# Stability selection. Performs stability selection based on gradient boosting. ```r stabsel(formula, data, family = "gaussian", q, maxit, B = 100, thr = .9, fraction = 0.5, seed = NULL, ...) ## Plot selection frequencies. ## S3 method for class 'stabsel' plot(x, show = NULL, pal = function(n) gray.colors(n, start = 0.9, end = 0.3), ...) ``` ## Arguments - `formula`: A formula or extended formula. - `data`: A `data.frame`. - `family`: A `bamlss.family` object. - `q`: An integer specifying how many terms to select in each boosting run. - `maxit`: An integer specifying the maximum number of boosting iterations. See `opt_boost`. Either choose `q` or `maxit` as hyper-parameter for regularization. - `B`: An integer. The boosting is run B times. - `thr`: Cut-off threshold of relative frequencies (between 0 and 1) for selection. - `fraction`: Numeric between 0 and 1. The fraction of data to be used in each boosting run. - `seed`: A seed to be set before the stability selection. - `x`: A object of class stabsel. - `show`: Number of terms to be shown. - `pal`: Color palette for different model terms. - ``...``: Not used yet in `stabsel`. ## Returns A object of class stabsel. ## Details `stabsel` performs stability selection based on gradient boosting (`opt_boost`): The boosting algorithm is run `B` times on a randomly drawn `fraction` of the `data`. Each boosting run is stopped either when `q` terms have been selected, or when `maxit` iterations have been performed, i.e. either `q` or `maxit` can be used to tune the regularization of the boosting. After the boosting the relative selection frequencies are evaluated. Terms with a relative selection frequency larger then `thr` are suggested for a final regression model. If neither `q` nor `maxit` has been specified, `q` will be set to the square root of the number of columns in `data`. Gradient boosting does not depend on random numbers. Thus, the individual boosting runs differ only in the subset of data which is used. ## Author(s) Thorsten Simon ## Examples ```r ## Not run: ## Simulate some data. set.seed(111) d <- GAMart() n <- nrow(d) ## Add some noise variables. for(i in 4:9) d[[paste0("x",i)]] <- rnorm(n) f <- paste0("~ ", paste("s(x", 1:9, ")", collapse = "+", sep = "")) f <- paste(f, "+ te(lon,lat)") f <- as.formula(f) f <- list(update(f, num ~ .), f) ## Run stability selection. sel <- stabsel(f, data = d, q = 6, B = 10) plot(sel) ## Estimate selected model. nf <- formula(sel) b <- bamlss(nf, data = d) plot(b) ## End(Not run) ```

stabsel function