Boosting models for fitting generalized additive models for location, shape and scale (GAMLSS) to potentially high dimensional data. The current relase version can be found on CRAN (https://cran.r-project.org/package=gamboostLSS).
In my opinion it would be nice to have a summary-function for mboostLSS-objects. In the following is code for a summary function (model taken from help).
library(gamboostLSS)
### Data generating process
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 +1 * x1 +0.5 * x2 -0.5 * x3 -1 * x4)
sigma <- exp(-0.4 * x3 -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)
### linear model with y ~ . for both components: 400 boosting iterations
model <- glmboostLSS(y ~ ., families = NBinomialLSS(), data = dat,
control = boost_control(mstop = 400),
center = TRUE)
### summary just tells you that the model is a list
summary(model)
### summary function based on print.mboostLSS() and summary.mboost()
summary.mboostLSS <- function(object, ...) {
####cl <- match.call()
cat("\n")
cat("\t LSS Models fitted via Model-based Boosting\n")
cat("\n")
if (!is.null(attr(object, "call")))
cat("Call:\n", deparse(attr(object, "call")), "\n\n", sep = "")
cat("Number of boosting iterations: mstop =", mstop(object), "\n")
cat("Step size: ", sapply(1:length(object), function(i) object[[i]]$control$nu), "\n\n")
cat("Families:\n")
lapply(object, function(xi){
show(xi$family)
nm <- variable.names(xi)
selprob <- tabulate(selected(xi), nbins = length(nm)) / length(selected(xi))
names(selprob) <- names(nm)
selprob <- sort(selprob, decreasing = TRUE)
selprob <- selprob[selprob > 0]
show(selprob)
} )
invisible(object)
}
### summary of the model
summary(model)
In my opinion it would be nice to have a summary-function for mboostLSS-objects. In the following is code for a summary function (model taken from help).