If doFit = FALSE
the result is an environment
representing the model ready to be optimized.
If doFit = TRUE
the result is an
object of class "clmm2"
with the following components:
stDevthe standard deviation of the random effects.
Niterthe total number of iterations in the Newton updates of the
conditional modes of the random effects.
grFacthe grouping factor defining the random effects.
nAGQthe number of quadrature points used in the adaptive Gauss-Hermite
Quadrature approximation to the marginal likelihood.
ranefthe conditional modes of the random effects, sometimes referred to
as "random effect estimates".
condVarthe conditional variances of the random effects at their conditional
modes.
betathe parameter estimates of the location part.
zetathe parameter estimates of the scale part on the log
scale; the scale parameter estimates on the original scale are given
by exp(zeta)
.
Alphavector or matrix of the threshold parameters.
Thetavector or matrix of the thresholds.
xivector of threshold parameters, which, given a
threshold function (e.g. "equidistant"
), and possible nominal
effects define the class boundaries, Theta
.
lambdathe value of lambda if lambda is supplied or estimated,
otherwise missing.
coefficientsthe coefficients of the intercepts
(theta
), the location (beta
), the scale
(zeta
), and the link function parameter (lambda
).
df.residualthe number of residual degrees of freedoms,
calculated using the weights.
fitted.valuesvector of fitted values conditional on the values
of the random effects. Use predict
to
get the fitted
values for a random effect of zero. An observation here is taken to
be each of the scalar elements of the multinomial table and not a
multinomial vector.
convergenceTRUE
if the optimizer terminates wihtout
error and FALSE
otherwise.
gradientvector of gradients for the unit-variance random
effects at their conditional modes.
optReslist with results from the optimizer. The contents of the
list depends on the choice of optimizer.
logLikthe log likelihood of the model at
optimizer termination.
Hessianif the model was fitted with Hess = TRUE
, this
is the Hessian matrix of the parameters at the optimum.
scalemodel.frame
for the scale model.
locationmodel.frame
for the location model.
nominalmodel.frame
for the nominal model.
edfthe (effective) number of degrees of freedom used by the
model.
startthe starting values.
methodcharacter, the optimizer.
ythe response variable.
levthe names of the levels of the response variable.
nobsthe (effective) number of observations, calculated
as the sum of the weights.
thresholdcharacter, the threshold function used in the model.
estimLambda1
if lambda is estimated in one of the
flexible link functions and 0
otherwise.
linkcharacter, the link function used in the model.
callthe matched call.
contrastscontrasts applied to terms in location and scale
models.
na.actionthe function used to filter missing data.