Conditional and unconditional prediction for censored ordered
probit. Unconditional prediction returns the fitted values (predicted
probabilities) from the
cpolr object. Conditional prediction
takes the observed range of the diff-corrected self-response output from
anchors and renormalizes the predicted
probabilities for each observation.
leave missing for unconditional prediction (or set unconditional=TRUE). For conditional prediction, specify the object of class anchors.rank used to run cpolr originally.
a logical value. See
Set to TRUE if you submit an anchors.object AND want the unconditional probabilities returned. One case that you would submit a anchors.rank object is if you did subsetting for the anchors object but not for the cpolr object, and want the intersection of the two objects used for the unconditional probabilities.
required for S3, but any other options will be ignored.
average = FALSE, a matrix of predicted probabilities
with rows corresponding to observations, and columns corresponding to
average = TRUE, the matrix of predicted probabilities
(conditional or unconditional) is summarized to a vector (summed by categories,
then renormalized to sum to 1).
anchors object has been specified, then each observation is
renormalized to fall into the range of the diff-corrected
self-response for that observation. If there are no ties for a given
observation, then that observation is a
vector consisting of (k-1) zeros and 1 one. If there are ties, then
the predicted probabilities for that observation are renormalized to
fall within the diff-corrected range.
anchors object is omitted, identical to the matrix of predicted
probabilities from the
anchors object is made using a subset of the data
used to create the
cpolr object, then invoking
fitted.cpolr will not use the same cases in calculating the
conditional probabilities as it would if the
anchors object is
If you want to have the same cases used in the unconditional
calculation as in the conditional with a subsetted
anchors object and set
unconditional.override = TRUE.
Related materials and worked examples are available at http://wand.stanford.edu/anchors/
Jonathan Wand http://wand.stanford.edu
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. 4th edition. Springer.
Wand, Jonathan; Gary King; and Olivia Lau. (2007) “Anchors: Software for Anchoring Vignettes”. Journal of Statistical Software. Forthcoming. copy at http://wand.stanford.edu/research/anchors-jss.pdf
Wand, Jonathan and Gary King. (2007) Anchoring Vignetttes in R: A (different kind of) Vignette copy at http://wand.stanford.edu/anchors/doc/anchors.pdf
Gary King and Jonathan Wand. "Comparing Incomparable Survey Responses: New Tools for Anchoring Vignettes," Political Analysis, 15, 1 (Winter, 2007): Pp. 46-66, copy at http://gking.harvard.edu/files/abs/c-abs.shtml.
## Basic usage: see cpolr