Binorm | R Documentation |
Density, cumulative distribution function and random generation for the bivariate normal distribution distribution.
dbinorm(x1, x2, mean1 = 0, mean2 = 0, var1 = 1, var2 = 1, cov12 = 0,
log = FALSE)
pbinorm(q1, q2, mean1 = 0, mean2 = 0, var1 = 1, var2 = 1, cov12 = 0)
rbinorm(n, mean1 = 0, mean2 = 0, var1 = 1, var2 = 1, cov12 = 0)
pnorm2(x1, x2, mean1 = 0, mean2 = 0, var1 = 1, var2 = 1, cov12 = 0)
x1 , x2 , q1 , q2 |
vector of quantiles. |
mean1 , mean2 , var1 , var2 , cov12 |
vector of means, variances and the covariance. |
n |
number of observations.
Same as |
log |
Logical.
If |
The default arguments correspond to the standard bivariate normal
distribution with correlation parameter \rho = 0
.
That is, two independent standard normal distributions.
Let sd1
(say) be sqrt(var1)
and
written \sigma_1
, etc.
Then the general formula for the correlation coefficient is
\rho = cov / (\sigma_1 \sigma_2)
where cov
is argument cov12
.
Thus if arguments var1
and var2
are left alone then
cov12
can be inputted with \rho
.
One can think of this function as an extension of
pnorm
to two dimensions, however note
that the argument names have been changed for VGAM
0.9-1 onwards.
dbinorm
gives the density,
pbinorm
gives the cumulative distribution function,
rbinorm
generates random deviates (n
by 2 matrix).
Being based on an approximation, the results of pbinorm()
may be negative!
Also,
pnorm2()
should be withdrawn soon;
use pbinorm()
instead because it is identical.
For rbinorm()
,
if the i
th variance-covariance matrix is not
positive-definite then the i
th row is all NA
s.
pbinorm()
is
based on Donnelly (1973),
the code was translated from FORTRAN to ratfor using struct, and
then from ratfor to C manually.
The function was originally called bivnor
, and TWY only
wrote a wrapper function.
Donnelly, T. G. (1973). Algorithm 462: Bivariate Normal Distribution. Communications of the ACM, 16, 638.
pnorm
,
binormal
,
uninormal
.
yvec <- c(-5, -1.96, 0, 1.96, 5)
ymat <- expand.grid(yvec, yvec)
cbind(ymat, pbinorm(ymat[, 1], ymat[, 2]))
## Not run: rhovec <- seq(-0.95, 0.95, by = 0.01)
plot(rhovec, pbinorm(0, 0, cov12 = rhovec),
xlab = expression(rho), lwd = 2,
type = "l", col = "blue", las = 1)
abline(v = 0, h = 0.25, col = "gray", lty = "dashed")
## End(Not run)
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