plot.aldmck_negative | R Documentation |
plot.aldmck_negative
reads an aldmck
object and plots the probability distribution
of the respondents and stimuli with negative weights.
## S3 method for class 'aldmck_negative'
plot(x, xlim=c(-2,2), ...)
x |
an |
xlim |
vector of length 2, fed to the |
... |
other arguments to |
A plot of the probability distribution of the respondent ideal points, along with the locations of the stimuli. If no negative weights exist, either because respondent self-placements are not specified, or because all weights are positive, a plot indicating this in text is given.
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
Christopher Hare cdhare@ucdavis.edu
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): 111-130. doi: 10.2307/1956957
David A. Armstrong II, Ryan Bakker, Royce Carroll, Christopher Hare, Keith T. Poole, and Howard Rosenthal. 2021. Analyzing Spatial Models of Choice and Judgment. 2nd ed. Statistics in the Social and Behavioral Sciences Series. Boca Raton, FL: Chapman & Hall/CRC. doi: 10.1201/9781315197609
Thomas R. Palfrey and Keith T. Poole. 1987. “The Relationship between Information, Ideology, and Voting Behavior.” American Journal of Political Science 31(3): 511-530. doi: 10.2307/2111281
Keith T. Poole, Jeffrey B. Lewis, Howard Rosenthal, James Lo, and 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
Keith T. Poole. 1998. “Recovering a Basic Space From a Set of Issue Scales.” American Journal of Political Science 42(3): 954-993. doi: 10.2307/2991737
'aldmck', 'LC1980', 'summary.aldmck', 'plot.cdf', 'plot.aldmck'
### Loads the Liberal-Conservative scales from the 1980 ANES.
data(LC1980)
result <- aldmck(data=LC1980, polarity=2, respondent=1, missing=c(0,8,9), verbose=TRUE)
summary(result)
plot.aldmck_negative(result, xlim=c(-1.5,1.5))
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