cpolr: Censored ordered probit
In anchors: Statistical analysis of surveys with anchoring vignettes

Description Usage Arguments Details Value Note Author(s) References See Also Examples

Censored ordered probit for analysis of anchoring vignettes. Used in the context of anchoring vignettes as a parametric model for breaking ties/interval in non-parametric ranks.

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  cpolr(formula, data, weights, start, ..., subset, na.action,
                 contrasts = NULL, Hess = TRUE, model = TRUE, method =
                 c("probit", "logistic", "cloglog", "cauchit"), debug = 0)

`formula`	A formula representing 'C' range produced by `anchors` as a function of other variables: `cbind(Cs, Ce) ~ x1 + x2`
`data`	a data frame containing two columns Cs, Ce and the covariates identified in the formula.
`weights`	optional case weights in fitting. Default to 1.
`start`	initial values for the parameters. This is in the format 'c(coefficients, zeta)'
`...`	additional arguments to be passed to `optim[stats]`, most often a 'control' argument.
`subset`	expression saying which subset of the rows of the data should be used in the fit. All observations are included by default.
`na.action`	a function to filter missing data.
`contrasts`	a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
`Hess`	logical for whether the Hessian (the observed information matrix) should be returned.
`model`	logical for whether the model matrix should be returned.
`method`	default is probit; alternatives are logistic or complementary log-log or cauchit (corresponding to a Cauchy latent variable and only available in R >= 2.1.0).
`debug`	additional printing if > 0

For cpolr, cpolr.method default is probit; for additional options, see method option in polr

An object of classes c("cpolr", "polr"). This has components

`coefficients`	the coefficients of the linear predictor, which has no intercept.
`zeta`	the intercepts for the class boundaries.
`deviance`	the residual deviance.
`fitted.values`	a matrix, with a column for each level of the response.
`lev`	the names of the response levels.
`terms`	the 'terms' structure describing the model.
`df.residual`	the number of residual degrees of freedoms, calculated using the weights.
`edf`	the (effective) number of degrees of freedom used by the model.
`n, nobs`	the (effective) number of observations, calculated using the weights. ('nobs' is for use by 'stepAIC').
`call`	the matched call.
`convergence`	the convergence code returned by `optim`.
`niter`	the number of function and gradient evaluations used by `optim`.
`Hessian`	Hessian matrix from `optim`.

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

Based on polr function written by Brian Ripley, modifications by Jonathan Wand

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.

anchors, polr

data(freedom)

## an example of directly using cpolr:
ra <- anchors(self ~ vign1 + vign3 + vign6, data = freedom, method ="C")
freedom2 <- insert(freedom, ra )
out <- cpolr(cbind(Cs, Ce) ~ as.factor(country) + sex + educ, 
            data = freedom2)
summary(out)


## simplified in the context of anchors:
fo <- list(self= self ~ 1,
           vign = cbind(vign1,vign3,vign6) ~ 1,
           cpolr= ~ as.factor(country) + sex + educ)
ra2 <- anchors(self ~ vign1 + vign3 + vign6, data = freedom, method ="C")
summary(ra, ties="cpolr")

## AVERAGE fitted values
## conditional on observed 
fitted(ra2, ties="cpolr", unconditional=FALSE,average=TRUE)
## unconditional prediction
fitted(ra2, ties="cpolr", unconditional=TRUE,average=TRUE)

## fitted probability for each observation
## conditional on observed 
fitted(ra2, ties="cpolr", unconditional=TRUE, average=FALSE)
## unconditional prediction
fitted(ra2, ties="cpolr", unconditional=TRUE, average=FALSE)