R: NPML estimation or Gaussian quadrature for overdispersed...

alldist {npmlreg}

R Documentation

NPML estimation or Gaussian quadrature for overdispersed GLM's and variance component models

Description

Fits a random effect model using Gaussian quadrature (Hinde, 1982) or nonparametric maximum likelihood (Aitkin, 1996a). The function alldist is designed to account for overdispersion, while allvc fits variance component models.

Usage

alldist(formula, 
        random = ~1, 
        family = gaussian(), 
        data,
        k = 4, 
        random.distribution = "np", 
        tol = 0.5, 
        offset, 
        weights, 
        pluginz, 
        na.action, 
        EMmaxit = 500, 
        EMdev.change = 0.001, 
        lambda = 0, 
        damp = TRUE, 
        damp.power = 1, 
        spike.protect = 0, 
        sdev,
        shape, 
        plot.opt = 3, 
        verbose = TRUE,
        ...)
        
allvc(formula, 
        random = ~1, 
        family = gaussian(), 
        data, 
        k = 4, 
        random.distribution = "np", 
        tol = 0.5, 
        offset, 
        weights, 
        pluginz, 
        na.action, 
        EMmaxit = 500, 
        EMdev.change = 0.001, 
        lambda=0,
        damp = TRUE, 
        damp.power = 1, 
        spike.protect=0,
        sdev,
        shape, 
        plot.opt = 3, 
        verbose = TRUE,
        ...)

Arguments

`formula`	a formula defining the response and the fixed effects (e.g. `y ~ x`).
`random`	a formula defining the random model. In the case of `alldist`, set `random = ~1` to model overdispersion, and for instance `random = ~x` to introcude a random coefficient `x`. In the case of `allvc`, set `random=~1\|PSU` to model overdispersion on the upper level, where PSU is a factor for the primary sampling units, e.g. groups, clusters, classes, or individuals in longitudinal data, and define random coefficients accordingly.
`family`	conditional distribution of responses: "gaussian", "poisson", "binomial", "Gamma", or "inverse.gaussian" can be set. If "gaussian", "Gamma", or "inverse.gaussian", then equal component dispersion parameters are assumed, except if the optional parameter `lambda` is modified. The same link functions as for function `glm` are supported.
`data`	the data frame (mandatory, even if it is attached to the workspace!).
`k`	the number of mass points/integration points (supported are up to 600 mass points).
`random.distribution`	the mixing distribution, Gaussian Quadrature (`gq`) or NPML (`np`) can be set.
`tol`	the tol scalar (usually, `0<tol` `\le 1`)
`offset`	an optional offset to be included in the model.
`weights`	optional prior weights for the data.
`pluginz`	optional numerical vector of length `k` specifying the starting mass points of the EM algorithm.
`na.action`	a function indicating what should happen when `NA`'s occur, with possible arguments `na.omit` and `na.fail`. The default is set by the `na.action` setting in `options()`.
`EMmaxit`	maximum number of EM iterations.
`EMdev.change`	stops EM algorithm when deviance change falls below this value.
`lambda`	only applicable for Gaussian, Gamma, and Inverse Gaussian mixtures. If set, standard deviations/ shape parameters are calculated smoothly across components via an Aitchison-Aitken kernel (`dkern`) with parameter `lambda`. The setting `lambda= 0` is automatically mapped to `lambda =1/k` and corresponds to the case 'maximal smoothing' (i.e. equal component dispersion parameters), while `lambda=1` means 'no smoothing' (unequal disp. param.)
`damp`	switches EM damping on or off.
`damp.power`	steers degree of damping applied on dispersion parameter according to formula `1-(1-tol)^(damp.power*iter+1)`, see Einbeck & Hinde (2006).
`spike.protect`	for Gaussian, Gamma, and Inverse Gaussian mixtures with unequal or smooth component standard deviations: protects algorithm to converge into likelihood spikes by stopping the EM algorithm if one of the component standard deviations (shape parameters, resp.), divided by the fitted mass points, falls below (exceeds, resp.) a certain threshold, which is `0.000001spike.protect` (`10^6spike.protect`, resp.) Setting `spike.protect=0` means disabling the spike protection. If set, then `spike.protect=1` is recommended. Note that the displayed disparity may not be correct when convergence is not achieved. This can be checked with `EMconverged`.
`sdev`	optional; specifies standard deviation for normally distributed response. If unspecified, it will be estimated from the data.
`shape`	optional; specifies shape parameter for Gamma-and IG-distributed response. For Gamma, setting `shape=1` gives an exponential distribution. If unspecified, it will be estimated from the data.
`plot.opt`	if equal to zero, then no graphical output is given. For `plot.opt=1` the development of the disparity `-2\log L` over iteration number is plotted, for `plot.opt=2` the EM trajectories are plotted, and for `plot.opt=3` both plots are shown.
`verbose`	if set to `FALSE`, no printed output is given during function execution. Useful for `tolfind`.
`...`	generic options for the `glm` function. Not all options may be supported under any circumstances.

