Optimal Classification Roll Call Scaling
Description
oc
is the function that takes a rollcall
object and estimates nonmetric
Optimal Classification scores with them.
Usage
1 
Arguments
rcObject 
An object of class 
dims 
integer, number of dimensions to estimate. Must be nonnegative and cannot exceed 10 dimensions. 
minvotes 
minimum number of votes a legislator must vote in for them to be analyzed. 
lop 
A proportion between 0 and 1, the cutoff used for excluding lopsided
votes, expressed as the proportion of nonmissing votes on the minority side.
The default, 
polarity 
a vector specifying the legislator in the data set who is conservative on
each dimension. For example, 
verbose 
logical, indicates whether bills and legislators to be deleted should be printed while data is being checked before ideal points are estimated. 
Value
An object of class OCobject
, with elements as follows:
legislators 
data frame, containing all data from the old

rollcalls 
data frame, containing all data from the old

dimensions 
integer, number of dimensions estimated. 
eigenvalues 
A vector of roll call eigenvalues. 
fits 
A vector of length 2 with the classic measures of fit, containing the percent correct classification and the APRE. 
Author(s)
Keith Poole ktpoole@uga.edu
Jeffrey Lewis jblewis@ucla.edu
James Lo lojames@usc.edu
Royce Carroll rcarroll@rice.edu
References
Keith Poole. 2000. 'Nonparametric Unfolding of Binary Choice Data.' Political Analysis, 8(3):211237
Keith Poole. 2005. 'Spatial Models of Parliamentary Voting.' Cambridge: Cambridge University Press.
Keith Poole. http://voteview.com/
See Also
'plot.OCobject','summary.OCobject'.
Examples
1 2 3 4 5 6 7 8 9 10 11  #This data file is the same as reading file using:
#sen90 < readKH("ftp://voteview.com/sen90kh.ord")
#All ORD files can be found on www.voteview.com
data(sen90)
summary(sen90)
#Output file identical to one produced by command below
#sen90oc <oc(sen90,dims=2,polarity=c(7,2))
data(sen90oc)
summary(sen90oc)
plot(sen90oc)
