eVCGsampler (version 0.9.2)

multiSampler: Multi-Sample VCG Generator and Overlap Visualization

Description

Repeatedly samples VCGs (via `VCG_sampler` and `random=TRUE`) from the pool, optionally plots the overlap of VCGs.

Usage

multiSampler(formula, data, n, c_w = NULL, Nsamples = 20, plot = TRUE)

Value

If `plot = TRUE`, returns a list with:

data

The original data frame with additional VCG columns (`VCG_1`, ..., `VCG_Nsamples`).

p

A `ggplot2` object showing the number of times each observation was selected across VCG samples.

If `plot = FALSE`, returns the modified data frame only.

Arguments

formula

A formula specifying the treated and covariates, e.g., `treated ~ cov1 + cov2`. The treated variable must be binary (0=pool, 1=treated)

data

A data frame containing the variables specified in the formula.

n

Integer. Number of observations to sample from the pool. Or a vector of n for each stratum.

c_w

Optional: Vector of positive weights for covariates, reflecting the relative importance of the covariates for the balancing.

Nsamples

Number of VCGs to generate (default is 20).

plot

Logical; if `TRUE`, returns a ggplot2 plot showing the overlap of VCGs (default is `TRUE`).

Details

The function repeatedly calls `VCG_sampler` with `random` set to TRUE to generate multiple VCG groups. It calculates the frequency of selection for each observation and computes the average percentage of overlapping observations. This function should only be used if you really need multiple VCG, e.g. for PoC studies. It is not intended for selecting one VCG from them afterwards! In this case, the VCG_sampler function should be used directly and only one VCG should be generated.

Examples

  dat <- data.frame(
  treat = rep(0:1, c(50, 30)),
  cov_1 = c(rnorm(50, 5, 2),   rnorm(30, 5, 1)),
  cov_2 = c(rnorm(50, 11, 2),  rnorm(30, 10, 1))
  )

  result <- multiSampler(treat ~ cov_1 + cov_2, data = dat, n = 10, Nsamples = 10)