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Simulate correlated regressor values

Source: R/correlated_regressors.R
correlated_regressors.Rd

This function simulates regressor values from various marginal distributions with custom correlations.

Usage

correlated_regressors(
  labels,
  n = 100,
  marginals = list(),
  correlation = diag(length(labels)),
  verbose = FALSE
)

Arguments

labels

[character()]
Unique labels for the regressors.

n

[integer(1)]
The number of values per regressor.

marginals

[list()]
Optionally marginal distributions for regressors. If not specified, standard normal marginal distributions are used.

Each list entry must be named according to a regressor label, and the following distributions are currently supported:

discrete distributions

  • Poisson: list(type = "poisson", lambda = ...)

  • categorical: list(type = "categorical", p = c(...))

continuous distributions

  • normal: list(type = "normal", mean = ..., sd = ...)

  • uniform: list(type = "uniform", min = ..., max = ...)

correlation

[matrix()]
A correlation matrix of dimension length(labels), where the (p, q)-th entry defines the correlation between regressor labels[p] and labels[q].

verbose

[logical(1)]
Print information about the simulated regressors?

Value

A data.frame with n rows and length(labels) columns.

References

This function heavily depends on the {SimMultiCorrData} package.

See also

Other simulation helpers: Simulator, ddirichlet_cpp(), dmixnorm_cpp(), dmvnorm_cpp(), dtnorm_cpp(), dwishart_cpp(), gaussian_tv(), simulate_markov_chain()

Examples

labels <- c("P", "C", "N1", "N2", "U")
n <- 100
marginals <- list(
  "P" = list(type = "poisson", lambda = 2),
  "C" = list(type = "categorical", p = c(0.3, 0.2, 0.5)),
  "N1" = list(type = "normal", mean = -1, sd = 2),
  "U" = list(type = "uniform", min = -2, max = -1)
)
correlation <- matrix(
  c(1, -0.3, -0.1, 0, 0.5,
    -0.3, 1, 0.3, -0.5, -0.7,
    -0.1, 0.3, 1, -0.3, -0.3,
    0, -0.5, -0.3, 1, 0.1,
    0.5, -0.7, -0.3, 0.1, 1),
  nrow = 5, ncol = 5
)
data <- correlated_regressors(
  labels = labels, n = n, marginals = marginals, correlation = correlation
)
head(data)
#>   P C         N1         N2         U
#> 1 1 1 -2.6373250  0.2665329 -1.172598
#> 2 1 3 -0.4739260 -0.9327829 -1.974470
#> 3 2 3 -2.1400109 -0.6313114 -1.576500
#> 4 0 2  0.4310060  1.1888854 -1.815358
#> 5 1 3  0.5392228 -0.8698426 -1.785320
#> 6 3 1 -0.8406857  1.0096034 -1.236701