Learn R Programming

basicspace (version 0.25)

individuals: Extraction function for scaled individuals

Description

individuals is a convenience function to extract the individual/respondent parameters from an aldmck, blackbox, or blackbt object.

Usage

individuals(object)

Value

The individual parameters of the estimated output, which can also be recovered as

object$individuals (for blackbox or blackbt objects) or

object$respondents (for aldmck objects). Please refer to the documentation of aldmck, blackbox, or blackbox_transpose

for specifics.

Arguments

object

an aldmck, blackbox, or blackbt output object.

Author

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

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', 'blackbox', 'blackbox_transpose'.

Examples

Run this code
  ### Loads issue scales from the 1980 NES.
  data(Issues1980)
  Issues1980[Issues1980[,"abortion1"]==7,"abortion1"] <- 8	#missing recode
  Issues1980[Issues1980[,"abortion2"]==7,"abortion2"] <- 8	#missing recode

  # \donttest{ 
  Issues1980_bb <- blackbox(Issues1980, missing=c(0,8,9), verbose=FALSE, 
    dims=3, minscale=8)
  # }
  ### 'Issues1980_bb' can be retrieved quickly with: 
  data(Issues1980_bb)

  individuals(Issues1980_bb)

Run the code above in your browser using DataLab