# Conditional and unconditional prediction for censored ordered probit

### Description

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.

### Usage

1 2 |

### Arguments

`object` |
output from |

`anchors` |
leave missing for unconditional prediction (or set unconditional=TRUE). For conditional prediction, specify the object of class anchors.rank used to run cpolr originally. |

`average` |
a logical value. See |

`unconditional` |
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. |

### Value

If `average = FALSE`

, a matrix of predicted probabilities
with rows corresponding to observations, and columns corresponding to
categories.

If `average = TRUE`

, the matrix of predicted probabilities
(conditional or unconditional) is summarized to a vector (summed by categories,
then renormalized to sum to 1).

If `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.

If `anchors`

object is omitted, identical to the matrix of predicted
probabilities from the `cpolr`

output.

### Note

If 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
omitted!

If you want to have the same cases used in the unconditional
calculation as in the conditional with a subsetted `anchors`

object,
then include `anchors`

object and set
`unconditional.override = TRUE`

.

Related materials and worked examples are available at http://wand.stanford.edu/anchors/

### Author(s)

Jonathan Wand http://wand.stanford.edu

### References

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.

### See Also

`anchors`

, `cpolr`

### Examples

1 | ```
## Basic usage: see cpolr
``` |