bilogis {VGAM} | R Documentation |
Bivariate Logistic Distribution
Description
Density, distribution function, quantile function and random generation for the 4-parameter bivariate logistic distribution.
Usage
dbilogis(x1, x2, loc1 = 0, scale1 = 1, loc2 = 0, scale2 = 1,
log = FALSE)
pbilogis(q1, q2, loc1 = 0, scale1 = 1, loc2 = 0, scale2 = 1)
rbilogis(n, loc1 = 0, scale1 = 1, loc2 = 0, scale2 = 1)
Arguments
x1 , x2 , q1 , q2 |
vector of quantiles. |
n |
number of observations.
Same as |
loc1 , loc2 |
the location parameters |
scale1 , scale2 |
the scale parameters |
log |
Logical.
If |
Details
See bilogis
, the VGAM family function for
estimating the four parameters by maximum likelihood estimation,
for the formula of the cumulative distribution function and
other details.
Value
dbilogis
gives the density,
pbilogis
gives the distribution function, and
rbilogis
generates random deviates (a two-column matrix).
Note
Gumbel (1961) proposed two bivariate logistic distributions with
logistic distribution marginals, which he called Type I and Type II.
The Type I is this one.
The Type II belongs to the Morgenstern type.
The biamhcop
distribution has, as a special case,
this distribution, which is when the random variables are
independent.
Author(s)
T. W. Yee
References
Gumbel, E. J. (1961). Bivariate logistic distributions. Journal of the American Statistical Association, 56, 335–349.
See Also
Examples
## Not run: par(mfrow = c(1, 3))
ymat <- rbilogis(n = 2000, loc1 = 5, loc2 = 7, scale2 = exp(1))
myxlim <- c(-2, 15); myylim <- c(-10, 30)
plot(ymat, xlim = myxlim, ylim = myylim)
N <- 100
x1 <- seq(myxlim[1], myxlim[2], len = N)
x2 <- seq(myylim[1], myylim[2], len = N)
ox <- expand.grid(x1, x2)
z <- dbilogis(ox[,1], ox[,2], loc1 = 5, loc2 = 7, scale2 = exp(1))
contour(x1, x2, matrix(z, N, N), main = "density")
z <- pbilogis(ox[,1], ox[,2], loc1 = 5, loc2 = 7, scale2 = exp(1))
contour(x1, x2, matrix(z, N, N), main = "cdf")
## End(Not run)