Skip to Content

family {stats}

Family Objects for Models
Package: 
stats
Version: 
R 3.0.2

Description

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.

Usage

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")
quasibinomial(link = "logit")
quasipoisson(link = "log")

Arguments

link
a specification for the model link function. This can be a name/expression, a literal character string, a length-one character vector or an object of class "link-glm" (such as generated by make.link) provided it is not specified via one of the standard names given next.

The gaussian family accepts the links (as names) identity, log and inverse; the binomial family the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (complementary log-log); the Gamma family the links inverse, identity and log; the poisson family the links log, identity, and sqrt and the inverse.gaussian family the links 1/mu^2, inverse, identity and log.

The quasi family accepts the links logit, probit, cloglog, identity, inverse, log, 1/mu^2 and sqrt, and the function power can be used to create a power link function.

variance
for all families other than quasi, the variance function is determined by the family. The quasi family will accept the literal character string (or unquoted as a name/expression) specifications "constant", "mu(1-mu)", "mu", "mu^2" and "mu^3", a length-one character vector taking one of those values, or a list containing components varfun, validmu, dev.resids, initialize and name.
object
the function family accesses the family objects which are stored within objects created by modelling functions (e.g., glm).
...
further arguments passed to methods.

Details

family is a generic function with methods for classes "glm" and "lm" (the latter returning gaussian()).

The quasibinomial and quasipoisson families differ from the binomial and poisson families only in that the dispersion parameter is not fixed at one, so they can model over-dispersion. For the binomial case see McCullagh and Nelder (1989, pp. 124--8). Although they show that there is (under some restrictions) a model with variance proportional to mean as in the quasi-binomial model, note that glm does not compute maximum-likelihood estimates in that model. The behaviour of S is closer to the quasi- variants.

Values

An object of class "family" (which has a concise print method). This is a list with elements

family
character: the family name.
link
character: the link name.
linkfun
function: the link.
linkinv
function: the inverse of the link function.
variance
function: the variance as a function of the mean.
dev.resids
function giving the deviance residuals as a function of (y, mu, wt).
aic
function giving the AIC value if appropriate (but NA for the quasi- families). See logLik for the assumptions made about the dispersion parameter.
mu.eta
function: derivative function(eta) dμ/dη.
initialize
expression. This needs to set up whatever data objects are needed for the family as well as n (needed for AIC in the binomial family) and mustart (see glm.
valid.mu
logical function. Returns TRUE if a mean vector mu is within the domain of variance.
valid.eta
logical function. Returns TRUE if a linear predictor eta is within the domain of linkinv.
simulate
(optional) function simulate(object, nsim) to be called by the "lm" method of simulate. It will normally return a matrix with nsim columns and one row for each fitted value, but it can also return a list of length nsim. Clearly this will be missing for ‘quasi-’ families.

References

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.

Hastie, T. J. and Pregibon, D. (1992) Generalized linear models. Chapter 6 of Statistical Models in S eds J. M. Chambers and T. J. Hastie, Wadsworth & Brooks/Cole.

Note

The link and variance arguments have rather awkward semantics for back-compatibility. The recommended way is to supply them is as quoted character strings, but they can also be supplied unquoted (as names or expressions). In addition, they can also be supplied as a length-one character vector giving the name of one of the options, or as a list (for link, of class "link-glm"). The restrictions apply only to links given as names: when given as a character string all the links known to make.link are accepted.

This is potentially ambiguous: supplying link = logit could mean the unquoted name of a link or the value of object logit. It is interpreted if possible as the name of an allowed link, then as an object. (You can force the interpretation to always be the value of an object via logit[1].)

See Also

glm, power, make.link.

For binomial coefficients, choose; the binomial and negative binomial distributions, Binomial, and NegBinomial.

Examples

require(utils) # for str
 
nf <- gaussian()  # Normal family
nf
str(nf)
 
gf <- Gamma()
gf
str(gf)
gf$linkinv
gf$variance(-3:4) #- == (.)^2
 
 
## quasipoisson. compare with example(glm)
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)
d.AD <- data.frame(treatment, outcome, counts)
glm.qD93 <- glm(counts ~ outcome + treatment, family = quasipoisson())
 
glm.qD93
anova(glm.qD93, test = "F")
summary(glm.qD93)
## for Poisson results use
anova(glm.qD93, dispersion = 1, test = "Chisq")
summary(glm.qD93, dispersion = 1)
 
 
## Example of user-specified link, a logit model for p^days
## See Shaffer, T.  2004. Auk 121(2): 526-540.
logexp <- function(days = 1)
{
    linkfun <- function(mu) qlogis(mu^(1/days))
    linkinv <- function(eta) plogis(eta)^days
    mu.eta <- function(eta) days * plogis(eta)^(days-1) * binomial()$mu_eta
    valideta <- function(eta) TRUE
    link <- paste0("logexp(", days, ")")
    structure(list(linkfun = linkfun, linkinv = linkinv,
                   mu.eta = mu.eta, valideta = valideta, name = link),
              class = "link-glm")
}
binomial(logexp(3))
## in practice this would be used with a vector of 'days', in
## which case use an offset of 0 in the corresponding formula
## to get the null deviance right.
 
## Binomial with identity link: often not a good idea.
 
## Not run:binomial(link = make.link("identity"))## End(Not run)
 
 
## tests of quasi
x <- rnorm(100)
y <- rpois(100, exp(1+x))
glm(y ~ x, family = quasi(variance = "mu", link = "log"))
# which is the same as
glm(y ~ x, family = poisson)
glm(y ~ x, family = quasi(variance = "mu^2", link = "log"))
 
## Not run:glm(y ~ x, family = quasi(variance = "mu^3", link = "log")) # fails## End(Not run)
 
y <- rbinom(100, 1, plogis(x))
# needs to set a starting value for the next fit
glm(y ~ x, family = quasi(variance = "mu(1-mu)", link = "logit"), start = c(0,1))

Author(s)

The design was inspired by S functions of the same names described in Hastie & Pregibon (1992) (except quasibinomial and quasipoisson).

Documentation reproduced from R 3.0.2. License: GPL-2.