addreg {addreg}  R Documentation 
addreg
fits additive (identitylink) Poisson, negative binomial
and binomial regression models using a stable combinatorial EM algorithm.
addreg(formula, mono = NULL, family, data, standard, subset, na.action,
start = NULL, offset, control = list(...), model = TRUE,
method = c("cem", "em"),
accelerate = c("em", "squarem", "pem", "qn"),
control.method = list(), warn = TRUE, ...)
formula 
an object of class 
mono 
a vector indicating which terms in

family 
a description of the error distribution to
be used in the model. This can be a character string
naming a family function, a family function or the result
of a call to a family function (see

data 
an optional data frame, list or environment
(or object coercible by 
standard 
a numeric vector of length equal to the number of cases, where each element is a positive constant that (multiplicatively) standardises the fitted value of the corresponding element of the response vector. Ignored for binomial family (twocolumn specification of response should be used instead). 
subset 
an optional vector specifying a subset of observations to be used in the fitting process. 
na.action 
a function which indicates what should happen when the data
contain 
start 
starting values for the parameters in the
linear predictor, also with the starting value for
the 
offset 
this can be used to specify an a
priori known component to be included in the linear
predictor during fitting. This should be Ignored for binomial family; not yet implemented for negative binomial models. 
control 
list of parameters for controlling the
fitting process, passed to

model 
a logical value indicating whether the model frame (and, for binomial models, the equivalent Poisson model) should be included as a component of the returned value. 
method 
a character string that determines which algorithm to use to
find the MLE: 
accelerate 
a character string that determines the acceleration
algorithm to be used, (partially) matching one of 
control.method 
a list of control parameters for the acceleration algorithm, which are passed to
the If any items are not specified, the defaults are used. 
warn 
a logical indicating whether or not warnings should be provided for nonconvergence or boundary values. 
... 
arguments to be used to form the default

addreg
fits a generalised linear model (GLM) with a
Poisson or binomial error distribution and identity link
function, as well as additive NegBin I models (which are not
GLMs). Predictors are assumed to be continuous, unless
they are of class factor
, or are character or
logical (in which case they are converted to
factor
s). Specifying a predictor as monotonic using
the mono
argument means that for continuous terms,
the associated coefficient will be restricted to be
nonnegative, and for categorical terms, the coefficients
will be nondecreasing in the order of the factor
levels
. This allows semiparametric monotonic regression
functions, in the form of unsmoothed stepfunctions. For
smooth regression functions see addreg.smooth
.
As well as allowing monotonicity constraints, the function
is useful when a standard GLM routine, such as
glm
, fails to converge with an identitylink Poisson
or binomial model. If glm
does achieve successful convergence,
and addreg
converges to an interior point, then the two
results will be identical. However, glm
may still experience convergence
problems even when addreg
converges to an interior point.
Note that if addreg
converges to a boundary point, then it
may differ slightly from glm
even if glm
successfully
converges, because of differences in the definition of the parameter
space. addreg
produces valid fitted values for covariate
values within the Cartesian product of the observed range of covariate
values, whereas glm
produces valid fitted values just
for the observed covariate combinations (assuming it successfully
converges). This issue is only relevant when addreg
converges to a boundary point.
The computational method is a combinatorial EM algorithm (Marschner, 2014), which accommodates the parameter contraints in the model and is more stable than iteratively reweighted least squares. A collection of restricted parameter spaces is defined which covers the full parameter space, and the EM algorithm is applied within each restricted parameter space in order to find a collection of restricted maxima of the loglikelihood function, from which can be obtained the global maximum over the full parameter space. See Marschner (2010), Donoghoe and Marschner (2014) and Donoghoe and Marschner (2016) for further details.
Acceleration of the EM algorithm can be achieved through the
methods of the turboEM package, specified
through the accelerate
argument. However, note that these
methods do not have the guaranteed convergence of the standard
EM algorithm, particularly when the MLE is on the boundary of
its (possibly constrained) parameter space.
addreg
returns an object of class "addreg"
,
which inherits from classes "glm"
and "lm"
.
The function summary.addreg
can be used
to obtain or print a summary of the results.
The generic accessor functions coefficients
,
fitted.values
and residuals
can be used to
extract various useful features of the value returned by
addreg
. Note that effects
will not work.
An object of class "addreg"
is a list containing the
same components as an object of class "glm"
(see the
"Value" section of glm
), but without
contrasts
, qr
, R
or effects
components. It also includes:
loglik 
the maximised loglikelihood. 
aic.c 
a smallsample corrected
version of Akaike's An Information Criterion
(Hurvich, Simonoff and Tsai, 1998). This is used by

