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Generate Parameter Grid for Simulations

Source: R/generate_param_grid.R
generate_param_grid.Rd

Generate a grid of parameter combinations for simulation studies using various sampling methods. Can be called with no arguments for a demo grid.

Usage

generate_param_grid(param_ranges = NULL, n = 10, method = "random")

create_param_grid(...)

Arguments

param_ranges

Named list of parameter ranges. Each element can be:

  • A numeric vector of length 2 (min, max) for continuous/integer parameters.

  • A vector of values for categorical parameters.

If NULL (default), uses demo ranges for TNA simulation: list(n_sequences = c(50, 500), seq_length = c(10, 50), n_states = c(4, 12)).

n

Integer. Number of parameter combinations to generate. Default: 10.

method

Character. Sampling method. One of:

"random"

Random uniform sampling within ranges.

"grid"

Regular grid sampling (may exceed n, then subsampled).

"lhs"

Latin Hypercube Sampling for better coverage (requires lhs package).

Default: "random".

...

Arguments passed to generate_param_grid.

Value

A data frame with n rows and columns for each parameter.

Details

Method Details:

  • "random": Draws uniform random values within each range. Integer parameters (detected when min and max are both integers) are rounded.

  • "grid": Creates a regular grid with approximately n^(1/d) points per dimension (where d is the number of parameters). If the resulting grid exceeds n points, it is randomly subsampled.

  • "lhs": Uses Latin Hypercube Sampling for space-filling designs that provide better coverage of the parameter space than random sampling.

Categorical parameters are sampled uniformly with replacement for all methods.

Examples

# Simplest usage: demo grid with default TNA parameters
grid <- generate_param_grid()
head(grid)
#>   n_sequences seq_length n_states
#> 1         490         15        8
#> 2         343         43       10
#> 3         413         21        4
#> 4         460         48       10
#> 5         271         36        6
#> 6          71         16       11

# Define custom parameter ranges
ranges <- list(
  num_rows = c(50, 500),       # Integer parameter
  max_seq_length = c(10, 100), # Integer parameter
  stability_prob = c(0.7, 1.0) # Continuous parameter
)

# Random sampling
grid_random <- generate_param_grid(ranges, n = 20, method = "random")

# Latin Hypercube Sampling
grid_lhs <- generate_param_grid(ranges, n = 20, method = "lhs")

# Grid sampling
grid_regular <- generate_param_grid(ranges, n = 20, method = "grid")