Description Usage Arguments Details Value References See Also Examples
IMPORTANT: This function and its methods are no longer supported. The user is adviced to use clm() from package ordinal instead.
Fits a cumulative link locationscale model to an ordered response variable. When the scale part is left unspecified, the model reduces to a cumulative link model assuming a constant scale. With the default logistic link function, the model reduces to the famous Proportional Odds Model. With the probit link and a single twolevel factor in both location and scale parts, the model is known as the Binormal model in the Signal Detection Theory and the Psychometric literature.
1 2 3 
location 
a formula expression as for regression models, of the form

scale 
a optional formula expression as for the location part, of the form

data 
an optional data frame in which to interpret the variables occurring
in 
weights 
optional case weights in fitting. Default to 1. 
start 
initial values for the parameters. This is in the format

... 
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. Use this if you intend to call 
model 
logical for whether the model matrix should be returned. 
method 
logistic or probit or complementary loglog or cauchit (corresponding to a Cauchy latent variable). 
The implementation is highly inspired by polr
in
package MASS and should give compatible results, if scale
is
left unspecified.
Note that standard errors are appropriate for tau
=
log sigma
and not for sigma
, because the profile
likelihood is usually more symmetric for tau
than for
sigma
. Therefore vcov
will give the
variancecovariance matrix of the parameters with tau
rather
than sigma
and summary.clls
will report standard errors
for log sigma
. Notice also that a relevant test for
sigma
is H_0: sigma = 1, so the relevant test for log
sigma
is H_0: log(sigma) = 0. This is reflected in the z
value for sigma
returned by summary.clls
.
There are methods for the standard modelfitting functions, including
summary
, vcov
,
anova
, and an
extractAIC
method.
A object of class "clls"
. This has components
coefficients 
the coefficients of the location
( 
beta 
the parameter estimates of the location part. 
theta 
the intercepts/thresholds for the class boundaries. 
sigma 
the parameter estimates of the scale part. 
tau 
parameter estimates of the scale part on the log scale;
ie. 
deviance 
the residual deviance. 
fitted.values 
a matrix, with a column for each level of the response with the fitted probabilities. 
fitted.case 
a vector of same length as 
lev 
the names of the response levels. 
terms.location 
a 
terms.scale 
a 
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. 
call 
the matched call. 
method 
the matched method used. 
convergence 
the convergence code returned by 
niter 
the number of function and gradient evaluations used by

Hessian 
if 
location 
if 
scale 
if 
Agresti, A. (2002) Categorical Data. Second edition. Wiley.
Christensen, R. H. B., Brockhoff, P. B. and Cleaver, G. (2008) Estimation and Inference in the ANot A test with Sureness. Manuscript for Food Quality and Preference.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17  ## Not run:
options(contrasts = c("contr.treatment", "contr.poly"))
## Extend example from polr in package MASS:
## Fit model from polr example:
data(housing, package = "MASS")
fm1 < clls(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
fm1
summary(fm1)
## With probit link:
summary(update(fm1, method = "probit"))
## Allow scale to depend on Contvariable
summary(fm2 < update(fm1, scale =~ Cont))
anova(fm1, fm2)
## which seems to improve the fit
## End(Not run)

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