methods: Methods for Gradient Boosting for Mixed Models Objects

In mermboost: Gradient Boosting for Generalized Additive Mixed Models

Methods for Gradient Boosting for Mixed Models Objects

Description

Methods for models fitted by mixed model boosting algorithms.

Usage

## S3 method for class 'mermboost'
predict(object, newdata = NULL, RE = TRUE,
        type = c("link", "response", "class"), which = NULL,
        aggregate = c("sum", "cumsum", "none"), ...)
## S3 method for class 'glmermboost'
predict(object, newdata = NULL, RE = TRUE,
        type = c("link", "response", "class"), which = NULL,
        aggregate = c("sum", "cumsum", "none"), ...)

## S3 method for class 'mermboost'
 ranef(object, iteration = mstop(object), ...)
## S3 method for class 'glmermboost'
 ranef(object, iteration = mstop(object), ...)

## S3 method for class 'mermboost'
VarCorr(x, sigma=1, iteration = mstop(x), ...)
## S3 method for class 'glmermboost'
VarCorr(x, sigma=1, iteration = mstop(x), ...)

## S3 method for class 'mer_cv'
mstop(object, ...)
## S3 method for class 'mer_cv'
plot(x, ...)

Arguments

object	objects of class glmermboost or mermboost. If you are using mstop.mer_cv it refers to an object resulting from mer_cvrisk.
newdata	optionally, a data frame in which to look for variables with which to predict. In case the model was fitted using the matrix interface to glmermboost, newdata must be a matrix as well (an error is given otherwise). If RE = TRUE but not the same cluster-identifier is found in the newdata object, it gets set to FALSE, RE = FALSE.
RE	a logical values (TRUE/FALSE) indicating whether to include random effects.
which	a subset of base-learners to take into account for computing predictions or coefficients. If which is given (as an integer vector or characters corresponding to base-learners) a list or matrix is returned. This ignores the random effects.
type	the type of prediction required. The default is on the scale of the predictors; the alternative "response" is on the scale of the response variable. Thus for a binomial model the default predictions are of log-odds (probabilities on logit scale) and type = "response" gives the predicted probabilities. The "class" option returns predicted classes for binomial data.
aggregate	a character specifying how to aggregate predictions or coefficients of single base-learners. The default returns the prediction or coefficient for the final number of boosting iterations. "cumsum" returns a matrix (one row per base-learner) with the cumulative coefficients for all iterations simultaneously (in columns). "none" returns a list of matrices where the jth columns of the respective matrix contains the predictions of the base-learner of the jth boosting iteration (and zero if the base-learner is not selected in this iteration). Therefore, no random effects are considered.
iteration	an integer input that specifies from which iteration the random component is to be drawn.
sigma	an argument used in lme4. Exists for technical reasons but finds no application here.
x	a cross-validation object for plot.mer_cv or an mermboost object for VarCorr.mermboost.
...	additional arguments passed to callies.

Details

The methods should correspond to equivalent mboost and lme4 functions. However, additional arguments about random effects handling might be of interest.

Value

The predict.mermboost-methods give a vector, matrix or a list depending on the arguments.

A matrix with cluster-identifier as rownames and random effects as element results from ranef.mermboost.

A VarrCorr.merMod is the result of applying VarCorr.mermboost to a mermboost model.

To deal with cross validtion objects, class mer_cv, mstop.mer_cv gives a numeric value of the optimal stopping iteration while plot.mer_cv plots cross-validation risk-paths.

See Also

mstop.mer_cv and plot.mer_cv

Examples

data(Orthodont)

mod <- glmermboost(distance ~ age + Sex + (1 |Subject),
                   data = Orthodont, family = gaussian,
                   control = boost_control(mstop = 50))

any(predict(mod, RE = FALSE) == predict(mod, RE = TRUE))
all(predict(mod, RE = FALSE) ==
    predict.glmboost(mod) + mod$nuisance()[[mstop(mod)]]$ff
    )
ranef(mod)
VarCorr(mod, iteration = 10)

mermboost documentation built on April 4, 2025, 1:41 a.m.