Generalized Additive Mixed Models
Fits the specified generalized additive mixed model (GAMM) to
data, by a call to lme in the normal errors identity link case, or by
a call to gammPQL (a modification of glmmPQL from the MASS library) otherwise.
In the latter case estimates are only approximately MLEs. The routine is typically
slower than gam, and not quite as numerically robust.
To use lme4 in place of nlme as the underlying fitting engine,
see gamm4 from package gamm4.
Smooths are specified as in a call to gam as part of the fixed
effects model formula, but the wiggly components of the smooth are treated as
random effects. The random effects structures and correlation structures
available for lme are used to specify other random effects and
correlations.
It is assumed that the random effects and correlation structures are employed primarily to model residual correlation in the data and that the prime interest is in inference about the terms in the fixed effects model formula including the smooths. For this reason the routine calculates a posterior covariance matrix for the coefficients of all the terms in the fixed effects formula, including the smooths.
gamm(formula,random=NULL,correlation=NULL,family=gaussian(), data=list(),weights=NULL,subset=NULL,na.action,knots=NULL, control=list(niterEM=0,optimMethod="L-BFGS-B",returnObject=TRUE), niterPQL=20,verbosePQL=TRUE,method="ML",drop.unused.levels=TRUE, mustart=NULL, etastart=NULL,...)
formula |
A GAM formula (see also |
random |
The (optional) random effects structure as specified in a call to
|
correlation |
An optional |
family |
A |
data |
A data frame or list containing the model response variable and
covariates required by the formula. By default the variables are taken
from |
weights |
In the generalized case, weights with the same meaning as
|
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 ‘NA’s. The default is set by the ‘na.action’ setting of ‘options’, and is ‘na.fail’ if that is unset. The “factory-fresh” default is ‘na.omit’. |
knots |
this is an optional list containing user specified knot values to be used for basis construction. Different terms can use different numbers of knots, unless they share a covariate. |
control |
A list of fit control parameters for |
niterPQL |
Maximum number of PQL iterations (if any). |
verbosePQL |
Should PQL report its progress as it goes along? |
method |
Which of |
drop.unused.levels |
by default unused levels are dropped from factors before fitting. For some smooths involving factor variables you might want to turn this off. Only do so if you know what you are doing. |
mustart |
starting values for mean if PQL used. |
etastart |
starting values for linear predictor if PQL used (over-rides |
... |
further arguments for passing on e.g. to |
The Bayesian model of spline smoothing introduced by Wahba (1983) and Silverman (1985) opens
up the possibility of estimating the degree of smoothness of terms in a generalized additive model
as variances of the wiggly components of the smooth terms treated as random effects. Several authors
have recognised this (see Wang 1998; Ruppert, Wand and Carroll, 2003) and in the normal errors,
identity link case estimation can
be performed using general linear mixed effects modelling software such as lme. In the generalized case only
approximate inference is so far available, for example using the Penalized Quasi-Likelihood approach of Breslow
and Clayton (1993) as implemented in glmmPQL by Venables and Ripley (2002).
One advantage of this approach is that it allows correlated errors to be dealt with via random effects
or the correlation structures available in the nlme library (using correlation structures beyond the
strictly additive case amounts to using a GEE approach to fitting).
Some details of how GAMs are represented as mixed models and estimated using
lme or gammPQL in gamm can be found in Wood (2004 ,2006a,b). In addition gamm obtains a posterior covariance matrix for the parameters of all the fixed effects and the smooth terms. The approach is similar to that described in Lin & Zhang (1999) - the covariance matrix of the data (or pseudodata in the generalized case) implied by the weights, correlation and random effects structure is obtained, based on the estimates of the parameters of these terms and this is used to obtain the posterior covariance matrix of the fixed and smooth effects.
The bases used to represent smooth terms are the same as those used in gam, although adaptive
smoothing bases are not available. Prediction from the returned gam object is straightforward using predict.gam, but this will set the random effects to zero. If you want to predict with random effects set to their predicted values then you can adapt the prediction code given in the examples below.
In the event of lme convergence failures, consider
modifying options(mgcv.vc.logrange): reducing it helps to remove
indefiniteness in the likelihood, if that is the problem, but too large a
reduction can force over or undersmoothing. See notExp2 for more
information on this option. Failing that, you can try increasing the
niterEM option in control: this will perturb the starting values
used in fitting, but usually to values with lower likelihood! Note that this
version of gamm works best with R 2.2.0 or above and nlme, 3.1-62 and above,
since these use an improved optimizer.
