Package 'stabiliser'

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

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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

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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.

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stab_plot

Description

Plot from stability object

Arguments

stabiliser_outcome

Outcome from stabilise() or triangulate() function.

Value

A ggplot object.

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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.

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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").
level_2_id	The variable name determining level 2 status as a string (i.e., "level_2_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.

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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...

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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.

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