VGAM (version 1.0-4)

Sinmad: The Singh-Maddala Distribution

Description

Density, distribution function, quantile function and random generation for the Singh-Maddala distribution with shape parameters a and q, and scale parameter scale.

Usage

dsinmad(x, scale = 1, shape1.a, shape3.q, log = FALSE)
psinmad(q, scale = 1, shape1.a, shape3.q, lower.tail = TRUE, log.p = FALSE)
qsinmad(p, scale = 1, shape1.a, shape3.q, lower.tail = TRUE, log.p = FALSE)
rsinmad(n, scale = 1, shape1.a, shape3.q)

Arguments

x, q

vector of quantiles.

p

vector of probabilities.

n

number of observations. If length(n) > 1, the length is taken to be the number required.

shape1.a, shape3.q

shape parameters.

scale

scale parameter.

log

Logical. If log = TRUE then the logarithm of the density is returned.

lower.tail, log.p

Same meaning as in pnorm or qnorm.

Value

dsinmad gives the density, psinmad gives the distribution function, qsinmad gives the quantile function, and rsinmad generates random deviates.

Details

See sinmad, which is the VGAM family function for estimating the parameters by maximum likelihood estimation.

References

Kleiber, C. and Kotz, S. (2003) Statistical Size Distributions in Economics and Actuarial Sciences, Hoboken, NJ, USA: Wiley-Interscience.

See Also

sinmad, genbetaII.

Examples

Run this code
# NOT RUN {
sdata <- data.frame(y = rsinmad(n = 3000, scale = exp(2),
                                shape1 = exp(1), shape3 = exp(1)))
fit <- vglm(y ~ 1, sinmad(lss = FALSE, ishape1.a = 2.1), data = sdata,
            trace = TRUE, crit = "coef")
coef(fit, matrix = TRUE)
Coef(fit)
# }

Run the code above in your browser using DataCamp Workspace