plot.ideal: plots an ideal object
In pscl: Political Science Computational Laboratory
plot.ideal R Documentation
plots an ideal object
Description
Plot of the results of an ideal point estimation contained
in an object of class ideal.
Usage
## S3 method for class 'ideal'
plot(x, conf.int=0.95, burnin=NULL, ...)
plot1d(x, d=1, conf.int=0.95, burnin=NULL,
showAllNames = FALSE, ...)
plot2d(x, d1=1, d2=2, burnin=NULL,
overlayCuttingPlanes=FALSE, ...)
Arguments
x
an object of class ideal
conf.int
for "ideal" objects with 1 dimension estimated, the
level of the confidence interval to plot around the
posterior mean for each legislator. If 2 or more dimensions were
estimated, conf.int is ignored.
d
integer, which dimension to display in a 1d plot, if the
object is a multidimensional 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.
showAllNames
logical, if TRUE, the
vertical axis will the names of all legislators. Default is
FALSE to reduce clutter on typical-sized graph.
d1
integer, the number of the first dimension to plot when
plotting multi-dimensional ideal objects. This
dimension will appear on the horizontal (x) axis.
d2
integer, the number of the second dimension to plot when
plotting multi-dimensional ideal objects. This dimension
will appear on the vertical (y) axis.
overlayCuttingPlanes
logical, if TRUE, overlay the
estimated bill-specific cutting planes
...
other parameters to be passed through to plotting functions.
Details
If the ideal object comes from fitting a d=1
dimensional model, then plot.ideal plots the mean of the
posterior density over each legislator's ideal point, accompanied by a
conf.int confidence interval. In this case, plot.ideal
is simply a wrapper function to plot1d.
If the ideal object has d=2 dimensions, then
plot2d is called, which plots the (estimated) mean of
the posterior density of each legislator's ideal point (i.e., the
ideal point/latent trait is a point in 2-dimensional Euclidean space,
and the posterior density for each ideal point is a bivariate
density). Single dimension summaries of the estimated ideal points
(latent traits) can be obtained for multidimensional
ideal objects by passing the ideal object
directly to plot1d with d set appropriately.
If the ideal object has d>2 dimensions, a
scatterplot matrix is produced via pairs, with the
posterior means of the ideal points (latent traits) plotted against
one another, dimension by dimension.
For unidimensional and two-dimensional models, if party information is
available in the rollcall object contained in the ideal
object, legislators from different parties are plotted in different
colors. If the ideal object has more than 2 dimensions,
plot.ideal() produces a matrix of plots of the mean ideal
points of each dimension against the posterior mean ideal points of
the other dimensions.
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;
tracex for trace plots, a graphical aid useful in
diagnosing convergence of the MCMC algorithms.
Examples
## Not run:
data(s109)
id1 <- ideal(s109,
d=1,
normalize=TRUE,
store.item=TRUE,
maxiter=500, ## short run for examples
burnin=100,
thin=10)
plot(id1)
id2 <- ideal(s109,
d=2,
store.item=TRUE,
maxiter=11e2,
burnin=1e2,
verbose=TRUE,
thin=25)
plot(id2,overlayCuttingPlanes=TRUE)
id2pp <- postProcess(id2,
constraints=list(BOXER=c(-1,0),
INHOFE=c(1,0),
CHAFEE=c(0,.25)))
plot(id2pp,overlayCuttingPlanes=TRUE)
## End(Not run)
pscl documentation built on May 29, 2024, 9:09 a.m.
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| plot.ideal | R Documentation |
plots an ideal object
Description
Plot of the results of an ideal point estimation contained
in an object of class ideal.
Usage
## S3 method for class 'ideal'
plot(x, conf.int=0.95, burnin=NULL, ...)
plot1d(x, d=1, conf.int=0.95, burnin=NULL,
showAllNames = FALSE, ...)
plot2d(x, d1=1, d2=2, burnin=NULL,
overlayCuttingPlanes=FALSE, ...)
Arguments
x |
an object of class |
conf.int |
for "ideal" objects with 1 dimension estimated, the
level of the confidence interval to plot around the
posterior mean for each legislator. If 2 or more dimensions were
estimated, |
d |
integer, which dimension to display in a 1d plot, if the object is a multidimensional ideal object |
.
burnin |
of the recorded MCMC samples, how many to discard as
burnin? Default is |
showAllNames |
|
d1 |
integer, the number of the first dimension to plot when
plotting multi-dimensional |
d2 |
integer, the number of the second dimension to plot when
plotting multi-dimensional |
overlayCuttingPlanes |
logical, if |
... |
other parameters to be passed through to plotting functions. |
Details
If the ideal object comes from fitting a d=1
dimensional model, then plot.ideal plots the mean of the
posterior density over each legislator's ideal point, accompanied by a
conf.int confidence interval. In this case, plot.ideal
is simply a wrapper function to plot1d.
If the ideal object has d=2 dimensions, then
plot2d is called, which plots the (estimated) mean of
the posterior density of each legislator's ideal point (i.e., the
ideal point/latent trait is a point in 2-dimensional Euclidean space,
and the posterior density for each ideal point is a bivariate
density). Single dimension summaries of the estimated ideal points
(latent traits) can be obtained for multidimensional
ideal objects by passing the ideal object
directly to plot1d with d set appropriately.
If the ideal object has d>2 dimensions, a
scatterplot matrix is produced via pairs, with the
posterior means of the ideal points (latent traits) plotted against
one another, dimension by dimension.
For unidimensional and two-dimensional models, if party information is
available in the rollcall object contained in the ideal
object, legislators from different parties are plotted in different
colors. If the ideal object has more than 2 dimensions,
plot.ideal() produces a matrix of plots of the mean ideal
points of each dimension against the posterior mean ideal points of
the other dimensions.
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;
tracex for trace plots, a graphical aid useful in
diagnosing convergence of the MCMC algorithms.
Examples
## Not run:
data(s109)
id1 <- ideal(s109,
d=1,
normalize=TRUE,
store.item=TRUE,
maxiter=500, ## short run for examples
burnin=100,
thin=10)
plot(id1)
id2 <- ideal(s109,
d=2,
store.item=TRUE,
maxiter=11e2,
burnin=1e2,
verbose=TRUE,
thin=25)
plot(id2,overlayCuttingPlanes=TRUE)
id2pp <- postProcess(id2,
constraints=list(BOXER=c(-1,0),
INHOFE=c(1,0),
CHAFEE=c(0,.25)))
plot(id2pp,overlayCuttingPlanes=TRUE)
## End(Not run)
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