R: Fit Generalized Mixed-Effects Models

OrdinalBoost {GMMBoost}

R Documentation

Fit Generalized Mixed-Effects Models

Description

Fit a generalized linear mixed model with ordinal response.

Usage

OrdinalBoost(fix=formula, rnd=formula, data,model="sequential",control=list())

Arguments

`fix`	a two-sided linear formula object describing the fixed-effects part of the model, with the response on the left of a `~` operator and the terms, separated by `+` operators, on the right. For categorical covariables use `as.factor(.)` in the formula. Note, that the corresponding dummies are treated as a group and are updated blockwise
`rnd`	a two-sided linear formula object describing the random-effects part of the model, with the grouping factor on the left of a `~` operator and the random terms, separated by `+` operators, on the right.
`data`	the data frame containing the variables named in `formula`.
`model`	Two models for repeatedly assessed ordinal scores, based on the threshold concept, are available, the "sequential" and the "cumulative" model. Default is "sequential".
`control`	a list of control values for the estimation algorithm to replace the default values returned by the function `OrdinalBoostControl`. Defaults to an empty list.

Value

Generic functions such as print, predict and summary have methods to show the results of the fit. The predict function shows the estimated probabilities for the different categories for each observation, either for the data set of the OrdinalBoost object or for newdata. Default is newdata=Null. It uses also estimates of random effects for prediction, if possible (i.e. for known subjects of the grouping factor).

`call`	a list containing an image of the `OrdinalBoost` call that produced the object.
`coefficients`	a vector containing the estimated fixed effects
`ranef`	a vector containing the estimated random effects.
`StdDev`	a scalar or matrix containing the estimates of the random effects standard deviation or variance-covariance parameters, respectively.
`fitted.values`	a vector of fitted values.
`HatMatrix`	hat matrix corresponding to the final fit.
`IC`	a matrix containing the evaluated information criterion for the different covariates (columns) and for each boosting iteration (rows).
`IC_sel`	a vector containing the evaluated information criterion for the selected covariate at different boosting iterations.
`components`	a vector containing the selected components at different boosting iterations.
`opt`	number of optimal boosting steps with respect to AIC or BIC, respectively, if `OPT=TRUE`. Otherwise, `opt` is equal to the number of iterations. Note, that the boosting algorithm is also stopped, if it has converged with respect to the parameter estimates `[coefficients,ranef]` or with respect to the `IC_sel`.
`Deltamatrix`	a matrix containing the estimates of fixed and random effects (columns) for each boosting iteration (rows).
`Q_long`	a list containing the estimates of the random effects standard deviation or variance-covariance parameters, respectively, for each boosting iteration.
`fixerror`	a vector with standrad errors for the fixed effects.
`ranerror`	a vector with standrad errors for the random effects.

Author(s)

Andreas Groll andreas.groll@stat.uni-muenchen.de

References

Tutz, G. and A. Groll (2012). Likelihood-based boosting in binary and ordinal random effects models. Journal of Computational and Graphical Statistics. To appear.

Examples

 
## Not run: 
data(knee)

# fit a sequential model
# (here only one step is performed in order to
# save computational time)

glm1 <- OrdinalBoost(pain ~ time + th + age + sex, rnd = list(id=~1),
        data = knee, model = "sequential", control = list(steps=1))

# see also demo("OrdinalBoost-knee") for more extensive examples

## End(Not run)

[Package GMMBoost version 1.1.5 Index]