| gam.models {mgcv} | R Documentation |
This page is intended to provide some more information on how to specify GAMs. A GAM is a GLM in which the linear predictor depends, in part, on a sum of smooth functions of predictors and (possibly) linear functionals of smooth functions of (possibly dummy) predictors.
Specifically let y_i denote an independent random variable with mean mu_i and an exponential family distribution, or failing that a known mean variance relationship suitable for use of quasi-likelihood methods. Then the the linear predictor of a GAM has a structure something like
g(mu_i)=X_i b + f_1(x_1i,x_2i) + f_2(x_3i) + L_i f_3(x_4) + ...
where g is a known smooth monotonic `link' function, X_i b is the parametric part of the linear predictor, the x_j are predictor variables, the f_j are smooth functions and L_i is some linear functional of f_3. There may of course be multiple linear functional terms, or none.
The key idea here is that the dependence of the response on the predictors can be represented as a parametric sub-model plus the sum of some (functionals of) smooth functions of one or more of the predictor variables. Thus the model is quite flexible relative to strictly parametric linear or generalized linear models, but still has much more structure than the completely general model that says that the response is just some smooth function of all the covariates.
Note one important point. In order for the model to be identifiable
the smooth functions usually have to be constrained to have zero mean (usually
taken over the set of covariate values). The constraint is needed if the term involving the
smooth includes a constant function in its span. gam always applies such constraints
unless there is a by variable present, in which case an assessment is made of whether
the constraint is needed or not (see below).
The following sections discuss specifying model structures for gam.
Specification of the distribution and link function is done using the family
argument to gam and works in the same way as for glm.
This page therefore concentrates on the model formula for gam.
Consider the example model.
g(mu_i) = b_0 + b_1 x_1i + b_2 x_2i + f1(x_3i) + f2(x_4i,x_5i)
where the response variables y_i has expectation mu_i and g is a link function.
The gam formula for this would be
y ~ x1 + x2 + s(x3) + s(x4,x5).
This would use the default basis for the smooths (a thin plate
regression spline basis for each), with automatic selection of the
effective degrees of freedom for both smooths. The dimension of the
smoothing basis is given a default value as well (the dimension of the
basis sets an upper limit on the maximum possible degrees of
freedom for the basis - the limit is typically one less than basis
dimension). Full details of how to control smooths are given in
s and te, and further discussion of basis
dimension choice can be found in choose.k.
For the moment suppose that we would like to change
the basis of the first smooth to a cubic regression spline basis with
a dimension of 20, while fixing the second term at 25 degrees of
freedom. The appropriate formula would be:
y ~ x1 + x2 + s(x3,bs="cr",k=20) + s(x4,x5,k=26,fx=TRUE).
The above assumes that x_4 and x_5 are naturally on
similar scales (e.g. they might be co-ordinates), so that isotropic smoothing
is appropriate. If this assumption is false then tensor product smoothing might be
better (see te).
y ~ x1 + x2 + s(x3) + te(x4,x5)
would generate a tensor product smooth of x_4 and x_5.
By default this smooth would have basis dimension 25 and use cubic regression spline marginals.
Varying the defaults is easy. For example
y ~ x1 + x2 + s(x3) + te(x4,x5,bs=c("cr","ps"),k=c(6,7))
specifies that the tensor product should use a rank 6 cubic regression spline marginal
and a rank 7 P-spline marginal to create a smooth with basis dimension 42.
Another common modelling task is to decide if a model like:
E(y)=f(x,z)
is really necessary or whether:
E(y)=f1(x)+f2(z)
would do just as well. One possibility is to examine the results
of fitting:
y ~ s(x) + s(z) + s(x,z).
gam automatically generates side conditions to make this model
identifiable. You can also estimate `overlapping' models like:
y ~ s(x,z) + s(z,v).
