Function used in definition of smooth terms within
gam model formulae. The function does not evaluate a (spline) smooth - it exists purely to help set up a model using spline based smooths.
- a list of variables that are the covariates that this smooth is a function of.
- the dimension of the basis used to represent the smooth term. The default depends on the number of variables that the smooth is a function of.
kshould not be less than the dimension of the null space of the penalty for the term (see
null.space.dimension), but will be reset if it is. See
choose.kfor further information.
- indicates whether the term is a fixed d.f. regression spline (
TRUE) or a penalized regression spline (
- a two letter character string indicating the (penalized) smoothing basis to use. (eg
"tp"for thin plate regression spline,
"cr"for cubic regression spline). see
smooth.termsfor an over view of what is available.
- The order of the penalty for this term (e.g. 2 for normal cubic spline penalty with 2nd derivatives when using default t.p.r.s basis).
NAsignals autoinitialization. Only some smooth classes use this. The
"ps"class can use a 2 item array giving the basis and penalty order separately.
- a numeric or factor variable of the same dimension as each covariate. In the numeric vector case the elements multiply the smooth, evaluated at the corresponding covariate values (a `varying coefficient model' results). For the numeric
byvariable case the resulting smooth is not usually subject to a centering constraint (so the
by variableshould not be added as an additional main effect). In the factor
byvariable case a replicate of the smooth is produced for each factor level (these smooths will be centered, so the factor usually needs to be added as a main effect as well). See
gam.modelsfor further details. A
byvariable may also be a matrix if covariates are matrices: in this case implements linear functional of a smooth (see
- Any extra information required to set up a particular basis. Used e.g. to set large data set handling behaviour for
- A label or integer identifying this term in order to link its smoothing parameters to others of the same type. If two or more terms have the same
idthen they will have the same smoothing paramsters, and, by default, the same bases (first occurance defines basis type, but data from all terms used in basis construction). An
idwith a factor
byvariable causes the smooths at each factor level to have the same smoothing parameter.
- any supplied smoothing parameters for this term. Must be an array of the same length as the number of penalties for this smooth. Positive or zero elements are taken as fixed smoothing parameters. Negative elements signal auto-initialization. Over-rides values supplied in
gam. Ignored by
The function does not evaluate the variable arguments. To use this function to specify use of your own smooths, note the relationships between the inputs and the output object and see the example in
xx.smooth.spec object, where
xx is a basis identifying code given by the
bs argument of
smooth.spec objects define smooths and are turned into bases and penalties by
smooth.construct method functions.
The returned object contains the following items:
- An array of text strings giving the names of the covariates that the term is a function of.
- The dimension of the basis used to represent the smooth.
- TRUE if the term is to be treated as a pure regression spline (with fixed degrees of freedom); FALSE if it is to be treated as a penalized regression spline
- The dimension of the smoother - i.e. the number of covariates that it is a function of.
- The order of the t.p.r.s. penalty, or 0 for auto-selection of the penalty order.
- is the name of any
byvariable as text (
- A suitable text label for this smooth term.
- The object passed in as argument
- An identifying label or number for the smooth, linking it to other smooths. Defaults to
NULLfor no linkage.
- array of smoothing parameters for the term (negative for auto-estimation). Defaults to
Wood, S.N. (2003) Thin plate regression splines. J.R.Statist.Soc.B 65(1):95-114
Wood S.N. (2006) Generalized Additive Models: An Introduction with R. Chapman and Hall/CRC Press.
# example utilising `by' variables library(mgcv) set.seed(0) n<-200;sig2<-4 x1 <- runif(n, 0, 1);x2 <- runif(n, 0, 1);x3 <- runif(n, 0, 1) fac<-c(rep(1,n/2),rep(2,n/2)) # create factor fac.1<-rep(0,n)+(fac==1);fac.2<-1-fac.1 # and dummy variables fac<-as.factor(fac) f1 <- exp(2 * x1) - 3.75887 f2 <- 0.2 * x1^11 * (10 * (1 - x1))^6 + 10 * (10 * x1)^3 * (1 - x1)^10 f<-f1*fac.1+f2*fac.2+x2 e <- rnorm(n, 0, sqrt(abs(sig2))) y <- f + e # NOTE: smooths will be centered, so need to include fac in model.... b<-gam(y~fac+s(x1,by=fac)+x2) plot(b,pages=1)
Documentation reproduced from package mgcv, version 1.8-12. License: GPL (>= 2)