Description Usage Arguments Details Value References See Also Examples

Function for stability selection with functional response. Per default the sampling is done on the level of curves and if the model contains a smooth functional intercept, this intercept is refittedn in each sampling fold.

1 2 3 4 5 6 | ```
## S3 method for class 'FDboost'
stabsel(x, refitSmoothOffset = TRUE, cutoff, q, PFER,
folds = cvLong(x$id, weights = rep(1, l = length(x$id)), type =
"subsampling", B = B), B = ifelse(sampling.type == "MB", 100, 50),
assumption = c("unimodal", "r-concave", "none"), sampling.type = c("SS",
"MB"), papply = mclapply, verbose = TRUE, eval = TRUE, ...)
``` |

`x` |
fitted FDboost-object |

`refitSmoothOffset` |
logical, should the offset be refitted in each learning sample?
Defaults to |

`cutoff` |
cutoff between 0.5 and 1. Preferably a value between 0.6 and 0.9 should be used. |

`q` |
number of (unique) selected variables (or groups of variables depending on the model) that are selected on each subsample. |

`PFER` |
upper bound for the per-family error rate. This specifies the amount of falsely
selected base-learners, which is tolerated. See details of |

`folds` |
a weight matrix with number of rows equal to the number of observations,
see |

`B` |
number of subsampling replicates. Per default, we use 50 complementary pairs for the error
bounds of Shah & Samworth (2013) and 100 for the error bound derived in Meinshausen & Buehlmann (2010).
As we use |

`assumption` |
Defines the type of assumptions on the distributions of the selection probabilities
and simultaneous selection probabilities. Only applicable for |

`sampling.type` |
use sampling scheme of of Shah & Samworth (2013), i.e., with complementary pairs
( |

`papply` |
(parallel) apply function, defaults to mclapply. Alternatively, parLapply can be used. In the latter case, usually more setup is needed (see example of cvrisk for some details). |

`verbose` |
logical (default: TRUE) that determines wether warnings should be issued. |

`eval` |
logical. Determines whether stability selection is evaluated ( |

`...` |
additional arguments to |

The number of boosting iterations is an important hyper-parameter of the boosting algorithms
and can be chosen using the functions `cvrisk.FDboost`

and `validateFDboost`

as they compute
honest, i.e. out-of-bag, estimates of the empirical risk for different numbers of boosting iterations.
The weights (zero weights correspond to test cases) are defined via the folds matrix,
see `cvrisk`

in package mboost.
See Hofner et al. (2015) for the combination of stability selection and component-wise boosting.

An object of class `stabsel`

with a special print method.
For the elements of the object, see `stabsel`

B. Hofner, L. Boccuto and M. Goeker (2015), Controlling false discoveries in high-dimensional situations: boosting with stability selection. BMC Bioinformatics, 16, 1-17.

N. Meinshausen and P. Buehlmann (2010), Stability selection. Journal of the Royal Statistical Society, Series B, 72, 417-473.

R.D. Shah and R.J. Samworth (2013), Variable selection with error control: another look at stability selection. Journal of the Royal Statistical Society, Series B, 75, 55-80.

`stabsel`

to perform stability selection for a mboost-object.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | ```
######## Example for function-on-scalar-regression
data("viscosity", package = "FDboost")
## set time-interval that should be modeled
interval <- "101"
## model time until "interval" and take log() of viscosity
end <- which(viscosity$timeAll == as.numeric(interval))
viscosity$vis <- log(viscosity$visAll[,1:end])
viscosity$time <- viscosity$timeAll[1:end]
# with(viscosity, funplot(time, vis, pch = 16, cex = 0.2))
## fit a model cotaining all main effects
modAll <- FDboost(vis ~ 1
+ bolsc(T_C, df=1) %A0% bbs(time, df=5)
+ bolsc(T_A, df=1) %A0% bbs(time, df=5)
+ bolsc(T_B, df=1) %A0% bbs(time, df=5)
+ bolsc(rspeed, df=1) %A0% bbs(time, df=5)
+ bolsc(mflow, df=1) %A0% bbs(time, df=5),
timeformula = ~bbs(time, df=5),
numInt = "Riemann", family = QuantReg(),
offset = NULL, offset_control = o_control(k_min = 10),
data = viscosity,
control = boost_control(mstop = 100, nu = 0.2))
## create folds for stability selection
## only 5 folds for a fast example, usually use 50 folds
set.seed(1911)
folds <- cvLong(modAll$id, weights = rep(1, l = length(modAll$id)),
type = "subsampling", B = 5)
## Not run:
## stability selection with refit of the smooth intercept
stabsel_parameters(q = 3, PFER = 1, p = 6, sampling.type = "SS")
sel1 <- stabsel(modAll, q = 3, PFER = 1, folds = folds, grid = 1:200, sampling.type = "SS")
sel1
## stability selection without refit of the smooth intercept
sel2 <- stabsel(modAll, refitSmoothOffset = FALSE, q = 3, PFER = 1,
folds = folds, grid = 1:200, sampling.type = "SS")
sel2
## End(Not run)
``` |

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