R: Present Parameters of General Location Model in an...
getparam.mix
R Documentation
Present Parameters of General Location Model in an Understandable Format
Description
Present parameters of general location model in an understandable format.
Usage
getparam.mix(s, theta, corr=FALSE)
Arguments
s
summary list of an incomplete normal data matrix created by the
function prelim.mix.
theta
list of parameters such as one produced by the function em.mix,
da.mix, ecm.mix, or dabipf.mix.
corr
if FALSE, returns a list containing an array of cell probabilities,
a matrix of cell means, and a variance-covariance matrix.
If TRUE, returns a list containing an array of cell probabilities,
a matrix of cell means, a vector of standard deviations, and a correlation
matrix.
Value
if corr=FALSE, a list containing the components pi,
mu and sigma; if
corr=TRUE, a list containing the components pi, mu,
sdv, and r.
The components are:
pi
array of cell probabilities whose dimensions correspond to the
columns of the categorical part of $x$. The dimension is
c(max(x[,1]),max(x[,2]),...,max(x[,p])) where p
is the number of categorical variables.
mu
Matrix of cell means. The dimension is c(q,D) where q is the
number of continuous variables in x, and D is
length(pi). The order of the rows, corresponding to the
elements of pi, is the same order we would get by
vectorizing pi, as in as.vector(pi); it is
the usual lexicographic order used by S and Fortran, with the
subscript corresponding to x[,1] varying the fastest, and the
subscript corresponding to x[,p] varying the slowest.
sigma
matrix of variances and covariances corresponding to the continuous
variables in x.
sdv
vector of standard deviations corresponding to the continuous
variables in x.
r
matrix of correlations corresponding to the continuous
variables in x.
Note
In a restricted general location model, the matrix of means is
required to satisfy t(mu)=A%*%beta for a given design matrix
A. To obtain beta, perform a multivariate regression
of t(mu) on A — for
example, beta <- lsfit(A, t(mu), intercept=FALSE)$coef.
References
Schafer, J. L. (1996) Analysis of Incomplete Multivariate Data.
Chapman & Hall, Chapter 9.
See Also
prelim.mix, em.mix, ecm.mix,
da.mix, dabipf.mix.
Examples
data(stlouis)
s <- prelim.mix(stlouis,3) # do preliminary manipulations
thetahat <- em.mix(s) # compute ML estimate
getparam.mix(s, thetahat, corr=TRUE)$r # look at estimated correlations