| getparam.mix {mix} | R Documentation |
Present parameters of general location model in an understandable format.
getparam.mix(s, theta, corr=FALSE)
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.
|
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.
|
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)$coef.
Schafer, J. L. (1996) Analysis of Incomplete Multivariate Data. Chapman & Hall, Chapter 9.
prelim.mix, em.mix, ecm.mix,
da.mix, dabipf.mix.
data(stlouis) s <- prelim.mix(stlouis,3) # do preliminary manipulations thetahat <- em.mix(s) # compute ML estimate getparam.mix(s, thetahat, corr=TRUE)$r # look at estimated correlations