| family {base} | R Documentation |
Family objects provide a convenient way to specify the details of the
models used by functions such as glm. See the
documentation for glm for the details on how such model
fitting takes place.
family(object) binomial(link = "logit") gaussian(link ="identity") Gamma(link = "inverse") inverse.gaussian(link = "1/mu^2") poisson(link = "log") quasi(link = "identity", variance = "constant") print.family(x, ...)
link |
a specification for the model link function.
The binomial family admits the links "logit",
"probit", "log", and "cloglog" (complementary
log-log);
the Gamma family the links "identity",
"inverse", and "log";
the poisson family the links "identity", "log",
and "sqrt";
the quasi family the links "logit", "probit",
"cloglog", "identity", "inverse",
"log", "1/mu^2" and "sqrt".
The function power can also be used to create a
power link function for the quasi family.
The other families have only one permissible link function:
|
variance |
for all families, other than quasi, the
variance function is determined by the family. The quasi
family will accept the specifications "constant",
"mu(1-mu)", "mu", "mu^2" and "mu^3" for
the variance function. |
object |
the function family accesses the family
objects which are stored within objects created by modelling
functions (e.g. glm). |
McCullagh P. and Nelder, J. A. (1989) Generalized Linear Models. London: Chapman and Hall.
Dobson, A. J. (1983) An Introduction to Statistical Modelling. London: Chapman and Hall.
Cox, D. R. and Snell, E. J. (1981). Applied Statistics; Principles and Examples. London: Chapman and Hall.
nf <- gaussian()# Normal family nf str(nf)# internal STRucture gf <- Gamma() gf str(gf) gf$linkinv all(1:10 == gf$linkfun(gf$linkinv(1:10)))# is TRUE gf$variance(-3:4) #- == (.)^2 ## tests of quasi x <- rnorm(100) y <- rpois(100, exp(1+x)) glm(y ~x, family=quasi(var="mu", link="log")) # which is the same as glm(y ~x, family=poisson) glm(y ~x, family=quasi(var="mu^2", link="log")) glm(y ~x, family=quasi(var="mu^3", link="log")) # should fail y <- rbinom(100, 1, plogis(x)) # needs to set a starting value for the next fit glm(y ~x, family=quasi(var="mu(1-mu)", link="logit"), start=c(0,1))