missmodel
objects
missmodel
objects produced by data augmentation. These may be used to help
diagnose convergence of a data augmentation algorithm.
acf
and/or
acf.plot
directly.
daAcfPlot(da.missmod, subset = 1:100, lag.max = 100, select = F, variables = (all), ask = T, ...)
"missmodel"
object produced by a data augmentation algorithm.
paramIter
component of the
missmodel
object)
should be used to calculate autocorrelations.
This can be a logical vector (which is
replicated to have length equal to the number of rows),
a numeric vector indicating the observation numbers to be
included, or a character vector of the row names to
be included.
The subset should specify a consecutive set of iterates.
acf
variables
argument are plotted.
Schafer, J. L. (1997), Analysis of Incomplete Multivariate Data, Chapman & Hall, London.
crime.DA <- daLoglin(crime, margins = count~Visit.1:Visit.2, control = list(save = 101:500)) daAcfPlot(crime.DA) # plot the last 2 parameters daAcfPlot(crime.DA, variables = 3:4) # plot the first 3 parameters, interactively daAcfPlot(crime.DA, select = T) Select Variable Indices (or 0 to exit): 1 Visit.1=1;Visit.2=1 2 Visit.1=2;Visit.2=1 3 Visit.1=1;Visit.2=2 4 Visit.1=2;Visit.2=2 ? 1:3