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.
obsfit.moc
computes and prints the mean posterior
probabilities and the posterior means of a user specified function of
the expected and observed values, splitted with respect
to the specified variable.
print(x,digits=5,...) fitted(object,...) obsfit.moc(object,along=NULL,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
tau[i,k] = P( G[i] = k | z[i]) h( y[i] | G[i] = k, x[i]) / Sum_k (P( G[i] = k | z[i]) h( y[i] | G[i] = k, x[i]) )
using post.moc
and then the weighted posterior mean probabilities
Sum_i (wt[i] * tau[i,k]) / Sum_i wt[i]
The weighted posterior means of a function f() of the data are computed as
Sum_i (wt[i] * tau[i,k] * f(y[i])) / Sum_i (wt[i] * tau[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@umontreal.ca>
moc
,residuals.moc
,plot.moc
,
AIC.moc