clmmOld: Cumulative link mixed models
In ordinal: Regression Models for Ordinal Data

Description Usage Arguments Details Value Author(s) References Examples

Fits cumulative link mixed models, i.e. cumulative link models with random effects via the Laplace approximation or the standard and the adaptive Gauss-Hermite quadrature approximation. The functionality in clm2 is also implemented here. Currently only a single random term is allowed in the location-part of the model.

A new implementation is available in clmm that allows for more than one random effect.

clmm2(location, scale, nominal, random, data, weights, start, subset,
     na.action, contrasts, Hess = FALSE, model = TRUE, sdFixed,
     link = c("logistic", "probit", "cloglog", "loglog",
     "cauchit", "Aranda-Ordaz", "log-gamma"), lambda,
     doFit = TRUE, control, nAGQ = 1,
     threshold = c("flexible", "symmetric", "equidistant"), ...)

`location`	as in `clm2`.
`scale`	as in `clm2`.
`nominal`	as in `clm2`.
`random`	a factor for the random effects in the location-part of the model.
`data`	as in `clm2`.
`weights`	as in `clm2`.
`start`	initial values for the parameters in the format `c(alpha, beta, log(zeta), lambda, log(stDev))` where `stDev` is the standard deviation of the random effects.
`subset`	as in `clm2`.
`na.action`	as in `clm2`.
`contrasts`	as in `clm2`.
`Hess`	logical for whether the Hessian (the inverse of the observed information matrix) should be computed. Use `Hess = TRUE` if you intend to call `summary` or `vcov` on the fit and `Hess = FALSE` in all other instances to save computing time.
`model`	as in `clm2`.
`sdFixed`	If `sdFixed` is specified (a positive scalar), a model is fitted where the standard deviation for the random term is fixed at the value of `sdFixed`. If `sdFixed` is left unspecified, the standard deviation of the random term is estimated from data.
`link`	as in `clm2`.
`lambda`	as in `clm2`.
`doFit`	as in `clm2` although it can also be one of `c("no", "R" "C")`, where `"R"` use the R-implementation for fitting, `"C"` (default) use C-implementation for fitting and `"no"` behaves as `FALSE` and returns the environment.
`control`	a call to `clmm2.control`.
`threshold`	as in `clm2`.
`nAGQ`	the number of quadrature points to be used in the adaptive Gauss-Hermite quadrature approximation to the marginal likelihood. Defaults to `1` which leads to the Laplace approximation. An odd number of quadrature points is encouraged and 3, 5 or 7 are usually enough to achive high precision. Negative values give the standard, i.e. non-adaptive Gauss-Hermite quadrature.
`...`	additional arguments are passed on to `clm2.control` and possibly further on to the optimizer, which can lead to surprising error or warning messages when mistyping arguments etc.

There are methods for the standard model-fitting functions, including summary, vcov, profile, plot.profile, confint, anova, logLik, predict and an extractAIC method.

A Newton scheme is used to obtain the conditional modes of the random effects for Laplace and AGQ approximations, and a non-linear optimization is performed over the fixed parameter set to get the maximum likelihood estimates. The Newton scheme uses the observed Hessian rather than the expected as is done in e.g. glmer, so results from the Laplace approximation for binomial fits should in general be more precise - particularly for other links than the "logistic".

Core parts of the function are implemented in C-code for speed.

The function calls clm2 to up an environment and to get starting values.

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:

`stDev`	the standard deviation of the random effects.
`Niter`	the total number of iterations in the Newton updates of the conditional modes of the random effects.
`grFac`	the grouping factor defining the random effects.
`nAGQ`	the number of quadrature points used in the adaptive Gauss-Hermite Quadrature approximation to the marginal likelihood.
`ranef`	the conditional modes of the random effects, sometimes referred to as "random effect estimates".
`condVar`	the conditional variances of the random effects at their conditional modes.
`beta`	the parameter estimates of the location part.
`zeta`	the parameter estimates of the scale part on the log scale; the scale parameter estimates on the original scale are given by `exp(zeta)`.
`Alpha`	vector or matrix of the threshold parameters.
`Theta`	vector or matrix of the thresholds.
`xi`	vector of threshold parameters, which, given a threshold function (e.g. `"equidistant"`), and possible nominal effects define the class boundaries, `Theta`.
`lambda`	the value of lambda if lambda is supplied or estimated, otherwise missing.
`coefficients`	the coefficients of the intercepts (`theta`), the location (`beta`), the scale (`zeta`), and the link function parameter (`lambda`).
`df.residual`	the number of residual degrees of freedoms, calculated using the weights.
`fitted.values`	vector 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.
`convergence`	`TRUE` if the optimizer terminates wihtout error and `FALSE` otherwise.
`gradient`	vector of gradients for the unit-variance random effects at their conditional modes.
`optRes`	list with results from the optimizer. The contents of the list depends on the choice of optimizer.
`logLik`	the log likelihood of the model at optimizer termination.
`Hessian`	if the model was fitted with `Hess = TRUE`, this is the Hessian matrix of the parameters at the optimum.
`scale`	`model.frame` for the scale model.
`location`	`model.frame` for the location model.
`nominal`	`model.frame` for the nominal model.
`edf`	the (effective) number of degrees of freedom used by the model.
`start`	the starting values.
`method`	character, the optimizer.
`y`	the response variable.
`lev`	the names of the levels of the response variable.
`nobs`	the (effective) number of observations, calculated as the sum of the weights.
`threshold`	character, the threshold function used in the model.
`estimLambda`	`1` if lambda is estimated in one of the flexible link functions and `0` otherwise.
`link`	character, the link function used in the model.
`call`	the matched call.
`contrasts`	contrasts applied to terms in location and scale models.
`na.action`	the function used to filter missing data.

Rune Haubo B Christensen

Agresti, A. (2002) Categorical Data Analysis. Second edition. Wiley.

options(contrasts = c("contr.treatment", "contr.poly"))

## More manageable data set:
dat <- subset(soup, as.numeric(as.character(RESP)) <=  24)
dat$RESP <- dat$RESP[drop=TRUE]

m1 <- clmm2(SURENESS ~ PROD, random = RESP, data = dat, link="probit",
           Hess = TRUE, method="ucminf", threshold = "symmetric")

m1
summary(m1)
logLik(m1)
vcov(m1)
extractAIC(m1)
anova(m1, update(m1, location = SURENESS ~ 1, Hess = FALSE))
anova(m1, update(m1, random = NULL))

## Use adaptive Gauss-Hermite quadrature rather than the Laplace
## approximation:
update(m1, Hess = FALSE, nAGQ = 3)

## Use standard Gauss-Hermite quadrature:
update(m1, Hess = FALSE, nAGQ = -7)

##################################################################
## Binomial example with the cbpp data from the lme4-package:
if(require(lme4)) {
    cbpp2 <- rbind(cbpp[,-(2:3)], cbpp[,-(2:3)])
    cbpp2 <- within(cbpp2, {
        incidence <- as.factor(rep(0:1, each=nrow(cbpp)))
        freq <- with(cbpp, c(incidence, size - incidence))
    })

    ## Fit with Laplace approximation:
    fm1 <- clmm2(incidence ~ period, random = herd, weights = freq,
                 data = cbpp2, Hess = 1)
    summary(fm1)

    ## Fit with the adaptive Gauss-Hermite quadrature approximation:
    fm2 <- clmm2(incidence ~ period, random = herd, weights = freq,
                 data = cbpp2, Hess = 1, nAGQ = 7)
    summary(fm2)
}