clmm  R Documentation 
Fits Cumulative Link Mixed Models with one or more random effects via the Laplace approximation or quadrature methods
clmm(formula, data, weights, start, subset, na.action, contrasts, Hess = TRUE, model = TRUE, link = c("logit", "probit", "cloglog", "loglog", "cauchit"), doFit = TRUE, control = list(), nAGQ = 1L, threshold = c("flexible", "symmetric", "symmetric2", "equidistant"), ...)
formula 
a twosided linear formula object describing the fixedeffects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right. The vertical bar character "" separates an expression for a model matrix and a grouping factor. 
data 
an optional data frame in which to interpret the variables occurring in the formula. 
weights 
optional case weights in fitting. Defaults to 1. 
start 
optional initial values for the parameters in the format

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 inverse of the observed
information matrix)
should be computed.
Use 
model 
logical for whether the model frames should be part of the returned object. 
link 
link function, i.e. the type of locationscale distribution
assumed for the latent distribution. The default 
doFit 
logical for whether the model should be fit or the model environment should be returned. 
control 
a call to 
nAGQ 
integer; the number of quadrature points to use in the adaptive
GaussHermite quadrature approximation to the likelihood
function. The default ( 
threshold 
specifies a potential structure for the thresholds
(cutpoints). 
... 
additional arguments are passed on to 
This is a new (as of August 2011) improved implementation of CLMMs. The
old implementation is available in clmm2
. Some features
are not yet available in clmm
; for instance
scale effects, nominal effects and flexible link functions are
currently only available in clmm2
. clmm
is expected to
take over clmm2
at some point.
There are standard print, summary and anova methods implemented for
"clmm"
objects.
a list containing
alpha 
threshold parameters. 
beta 
fixed effect regression parameters. 
stDev 
standard deviation of the random effect terms. 
tau 

coefficients 
the estimated model parameters = 
control 
List of control parameters as generated by 
Hessian 
Hessian of the model coefficients. 
edf 
the estimated degrees of freedom used by the model =

nobs 

n 
length(y). 
fitted.values 
fitted values evaluated with the random effects at their conditional modes. 
df.residual 
residual degrees of freedom; 
tJac 
Jacobian of the threshold function corresponding to the mapping from standard flexible thresholds to those used in the model. 
terms 
the terms object for the fixed effects. 
contrasts 
contrasts applied to the fixed model terms. 
na.action 
the function used to filter missing data. 
call 
the matched call. 
logLik 
value of the loglikelihood function for the model at the optimum. 
Niter 
number of Newton iterations in the inner loop update of the conditional modes of the random effects. 
optRes 
list of results from the optimizer. 
ranef 
list of the conditional modes of the random effects. 
condVar 
list of the conditional variance of the random effects at their conditional modes. 
Rune Haubo B Christensen
## Cumulative link model with one random term: fmm1 < clmm(rating ~ temp + contact + (1judge), data = wine) summary(fmm1) ## Not run: ## May take a couple of seconds to run this. ## Cumulative link mixed model with two random terms: mm1 < clmm(SURENESS ~ PROD + (1RESP) + (1RESP:PROD), data = soup, link = "probit", threshold = "equidistant") mm1 summary(mm1) ## test random effect: mm2 < clmm(SURENESS ~ PROD + (1RESP), data = soup, link = "probit", threshold = "equidistant") anova(mm1, mm2) ## End(Not run)
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