aldmck {basicspace}  R Documentation 
aldmck
is a function that takes a matrix of perceptual data, such as
liberalconservative rankings of various stimuli, and recovers the true
location of those stimuli in a spatial model. It differs from procedures
such as wnominate
, which instead use preference data to estimate
candidate and citizen positions. The procedure here, developed by John
Aldrich and Richard McKelvey in 1977, is restricted to estimating data
with no missing values and only in one dimension. Please refer to the
blackbox
and blackbox_transpose
functions in this package for
procedures that accomodate missing data and multidimensionality estimates.
aldmck(data, respondent = 0, missing=NULL, polarity, verbose=FALSE)
data 
matrix of numeric values, containing the perceptual data. Respondents should be organized on rows, and stimuli on columns. It is helpful, though not necessary, to include row names and column names. 
respondent 
integer, specifies the column in the data matrix of the stimuli that contains the respondent's selfplacement on the scale. Setting respondent = 0 specifies that the selfplacement data is not available. Selfplacement data is not required to estimate the locations of the stimuli, but is required if recovery of the respondent ideal points, or distortion parameters is desired. Note that no distortion parameters are estimated in AM without selfplacements because they are not needed, see equation (24) in Aldrich and McKelvey (1977) for proof. 
missing 
vector or matrix of numeric values, sets the missing values for the data. NA values are always treated as missing regardless of what is set here. Observations with missing data are discarded before analysis. If input is a vector, then the vector is assumed to contain the missing value codes for all the data. If the input is a matrix, it must be of dimension p x q, where p is the maximum number of missing values and q is the number of columns in the data. Each column of the inputted matrix then specifies the missing data values for the respective variables in data. If null (default), no missing values are in the data other than the standard NA value. 
polarity 
integer, specifies the column in the data matrix of the stimuli that is to be set on the left side (generally this means a liberal) 
verbose 
logical, indicates whether 
An object of class aldmck
.
legislators 
vector, containing the recovered locations of the stimuli. The names of
the stimuli are attached if provided as column names in the argument 
respondents 
matrix, containing the information estimated for each respondent. Observations which were discarded in the estimation for missing data purposes have been NA'd out:

eigenvalues 
A vector of the eigenvalues from the estimation. 
AMfit 
Ratio of overall variance to perceptions in scaled data divided by average variance. This measure of fit, described by Aldrich and McKelvey, measures the amount of reduction of the variance of the scaled over unscaled data. 
N 
Number of respondents used in the estimation (i.e. had no missing data) 
N.neg 
Number of cases with negative weights. Only calculated if respondent selfplacements are specified, will equal 0 if not. 
N.pos 
Number of cases with positive weights. Only calculated if respondent selfplacements are specified, will equal 0 if not. 
Keith Poole ktpoole@uga.edu
Howard Rosenthal hr31@nyu.edu
Jeffrey Lewis jblewis@ucla.edu
James Lo lojames@usc.edu
Royce Carroll rcarroll@rice.edu
Keith Poole, Jeffrey Lewis, Howard Rosenthal, James Lo, Royce Carroll (2016) “Recovering a Basic Space from Issue Scales in R.” Journal of Statistical Software. 69(7), 1–21. doi:10.18637/jss.v069.i07
John H. Aldrich and Richard D. McKelvey (1977) “A Method of Scaling with Applications to the 1968 and 1972 Presidential Elections.” American Political Science Review. 71(1), 111130.
Thomas R. Palfrey and Keith T. Poole (1987) “The Relationship between Information, Ideology, and Voting Behavior.” American Journal of Political Science. 31(3), 511530.
Keith Poole. http://voteview.com
'LC1980', 'summary.aldmck', 'plot.aldmck', 'plot.cdf'.
### Loads and scales the LiberalConservative scales from the 1980 NES.
data(LC1980)
result < aldmck(data=LC1980, polarity=2, respondent=1, missing=c(0,8,9),verbose=TRUE)
summary(result)
plot.aldmck(result)