dDGEII function

Discrete generalized exponential distribution - a second type

Discrete generalized exponential distribution - a second type

These functions define the density, distribution function, quantile function and random generation for the Discrete generalized exponential distribution a second type with parameters μ\mu and σ\sigma.

dDGEII(x, mu = 0.5, sigma = 1.5, log = FALSE) pDGEII(q, mu = 0.5, sigma = 1.5, lower.tail = TRUE, log.p = FALSE) rDGEII(n, mu = 0.5, sigma = 1.5) qDGEII(p, mu = 0.5, sigma = 1.5, lower.tail = TRUE, log.p = FALSE)

Arguments

  • x, q: vector of (non-negative integer) quantiles.
  • mu: vector of the mu parameter.
  • sigma: vector of the sigma parameter.
  • log, log.p: logical; if TRUE, probabilities p are given as log(p).
  • lower.tail: logical; if TRUE (default), probabilities are P[X<=x]P[X <= x], otherwise, P[X>x]P[X > x].
  • n: number of random values to return.
  • p: vector of probabilities.

Returns

dDGEII gives the density, pDGEII gives the distribution function, qDGEII gives the quantile function, rDGEII

generates random deviates.

Details

The DGEII distribution with parameters μ\mu and σ\sigma

has a support 0, 1, 2, ... and mass function given by

f(xμ,σ)=(1μx+1)σ(1μx)σf(x | \mu, \sigma) = (1-\mu^{x+1})^{\sigma}-(1-\mu^x)^{\sigma}

with 0<μ<10 < \mu < 1 and σ>0\sigma > 0. If σ=1\sigma=1, the DGEII distribution reduces to the geometric distribution with success probability 1μ1-\mu.

Note: in this implementation we changed the original parameters pp to μ\mu and α\alpha to σ\sigma, we did it to implement this distribution within gamlss framework.

Examples

# Example 1 # Plotting the mass function for different parameter values x_max <- 40 probs1 <- dDGEII(x=0:x_max, mu=0.1, sigma=5) probs2 <- dDGEII(x=0:x_max, mu=0.5, sigma=5) probs3 <- dDGEII(x=0:x_max, mu=0.9, sigma=5) # To plot the first k values plot(x=0:x_max, y=probs1, type="o", lwd=2, col="dodgerblue", las=1, ylab="P(X=x)", xlab="X", main="Probability for DGEII", ylim=c(0, 0.60)) points(x=0:x_max, y=probs2, type="o", lwd=2, col="tomato") points(x=0:x_max, y=probs3, type="o", lwd=2, col="green4") legend("topright", col=c("dodgerblue", "tomato", "green4"), lwd=3, legend=c("mu=0.1, sigma=5", "mu=0.5, sigma=5", "mu=0.9, sigma=5")) # Example 2 # Checking if the cumulative curves converge to 1 #plot1 x_max <- 10 plot_discrete_cdf(x=0:x_max, fx=dDGEII(x=0:x_max, mu=0.3, sigma=15), col="dodgerblue", main="CDF for DGEII", lwd=3) legend("bottomright", legend="mu=0.3, sigma=15", col="dodgerblue", lty=1, lwd=2, cex=0.8) #plot2 plot_discrete_cdf(x=0:x_max, fx=dDGEII(x=0:x_max, mu=0.5, sigma=30), col="tomato", main="CDF for DGEII", lwd=3) legend("bottomright", legend="mu=0.5, sigma=30", col="tomato", lty=1, lwd=2, cex=0.8) #plot3 plot_discrete_cdf(x=0:x_max, fx=dDGEII(x=0:x_max, mu=0.5, sigma=50), col="green4", main="CDF for DGEII", lwd=3) legend("bottomright", legend="mu=0.5, sigma=50", col="green4", lty=1, lwd=2, cex=0.8) # Example 3 # Comparing the random generator output with # the theoretical probabilities x_max <- 15 probs1 <- dDGEII(x=0:x_max, mu=0.5, sigma=5) names(probs1) <- 0:x_max x <- rDGEII(n=1000, mu=0.5, sigma=5) probs2 <- prop.table(table(x)) cn <- union(names(probs1), names(probs2)) height <- rbind(probs1[cn], probs2[cn]) nombres <- cn mp <- barplot(height, beside=TRUE, names.arg=nombres, col=c('dodgerblue3','firebrick3'), las=1, xlab='X', ylab='Proportion') legend('topright', legend=c('Theoretical', 'Simulated'), bty='n', lwd=3, col=c('dodgerblue3','firebrick3'), lty=1) # Example 4 # Checking the quantile function mu <- 0.5 sigma <- 5 p <- seq(from=0, to=1, by=0.01) qxx <- qDGEII(p=p, mu=mu, sigma=sigma, lower.tail=TRUE, log.p=FALSE) plot(p, qxx, type="s", lwd=2, col="green3", ylab="quantiles", main="Quantiles of DDGEII(mu=0.5, sigma=5)")

References

\insertRef nekoukhou2013DiscreteDists

See Also

DGEII .

Author(s)

Valentina Hurtado Sepulveda, vhurtados@unal.edu.co

  • Maintainer: Freddy Hernandez-Barajas
  • License: MIT + file LICENSE
  • Last published: 2024-09-13