loglikNorm {norm2} | R Documentation |
Computes the observed-data loglikelihood function at given parameter values for an incomplete dataset under a normal model.
loglikNorm(obj, ...) ## Default S3 method: loglikNorm(obj, x = NULL, intercept = TRUE, param, ...) ## S3 method for class 'formula' loglikNorm(formula, data, param, ...) ## S3 method for class 'norm' loglikNorm(obj, param = obj$param, ...)
obj |
an object used to select a method. It may be |
x |
a numeric matrix, vector or data frame of covariates to be
used as predictors for |
intercept |
if |
formula |
an object of class |
data |
an optional data frame, list or environment (or object
coercible by |
param |
assumed values for the model
parameters. This must be a list with two named components,
|
... |
values to be passed to the methods. |
The simplest way to call
loglikNorm
is to provide an object of class "norm"
as its
sole argument, where that object is the result of a call to
emNorm
or mcmcNorm
. The parameter values
stored in that object will then be passed to loglikNorm
automatically.
Alternatively, one may call loglikNorm
by providing as the first
argument y
, a vector or matrix of data to be modeled as
normal, and an optional vector or matrix of predictors x
.
Missing values NA
are allowed in y
but not in x
.
A third way to call loglikNorm
is to provide
formula
, a formula for a (typically
multivariate) linear regression model in the manner expected by
lm
. A formula is given as y ~ model
, where
y
is either a single numeric variable or a matrix of numeric
variables bound together with the function cbind
. The
right-hand side of the formula (everything to the right of ~
) is a
linear predictor, a series of terms separated by operators +
,
:
or *
to specify main effects and
interactions. Factors are allowed on the right-hand side and will
enter the model as contrasts among the levels
. The
intercept term 1
is included by default; to remove the
intercept, use -1
.
Calling loglikNorm
is equivalent to calling
logpostNorm
with prior="uniform"
.
a numeric value reporting the observed-data loglikelihood
Joe Schafer Joseph.L.Schafer@census.gov
Schafer, J.L. (1997) Analysis of Incomplete Multivariate
Data. London: Chapman & Hall/CRC Press.
For more information about this function and other functions in
the norm2
package, see User's Guide for norm2
in the library subdirectory doc
.
## run EM for cholesterol data and display the ## loglikelihood values at all iterations data(cholesterol) emResult <- emNorm(cholesterol) print( emResult$loglik ) ## compute the loglikelihood at the final estimate ## and compare it to the last loglikelihood value ## reported by emNorm loglik.max <- loglikNorm(emResult) print( loglik.max - emResult$loglik[ emResult$iter ] ) ## The result from loglikNorm is slightly higher, ## because the last value reported by emNorm is the ## loglikelihood at the BEGINNING of the last iteration