Estimate weighting factors for mean and SD errors

Compute weighting factors to match a target mean and SD across variables.

Usage

weights_vec(
  N,
  target_mean,
  target_sd,
  range,
  obj_weight = c(1, 1),
  integer,
  int.probs = NULL,
  est_iter = 1000,
  eps = 0.001,
  max_weight = 10000,
  metric = "mean"
)

Arguments

N: Integer. Number of values in each vector.
target_mean: Named numeric vector. Desired means for each variable (names identify columns).
target_sd: Named numeric vector. Desired standard deviations for each variable.
range: Numeric vector of length 2 or numeric matrix. Allowed value range for all variables (vector), or per-variable bounds as a two-row matrix matching `target_mean`.
obj_weight: List of numeric vectors length 2, one per variable. Weights for mean vs. SD error. Default `c(1,1)`.
integer: Logical vector: TRUE for integer-valued vectors.
int.probs: List of numeric vectors, one per variable. Sampling probabilities for integer moves; NULL for uniform.
est_iter: Number of Monte Carlo iterations to estimate weights.
eps: Numeric. Small constant to avoid division by zero in objective. Default `1e-12`.
max_weight: Maximum allowed weight magnitude.
metric: Character: "mean" or "median" for summarizing estimated weights.

Value

A list of length equal to `target_mean`, where each element is a numeric vector of length 2 containing the estimated weights for mean vs. SD error for that variable.

Details

Computes quasi-optimal weights balancing mean and standard deviation errors for multiple variables by running Monte Carlo optimization to estimate relative baseline contribution of each term in the objective function.

Examples

 if (FALSE) { # \dontrun{
# Estimate weights
weights_vec(
  N = 100,
  target_mean = c(5,10),
  target_sd   = c(2,3),
  integer     = c(TRUE,TRUE),
  range       = matrix(c(0,15, 0,20), nrow = 2),
)
} # }