basicspace (version 0.25)

plot.AM: Aldrich-McKelvey Coordinate Distribution Plot

Description

plot.AM reads an aldmck object and plots the probability distribution of the respondents and stimuli.

Usage

# S3 method for AM
plot(x, xlim=c(-2,2), ...)

Value

A plot of the probability distribution of the respondent ideal points, along with the locations of the stimuli. If no self-placements were specified during estimation, no graphical plots will appear.

Arguments

x

an aldmck output object.

xlim

vector of length 2, fed to the plot function as the xlim argument, which sets the minimum and maximum range of the x-axis.

...

other arguments to plot.

Author

Keith Poole

Howard Rosenthal

Jeffrey Lewis

James Lo

Royce Carroll

Christopher Hare

References

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

See Also

'aldmck', 'LC1980', 'summary.aldmck', 'plot.cdf', 'plot.aldmck'

Examples

  ### Loads and scales 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.AM(result, xlim=c(-1.5,1.5))