dglm: Double Generalized Linear Models

In dglm: Double Generalized Linear Models

Double Generalized Linear Models

Description

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

Usage

dglm(formula=formula(data), dformula = ~ 1, family = gaussian, dlink = "log", 
data = parent.frame(), 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 \mu_i = \mbox{E}[y_i] for the expectation of the ith response. Then \mbox{Var}[Y_i] = \phi_i V(\mu_i) where V is the variance function and \phi_i is the dispersion of the ith response (often denoted as the Greek character ‘phi’). We assume the link linear models g(\mu_i) = \mathbf{x}_i^T \mathbf{b} and h(\phi_i) = \mathbf{z}_i^T \mathbf{z}, where \mathbf{x}_i and \mathbf{z}_i are vectors of covariates, and \mathbf{b} and \mathbf{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 \mu_i, \mathbf{b} and \phi_i respectively.

The parameters \mathbf{b} are estimated as for an ordinary glm. The parameters \mathbf{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$dispersion.fit) with a few more components for the outer iteration and overall likelihood. The summary and anova functions have special methods for dglm objects. Any generic function that has methods for glms or lms will work on out, giving information about the mean submodel. Information about the dispersion submodel can be obtained by using out$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. 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 has 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-class 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

References

Smyth, G. K. (1989). Generalized linear models with varying dispersion. J. R. Statist. Soc. B, 51, 47–60. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/j.2517-6161.1989.tb01747.x")}

Smyth, G. K., and Verbyla, A. P. (1999). Adjusted likelihood methods for modelling dispersion in generalized linear models. Environmetrics, 10, 696-709. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/(SICI)1099-095X(199911/12)10:6<695::AID-ENV385>3.0.CO;2-M")} https://gksmyth.github.io/pubs/Ties98-Preprint.pdf

Smyth, G. K., and Verbyla, A. P. (1999). Double generalized linear models: approximate REML and diagnostics. In Statistical Modelling: Proceedings of the 14th International Workshop on Statistical Modelling, Graz, Austria, July 19-23, 1999, H. Friedl, A. Berghold, G. Kauermann (eds.), Technical University, Graz, Austria, pages 66-80. https://gksmyth.github.io/pubs/iwsm99-Preprint.pdf

See Also

dglm-class, dglm.control, Digamma family, Polygamma.

See https://gksmyth.github.io/s/dglm.html for the original S-Plus code.

Examples

# Continuing the example from glm, but this time try
# fitting a Gamma double generalized linear model also.
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)) 

dglm documentation built on Nov. 25, 2023, 9:07 a.m.