Combine {sharp}

Merging stability selection outputs

Description

Merges the outputs from two runs of VariableSelection, GraphicalModel or Clustering. The two runs must have been done using the same methods and the same params but with different seeds. The combined output will contain results based on iterations from both stability1 and stability2. This function can be used for parallelisation.

Usage

Combine(stability1, stability2, include_beta = TRUE)

Arguments

stability1

output from a first run of VariableSelection, GraphicalModel, or Clustering.

stability2

output from a second run of VariableSelection, GraphicalModel, or Clustering.

include_beta

logical indicating if the beta coefficients of visited models should be concatenated. Only applicable to variable selection or clustering.

Value

A single output of the same format.

See Also

VariableSelection, GraphicalModel

Examples

## Variable selection

# Data simulation
set.seed(1)
simul <- SimulateRegression(n = 100, pk = 50, family = "gaussian")

# Two runs
stab1 <- VariableSelection(xdata = simul$xdata, ydata = simul$ydata, seed = 1, K = 10)
stab2 <- VariableSelection(xdata = simul$xdata, ydata = simul$ydata, seed = 2, K = 10)

# Merging the outputs
stab <- Combine(stability1 = stab1, stability2 = stab2, include_beta = FALSE)
str(stab)


## Graphical modelling

# Data simulation
simul <- SimulateGraphical(pk = 20)

# Two runs
stab1 <- GraphicalModel(xdata = simul$data, seed = 1, K = 10)
stab2 <- GraphicalModel(xdata = simul$data, seed = 2, K = 10)

# Merging the outputs
stab <- Combine(stability1 = stab1, stability2 = stab2)
str(stab)


## Clustering

# Data simulation
simul <- SimulateClustering(n = c(15, 15, 15))

# Two runs
stab1 <- Clustering(xdata = simul$data, seed = 1)
stab2 <- Clustering(xdata = simul$data, seed = 2)

# Merging the outputs
stab <- Combine(stability1 = stab1, stability2 = stab2)
str(stab)