R: predicted probabilities from an ideal object

predict.ideal {pscl} | R Documentation

## predicted probabilities 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,
                            burnin=NULL,
                            ...)

    ## 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; i.e., `store.item=TRUE` was set when the `ideal` object was fitted
`cutoff` | numeric, a value between 0 and 1, the threshold to be used for classifying predicted probabilities of a Yea votes as predicted Yea and Nay votes.
`burnin` | of the recorded MCMC samples, how many to discard as burnin? Default is `NULL`, in which case the value of `burnin` in the `ideal` object is used.
`x` | object of class `predict.ideal`
`digits` | number of digits in printed object
`...` | further arguments passed to or from other methods.

### Details

Predicted probabilities are computed using the mean of the posterior density of of x (ideal points, or latent ability) and β (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.

### 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 `eval`uated
`desc` | string, the descriptive text from the `rollcall` object passed to `ideal`

### Note

When specifying a value of `burnin` different from that used in fitting the `ideal` object, note a distinction between the iteration numbers of the stored iterations, and the number of stored iterations. That is, the `n`-th iteration stored in an `ideal` object will not be iteration `n` if the user specified `thin>1` in the call to `ideal`. Here, iterations are tagged with their iteration number. Thus, if the user called `ideal` with `thin=10` and `burnin=100` then the stored iterations are numbered `100, 110, 120, ...`. Any future subsetting via a `burnin` refers to this iteration number.

### See Also

`ideal`, `summary.ideal`, `plot.predict.ideal`

### Examples

    data(s109)

    f <- system.file("extdata","id1.rda",package="pscl")
    load(f)
    phat <- predict(id1)
    phat         ## print method

[Package pscl version 1.5.2 Index]