Returns a list with two items:
gam |
an object of class |
lme |
the fitted model object returned by |
gamm has a somewhat different argument list to gam,
gam arguments such as gamma supplied to gamm will
just be ignored.
gamm performs poorly with binary data, since it uses PQL. It is
better to use gam with s(...,bs="re") terms, or gamm4.
gamm assumes that you know what you are doing! For example, unlike
glmmPQL from MASS it will return the complete lme object
from the working model at convergence of the PQL iteration, including the 'log
likelihood', even though this is not the likelihood of the fitted GAMM.
The routine will be very slow and memory intensive if correlation structures are used for the very large groups of data. e.g. attempting to run the spatial example in the examples section with many 1000's of data is definitely not recommended: often the correlations should only apply within clusters that can be defined by a grouping factor, and provided these clusters do not get too huge then fitting is usually possible.
Models must contain at least one random effect: either a smooth with non-zero
smoothing parameter, or a random effect specified in argument random.
gamm is not as numerically stable as gam: an lme call
will occasionally fail. See details section for suggestions, or try the
‘gamm4’ package.
gamm is usually much slower than gam, and on some platforms you may need to
increase the memory available to R in order to use it with large data sets
(see memory.limit).
Note that the weights returned in the fitted GAM object are dummy, and not those used by the PQL iteration: this makes partial residual plots look odd.
Note that the gam object part of the returned object is not complete in
the sense of having all the elements defined in gamObject and
does not inherit from glm: hence e.g. multi-model anova calls will not work.
It is also based on the working model when PQL is used.
The parameterization used for the smoothing parameters in gamm, bounds
them above and below by an effective infinity and effective zero. See
notExp2 for details of how to change this.
Linked smoothing parameters and adaptive smoothing are not supported.
Simon N. Wood simon.wood@r-project.org
Breslow, N. E. and Clayton, D. G. (1993) Approximate inference in generalized linear mixed models. Journal of the American Statistical Association 88, 9-25.
Lin, X and Zhang, D. (1999) Inference in generalized additive mixed models by using smoothing splines. JRSSB. 55(2):381-400
Pinheiro J.C. and Bates, D.M. (2000) Mixed effects Models in S and S-PLUS. Springer
Ruppert, D., Wand, M.P. and Carroll, R.J. (2003) Semiparametric Regression. Cambridge
Silverman, B.W. (1985) Some aspects of the spline smoothing approach to nonparametric regression. JRSSB 47:1-52
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
Wahba, G. (1983) Bayesian confidence intervals for the cross validated smoothing spline. JRSSB 45:133-150
Wood, S.N. (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. Journal of the American Statistical Association. 99:673-686
Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95-114
Wood, S.N. (2006a) Low rank scale invariant tensor product smooths for generalized additive mixed models. Biometrics 62(4):1025-1036
Wood S.N. (2006b) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.
Wang, Y. (1998) Mixed effects smoothing spline analysis of variance. J.R. Statist. Soc. B 60, 159-174
magic for an alternative for correlated data,
te, s,
predict.gam,
plot.gam, summary.gam, negbin,
vis.gam,pdTens, gamm4 (
https://cran.r-project.org/package=gamm4)
library(mgcv)
## simple examples using gamm as alternative to gam
set.seed(0)
dat <- gamSim(1,n=200,scale=2)
b <- gamm(y~s(x0)+s(x1)+s(x2)+s(x3),data=dat)
plot(b$gam,pages=1)
summary(b$lme) # details of underlying lme fit
summary(b$gam) # gam style summary of fitted model
anova(b$gam)
gam.check(b$gam) # simple checking plots
b <- gamm(y~te(x0,x1)+s(x2)+s(x3),data=dat)
op <- par(mfrow=c(2,2))
plot(b$gam)
par(op)
rm(dat)
## Add a factor to the linear predictor, to be modelled as random
dat <- gamSim(6,n=200,scale=.2,dist="poisson")
b2 <- gamm(y~s(x0)+s(x1)+s(x2),family=poisson,
data=dat,random=list(fac=~1))
plot(b2$gam,pages=1)
fac <- dat$fac
rm(dat)
vis.gam(b2$gam)
## In the generalized case the 'gam' object is based on the working
## model used in the PQL fitting. Residuals for this are not
## that useful on their own as the following illustrates...
gam.check(b2$gam)
