| posPoiMlink {VGAMextra} | R Documentation |
Link functions for the mean of 1–parameter discrete distributions: The Positive Poisson Distribution.
Description
Computes the posPoiMlink transformation,
its inverse and the first two derivatives.
Usage
posPoiMlink(theta, bvalue = NULL,
alg.roots = c("Newton-Raphson", "bisection")[1],
inverse = FALSE, deriv = 0, short = TRUE, tag = FALSE)
Arguments
theta |
Numeric or character. This is |
bvalue |
Details at |
alg.roots |
Character. The iterative method to find the inverse of this link
function. Same as |
inverse, deriv, short, tag |
Details at |
Details
This is a link function for the mean of the positive Poisson distribution. It is defined as
\eta = \tt{posPoiMlink}(\lambda) =
-\log (\lambda^{-1} - \lambda^{-1} e^{-\lambda}),
where \lambda > 0 stands for the single parameter of
pospoisson, i.e.
theta in the VGLM/VGAM context.
Notice, the mean of the positive Poisson is given by
\frac{\lambda}{1 - e^{-\lambda}}.
This link function comes up by taking the logarithm on both sides of this equation.
The domain set for \lambda is (0, \infty).
Hence, non–positive values of \lambda will result in NaN
or NA. Use argument bvalue to properly replace
them before computing the link function.
posPoiMlink tends to infinity as \lambda
increases. Specially, its inverse grows at a higher rate. Therefore,
large values of \lambda will result in Inf accordingly.
See example 2 below.
If theta is a character, arguments inverse and
deriv are disregarded.
Value
For deriv = 0, the posPoiMlink transformation of
theta, if inverse = FALSE. When inverse = TRUE,
theta becomes \eta and the inverse of
posPoiMlink is needed but cannot be written in
closed–form. Instead this link function returns the approximated
inverse image of \eta, say \theta_\eta,
such that
\tt{posPoiMlink}(\theta_\eta) = \eta.
Here, \theta_\eta is iteratively computed
as the unique root of the auxiliary function
f(\theta; \eta) = \tt{posPoiMlink}(\theta) - \eta,
as a function of \theta.
This work is performed via Newton–Raphson or bisection,
as per argument alg.roots.
For deriv = 1, d eta / d theta
as a function of theta if inverse = FALSE, else
the reciprocal d theta / d eta.
Similarly, when deriv = 2 the second order derivatives
are returned in terms of theta.
Warning
This link function is monotonic increasing in (0, \infty) so that
the horizontal axis is an asymptote. Then, in order to assure the root
of the auxiliary
f(\theta; \eta) = \tt{posPoiMlink}(\theta) - \eta
to be real, \eta must be positive. As a result,
posPoiMlink is shited–down and hence intersecting
the horizontal axis uniquely.
Note
This link function is useful to model any parameter
in (0, \infty). Some numerical issues may arise if there are
covariates causing negative values the parameter.
Try identitylink alternatively.
Author(s)
V. Miranda and T. W. Yee.
See Also
pospoisson,
newtonRaphson.basic,
bisection.basic,
Links,
identitylink.
Examples
## Example 1. Special values for theta (or eta, accordingly) ##
m.lambda <- c(0, 0.5, 1, 10, 20, 25, 1e2, 1e3, Inf, -Inf, NaN, NA)
# The 'posPoiMlink' transformation and the first two derivatives.
print(rbind(m.lambda,
deriv1 = posPoiMlink(theta = m.lambda, inverse = FALSE, deriv = 1),
deriv2 = posPoiMlink(theta = m.lambda, inverse = FALSE, deriv = 2)),
digits = 2)
# The inverse of 'posPoiMlink' and the first two derivatives.
print(rbind(m.lambda,
Invderiv1 = posPoiMlink(theta = m.lambda, inverse = TRUE, deriv = 1),
Invderiv2 = posPoiMlink(theta = m.lambda, inverse = TRUE, deriv = 2)),
digits = 2)
## Example 2. The inverse of 'posPoiMlink' ##
m.lambda <- c(0, 1, 5, 10, 1e2, 1e3)
posPoiMlink(theta = posPoiMlink(m.lambda, inverse = TRUE))
pospoi.inv <- posPoiMlink(posPoiMlink(m.lambda, inverse = TRUE)) - m.lambda
summary(pospoi.inv) ## Should be zero.
## Example 3. Plot of 'posPoiMlink' and its first two derivatives ##
## inverse = FALSE, deriv = 0, 1, 2. ##
m.lambda <- seq(0, 35, by = 0.01)[-1]
y.lambda <- posPoiMlink(theta = m.lambda, deriv = 0)
der.1 <- posPoiMlink(theta = m.lambda, deriv = 1)
der.2 <- posPoiMlink(theta = m.lambda, deriv = 2)
plot(y.lambda ~ m.lambda, col = "black",
main = "log(mu), mu = E[Y], Y ~ pospoisson(lambda).",
ylim = c(-1, 10), xlim = c(-1, 26),
lty = 1, type = "l", lwd = 3)
abline(v = 0, h = 0, col = "gray50", lty = "dashed")
lines(m.lambda, der.1, col = "blue", lty = 5, lwd = 3)
lines(m.lambda, der.2, col = "chocolate", lty = 4, lwd = 3)
legend(5, 9, legend = c("posPoiMlink", "deriv = 1", "deriv = 2"),
col = c("black", "blue", "chocolate"), lty = c(1, 5, 4), lwd = c(3, 3, 3))