, 7.5)[fac] + rnorm(n, sd = 0.6))
# 
# ## estimate model
# b1 <- bayesx(y ~ sx(x) + sx(z, w, bs = "te") + fac,
#    data = dat, method = "MCMC")
# 
# ## plot p-spline term
# plot(b1, term = 1)
# ## same with
# plot(b1, term = "sx(x)")
# 
# ## with residuals
# plot(b1, term = "sx(x)", residuals = TRUE)
# 
# ## plot tensor term
# plot(b1, term = "sx(z,w)")
# 
# ## use other palette
# plot(b1, term = "sx(z,w)", col.surface = heat.colors)
# 
# ## swap colors
# plot(b1, term = "sx(z,w)", col.surface = heat.colors, swap = TRUE)
# 
# ## plot tensor term with residuals
# plot(b1, term = "sx(z,w)", residuals = TRUE)
# 
# ## plot image and contour
# plot(b1, term = "sx(z,w)", image = TRUE)
# plot(b1, term = "sx(z,w)", image = TRUE, contour = TRUE)
# 
# ## increase the grid
# plot(b1, term = "sx(z,w)", image = TRUE, contour = TRUE, grid = 100)
# 
# ## plot factor term
# plot(b1, term = "fac")
# 
# ## plot factor term with residuals
# plot(b1, term = "fac", resid = TRUE, cex = 0.5)
# 
# ## plot residual dignostics
# plot(b1, which = 5:8)
# 
# ## plot variance sampling path of term sx(x)
# plot(b1, term = 1, which = "var-samples")
# 
# ## plot coefficients sampling paths of term sx(x)
# plot(b1, term = 1, which = "coef-samples")
# 
# ## plot the sampling path of the intercept
# par(mfrow = c(1, 1))
# plot(b1, which = "intcpt-samples")
# 
# ## plot the autcorrelation function  
# ## of the sampled intercept
# plot(b1, which = "intcpt-samples", 
#   acf = TRUE, lag.max = 50)
# 
# ## increase lags
# plot(b1, which = "intcpt-samples", 
#   acf = TRUE, lag.max = 200)
# 
# ## plot maximum autocorrelation 
# ## of all sampled parameters in b1
# plot(b1, which = "max-acf")
# 
# ## plot maximum autocorrelation of 
# ## all sampled coefficients of term sx(x)
# plot(b1, term = "sx(x)", which = "coef-samples", 
#   max.acf = TRUE, lag.max = 100)
# 
# 
# ## now a spatial example
# set.seed(333)
# 
# ## simulate some geographical data
# data("MunichBnd")
# N <- length(MunichBnd); names(MunichBnd) <- 1:N
# n <- N*5
# 
# ## regressors
# dat <- data.frame(id = rep(1:N, n/N), x1 = runif(n, -3, 3))
# dat$sp <- with(dat, sort(runif(N, -2, 2), decreasing = TRUE)[id])
# dat$re <- with(dat, rnorm(N, sd = 0.6)[id])
# 
# ## response
# dat$y <- with(dat, 1.5 + sin(x1) + sp + re + rnorm(n, sd = 0.6))
# 
# ## estimate model
# b2 <- bayesx(y ~ sx(x1) + sx(id, bs = "mrf", map = MunichBnd) +
#   sx(id, bs = "re"), method = "MCMC", data = dat)
# 
# ## summary statistics
# summary(b2)
# 
# ## plot structured spatial effect
# plot(b2, term = "sx(id)", map = MunichBnd)
# 
# ## plot unstructured spatial effect
# plot(b2, term = "sx(id):re", map = MunichBnd)
# 
# ## now without map
# ## generates a kernel density plot
# ## of the effects
# plot(b2, term = "sx(id):mrf", map = FALSE)
# plot(b2, term = "sx(id):re", map = FALSE)
# 
# ## with approximate quantiles of the  
# ## kernel density estimate 
# plot(b2, term = "sx(id):re", map = FALSE, 
#   kde.quantiles = TRUE, probs = c(0.025, 0.5, 0.975))
# 
# ## plot the total spatial effect
# plot(b2, term = "sx(id):total")
# plot(b2, term = "sx(id):total", map = MunichBnd)
# 
# ## combine model objects
# b <- c(b1, b2)
# 
# ## plot first term of second model
# plot(b, model = 2, term = 1)
# plot(b, model = "b2", term = "sx(x1)")
# 
# ## plot second term of both models
# plot(b, term = 2, map = MunichBnd)
# 
# ## plot everything
# plot(b)
# ## End(Not run)