### Description

Plot seats-votes curves produced by seatsVotes

### Usage

## S3 method for class 'seatsVotes'
legend = "bottomright", transform=FALSE, ...)


### Arguments

 x an object of class seatsVotes type character, partially matching the options above; see details legend where to put the legend when plotting with type="seatsVotes" transform logical, whether to transform the vote shares for type="density"; see Details ... arguments passed to or from other functions (e.g., options for the density function, when type="density")

### Details

A seats-votes curve (with various annotations) is produced with option type="seatsVotes".

A density plot of the vote shares is produced with type="density". A bimodal density corresponds to an electoral system with a proliferation of safe seats for both parties, and a seats-votes curve that is relatively flat (or “unresponsive”) in the neighborhood of average district-level vote shares of 50 percent. The density is fitted using the defaults in the density function, but with the density constrained to fall to zero at the extremes of the data, via the from and to options to density. A rug is added to the density plot.

If transform=TRUE, the vote shares are transformed prior to plotting, so as to reduce the extent to which extreme vote shares close to zero or 1 determine the shape of the density (i.e., this option is available only for plots of type="density"). The transformation is a sinusoidal function, a scaled “log-odds/inverse-log-odds” function mapping from (0,1) to (0,1): i.e., f(x) = g(k\cdot h(x)) where h(x) is the log-odds transformation h(x) = \log(x/(1-x)), k is a scaling parameter set to \sqrt{3}, and g(x) is the inverse-log-odds transformation g(x) = \exp(x)/(1+\exp(x)). Note that this transformation is cosmetic, with the effect of assigning more of the graphing region to be devoted to marginal seats.

### Value

The function performs the requested plots and exits silently with invisible{NULL}.

### Author(s)

Simon Jackman simon.jackman@sydney.edu.au

density, rug

### Examples

data(ca2006)
x <- ca2006$D/(ca2006$D+ca2006\$R)