Package 'stabiliser'

Title: Stabilising Variable Selection
Description: A stable approach to variable selection through stability selection and the use of a permutation-based objective stability threshold. Lima et al (2021) <doi:10.1038/s41598-020-79317-8>, Meinshausen and Buhlmann (2010) <doi:10.1111/j.1467-9868.2010.00740.x>.
Authors: Robert Hyde [aut, cre] (ORCID: <https://orcid.org/0000-0002-8705-9405>), Eliana Lima [aut], Matthew Barden [aut], Kate Lewis [aut], Martin Green [aut]
Maintainer: Robert Hyde <[email protected]>
License: MIT + file LICENSE
Version: 1.0.7
Built: 2026-06-07 07:24:02 UTC
Source: https://github.com/roberthyde/stabiliser

Help Index

simulate_data

Description

Simulate a dataset. This can optionally include variables with a given associated with the outcome.

Usage

simulate_data(nrows, ncols, n_true = 0, amplitude = 0)

Arguments

nrows

The number of rows to simulate.
ncols

The number of columns to simulate.
n_true

The number of variables truly associated with the outcome.
amplitude

The strength of association between true variables and the outcome.

Value

A simulated dataset

simulate_data_re

Description

Simulate a 500x500 dataset with 8 true fixed effects, 492 junk variables and a clustered outcome suitable for a 2 level random effects analysis. The strength of association between true variables and the outcome is governed by the error added at level 1 (defined by parameter sd_level_1) and level 2 (sd_level_2).

Arguments

sd_level_1

Standard deviation of level 1 variables
sd_level_2

Standard deviation of level 2 variables

Value

A simulated dataset with a clustered outcome sutable for random effects analysis

simulate_data_re_glmer

Description

Simulate a dataset where some variables are associated with the outcome and some are unk

Usage

simulate_glmer_re_data(
  n_subjects = 100,
  obs_per_subject = 10,
  n_signal = 2,
  n_noise = 3,
  beta0 = -1,
  beta_signal = NULL,
  sigma_u = 1
)

Arguments

n_subjects

The number of individual subjects, e.g. participations
obs_per_subject

The number of observations per subject
n_signal

The number of causal predictors
n_noise

The number of junk predictors
beta0

Intercept
beta_signal

signal size for causal parameters
sigma_u

standard deviation for random intercepts

Value

A simulated dataset with a clustered outcome suitable for random effects analysis with a binary outcome

simulate_selection_bias

Description

An function to illustrate the risk of selection bias in conventional modelling approaches by simulating a dataset with no information and conducting conventional modelling with prefiltration.

Arguments

nrows

A vector of the number of rows to simulate (i.e., c(100, 200)).
ncols

A vector of the number of columns to simulate (i.e., c(100, 200)).
p_thresh

A vector of the p-value threshold to use in univariate pre-filtration (i.e., c(0.1, 0.2)).

Value

A list including a dataframe of results, a dataframe of the median number of variables selected and a plot illustrating false positive selection.

stab_plot

Description

Plot from stability object

Arguments

stabiliser_outcome

Outcome from stabilise() or triangulate() function.

Value

A ggplot object.

stabilise

Description

Function to calculate stability of variables' association with an outcome for a given model over a number of bootstrap repeats

Arguments

data

A dataframe containing an outcome variable to be permuted.
outcome

The outcome as a string (i.e. "y").
boot_reps

The number of bootstrap samples. Default is "auto" which selects number based on dataframe size.
permutations

The number of times to be permuted per repeat. Default is "auto" which selects number based on dataframe size.
perm_boot_reps

The number of times to repeat each set of permutations. Default is 20.
models

The models to select for stabilising. Default is elastic net (models = c("enet")), other available models include "lasso", "mbic", "mcp".
type

The type of model, either "linear" or "logistic"
quantile

The quantile of null stabilities to use as a threshold.
normalise

Normalise numeric variables (TRUE/FALSE)
dummy

Create dummy variables for factors/characters (TRUE/FALSE)
impute

Impute missing data (TRUE/FALSE)

Value

A list for each model selected. Each list contains a dataframe of variable stabilities, a numeric permutation threshold, and a dataframe of coefficients for both bootstrap and permutation.

stabilise_re

Description

Function to calculate stability of variables' association with an outcome for a given model over a number of bootstrap repeats using clustered data.

Arguments

data

A dataframe containing an outcome variable to be permuted.
outcome

The outcome as a string (i.e. "y").
intercept_level_ids

A vector names defining which variables are random effect, i.e., c("level_2_column_name", "level_3_column_name").
n_top_filter

The number of variables to filter for final model (Default = 50).
boot_reps

The number of bootstrap samples. Default is "auto" which selects number based on dataframe size.
permutations

The number of times to be permuted per repeat. Default is "auto" which selects number based on dataframe size.
perm_boot_reps

The number of times to repeat each set of permutations. Default is 20.
normalise

Normalise numeric variables (TRUE/FALSE)
dummy

Create dummy variables for factors/characters (TRUE/FALSE)
impute

Impute missing data (TRUE/FALSE)

Value

A list containing a table of variable stabilities and a numeric permutation threshold.

stabilise_re_glmer

Description

Function to calculate stability of variables' association with an outcome for a given model over a number of bootstrap repeats using clustered data.

Arguments

data

A dataframe containing an outcome variable to be permuted.
outcome

The outcome as a string (i.e. "y").
intercept_level_ids

A vector names defining which variables are random effect, i.e., c("level_2_column_name", "level_3_column_name").
n_top_filter

The number of variables to filter for final model (Default = 50).
boot_reps

The number of bootstrap samples. Default is "auto" which selects number based on dataframe size. For glmer models, these are subsamples of the dataset, set to 80%.
permutations

The number of times to be permuted per repeat. Default is "auto" which selects number based on dataframe size.
perm_boot_reps

The number of times to repeat each set of permutations. Default is 20.
normalise

Normalise numeric variables (TRUE/FALSE)
dummy

Create dummy variables for factors/characters (TRUE/FALSE)
impute

Impute missing data (TRUE/FALSE)
base_id

level of the random effect to bootstrap by, e.g individual. This is likely the lower level of random effect specified
parallel

TRUE or FALSE, whether to set up parallel processing
num_cores

Number of cores to use if parallel processing required

Value

A list containing a table of variable stabilities and a numeric permutation threshold.

stabiliser_example

Description

A simulated dataset

Usage

stabiliser_example

Format

A data frame with 50 rows and 100 variables.

The stabiliser_example dataset is a simulated example with the following properties: 1 simulated outcome variable: y 4 variables simulated to be associated with y: causal1, causal2... 95 variables simulated to have no association with y: junk1, junk2...

triangulate

Description

Triangulate multiple models using a stability object

Arguments

object

An object generated through the stabilise() function.
quantile

The quantile of null stabilities to use as a threshold.

Value

A combined list of model results including a dataframe of stability results for variables and a numeric permutation threshold.