print.moc: Summary methods for fitted MOC models

In moc: General Nonlinear Multivariate Finite Mixtures

Description

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.

coef.moc returns the coefficients (estimated parameters) of a fitted moc object.

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.

Usage

## S3 method for class 'moc'
print(x, digits = 5, expand = TRUE, transpose = FALSE, ...)

## S3 method for class 'moc'
coef(object, split=FALSE, ...)

## S3 method for class 'moc'
fitted(object, ...)

obsfit.moc(object, along = list(cons = rep(1, object$nsubject)),
           FUN = function(x) x)

 

Arguments

x, object	Objects of class moc.
split	If split is TRUE, returns a list with elements corresponding to mu, shape, extra and mixture parameters.
digits	Number of digits to be printed.
expand	Expand density, gmu, gshape, gextra, gmixture function body in the print.
transpose	Transpose fitted.mean and observed.mean in the print.
along	Splitting variable.
FUN	User defined function to apply to observed and expected values.
...	Unused.

Details

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].

Value

All these methods return their results invisibly.

Author(s)

Bernard Boulerice <[e-mail]>

See Also

moc, residuals.moc, post.moc, plot.moc, AIC.moc

moc documentation built on May 1, 2019, 7:32 p.m.