alldist {npmlreg} | R Documentation |
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.
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, ...)
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 coeffcient 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 coefficents accordingly. |
family |
conditional distribution of responses. "gaussian", "poisson", "binomial", or "Gamma" can be set. If "gaussian" or "Gamma", then equal component dispersion parameters are assumed, except if the optional parameter lambda is modified. |
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 <= 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 and Gamma mixtures. If set, standard
deviations/ shape parameters are calculated smoothly across components via
a 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 (2005). |
spike.protect |
protects algorithm to converge into likelihood spikes for
Gaussian and Gamma mixtures with unequal or smooth component standard deviations,
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 0.000001*spike.protect
(10^6*spike.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-distributed response.
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 -2logL 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. |
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, 2005, pp. 474, p. 491) 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,
that only one random coefficient can be specified, and that the option
random.distribution
is restricted to np
in this case.
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.
For k
>= 70, mass points with negligible mass
(i.e. < 1e-55) are omitted. The maximum number of 'real' mass points
is then 240.
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 emprical 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 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.
In contrast to the GLIM 4 version, this R implementation
uses for Gaussian and Gamma 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 or gamma-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.
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).
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. (2005). Statistical Modelling in GLIM 4. Second Edition, 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.
glm
, summary.glmmNPML
,
predict.glmmNPML
family.glmmNPML
,
plot.glmmNPML
.
# 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] 0.906 0.435 0.218 0.676 1.205 0.216 0.412 0.295 # The toxoplasmosis data data(rainfall, package="forward") 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, package="gamlss") 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] 50.75232 fitnp3<- alldist(duration~age+temp1, data=hosp, k=3, family=Gamma(link=log), tol=0.5, lambda=0.9) fitnp3$shape$shapek #[1] 49.03108 42.79532 126.64046 # 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