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