Double generalised linear models

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Description

Fits a generalized linear model with a link-linear model for the dispersion as well as for the mean.

Usage

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dglm(formula=formula(data), dformula = ~ 1, family = stats::gaussian, dlink = "log", 
data = sys.parent(), subset = NULL, weights = NULL, contrasts = NULL, 
method = "ml", mustart = NULL, betastart = NULL, etastart = NULL, phistart = NULL, 
control = dglm.control(...), ykeep = TRUE, xkeep = FALSE, zkeep = FALSE, ...)

dglm.constant(y,family,weights=1)

Arguments

formula

a symbolic description of the model to be fit. The details of model specification are found in dglm.

dformula

a formula expression of the form ~ predictor, the response being ignored. This specifies the linear predictor for modelling the dispersion. A term of the form offset(expression) is allowed.

family

a description of the error distribution and link function to be used in the model. See glm for more information.

dlink

link function for modelling the dispersion. Any link function accepted by the quasi family is allowed, including power(x). See details below.

data

an optional data frame containing the variables in the model. See glm for more information.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

weights

an optional vector of weights to be used in the fitting process.

contrasts

an optional list. See the contrasts.arg of model.matrix.default.

method

the method used to estimate the dispersion parameters; the default is "reml" for restricted maximum likelihood and the alternative is "ml" for maximum likelihood. Upper case and partial matches are allowed.

mustart

numeric vector giving starting values for the fitted values or expected responses. Must be of the same length as the response, or of length 1 if a constant starting vector is desired. Ignored if betastart is supplied.

betastart

numeric vector giving starting values for the regression coefficients in the link-linear model for the mean.

etastart

numeric vector giving starting values for the linear predictor for the mean model.

phistart

numeric vector giving starting values for the dispersion parameters.

control

a list of iteration and algorithmic constants. See dglm.control for their names and default values. These can also be set as arguments to dglm itself.

ykeep

logical flag: if TRUE, the vector of responses is returned.

xkeep

logical flag: if TRUE, the model.matrix for the mean model is returned.

zkeep

logical flag: if TRUE, the model.matrix for the dispersion model is returned.

...

further arguments passed to or from other methods.

y

numeric response vector

Details

Write m_i = E(y_i) for the expectation of the ith response. Then Var(y_i) = s_iV(m_i) where V is the variance function and s_i is the dispersion of the ith response (often denoted as the Greek character ‘phi’). We assume the link linear models g(m_i) = x_i^T b and h(s_i) = z_i^T a, where x_i and z_i are vectors of covariates, and b and a are vectors of regression cofficients affecting the mean and dispersion respectively. The argument dlink specifies h. See family for how to specify g. The optional arguments mustart, betastart and phistart specify starting values for m_i, b and s_i respectively.

The parameters b are estimated as for an ordinary glm. The parameters a are estimated by way of a dual glm in which the deviance components of the ordinary glm appear as responses. The estimation procedure alternates between one iteration for the mean submodel and one iteration for the dispersion submodel until overall convergence.

The output from dglm, out say, consists of two glm objects (that for the dispersion submodel is out$dispersion.fit) with a few more components for the outer iteration and overall likelihood. The summary and anova functions have special methods for dglm objects. Any generic function which has methods for glms or lms will work on out, giving information about the mean submodel. Information about the dispersion submodel can be obtained by using out$dispersion.fit as argument rather than out itself. In particular drop1(out,scale=1) gives correct score statistics for removing terms from the mean submodel, while drop1(out$dispersion.fit,scale=2) gives correct score statistics for removing terms from the dispersion submodel.

The dispersion submodel is treated as a gamma family unless the original reponses are gamma, in which case the dispersion submodel is digamma. (Note that the digamma and trigamma functions are required to fit a digamma family.) This is exact if the original glm family is gaussian, Gamma or inverse.gaussian. In other cases it can be justified by the saddle-point approximation to the density of the responses. The results will therefore be close to exact ML or REML when the dispersions are small compared to the means. In all cases the dispersion submodel as prior weights 1, and has its own dispersion parameter which is 2.

Value

an object of class dglm is returned, which inherits from glm and lm. See dglm.object for details.

Note

The anova method is questionable when applied to an dglm object with method="reml" (stick to method="ml").

Author(s)

Gordon Smyth, ported to R\ by Peter Dunn (pdunn2@usc.edu.au)

References

Smyth, G. K. (1989). Generalized linear models with varying dispersion. J. R. Statist. Soc. B, 51, 47–60.

Smyth, G. K., and Verbyla, A. P. (1999). Adjusted likelihood methods for modelling dispersion in generalized linear models. Environmetrics, 10, 696-709.

Verbyla, A. P., and Smyth, G. K. (1998). Double generalized linear models: approximate residual maximum likelihood and diagnostics. Research Report, Department of Statistics, University of Adelaide.

See Also

dglm.object, dglm.control, Digamma family, Polygamma

Examples

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# Continuing the example from  glm, but this time try
# fitting a Gamma double generalized linear model also.
library(statmod)
clotting <- data.frame(
      u = c(5,10,15,20,30,40,60,80,100),
      lot1 = c(118,58,42,35,27,25,21,19,18),
      lot2 = c(69,35,26,21,18,16,13,12,12))
         
# The same example as in  glm: the dispersion is modelled as constant
# However, dglm uses  ml  not  reml,  so the results are slightly different:
out <- dglm(lot1 ~ log(u), ~1, data=clotting, family=Gamma)
summary(out)

# Try a double glm 
out2 <- dglm(lot1 ~ log(u), ~u, data=clotting, family=Gamma)

summary(out2)
anova(out2)

# Summarize the mean model as for a glm
summary.glm(out2)
    
# Summarize the dispersion model as for a glm
summary(out2$dispersion.fit)

# Examine goodness of fit of dispersion model by plotting residuals
plot(fitted(out2$dispersion.fit),residuals(out2$dispersion.fit))