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 non-parametric 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
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.
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)