cvamEstimate: Obtain Estimated Probabilities from a Fitted Model
In cvam: Coarsened Variable Modeling

Description Usage Arguments Details Value Note Author(s) References See Also Examples

After fitting a log-linear model with cvam, the fitted model object may be passed to this function to obtain estimated marginal and conditional probabilities for model factors.

cvamEstimate(estimate, obj, meanSeries = TRUE,
   confidence = obj$control$confidence,
   probRound = obj$control$probRound, probDigits = obj$control$probDigits, ...)

## S3 method for class 'cvamEstimate'
print(x, showHeader = TRUE, ...)

## S3 method for class 'cvamEstimateList'
print(x, showHeader = TRUE, ...)

`estimate`	a formula or list of formulas indicating the desired probabilities; see DETAILS.
`obj`	an object produced by `cvam` containing results from a model fit
`meanSeries`	applies when `obj` contains results from a simulation run. If `TRUE`, then the requested estimates are computed based on a running mean of cell probabilities over all iterations after the burn-in period. If `FALSE`, then the requested estimates are based only on the cell probabilities from the final iteration, and (assuming the run was sufficiently long, if it is MCMC) can be regarded as a single draw from their posterior distribution.
`confidence`	confidence coefficient for asymmetric interval estimates; see DETAILS.
`probRound`	if TRUE, probabilities will be rounded.
`probDigits`	number of digits for rounding probabilities.
`x`	a set of estimates to be printed.
`showHeader`	if `TRUE`, a descriptive header is printed.
`...`	additional arguments to be passed to `print`.

The argument estimate should be a one-sided formula or a list of one-sided formulas, with variables separated by '+', and variables to be conditioned on appearing after '|'. For example, ~ A requests marginal probabilities for every level of A; ~ A + B | C + D requests conditional probabilities for every level combination of A and B given every level combination of C and D.

If obj was produced with saturated=FALSE and method="EM", then standard errors for all probabilities are computed using Taylor linearization, also known as the delta method, based on the asymptotic covariance matrix for the log-linear coefficients.
If obj was produced with saturated=FALSE and method="MCMC" or "approxBayes", then standard errors are computed with Taylor linearization, based on the covariance matrix for the simulated log-linear coefficients from all iterations after the burn-in period.
If obj was produced with saturated=TRUE, then standard errors are not computed.

A symmetric confidence interval for a probability may be problematic, especially if the estimate is close to zero or one. Asymmetric confidence intervals are computed by applying a normal approximation on the logistic (log-odds) scale and translating the endpoints back to the probability scale.

if estimate is a single formula, this function returns a data frame containing estimated probabilities, standard errors, and endpoints of approximate confidence intervals. If estimate is a list of formulas, then a list of data frames is returned.

Estimates may also be requested when fitting a model with cvam, by providing a formula or list of formulas to the optional argument estimate.

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.

cvam cvamPredict cvamImpute cvamLik