Horizontal interval plot of components of a random vector

mlplot plots the scalar components as of the given random array or vector as horizontal intervals, grouped by row.

mlplot(X, ...)

# S3 method for default
mlplot(
  X,
  y.center = TRUE,
  y.shift = 0,
  y.map = NULL,
  mar = par("mar"),
  left.margin = 3,
  vline = NULL,
  top.axis = TRUE,
  exp.labels = FALSE,
  x.ticks = NULL,
  axes = NULL,
  xlim = NULL,
  ylim = NULL,
  xlab = deparse(substitute(X)),
  ylab = NULL,
  las = NULL,
  add = FALSE,
  ...
)

Arguments

X: a random array or vector
...: further arguments passed to plot and points
y.center: center the intervals nicely at each y-coordinate?
y.shift: add this amount to each y coordinate of an interval
y.map: optional function to compute the y-coordinates, given X
mar: the margins of the plot
left.margin: offset to add to the left margin of the plot (to add space for the labels)
vline: if numeric, plot vertical lines at these (horizontal) coordinates
top.axis: (logical) plot the top axis?
exp.labels: (logical) if the original scale is logarithmic, label ticks in original (exp) scale?
x.ticks: positions for the ticks of the x-axis
axes: (logical) plot the axes at all?
xlim: x limits
ylim: y limits
xlab: x label
ylab: not used (instead of labels, the row names are shown)
las: the style of axis labels, see par
add: (logical) add the intervals to an existing plot?

Details

mlplot plots the scalar components of a vector or an array (2 or 3-dimensional) vertically (up to down) so that a component of a vector or a row of a matrix is plotted at vertical points 1...nrow(x).

An 'mlplot' of a vector implements a "forest plot."

Scalars on the same row are plotted closely together. The positioning of the scalars within a row are controlled by the arguments y.center, y.shift, y.map. These do not need to be set for the default plot; if two arrays or vectors are plotted over on top of each other (using add=TRUE) then you should probably change y.shift which controls the vertical position of the array elements.

See demo(mlplot) for a detailed

To change the color of the random components of the vector, use rvcol. Typically this is of the same length as X, giving the color `theme' for each component.

If X is a 3-dimensional array, mlplot is called repeatedly for each 2-dimensional array X[,,k] for each k.

X may also be a fixed numeric object.

NAs (or random scalars with 100\

mlplot is still experimental.

References

Kerman, J. and Gelman, A. (2007). Manipulating and Summarizing Posterior Simulations Using Random Variable Objects. Statistics and Computing 17:3, 235-244.

Author

Jouni Kerman jouni@kerman.com

Examples



if (FALSE) {
  # You can run this complete example by typing demo("mlplot")

  n.rows <- 4; n.cols <- 5; n <- (n.rows*n.cols)
  # Draw some fixed numbers
  mu.true <- rnorm(1:n.rows, mean=1:n.rows, sd=1)
  sigma.true <- 1
  theta <- rvmatrix(rvnorm(n=n.cols, mean=mu.true, sd=sigma.true), nrow=n.rows)
  #
  col.labels <- paste("Time", 1:n.cols, sep=":")
  row.labels <- paste("Unit", 1:n.rows, sep=":")
  dimnames(theta) <- list(row.labels, col.labels)
  #
  par(mfrow=c(2,2))
  mlplot(theta, main="theta")
  abline(v=0, lty="dotted")
  mlplot(t(theta), main="theta transposed")
  abline(v=0, lty="dotted")
  row.sd <- apply.rv(theta, 1, sd.rv)
  col.sd <- apply.rv(theta, 2, sd.rv)
  x.max <- max(rvquantile(c(row.sd, col.sd), 0.99))
  mlplot(row.sd, xlim=c(0, x.max), main="theta: within-row sd for each unit")
  abline(v=0)
  mlplot(col.sd, xlim=c(0, x.max), main="theta: between-row sd for each time point")
  abline(v=0)
}