summary.ideal: Summarize an Ideal object

Description

Provides a summary of the output from ideal point estimation contained in an object of class ideal.

Usage

## S3 method for class 'ideal':
summary(object, quantiles = c(.025, .975),
              start = rownames(object$x)[1],
              include.beta=FALSE,...)

Arguments

object

an object of class ideal.

quantiles

a list of quantiles to report for each legislator's ideal point and each item's discrimination parameter (if stored in the ideal object).

start

the number of the iteration with which to begin when computing summary quantities and means.

include.beta

whether or not to calculate summary statistics of beta, if beta is available. If the item parameters were not stored in the ideal object, then include.beta is ignored.

...

further arguments passed to or from other functions

Value

An item of class summary.ideal with elements:
objectthe name of the ideal object as an unevaluated expression, produced by match.call()$object
xResultsa list of length d (the dimension of the fitted model). Component i of the list is a matrix summarizing the MCMC output for the n legislators' ideal points on the i-th dimension of the model. The columns of this matrix contain the mean of the MCMC draws from the posterior density of the legislators ideal points, the standard deviation, and the requested quantiles.
bResultsa list of length d+1, similar to xResults, but containing summaries of the bill parameters; i.e., there are d discrimination parameters per bill, plus an intercept. If the bill/item parameters were not stored when ideal was called (store.item=FALSE), or include.beta=FALSE, then bResults is a list of length zero.
bSiga link{list} of length d, each component a vector of length m, of mode logical, equal to TRUE if the corresponding discrimination parameter is distinguishible from zero; see Details. If store.item was set to FALSE when ideal was invoked, then bSig is a list of length zero.
party.quantif party information is available through the rollcall object that was used to run ideal, then party.quant gives the posterior mean of the legislators' ideal points by party, by dimension. If no party information is available, then party.quant=NULL.

Details

The tests of whether a discrimination parameters are distinguishible from zero first checks to see if the two most extreme quantiles are symmetric (e.g., as are the default value of .025 and .975). If so, the corresponding quantiles of the MCMC samples for each discrimination parameter are inspected to see if they have the same sign. If they do, then the corresponding discrimination parameter is flagged as distinguishible from zero; otherwise not.

Examples

Run this code

## fake example
set.seed(314159265)
fakeData <- matrix(sample(x=c(0,1),size=1000,replace=TRUE),
                   10,100)
rc <- rollcall(fakeData)
## short-run for demo purposes
idFake <- ideal(rc,maxiter=1000,burnin=100,thin=10)
summary(idFake)

## Supreme Court Example
data(sc9497)
rc <- rollcall(data=sc9497$votes,
               legis.names=sc9497$legis.names,
               desc=sc9497$desc)
id1 <- ideal(rc)
summary(id1)

data(s109)
cl2 <- constrain.legis(s109,
                       x=list("KENNEDY (D MA)"=c(-1,0),
                         "ENZI (R WY)"=c(1,0),
                         "CHAFEE (R RI)"=c(0,-.5)),
                       d=2)
id2Constrained <- ideal(s109,
                        d=2,
                        priors=cl2,      ## priors (w constraints)
                        startvals=cl2,   ## start value (w constraints)
                        store.item=TRUE,
                        maxiter=5000,
                        burnin=500,
                        thin=25)

summary(id2Constrained,
        include.items=TRUE)