predict.blackbt: Predict method of blackbt objects

Description

predict.blackbt reads an blackbt object and uses the estimates to generate a matrix of predicted values.

Usage

# S3 method for blackbt
predict(object, dims=1, ...)

Value

A matrix of predicted values generated from the parameters estimated from a blackbt object.

Arguments

object: A blackbox output object.
dims: Number of dimensions used in prediction. Must be equal to or less than number of dimensions used in estimation.
...: Ignored.

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

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

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

Examples

Run this code

  ### Loads the Liberal-Conservative scales from the 1980 ANES.
  data(LC1980)
  LCdat <- LC1980[,-1]	#Dump the column of self-placements

  ### Estimate blackbt object from example and call predict function
  # \donttest{ 
  LC1980_bbt <- blackbox_transpose(LCdat, missing=c(0,8,9), dims=3, 
    minscale=5, verbose=TRUE)
  # }
  ### 'LC1980_bbt' can be retrieved quickly with: 
  data(LC1980_bbt)
  prediction <- predict.blackbt(LC1980_bbt, dims=2)

  ### Examine predicted vs. observed values for first 10 respondents
  ### First column of LC1980 are self-placements, which are excluded
  LC1980[1:10,-1]
  prediction[1:10,]

  ### Check correlation across all predicted vs. observed, excluding missing values
  prediction[which(LC1980[,-1] %in% c(0,8,9))] <- NA
  cor(as.numeric(prediction), as.numeric(LC1980[,-1]), use="pairwise.complete")

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