Details

The nonparametric maximum likelihood (NPML) approach was introduced in Aitkin (1996) as a tool to fit overdispersed generalized linear models. The idea is to approximate the unknown and unspecified distribution of the random effect by a discrete mixture of exponential family densities, leading to a simple expression of the marginal likelihood which can then be maximized using a standard EM algorithm.

Aitkin (1999) extended this method to generalized linear models with shared random effects arising through variance component or repeated measures structure. Applications are two-stage sample designs, when firstly the primary sampling units (the upper-level units, e.g. classes) and then the secondary sampling units (lower-level units, e.g. students) are selected, or longitudinal data. Models of this type have also been referred to as multi-level models (Goldstein, 2003). allvc is restricted to 2-level models.

The number of components k of the finite mixture has to be specified beforehand. When option 'gq' is set, then Gauss-Hermite masses and mass points are used, assuming implicitly a normally distributed random effect. When option 'np' is chosen, the EM algorithm uses the Gauss-Hermite masses and mass points as starting points. The position of the starting points can be concentrated or extended by setting tol smaller or larger than one, respectively.

Fitting random coefficient models (Aitkin, Francis & Hinde, 2009, pp. 496, p. 514) is possible by specifying the random term explicitly. Note that the setting random= ~ x gives a model with a random slope and a random intercept, and that only one random coefficient can be specified. The option random.distribution is restricted to np in this case, i.e. Gaussian Quadrature is not supported for random coefficient models (see also Aitkin, Francis & Hinde (2005), page 475 bottom).

As for glm, there are three different ways of specifying a binomial model, namley through

a two-column matrix before the '~' symbol, specifying the counts of successes and non-successes.
a vector of proportions of successes before the '~' symbol, and the associated number of trials provided in the weights argument.
a two-level factor before the '~' symbol (only for Bernoulli-response).

The weights have to be understood as frequency weights, i.e. setting all weights in alldist equal to 2 will duplicate each data point and hence double the disparity and deviance.

The Inverse Gaussian (IG) response distribution is parametrized as usual through the mean and a scaling parameter. We refer to the latter, which is the inverse of the dispersion parameter in exponential family formulation, as shape. The canonical "1/mu^2" link is supported, but it is quite tenuous since the linear predictor is likely to become negative after adding the random effect. The log link behaves more reliably for this distribution.

For k \ge 54, mass points with negligible mass (i.e. < 1e-50) are omitted. The maximum number of 'effective' mass points is then 198.

Value

The function alldist produces an object of class glmmNPML (if random.distributon is set to ‘np’) or glmmGQ (‘gq’). Both objects contain the following 29 components:

