cpolr {anchors}  R Documentation 
Censored ordered probit for analysis of anchoring vignettes. Used in the context of anchoring vignettes as a parametric model for breaking ties/interval in nonparametric ranks.
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

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

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 loglog 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 
niter 
the number of function and gradient evaluations used by

Hessian 
Hessian matrix from 
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/anchorsjss.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. 4666, copy at http://gking.harvard.edu/files/abs/cabs.shtml.
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)