Note that optimum interpretability of such models is obtained by ensuring that
lower order terms are strictly nested withing higher order ones. This is most easily achieved by
employing tensor product smooths for multidimensional terms, and ensuring that lower order terms are
using the same (marginal) bases as higher order ones. For example:
y ~ s(x,bs="cr",k=5) + s(z,bs="cr",k=5) + te(x,z)
would ensure strict nesting of the main effects within the interaction (given the te default
bs and k arguments), since the main effects employ the same bases used as marginals for the te term.
by variables are the means for constructing `varying-coefficient models' (geographic regression models) and
for letting smooths `interact' with factors or parametric terms. They are also the key to specifying general linear
functionals of smooths.
The s and te terms used to specify smooths accept an argument by,
which is a numeric or factor variable of the same dimension as the covariates of the smooth.
If a by variable is numeric, then its ith element multiples the ith
row of the model matrix corresponding to the smooth term concerned. If a by variable is a factor then it generates
an indicator vector for each level of the factor. The model matrix for the smooth term is then replicated for each factor level,
and each copy has its rows multiplied by the corresponding rows of its
indicator variable. The smoothness penalties are also duplicated for each
factor level. In short a different smooth is generated
for each factor level (the id argument to s and te can be used to force all
such smooths to have the same smoothing parameter).
As an example, consider the model
E(y_i) = b_0 + f(x_i)z_i
where f is a smooth function, and z_i is a numeric variable.
The appropriate formula is:
y ~ s(x,by=z)
- the by argument ensures that the smooth function gets multiplied by
covariate z. Note that when using factor variables, then the presence of centering constraints usually
means that the by variable should be included as a parametric term, as well.
The example code below also illustrates the use of factor by variables.
by variables may be supplied as numeric matrices as part of specifying general linear functional terms.
If a by variable is present and numeric (rather than a factor) then the corresponding smooth is only subjected
to an identifiability constraint if (i) the by variable is a constant vector, or, (ii) for a matrix
by variable, L, if L%*%rep(1,ncol(L)) is constant or (iii) if a user defined smooth constructor
supplies an identifiability constraint explicitly.
It is sometimes desirable to insist that different smooth terms have the same degree of smoothness.
This can be done by using the id argument to s or te terms. Smooths
which share an id will have the same smoothing parameter. Really this only makes sense if the
smooths use the same basis functions, and the default behaviour is to force this to happen: all smooths
sharing an id have the same basis functions as the first smooth occurring with that id. Note
that if you want exactly the same function for each smooth, then this is best achieved by making use of the
summation convention covered under `linear functional terms'.
As an example suppose that E(y_i)=mu_i and
g(mu_i) = f1(x_1i) + f2(x_2i,x_3i) + f3(x_4i)
but that f1 and f3 should have the same smoothing parameters (and x_2
and x_3 are on different scales). Then
the gam formula
y ~ s(x1,id=1) + te(x_2,x3) + s(x4,id=1)
would achieve the desired result. id can be numbers or character strings. Giving an id to a
term with a factor by variable causes the smooths at each level of the factor to have the same smoothing
parameter.
Smooth term ids are not supported by gamm.
General linear functional terms have a long history in the spline literature including in the penalized GLM context (see e.g. Wahba 1990). Such terms encompass varying coefficient models/ geographic regression, functional GLMs (i.e. GLMs with functional predictors), GLASS models, etc, and allow smoothing with respect to aggregated covariate values, for example.
Such terms are implemented in mgcv using a simple `summation convention' for smooth terms: If the covariates of a
smooth are supplied as matrices, then summation of the evaluated smooth over the columns of the matrices is implied. Each
covariate matrix and any by variable matrix must be of the same dimension. Consider, for example the term
s(X,Z,by=L)
where X, Z and L are n by p matrices. Let f denote the thin plate regression
spline specified. The resulting contibution to the ith
element of the linear predictor is
sum_j^p L_ij f(X_ij,Z_ij)
If no L is supplied then all its elements are taken as 1. In R code terms, let F denote the n by p
matrix obtained by evaluating the smooth at the values in X and Z. Then the contribution of the term to the
linear predictor is rowSums(L*F) (note that it's element by element multiplication here!).
The summation convention applies to te terms as well as s terms. More details and examples
are provided in
linear.functional.terms.