`coefficients`	a named vector of coefficients (including the mass points). In case of Gaussian quadrature, the coefficient given at `z` corresponds to the standard deviation of the mixing distribution.
`residuals`	the difference between the true response and the empirical Bayes predictions.
`fitted.values`	the empirical Bayes predictions (Aitkin, 1996b) on the scale of the responses.
`family`	the ‘family’ object used.
`linear.predictors`	the extended linear predictors `\hat{\eta}_{ik}`.
`disparity`	the disparity (`-2logL`) of the fitted mixture regression model.
`deviance`	the deviance of the fitted mixture regression model.
`null.deviance`	the deviance for the null model (just containing an intercept), comparable with `deviance`.
`df.residual`	the residual degrees of freedom of the fitted model (including the random part).
`df.null`	the residual degrees of freedom for the null model.
`y`	the (extended) response vector.
`call`	the matched call.
`formula`	the formula supplied.
`random`	the random term of the model formula.
`data`	the data argument.
`model`	the (extended) design matrix.
`weights`	the case weights initially supplied.
`offset`	the offset initially supplied.
`mass.points`	the fitted mass points.
`masses`	the mixture probabilities corresponding to the mass points.
`sdev`	a list of the two elements `sdev$sdev` and `sdev$sdevk`. The former is the estimated standard deviation of the Gaussian mixture components (estimated over all mixture components), and the latter gives the unequal or smooth component-specific standard deviations. All values are equal if `lambda=0`.
`shape`	a list of the two elements `shape$shape` and `shape$shapek`, to be interpreted in analogy to sdev.
`rsdev`	estimated random effect standard deviation.
`post.prob`	a matrix of posteriori probabilities.
`post.int`	a vector of ‘posteriori intercepts’ (as in Sofroniou et al. (2006)).
`ebp`	the empirical Bayes Predictions on the scale of the linear predictor. For compatibility with older versions.
`EMiter`	gives the number of iterations of the EM algorithm.
`EMconverged`	logical value indicating if the EM algorithm converged.
`lastglm`	the fitted `glm` object from the last EM iteration.
`Misc`	contains additional information relevant for the summary and plot functions, in particular the disparity trend and the EM trajectories.

If a binomial model is specified by giving a two-column response, the weights returned by weights are the total numbers of cases (factored by the supplied case weights) and the component y of the result is the proportion of successes.

As a by-product, alldist produces a plot showing the disparity in dependence of the iteration number. Further, a plot with the EM trajectories is given. The x-axis corresponds to the iteration number, and the y-axis to the value of the mass points at a particular iteration. This plot is not produced for GQ.

Note

In contrast to the GLIM 4 version, this R implementation uses for Gaussian (as well Gamma and IG) mixtures by default a damping procedure in the first cycles of the EM algorithm (Einbeck & Hinde, 2006), which stabilizes the algorithm and makes it less sensitive to the optimal choice of tol. If tol is very small (i.e. less than 0.1), it can be useful to set damp.power to values larger than 1 in order to accelerate convergence. Do not use damp.power=0, as this would mean permanent damping during EM. Using the option pluginz, one can to some extent circumvent the necessity to specify tol by giving the starting points explicitly. However, when using pluginz for normal, Gamma- or IG- distributed response, damping will be strictly necessary to ensure that the imposed starting points don't get blurred immediately due to initial fluctuations, implying that tol still plays a role in this case.

Author(s)

Originally translated from the GLIM 4 functions alldist and allvc (Aitkin & Francis, 1995) to R by Ross Darnell (2002). Modified, extended, and prepared for publication by Jochen Einbeck & John Hinde (2006).

References

Aitkin, M. and Francis, B. (1995). Fitting overdispersed generalized linear models by nonparametric maximum likelihood. GLIM Newsletter 25, 37-45.

Aitkin, M. (1996a). A general maximum likelihood analysis of overdispersion in generalized linear models. Statistics and Computing 6, 251-262.

Aitkin, M. (1996b). Empirical Bayes shrinkage using posterior random effect means from nonparametric maximum likelihood estimation in general random effect models. Statistical Modelling: Proceedings of the 11th IWSM 1996, 87-94.

Aitkin, M. (1999). A general maximum likelihood analysis of variance components in generalized linear models. Biometrics 55, 117-128.

Aitkin, M., Francis, B. and Hinde, J. (2009). Statistical Modelling in R. Oxford Statistical Science Series, Oxford, UK.

Einbeck, J. & Hinde, J. (2006). A note on NPML estimation for exponential family regression models with unspecified dispersion parameter. Austrian Journal of Statistics 35, 233-243.