## But more useful residuals are easy to produce on a model
## by model basis. For example...
fv <- exp(fitted(b2$lme)) ## predicted values (including re)
rsd <- (b2$gam$y - fv)/sqrt(fv) ## Pearson residuals (Poisson case)
op <- par(mfrow=c(1,2))
qqnorm(rsd);plot(fv^.5,rsd)
par(op)
## now an example with autocorrelated errors....
n <- 200;sig <- 2
x <- 0:(n-1)/(n-1)
f <- 0.2*x^11*(10*(1-x))^6+10*(10*x)^3*(1-x)^10
e <- rnorm(n,0,sig)
for (i in 2:n) e[i] <- 0.6*e[i-1] + e[i]
y <- f + e
op <- par(mfrow=c(2,2))
## Fit model with AR1 residuals
b <- gamm(y~s(x,k=20),correlation=corAR1())
plot(b$gam);lines(x,f-mean(f),col=2)
## Raw residuals still show correlation, of course...
acf(residuals(b$gam),main="raw residual ACF")
## But standardized are now fine...
acf(residuals(b$lme,type="normalized"),main="standardized residual ACF")
## compare with model without AR component...
b <- gam(y~s(x,k=20))
plot(b);lines(x,f-mean(f),col=2)
## more complicated autocorrelation example - AR errors
## only within groups defined by `fac'
e <- rnorm(n,0,sig)
for (i in 2:n) e[i] <- 0.6*e[i-1]*(fac[i-1]==fac[i]) + e[i]
y <- f + e
b <- gamm(y~s(x,k=20),correlation=corAR1(form=~1|fac))
plot(b$gam);lines(x,f-mean(f),col=2)
par(op)
## more complex situation with nested random effects and within
## group correlation
set.seed(0)
n.g <- 10
n<-n.g*10*4
## simulate smooth part...
dat <- gamSim(1,n=n,scale=2)
f <- dat$f
## simulate nested random effects....
fa <- as.factor(rep(1:10,rep(4*n.g,10)))
ra <- rep(rnorm(10),rep(4*n.g,10))
fb <- as.factor(rep(rep(1:4,rep(n.g,4)),10))
rb <- rep(rnorm(4),rep(n.g,4))
for (i in 1:9) rb <- c(rb,rep(rnorm(4),rep(n.g,4)))
## simulate auto-correlated errors within groups
e<-array(0,0)
for (i in 1:40) {
eg <- rnorm(n.g, 0, sig)
for (j in 2:n.g) eg[j] <- eg[j-1]*0.6+ eg[j]
e<-c(e,eg)
}
dat$y <- f + ra + rb + e
dat$fa <- fa;dat$fb <- fb
## fit model ....
b <- gamm(y~s(x0,bs="cr")+s(x1,bs="cr")+s(x2,bs="cr")+
s(x3,bs="cr"),data=dat,random=list(fa=~1,fb=~1),
correlation=corAR1())
plot(b$gam,pages=1)
summary(b$gam)
vis.gam(b$gam)
## Prediction from gam object, optionally adding
## in random effects.
## Extract random effects and make names more convenient...
refa <- ranef(b$lme,level=5)
rownames(refa) <- substr(rownames(refa),start=9,stop=20)
refb <- ranef(b$lme,level=6)
rownames(refb) <- substr(rownames(refb),start=9,stop=20)
## make a prediction, with random effects zero...
p0 <- predict(b$gam,data.frame(x0=.3,x1=.6,x2=.98,x3=.77))
## add in effect for fa = "2" and fb="2/4"...
p <- p0 + refa["2",1] + refb["2/4",1]
## and a "spatial" example...
library(nlme);set.seed(1);n <- 100
dat <- gamSim(2,n=n,scale=0) ## standard example
attach(dat)
old.par<-par(mfrow=c(2,2))
contour(truth$x,truth$z,truth$f) ## true function
f <- data$f ## true expected response
## Now simulate correlated errors...
cstr <- corGaus(.1,form = ~x+z)
cstr <- Initialize(cstr,data.frame(x=data$x,z=data$z))
V <- corMatrix(cstr) ## correlation matrix for data
Cv <- chol(V)
e <- t(Cv) %*% rnorm(n)*0.05 # correlated errors
## next add correlated simulated errors to expected values
data$y <- f + e ## ... to produce response
b<- gamm(y~s(x,z,k=50),correlation=corGaus(.1,form=~x+z),
data=data)
plot(b$gam) # gamm fit accounting for correlation
# overfits when correlation ignored.....
b1 <- gamm(y~s(x,z,k=50),data=data);plot(b1$gam)
b2 <- gam(y~s(x,z,k=50),data=data);plot(b2)
par(old.par)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.