| print.moc {moc} | R Documentation |
print.moc prints information contained in a fitted moc
object. The attributes parameters of the functions
gmu, gshape, gextra and gmixture will be
used to label the output.
fitted.moc computes the expected values for each observation
of a moc object using its expected function.
obsfit.moc computes and prints the mean posterior
probabilities and the posterior means of a user specified function of
the expected and observed values, separated with respect
to the specified variable.
## S3 method for class 'moc':
print(x, digits = 5, ...)
## S3 method for class 'moc':
fitted(object, ...)
obsfit.moc(object, along = list(cons = rep(1, object$nsubject)),
FUN = function(x) x)
x, object |
Objects of class moc. |
digits |
Number of digits to be printed. |
along |
Splitting variable. |
FUN |
User defined function to apply to observed and expected values. |
... |
Unused. |
obsfit.moc will first compute the posterior probabilities
for all subjects in each mixture using post.moc and
then the weighted posterior mean probabilities
Sum_i (wt[i] * post[i,k]) / Sum_i wt[i]
The weighted posterior means of a function g() of the data ( which are the empirical estimators of the conditional expectation given mixture group ) are computed as
Sum_i (wt[i] * post[i,k] * g(y[i])) / Sum_i (wt[i] * post[i,k])
where both sums are taken over index of valid data y[i].
All these methods return their results invisibly.
Bernard Boulerice <Bernard.Boulerice@sympatico.ca>
moc, residuals.moc, post.moc,
plot.moc, AIC.moc