Goldstein, H. (2003). Multilevel Statistical Models (3rd edition). Arnold, London, UK.

Hinde, J. (1982). Compound Poisson regression models. Lecture Notes in Statistics 14, 109-121.

Sofroniou, N., Einbeck, J., and Hinde, J. (2006). Analyzing Irish suicide rates with mixture models. Proceedings of the 21st International Workshop on Statistical Modelling in Galway, Ireland, 2006.

Examples


# The first three examples (galaxy data, toxoplasmosis data , fabric faults) 
# are based on GLIM examples in Aitkin et al. (2005), and the forth example using
# the Hospital-Stay-Data (Rosner, 2000) is taken from Einbeck & Hinde (2006).
# The fifth data example using the Oxford boys is again inspired by Aitkin et al. (2005).
# The sixth example on Irish suicide rates is taken from Sofroniou et al. (2006).
  

# The galaxy data   
  data(galaxies, package="MASS")
  gal<-as.data.frame(galaxies)
  galaxy.np6 <- alldist(galaxies/1000~1, random=~1, random.distribution="np", 
      data=gal, k=6)
  galaxy.np8u <- alldist(galaxies/1000~1, random=~1, random.distribution="np", 
      data=gal, k=8, lambda=0.99)
  round(galaxy.np8u$sdev$sdevk, digits=3)
  # [1]  0.912 0.435 0.220 0.675 1.214 0.264 0.413 0.297

# The toxoplasmosis data 
  data(rainfall)
  rainfall$x<-rainfall$Rain/1000  
  rainfall$x2<- rainfall$x^2; rainfall$x3<- rainfall$x^3
  toxo.np3<- alldist(cbind(Cases,Total-Cases) ~ x+x2+x3, random=~1, 
      random.distribution="np", family=binomial(link=logit), data=rainfall, k=3)
  toxo.np3x<- alldist(cbind(Cases,Total-Cases) ~ x, random=~x, 
      random.distribution="np", family=binomial(link=logit), data=rainfall, k=3)
  # is the same as 
  toxo.np3x<- alldist(Cases/Total ~ x, random = ~x, weights=Total, 
      family=binomial(link=logit), data=rainfall, k=3)
  # or
  toxo.np3x<-update(toxo.np3, .~.-x2-x3, random = ~x)

# The fabric faults data
  data(fabric)
  coefficients(alldist(y ~ x, random=~1, family=poisson(link=log), 
      random.distribution="gq", data= fabric, k=3, verbose=FALSE))
  # (Intercept)           x           z 
  #  -3.3088663   0.8488060   0.3574909
  
# The Pennsylvanian hospital stay data
  data(hosp)
  fitnp3<-  alldist(duration~age+temp1, data=hosp, k=3, family=Gamma(link=log),
      tol=0.5) 
  fitnp3$shape$shape
  # [1] 50.76636
  fitnp3<-  alldist(duration~age+temp1, data=hosp, k=3, family=Gamma(link=log),
      tol=0.5, lambda=0.9) 
  fitnp3$shape$shapek
  # [1]  49.03101  42.79522 126.64077
  
# The Oxford boys data
  data(Oxboys, package="nlme")
  Oxboys$boy <- gl(26,9)
  allvc(height~age, random=~1|boy, data=Oxboys, random.distribution='gq', k=20)
  allvc(height~age, random=~1|boy, data=Oxboys,random.distribution='np',k=8) 
  # with random coefficients:
  allvc(height~age,random=~age|boy, data=Oxboys, random.distribution='np', k=8)
    
# Irish suicide data
  data(irlsuicide)
  # Crude rate model:
  crude<- allvc(death~sex* age, random=~1|ID, offset=log(pop), 
      k=3, data=irlsuicide, family=poisson) 
  crude$disparity 
  # [1] 654.021
  # Relative risk model:
  relrisk<- allvc(death~1, random=~1|ID, offset=log(expected), 
      k=3, data=irlsuicide, family=poisson) 
  relrisk$disparity    
  # [1] 656.4955

[Package npmlreg version 0.46-5 Index]