plot.ideal {pscl} | 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)

[Package pscl version 1.5.2 Index]