pscl (version 0.58)

predict.ideal: oredicted orobabilities from an ideal object

Description

compute predicted probabilities from an ideal object. This predict method uses the posterior mean values of x and beta to make predictions.

Usage

## S3 method for class 'ideal':
predict(object,
                        cutoff=.5,
                        start=rownames(object$x)[1],
                        ...)
## S3 method for class 'predict.ideal':
print(x,digits=2,...)

Arguments

object

an object of class ideal (produced by ideal) with item parameters (beta) stored.

cutoff

numeric, a value between 0 and 1, the threshold to be used for sepatating predicted probabilities into yea and nay votes.

start

of the recorded MCMC samples, the sample that should be used as the first iteration included in prediction calculations.

x

object of class predict.ideal

digits

number of digits in printed object

...

further arguments passed to or from other methods.

Value

• An object of class predict.ideal, containing:
• pred.probs
  the calculated predicted probability for each legislator for each vote.
• prediction
  the calculated prediction (0 or 1) for each legislator for each vote.
• correct
  for each legislator for each vote, whether the prediction was correct.
• legis.percent
  for each legislator, the percent of votes correctly predicted.
• vote.percent
  for each vote, the percent correctly predicted.
• yea.percent
  the percent of yea votes correctly predicted.
• nay.percent
  the percent of nay votes correctly predicted.
• party.percent
  the average value of the percent correctly predicted by legislator, separated by party, if party information exists in the rollcall object used for ideal. If no party information is available, party.percent = NULL.
• overall.percent
  the total percent of votes correctly predicted.
• ideal
  the name of the ideal object, which can be later evaluated
• desc
  string, the descriptive text from the rollcall object passed to ideal

Details

Predicted probabilities are computed using the mean of the posterior density of of $$x$$ (ideal points, or latent ability) and $$beta$$ (bill or item parameters). The percentage correctly predicted are determined by counting the percentages of votes with predicted probabilities of a Yea vote greater than or equal to the cutoff as the threshold.

See Also

ideal, summary.ideal, plot.predict.ideal

Examples

data(s109)

id1 <- ideal(s109, meanzero=TRUE,
             store.item=TRUE)      ## too long for examples

id1 <- ideal(s109,
             d=1,
             meanzero=TRUE,
             store.item=TRUE,  ## need this to be TRUE for predict
             maxiter=1000,
             burnin=100,
             thin=10)  

phat <- predict(id1)
phat         ## print method