gamboostLSS (version 1.0-2)

methods: Methods for mboostLSS

Description

Methods for GAMLSS models fitted by boosting algorithms.

Usage

## S3 method for class 'mboostLSS':
coef(object, which = NULL,
                         aggregate = c("sum", "cumsum", "none"),
                         parameter = names(object), ...)
## S3 method for class 'glmboostLSS':
coef(object, which = NULL,
                           aggregate = c("sum", "cumsum", "none"),
                           off2int = FALSE, parameter = names(object), ...)
## S3 method for class 'mboostLSS':
risk(object, merge = FALSE, parameter = names(object), ...)
## S3 method for class 'mboostLSS':
mstop(object, parameter = names(object), ...)
## S3 method for class 'oobag':
mstop(object, parameter = names(object), ...)
## S3 method for class 'mboostLSS':
[(x, i, return = TRUE, ...)
## S3 method for class 'mboostLSS':
selected(object, parameter = names(object), ...)
## S3 method for class 'glmboostLSS':
plot(x, main = names(x), parameter = names(x),
                           off2int = FALSE, ...)
## S3 method for class 'gamboostLSS':
plot(x, main = names(x), parameter = names(x),
...)
## S3 method for class 'mboostLSS':
fitted(object, parameter = names(object), ...)
## S3 method for class 'mboostLSS':
predict(object, newdata = NULL,
                            type = c("link", "response", "class"),
                            which = NULL,
                            aggregate = c("sum", "cumsum", "none"),
                            parameter = names(object), ...)

Arguments

x

an object of the appropriate class (see usage).

object

an object of the appropriate class (see usage).

which

a subset of base-learners to take into account when computing predictions or coefficients. If which is given (as an integer vector or characters corresponding to base-learners), a list or matrix is returned.

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 with the

parameter

This can be either a vector of indices or a vector of parameter names which should be processed. See expamles for details. Per default all distribution parameters of the GAMLSS family are returned.

off2int

logical indicating whether the offset should be added to the intercept (if there is any) or if the offset is neglected for plotting (default).

merge

logical. Should the risk vectors of the single components be merged to one risk vector for the model in total? Per default (merge = FALSE) a (named) list of risk vectors is returned.

i

integer. Index specifying the model to extract. If i is smaller than the initial mstop, a subset is used. If i is larger than the initial mstop, additional boosting steps are performed until

return

a logical indicating whether the changed object is returned.

main

a title for the plots.

newdata

optionally; A data frame in which to look for variables with which to predict.

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 on the log-odds scale

...

Further arguments to the functions.

Warning

The [.mboostLSS function changes the original object, i.e., LSSmodel[10] changes LSSmodel directly!

Details

These functions can be used to extract details from fitted models. print shows a dense representation of the model fit.

The function coef extracts the regression coefficients of linear predictors fitted using the glmboostLSS function or additive predictors fitted using gamboostLSS. Per default, only coefficients of selected base-learners are returned for all distribution parameters. However, any desired coefficient can be extracted using the which argument. Furhtermore, one can extract only coefficients for a single distribution parameter via the parameter argument (see examples for details).

Analogical, the function plot per default displays the coefficient paths for the complete GAMLSS but can be restricted to single distribution parameters or covariates (or subsets) using the parameter or which arguments, respectively.

The predict function can be used for predictions for the distribution parameters depending on new observations whereas fitted extracts the regression fits for the observations in the learning sample. For predict, newdata can be specified -- otherwise the fitted values are returned. If which is specified, marginal effects of the corresponding base-learner(s) are returned. The argument type can be used to make predictions on the scale of the link (i.e., the linear predictor X * beta), the response (i.e. h(X * beta), where h is the response function) or the class (in case of classification).

References

Mayr, A., Fenske, N., Hofner, B., Kneib, T. and Schmid, M. (2011): GAMLSS for high-dimensional data - a flexible approach based on boosting. Journal of the Royal Statistical Society, Series C (Applied Statistics). Accepted.

Preliminary version: Department of Statistics, Technical Report 98. http://epub.ub.uni-muenchen.de/11938/

Buehlmann, P. and Hothorn, T. (2007), Boosting algorithms: regularization, prediction and model fitting. Statistical Science, 22(4), 477--505.

Rigby, R. A. and D. M. Stasinopoulos (2005). Generalized additive models for location, scale and shape (with discussion). Journal of the Royal Statistical Society, Series C (Applied Statistics), 54, 507-554.

See Also

glmboostLSS, gamboostLSS and blackboostLSS for fitting of GAMLSS. See methods in the mboost package for the corresponding methods for mboost objects.

Examples

### generate data
set.seed(1907)
x1 <- rnorm(1000)
x2 <- rnorm(1000)
x3 <- rnorm(1000)
x4 <- rnorm(1000)
x5 <- rnorm(1000)
x6 <- rnorm(1000)
mu    <- exp(1.5 + x1^2 +0.5 * x2 - 3 * sin(x3) -1 * x4)
sigma <- exp(-0.2 * x4 +0.2 * x5 +0.4 * x6)
y <- numeric(1000)
for( i in 1:1000)
    y[i] <- rnbinom(1, size = sigma[i], mu = mu[i])
dat <- data.frame(x1, x2, x3, x4, x5, x6, y)

### fit a model
model <- gamboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
                     control = boost_control(mstop = 400))

### extract coefficients
coef(model)

### only for distribution parameter mu
coef(model, parameter = "mu")

### only for covariate x1
coef(model, which = "x1")


### plot complete model
par(mfrow = c(4, 3))
plot(model)
### plot first parameter only
par(mfrow = c(2, 3))
plot(model, parameter = "mu")
### now plot only effect of x3 of both parameters
par(mfrow = c(1, 2))
plot(model, which = "x3")
### first component second parameter (sigma)
par(mfrow = c(1, 1))
plot(model, which = 1, parameter = 2)

### subset model for mstop = 300 (one-dimensional)
model[300]

# WARNING: Subsetting via model[mstopnew] changes the model directly!
# For the original fit one has to subset again: model[mstop]

par(mfrow = c(2, 2))
plot(risk(model, parameter = "mu")[[1]])
plot(risk(model, parameter = "sigma")[[1]])
### get back to orignal fit
model[400]
plot(risk(model, parameter = "mu")[[1]])
plot(risk(model, parameter = "sigma")[[1]])