| linkfcn | R Documentation |
Link function for the exponential family.
linkfcn(mu, linkp, family = "gaussian")
linkinv(z, linkp, family = "gaussian")
mu |
Numeric. The mean of the response variable. |
linkp |
The link function parameter. A scalar. |
family |
The distribution of the response variable from
|
z |
Numeric. The linear predictor. |
linkfcn maps the mean of the response variable mu to
the linear predictor z. linkinv is its inverse.
For the Gaussian family, if the link parameter is positive, then the extended link is used, defined by
z =
\frac{sign(\mu)|\mu|^\nu - 1}{\nu}
In the other case, the usual Box-Cox link is used.
For the Poisson and gamma families, if the link parameter is positive, then the link is defined by
z = \frac{sign(w)
(e^{\nu |w|}-1)}{\nu}
where
w = \log(\mu). In the other case, the usual
Box-Cox link is used.
For the GEV binomial family, the link function is defined by
\mu = 1 - \exp\{-\max(0, 1 + \nu z)^{\frac{1}{\nu}}\}
for any real \nu. At
\nu = 0 it reduces to the complementary log-log
link.
The Wallace binomial family is a fast approximation to the robit family. It is defined as
\mu =
\Phi(\mbox{sign}(z) c(\nu) \sqrt{\nu \log(1 + z^2/\nu)})
where c(\nu) = (8\nu+1)/(8\nu+3)
A numeric array of the same dimension as the function's first argument.
Evangelou, E., & Roy, V. (2019). Estimation and prediction for spatial generalized linear mixed models with parametric links via reparameterized importance sampling. Spatial Statistics, 29, 289-315.
## Not run:
mu <- seq(0.1, 0.9, 0.1)
linkfcn(mu, 7, "binomial") # robit(7) link function
linkfcn(mu, , "binomial.logit") # logit link function
mu <- seq(-3, 3, 1)
linkfcn(mu, 0.5, "gaussian") # sqrt transformation
linkinv(linkfcn(mu, 0.5, "gaussian"), 0.5, "gaussian")
curve(linkfcn(x, 0.5, "gaussian"), -3, 3)
## End(Not run)
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