xminmax 
the minimum and maximum observed values for each of the continuous covariates, to help define the covariate space of the model. 
As well as, for Poisson and negative binomial models:
nn.coefficients 
estimated coefficients associated with the nonnegative parameterisation corresponding to the MLE. 
nn.x 
nonnegative model matrix associated with

standard 
the 
Or, for binomial models:
model.addpois 
if requested, the 
The scale
component of the result is fixed at 1
for
Poisson and binomial models, and is the constant overdispersion parameter
for negative binomial models (that is, scale
= 1+\phi
) where
Var(\mu) = (1+\phi)\mu
).
Due to the way the covariate space is defined in the CEM algorithm,
specifying interactions in the formula is not currently supported
by addreg
. 2way interactions between factors can be
included by calculating a new factor term that has levels
corresponding to all possible combinations of the factor
levels. See the Example.
Mark W. Donoghoe markdonoghoe@gmail.com
Donoghoe, M. W. and I. C. Marschner (2014). Stable computational methods for additive binomial models with application to adjusted risk differences. Computational Statistics and Data Analysis 80: 184–196.
Donoghoe, M. W. and I. C. Marschner (2016). Estimation of adjusted rate differences using additive negative binomial regression. Statistics in Medicine 35(18): 3166–3178.
Hurvich, C. M., J. S. Simonoff and C.L. Tsai (1998). Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 60(2): 271–293.
Marschner, I. C. (2010). Stable computation of maximum likelihood estimates in identity link Poisson regression. Journal of Computational and Graphical Statistics 19(3): 666–683.
Marschner, I. C. (2014). Combinatorial EM algorithms. Statistics and Computing 24(6): 921–940.
require(glm2)
data(crabs)
#============================================================================
# identitylink Poisson model with periodic nonconvergence when glm is used
#============================================================================
crabs.boot < crabs[crabs$Rep1,c(5:6)]
crabs.boot$width.shifted < crabs.boot$Width  min(crabs$Width)
fit.glm < glm(Satellites ~ width.shifted + factor(Dark) + factor(GoodSpine),
family = poisson(identity), data = crabs.boot, start = rep(1,4),
control = glm.control(trace = TRUE))
fit.addreg < addreg(formula(fit.glm), family = poisson, data = crabs.boot,
trace = 1)
# Speed up convergence by using single EM algorithm
fit.addreg.em < update(fit.addreg, method = "em")
# Speed up convergence by using acceleration methods
fit.addreg.acc < update(fit.addreg, accelerate = "squarem")
fit.addreg.em.acc < update(fit.addreg.em, accelerate = "squarem")
# Usual S3 methods work on addreg objects
summary(fit.addreg)
vcov(fit.addreg)
confint(fit.addreg)
summary(predict(fit.addreg), type = "response")
fit.addreg2 < addreg(update(formula(fit.glm), ~ .  factor(GoodSpine)),
family = poisson, data = crabs.boot, trace = 1)
anova(fit.addreg2, fit.addreg, test = "LRT")
# Account for overdispersion (use start to speed it up a little)
fit.addreg.od < addreg(Satellites ~ factor(Dark) + factor(GoodSpine),
family = negbin1, data = crabs.boot, trace = 1,
start = c(4.3423675,2.4059273,0.4531984,5.969648))
summary(fit.addreg.od)