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Generate Correlation Matrix from Random Samples.

Source: R/cormat.R
cor_matrix.Rd

This function generates random samples from specified probability distributions and computes the correlation matrix for the generated samples.

Usage

cor_matrix(num_samples = 100, num_vars = 5, dists)

Arguments

num_samples

The number of samples to generate.

num_vars

The number of distributions to sample. The first num_vars elements of dists are used.

dists

A list describing each distribution. Each element should be a function that generates random samples. The names of the list elements are used to label the rows and columns of the result.

Value

The function returns the correlation matrix for the distributions, with rows and columns named after the distributions they were drawn from. Because the columns are sampled independently, the off-diagonal entries are sampling noise about zero: this generates correctly shaped, positive-definite input for testing and for a near-independent baseline, and is not an estimator of dependence between tasks.

References

Govan, Paul, and Ivan Damnjanovic. "The resource-based view on project risk management." Journal of construction engineering and management 142.9 (2016): 04016034.

Examples

# List of probability distributions
dists <- list(
  normal = function(n) rnorm(n, mean = 0, sd = 1),
  uniform = function(n) runif(n, min = 0, max = 1),
  exponential = function(n) rexp(n, rate = 1),
  poisson = function(n) rpois(n, lambda = 1),
  binomial = function(n) rbinom(n, size = 10, prob = 0.5)
)

# Generate correlation matrix
cor_matrix <- cor_matrix(num_samples = 100, num_vars = 5, dists = dists)

# Print correlation matrix
print(cor_matrix)
#>                 normal     uniform exponential     poisson    binomial
#> normal      1.00000000  0.06473076  0.05224556  0.27937665  0.03459448
#> uniform     0.06473076  1.00000000  0.05646605 -0.05419664 -0.01709332
#> exponential 0.05224556  0.05646605  1.00000000 -0.17056880 -0.10869859
#> poisson     0.27937665 -0.05419664 -0.17056880  1.00000000  0.11908758
#> binomial    0.03459448 -0.01709332 -0.10869859  0.11908758  1.00000000