predict.mnp  R Documentation 
Obtains posterior predictions under a fitted (Bayesian) multinomial probit
model. predict
method for class mnp
.
## S3 method for class 'mnp'
predict(
object,
newdata = NULL,
newdraw = NULL,
n.draws = 1,
type = c("prob", "choice", "order"),
verbose = FALSE,
...
)
object 
An output object from 
newdata 
An optional data frame containing the values of the predictor
variables. Predictions for multiple values of the predictor variables can be
made simultaneously if 
newdraw 
An optional matrix of MCMC draws to be used for posterior
predictions. The default is the original MCMC draws stored in 
n.draws 
The number of additional Monte Carlo draws given each MCMC
draw of coefficients and covariance matrix. The specified number of latent
variables will be sampled from the multivariate normal distribution, and the
quantities of interest will be calculated by averaging over these draws.
This will be particularly useful calculating the uncertainty of predicted
probabilities. The default is 
type 
The type of posterior predictions required. There are four
options: 
verbose 
logical. If 
... 
additional arguments passed to other methods. 
The posterior predictive values are computed using the Monte Carlo sample
stored in the mnp
output (or other sample if newdraw
is
specified). Given each Monte Carlo sample of the parameters and each vector
of predictor variables, we sample the vectorvalued latent variable from the
appropriate multivariate Normal distribution. Then, using the sampled
predictive values of the latent variable, we construct the most preferred
choice as well as the ordered preferences. Averaging over the Monte Carlo
sample of the preferred choice, we obtain the predictive probabilities of
each choice being most preferred given the values of the predictor
variables. Since the predictive values are computed via Monte Carlo
simulations, each run may produce somewhat different values. The computation
may be slow if predictions with many values of the predictor variables are
required and/or if a large Monte Carlo sample of the model parameters is
used. In either case, setting verbose = TRUE
may be helpful in
monitoring the progress of the code.
predict.mnp
yields a list of class
predict.mnp
containing at least one of the following elements:
o 
A three dimensional array of the Monte Carlo sample from the posterior predictive
distribution of the ordered preferences. The first dimension corresponds to
the rows of 
p 
A two or three dimensional array of the
posterior predictive probabilities for each alternative in the choice set
being most preferred. The first demension corresponds to the rows of

y 
A matrix of the Monte Carlo
sample from the posterior predictive distribution of the most preferred
choice. The first dimension correspond to the rows of 
x 
A matrix of covariates used for prediction 
Kosuke Imai, Department of Government and Department of Statistics, Harvard University imai@Harvard.Edu
mnp
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