rmc: Random Number Generation for the McDonald (Mc)/Beta Power...

In gkwreg: Generalized Kumaraswamy Regression Models for Bounded Data

Random Number Generation for the McDonald (Mc)/Beta Power Distribution

Description

Generates random deviates from the McDonald (Mc) distribution (also known as Beta Power) with parameters gamma (\gamma), delta (\delta), and lambda (\lambda). This distribution is a special case of the Generalized Kumaraswamy (GKw) distribution where \alpha = 1 and \beta = 1.

Usage

rmc(n, gamma, delta, lambda)

Arguments

n	Number of observations. If length(n) > 1, the length is taken to be the number required. Must be a non-negative integer.
gamma	Shape parameter gamma > 0. Can be a scalar or a vector. Default: 1.0.
delta	Shape parameter delta >= 0. Can be a scalar or a vector. Default: 0.0.
lambda	Shape parameter lambda > 0. Can be a scalar or a vector. Default: 1.0.

Details

The generation method uses the relationship between the GKw distribution and the Beta distribution. The general procedure for GKw (rgkw) is: If W \sim \mathrm{Beta}(\gamma, \delta+1), then X = \{1 - [1 - W^{1/\lambda}]^{1/\beta}\}^{1/\alpha} follows the GKw(\alpha, \beta, \gamma, \delta, \lambda) distribution.

For the Mc distribution, \alpha=1 and \beta=1. Therefore, the algorithm simplifies significantly:

1. Generate U \sim \mathrm{Beta}(\gamma, \delta+1) using rbeta.

2. Compute the Mc variate X = U^{1/\lambda}.

This procedure is implemented efficiently, handling parameter recycling as needed.

Value

A vector of length n containing random deviates from the Mc distribution, with values in (0, 1). The length of the result is determined by n and the recycling rule applied to the parameters (gamma, delta, lambda). Returns NaN if parameters are invalid (e.g., gamma <= 0, delta < 0, lambda <= 0).

Author(s)

Lopes, J. E.

References

McDonald, J. B. (1984). Some generalized functions for the size distribution of income. Econometrica, 52(3), 647-663.

Cordeiro, G. M., & de Castro, M. (2011). A new family of generalized distributions. Journal of Statistical Computation and Simulation,

Kumaraswamy, P. (1980). A generalized probability density function for double-bounded random processes. Journal of Hydrology, 46(1-2), 79-88.

Devroye, L. (1986). Non-Uniform Random Variate Generation. Springer-Verlag. (General methods for random variate generation).

See Also

rgkw (parent distribution random generation), dmc, pmc, qmc (other Mc functions), rbeta

Examples

set.seed(2028) # for reproducibility

# Generate 1000 random values from a specific Mc distribution
gamma_par <- 2.0
delta_par <- 1.5
lambda_par <- 1.0 # Equivalent to Beta(gamma, delta+1)

x_sample_mc <- rmc(1000, gamma = gamma_par, delta = delta_par,
                   lambda = lambda_par)
summary(x_sample_mc)

# Histogram of generated values compared to theoretical density
hist(x_sample_mc, breaks = 30, freq = FALSE, # freq=FALSE for density
     main = "Histogram of Mc Sample (Beta Case)", xlab = "x")
curve(dmc(x, gamma = gamma_par, delta = delta_par, lambda = lambda_par),
      add = TRUE, col = "red", lwd = 2, n = 201)
curve(stats::dbeta(x, gamma_par, delta_par + 1), add=TRUE, col="blue", lty=2)
legend("topright", legend = c("Theoretical Mc PDF", "Theoretical Beta PDF"),
       col = c("red", "blue"), lwd = c(2,1), lty=c(1,2), bty = "n")

# Comparing empirical and theoretical quantiles (Q-Q plot)
lambda_par_qq <- 0.7 # Use lambda != 1 for non-Beta case
x_sample_mc_qq <- rmc(1000, gamma = gamma_par, delta = delta_par,
                      lambda = lambda_par_qq)
prob_points <- seq(0.01, 0.99, by = 0.01)
theo_quantiles <- qmc(prob_points, gamma = gamma_par, delta = delta_par,
                      lambda = lambda_par_qq)
emp_quantiles <- quantile(x_sample_mc_qq, prob_points, type = 7)

plot(theo_quantiles, emp_quantiles, pch = 16, cex = 0.8,
     main = "Q-Q Plot for Mc Distribution",
     xlab = "Theoretical Quantiles", ylab = "Empirical Quantiles (n=1000)")
abline(a = 0, b = 1, col = "blue", lty = 2)

# Compare summary stats with rgkw(..., alpha=1, beta=1, ...)
# Note: individual values will differ due to randomness
x_sample_gkw <- rgkw(1000, alpha = 1.0, beta = 1.0, gamma = gamma_par,
                     delta = delta_par, lambda = lambda_par_qq)
print("Summary stats for rmc sample:")
print(summary(x_sample_mc_qq))
print("Summary stats for rgkw(alpha=1, beta=1) sample:")
print(summary(x_sample_gkw)) # Should be similar

gkwreg documentation built on April 16, 2025, 1:10 a.m.