plot_crosscorrPlot: Plot image of correlation matrix

In mcmcOutput: Functions to Store, Manipulate and Display Markov Chain Monte Carlo (MCMC) Output

Plot image of correlation matrix

Description

Displays graphically the lower triangle of the correlation matrix among the columns of the input.

Usage

crosscorrPlot(x, params=NULL, col, addSpace=c(0,0), ...)

Arguments

x	An object with MCMC chain values, of any class that can be coerced to mcmcOutput.
params	An optional vector of column numbers or names; names are partially matched, so params="alpha" will match all of alpha, alpha0, alpha[1], alphanew; negative indices indicate parameters to exclude, positive and negative indices cannot be mixed.
col	The colours to use to code the correlations; default is a blue-yellow-red ramp; NA correlations appear as white boxes.
addSpace	A length-2 vector to add extra white space right and above the main display, eg, to provide extra space for long parameter names; units are the width of one box of the display. Unlike mar, this inserts space left of the legend and below the title.
...	Additional graphical parameters, see Details.

Details

The usual graphical parameters can be added in the ... argument. Note the following:

* mar : a vector of length 4 specifying the width of the margins below, left, above and right of the main plot; you will need to increase the margins to insert xlab or ylab; default c(1,1,5,4); see the entry for mar at par.

* cex.axis : controls the size of the parameter names, default 1.2.

* srt : controls the rotation of the parameter names, 0 = horizontal, 90 = vertical, default 45.

* offset : controls the distance of the start of the parameter names from the corner of the box in units of box width, default 0.2.

* tcl : the length of the tick marks next to the parameter names in units of box widths, default 0.1.

* lwd.ticks : line width for the tick marks, default 1.

* legendAsp : aspect ratio for the legend, default 0.1.

Value

Returns the correlation matrix invisibly.

Author(s)

Mike Meredith

Examples

# Create a data frame of fake MCMC output:
mu0 <- rnorm(3000)         # normal, mean zero
mu10 <- rnorm(3000, rep(9:11, each=1000), 1) + mu0*0.5
  # approx normal, mean 10, correlated with mu0
fake <- data.frame(
  mu0 = mu0,
  mu10 = mu10,
  sigma=rlnorm(3000),        # non-negative, skewed
  prob = plogis(1-mu0),      # probability, central mode, neg. correlation with mu0
  prob0 = rbeta(3000, 1,2),  # probability, mode = 0
  N = rpois(3000, rep(c(24, 18, 18), each=1000)),
                             # large integers (no zeros), poor mixing
  n = rpois(3000, 2),        # small integers (some zeros)
  const1 = rep(1, 3000))     # all values = 1
str(fake)

tmp <- crosscorrPlot(fake)
round(tmp, 2)
crosscorrPlot(fake, main="Isn't this a really cool plot?")
crosscorrPlot(fake, main="A subset of parameters", params=c("mu", "prob", "N"))
crosscorrPlot(fake, main="Leave out 'sigma'", params=-3)
crosscorrPlot(fake, main="Just a few colours", col=c("blue","skyblue","pink","red"))
names(fake)[5] <- "A_parameter_with_a_very_long_name"
crosscorrPlot(fake, main="Is there room?")
crosscorrPlot(fake, main="With addSpace=c(2,0)", addSpace=c(2,0))

mcmcOutput documentation built on Nov. 18, 2022, 1:08 a.m.