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
and
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 |
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 |
x |
object of class |
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 (ideal points, or latent ability) and
(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 |
overall.percent |
the total percent of votes correctly predicted. |
ideal |
|
desc |
string, the descriptive text from the
|
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