get.coef | R Documentation |
This group of functions will extract various
summaries from a model fit by cvam
, either
with the EM algorithm or by Markov chain Monte Carlo.
get.coef(obj, withSE = FALSE, meanSeries = TRUE, msgIfNone = TRUE) get.covMat(obj, msgIfNone = TRUE) get.estimates(obj, msgIfNone = TRUE) get.loglik(obj, msgIfNone = TRUE) get.logP(obj, msgIfNone = TRUE) get.mfTrue(obj) get.modelMatrix(obj, msgIfNone = TRUE) get.offset(obj, mfTrue = FALSE, msgIfNone = TRUE) get.strZero(obj, mfTrue = FALSE, msgIfNone = TRUE) get.fitted(obj, type=c("prob", "mean", "logMean"), mfTrue = TRUE, meanSeries = TRUE, msgIfNone = TRUE ) get.imputedFreq(obj, msgIfNone = TRUE) get.minus2logPSeries(obj, startValShift = TRUE, msgIfNone = TRUE, coda = ( obj$method == "MCMC" ) ) get.coefSeries(obj, msgIfNone = TRUE, coda = ( obj$method == "MCMC" ) ) get.probSeries(obj, levelNames=TRUE, sep=".", msgIfNone = TRUE, coda = ( obj$method == "MCMC" ) )
obj |
an object resulting from a call to |
withSE |
if |
mfTrue |
if |
meanSeries |
applies when |
msgIfNone |
if |
type |
type of fitted values to be returned by
|
startValShift |
the function |
coda |
if |
levelNames |
the |
sep |
a character string used to separate the levels of multiple
factors when |
The series objects returned by get.minus2logPSeries
,
get.coefSeries
and get.probSeries
omit results from the
burn-in period, if any, and may also be thinned. The default behavior
is no burn-in period and no thinning. The burn-in period and thinning
interval are set by components of the control
argument to
cvam
, via the function cvamControl
; the relevant
components are control$burnMCMC
and control$thinMCMC
.
By default, cvam
does not save cell probabilities. To save them, set
control$saveProbSeries
to TRUE
.
get.imputedFreq
returns multiple imputations of frequencies for
the complete-data table generated and stored during an MCMC run after
the burn-in period. The default behavior is no imputation. This can be
changed by setting control$imputeEvery
to an integer greater
than zero.
Other useful information from a model fit can be extracted
with the summary
method for a cvam
object, and with the
functions cvamEstimate
, cvamPredict
,
cvamLik
, and cvamImpute
.
get.coef
returns a vector of estimated coefficients from the
log-linear model if withSE=FALSE
; if withSE=TRUE
, it
returns a data frame containing coefficients, standard errors,
t-statistics and p-values.
get.covMat
returns an estimated covariance matrix for the
estimated coefficients.
get.estimates
returns a data frame or a list of data frames
containing the estimates held in obj
.
get.loglik
and get.logP
return the value of the
loglikelihood function or log-posterior density from the beginning of
the final iteration of EM or MCMC. If the model was fit using
cvam(..., saturated=TRUE)
, the likelihood is based on a
multinomial or product-multinomial distribution over the cells of the
complete-data table. If the model was fit as a log-linear approach
using cvam(..., saturated=FALSE)
, the likelihood is based on a
surrogate Poisson model.
get.mfTrue
returns a data frame with one row per cell of the
complete-data table. The variables in this data frame include every
factor appearing in the model (the non-coarsened versions) and another
variable named freq
. If the model was fit using cvam(...,
method="EM")
, freq
contains the predicted cell
frequencies at the final iteration of EM. If the model was fit using
cvam(..., method="MCMC")
, freq
contains a running
average of imputed cell frequencies over all iterations of MCMC after the
burn-in period. In either case, if the data used to fit the model
contain no missing or coarsened values, then freq
will be equal
to the observed frequencies.
get.modelMatrix
returns the model matrix for the log-linear
model. The rows of the model matrix correspond to the rows of
mfTrue
, and the columns correspond to terms created from
the factors in mfTrue
.
get.offset
retrieves the offset for the log-linear model.
If mfTrue
is TRUE
, it returns the data frame
mfTrue
with a numeric variable named offset
. If
mfTrue
is FALSE
, it returns a numeric vector of length
NROW(mfTrue)
.
get.strZero
retrieves the logical values indicating whether
each cell is structural zero. If mfTrue
is TRUE
, it
returns the data frame mfTrue
with a logical variable named
strZero
. If mfTrue
is FALSE
, it returns a logical
vector of length NROW(mfTrue)
.
get.fitted
retrieves fitted values from the log-linear
model. If type="prob"
, the fitted values are cell
probabilities conditioned on any variables fixed in the model. If
type="mean"
or "logMean"
, the fitted values are cell
means or log-cell means from the log-linear model.
If mfTrue
is TRUE
, the function
returns the data frame mfTrue
with a numeric variable named
fit
. If mfTrue
is FALSE
, it returns a numeric
vector of length NROW(mfTrue)
.
get.imputedFreq
returns the data frame mfTrue
, with the
freq
variable replaced by multiply imputed versions of the
frequencies for the complete-data table.
get.minus2logPSeries
returns a series of (minus 2 times) the
log-posterior density values from the iterations of MCMC, either as a
numeric vector or as an mcmc
object used by the coda
package.
get.coefSeries
returns a series of log-linear coefficients from
the iterations of MCMC, either as a numeric matrix or as an
mcmc
object used by the coda
package.
get.probSeries
returns a series of cell probabilities from
the iterations of MCMC, either as a numeric matrix or as an
mcmc
object used by the coda
package.
Joe Schafer Joseph.L.Schafer@census.gov
For more information, refer to the package vignette Log-Linear Modeling with Missing and Coarsened Values Using the cvam Package.
For information about coda
, see:
Martyn Plummer, Nicky Best, Kate Cowles and Karen Vines (2006). CODA: Convergence Diagnosis and Output Analysis for MCMC, R News, vol 6, 7-11.
cvam
, summary.cvam
, cvamControl
,
cvamEstimate
, cvamPredict
,
cvamImpute
, cvamLik
fit <- cvam( ~ V1 * V2, data=crime, freq=n ) get.coef(fit, withSE=TRUE) get.covMat(fit) get.fitted(fit, type="mean") set.seed(6755) fit <- cvam(fit, method="MCMC", control=list(iterMCMC=5000, imputeEvery=500) ) get.imputedFreq(fit) ## Not run: plot( get.coefSeries(fit) ) # coda trace and density plots
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