In case the ability to add smooth classes, smooth identities, by variables and the summation convention are
still not sufficient to implement exactly the penalized GLM that you require, gam also allows you to penalize the
parametric terms in the model formula. This is mostly useful in
allowing one or more matrix terms to be included in the formula, along with a
sequence of quadratic penalty matrices for each.
Suppose that you have set up a model matrix X, and want to penalize the corresponding coefficients, b
with two penalties b'S1 b and b'S2 b.
Then something like the
following would be appropriate:
gam(y ~ X - 1,paraPen=list(X=list(S1,S1)))
The paraPen argument should be a list with elements having names corresponding to the terms being penalized.
Each element of paraPen is itself a list, with optional elements L, rank and sp: all other elements
must be penalty matrices. If present, rank is a vector giving the rank of each penalty matrix
(if absent this is determined numerically). L is a matrix that maps underlying log smoothing parameters to the
log smoothing parameters that actually multiply the individual quadratic penalties: taken as the identity if not supplied.
sp is a vector of (underlying) smoothing parameter values: positive values are taken as fixed, negative to signal that
the smoothing parameter should be estimated. Taken as all negative if not supplied.
An obvious application of paraPen is to incorporate random effects, and an example of this is provided below. In this, divide the scale parameter by
the estimated smoothing parameters to recover variance component estimates.
Simon N. Wood simon.wood@r-project.org
Wahba (1990) Spline Models of Observational Data SIAM.
Wood S.N. (2006) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.
set.seed(10)
## simulate date from y = f(x2)*x1 + error
dat <- gamSim(3,n=400)
b<-gam(y ~ s(x2,by=x1),data=dat)
plot(b,pages=1)
summary(b)
## Factor `by' variable example (with a spurious covariate x0)
## simulate data...
dat <- gamSim(4)
## fit model...
b <- gam(y ~ fac+s(x2,by=fac)+s(x0),data=dat)
plot(b,pages=1)
summary(b)
## note that the preceding fit is the same as....
b1<-gam(y ~ s(x2,by=as.numeric(fac==1))+s(x2,by=as.numeric(fac==2))+
s(x2,by=as.numeric(fac==3))+s(x0)-1,data=dat)
## ... the `-1' is because the intercept is confounded with the
## *uncentred* smooths here.
plot(b1,pages=1)
summary(b1)
## repeat forcing all s(x2) terms to have the same smoothing param
## (not a very good idea for these data!)
b2 <- gam(y ~ fac+s(x2,by=fac,id=1)+s(x0),data=dat)
plot(b2,pages=1)
summary(b2)
rm(dat)
## An example of a simple random effects term implemented via
## penalization of the parametric part of the model...
dat <- gamSim(1,n=400,scale=2) ## simulate 4 term additive truth
## Now add some random effects to the simulation. Response is
## grouped into one of 20 groups by `fac' and each groups has a
## random effect added....
fac <- as.factor(sample(1:20,400,replace=TRUE))
dat$X <- model.matrix(~fac-1)
b <- rnorm(20)*.5
dat$y <- dat$y + dat$X%*%b
## now fit appropriate random effect model...
PP <- list(X=list(rank=20,diag(20)))
rm <- gam(y~ X+s(x0)+s(x1)+s(x2)+s(x3),data=dat,paraPen=PP)
plot(rm,pages=1)
## Get estimated random effects standard deviation...
sig.b <- sqrt(rm$sig2/rm$sp[1]);sig.b
## Simple comparison with lme, using Rail data.
## "ML" used as "REML" treatments of residual variance
## differ slightly...
require(nlme)
b0 <- lme(travel~1,data=Rail,~1|Rail,method="ML")
Z <- model.matrix(~Rail-1,data=Rail,
contrasts.arg=list(Rail="contr.treatment"))
b <- gam(travel~Z,data=Rail,paraPen=list(Z=list(diag(6))),method="ML")
b0
(b$reml.scale/b$sp)^.5 ## `gam' ML estimate of Rail sd
b$reml.scale^.5 ## `gam' ML estimate of residual sd