AggregatedEffects {sharp}

Summarised coefficients conditionally on selection

Description

Computes descriptive statistics (defined by FUN) for coefficients of the (calibrated) models conditionally on selection across resampling iterations.

Usage

AggregatedEffects(
  stability,
  lambda_id = NULL,
  side = "X",
  comp = 1,
  FUN = stats::median,
  ...
)

Arguments

stability

output of VariableSelection or BiSelection.

lambda_id

parameter ID with respect to the grid Lambda. If NULL, aggregated coefficients across the models run with the calibrated parameter are returned.

side

character string indicating if coefficients of predictors (side="X") or outcomes (side="Y") should be returned. Only applicable to PLS models.

comp

component ID. Only applicable to PLS models.

FUN

function to use to aggregate coefficients of visited models over resampling iterations. Recommended functions include median or mean.

...

additional arguments to be passed to FUN.

Value

A matrix of summarised coefficients conditionally on selection across resampling iterations. Missing values (NA) are returned for variables that are never selected.

See Also

VariableSelection, BiSelection, Refit

Examples

# Example with univariate outcome
set.seed(1)
simul <- SimulateRegression(n = 100, pk = 50, family = "gaussian")
stab <- VariableSelection(xdata = simul$xdata, ydata = simul$ydata, family = "gaussian")
median_betas <- AggregatedEffects(stab)

# Comparison with refitted model
refitted <- Refit(xdata = simul$xdata, ydata = simul$ydata, stability = stab)
refitted_betas <- coef(refitted)[-1, 1]
plot(median_betas[names(refitted_betas), ], refitted_betas,
  panel.first = abline(0, 1, lty = 2)
)

# Extracting mean betas conditionally on selection
mean_betas <- AggregatedEffects(stab, FUN = mean)
plot(median_betas, mean_betas)

# Regression with multivariate outcomes
set.seed(1)
simul <- SimulateRegression(n = 100, pk = 50, q = 2, family = "gaussian")
stab <- VariableSelection(xdata = simul$xdata, ydata = simul$ydata, family = "mgaussian")
median_betas <- AggregatedEffects(stab)
dim(median_betas)

# Sparse PLS with multivariate outcome
if (requireNamespace("sgPLS", quietly = TRUE)) {
  set.seed(1)
  simul <- SimulateRegression(n = 50, pk = 15, q = 3, family = "gaussian")
  x <- simul$xdata
  y <- simul$ydata
  stab <- BiSelection(
    xdata = x, ydata = y,
    family = "gaussian", ncomp = 3,
    LambdaX = seq_len(ncol(x) - 1),
    implementation = SparsePLS
  )
  median_betas <- AggregatedEffects(stab)
  dim(median_betas)
  median_betas <- AggregatedEffects(stab, side = "Y")
  dim(median_betas)
}