Description Usage Arguments Details Value Author(s) References See Also Examples
Computes the observed-data log-posterior density function at given parameter values for an incomplete dataset under a normal model.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | logpostNorm(obj, ...)
## Default S3 method:
logpostNorm(obj, x = NULL, intercept = TRUE, param,
prior = "uniform", prior.df = NULL, prior.sscp = NULL, ...)
## S3 method for class 'formula'
logpostNorm(formula, data, param,
prior = "uniform", prior.df = NULL, prior.sscp = NULL, ...)
## S3 method for class 'norm'
logpostNorm(obj, param = obj$param, prior = obj$prior,
prior.df = obj$prior.df, prior.sscp = obj$prior.sscp, ...)
|
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,
|
prior |
should be |
prior.df |
prior degrees of freedom for a ridge
( |
prior.sscp |
prior sums of squares and cross-products (SSCP)
matrix for an inverted Wishart prior ( |
... |
values to be passed to the methods. |
The simplest way to call
logpostNorm
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 logpostNorm
automatically.
Alternatively, one may call logpostNorm
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 logpostNorm
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
.
a numeric value reporting the observed-data log-posterior density
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
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ## run EM for marijuana data with ridge prior and print the
## last value of the log-posterior density
data(marijuana)
emResult <- emNorm(marijuana, prior="ridge", prior.df=0.5)
print( emResult$logpost[ emResult$iter ] )
## compute the log-posterior density at the final estimate
## and compare it to the last value reported by emNorm
logpost.max <- logpostNorm(emResult)
print( logpost.max - emResult$logpost[ emResult$iter ] )
## The result from logpostNorm is slightly higher,
## because the last value reported by emNorm is the
## log-posterior at the BEGINNING of the last iteration
|
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