Nakagami {VGAM}

Nakagami Distribution

Description

Density, cumulative distribution function, quantile function and random generation for the Nakagami distribution.

Usage

dnaka(x, scale = 1, shape, log = FALSE)
pnaka(q, scale = 1, shape, lower.tail = TRUE, log.p = FALSE)
qnaka(p, scale = 1, shape, ...)
rnaka(n, scale = 1, shape, Smallno = 1.0e-6)

Arguments

x, q

vector of quantiles.

p

vector of probabilities.

n

number of observations. Same as in runif.

scale, shape

arguments for the parameters of the distribution. See nakagami for more details. For rnaka, arguments shape and scale must be of length 1.

Smallno

Numeric, a small value used by the rejection method for determining the upper limit of the distribution. That is, pnaka(U) > 1-Smallno where U is the upper limit.

...

Arguments that can be passed into uniroot.

log

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

lower.tail, log.p

Same meaning as in pnorm or qnorm.

Details

See nakagami for more details.

Value

dnaka gives the density, pnaka gives the cumulative distribution function, qnaka gives the quantile function, and rnaka generates random deviates.

Author(s)

T. W. Yee and Kai Huang

See Also

nakagami.

Examples

## Not run:  x <- seq(0, 3.2, len = 200)
plot(x, dgamma(x, shape = 1), type = "n", col = "black", ylab = "",
     ylim = c(0,1.5), main = "dnaka(x, shape = shape)")
lines(x, dnaka(x, shape = 1), col = "orange")
lines(x, dnaka(x, shape = 2), col = "blue")
lines(x, dnaka(x, shape = 3), col = "green")
legend(2, 1.0, col = c("orange","blue","green"), lty = rep(1, len = 3),
       legend = paste("shape =", c(1, 2, 3)))

plot(x, pnorm(x), type = "n", col = "black", ylab = "",
     ylim = 0:1, main = "pnaka(x, shape = shape)")
lines(x, pnaka(x, shape = 1), col = "orange")
lines(x, pnaka(x, shape = 2), col = "blue")
lines(x, pnaka(x, shape = 3), col = "green")
legend(2, 0.6, col = c("orange","blue","green"), lty = rep(1, len = 3),
       legend = paste("shape =", c(1, 2, 3))) 
## End(Not run)

probs <- seq(0.1, 0.9, by = 0.1)
pnaka(qnaka(p = probs, shape = 2), shape = 2) - probs  # Should be all 0