summary.ideal {pscl} | R Documentation |
Provides a summary of the output from ideal point estimation contained
in an object of class ideal
.
## S3 method for class 'ideal': summary(object, quantiles = c(.025, .975), burnin=NULL, sort=TRUE, include.beta=FALSE,...)
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). |
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. |
sort |
logical, default is TRUE , indicating that the
summary of the ideal points be sorted by the estimated posterior means
(lowest to highest) |
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 |
The test of whether a given discrimination parameter is
distinguishible from zero first checks to see if the two most extreme
quantiles
are symmetric around .5 (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.
An item of class summary.ideal
with elements:
object |
the name of the ideal object as an
unevaluated expression , produced
by match.call()$object |
xResults |
a 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 . |
bResults |
a 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. |
bSig |
a 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.quant |
if 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 . |
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.
Simon Jackman jackman@stanford.edu
## 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=500,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) ## Not run: 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) ## End(Not run)