https://github.com/cran/sparseFLMM
Tip revision: b182e9a
smooth.construct.symm.smooth.spec.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/tri_constraint_constructor.R
\name{smooth.construct.symm.smooth.spec}
\alias{smooth.construct.symm.smooth.spec}
\title{Symmetric bivariate smooths constructor}
\usage{
\method{smooth.construct}{symm.smooth.spec}(object, data, knots)
}
\arguments{
\item{object}{is a smooth specification object or a smooth object.}

\item{data}{a data frame, model frame or list containing the values
of the (named) covariates at which the smooth term is to be evaluated.}

\item{knots}{an optional data frame supplying any knot locations
to be supplied for basis construction.}
}
\value{
An object of class "symm.smooth". See \code{\link[mgcv]{smooth.construct}} for the elements it will contain.
}
\description{
The \code{symm} class is a smooth class that is appropriate for symmetric bivariate smooths, e.g. of covariance functions,
using tensor-product smooths in a \code{gam} formula. A constraint matrix is constructed
(see \code{\link[sparseFLMM]{make_summation_matrix}}) to impose
a (skew-)symmetry constraint on the (cyclic) spline coefficients,
which considerably reduces the number of coefficients that have to be estimated.
}
\details{
By default a symmetric bivariate B-spline smooth \eqn{g} is specified,
in the sense that \eqn{g(s, t) = g(t, s)}. By setting
\code{s(..., bs = "symm", xt = list(skew = TRUE))}, a skew-symmetric (or anti-smmetric)
smooth with \eqn{g(s, t) = -g(t, s)} can be specified instead.
In both cases, the smooth can also be constraint to be cyclic
with the property \eqn{g(s, t) = g(s + c, t) = g(s, t + c)}
for some fixed constant \eqn{c} via specifying \code{xt = list(cyclic = TRUE)}.
Note that this does not correspond to specifying a tensor-product smooth from
cyclic marginal B-splines as given by the \code{cp}-smooth.
In the cyclic case, it is recommended to explicitly specify the range of the domain
of the smooth via the \code{knots} argument, as this determines the period and
often deviates from the observed range.

The underlying procedure is the following: First, the marginal spline design matrices and the corresponding
marginal difference penalties are built. Second, the tensor product of the marginal design matrices is built
and the bivariate penalty matrix is set up. Third, the constraint matrix is applied
to the tensor product design matrix and to the penalty matrix.
}
\examples{
require(sparseFLMM)

# (skew-)symmetric smooths ---------------------------------------

# generate random surface
dat1 <- data.frame(arg1 = 1:50)
dat2 <- expand.grid(arg1 = 1:50, arg2 = 1:50)

Bskew <- Predict.matrix(
smooth.construct(
s(arg1, arg2, bs = "symm", xt = list(skew = TRUE)),
data = dat2, knots = NULL ),
data = dat2 )
Bsymm <- Predict.matrix(
smooth.construct(
s(arg1, arg2, bs = "symm", xt = list(skew = FALSE)),
data = dat2, knots = NULL ),
data = dat2 )

set.seed(934811)
dat2$yskew <- c(Bskew \%*\% rnorm(ncol(Bskew))) dat2$ysymm <- c(Bsymm \%*\% rnorm(ncol(Bsymm)))

# fit sum of skew-symmetric and symmetric parts with corresponding smooths
modpa <- gam( I(yskew + ysymm) ~ s(arg1, arg2, bs = "symm", xt = list(skew = TRUE)) +
s(arg1, arg2, bs = "symm", xt = list(skew = FALSE)), data = dat2)
# predict surfaces
preds <- predict(modpa, type = "terms")
dat1 <- as.list(dat1)
dat1$arg2 <- dat1$arg1
dat1$predskew <- matrix(preds[,1], nrow = length(dat1$arg1))
dat1$predsymm <- matrix(preds[,2], nrow = length(dat1$arg1))

cols <- hcl.colors(12, "RdBu")
opar <- par(mfcol = c(2,2))
# symm part (intercept missing)
with(dat1, image(arg1, arg2, predsymm, asp = 1,
main = "Symmetric part of y",
col = cols))
with(dat1, image(arg1, arg2, asp = 1,
main = "Fit via symm.smooth",
matrix(dat2$ysymm, nrow = length(arg1)), col = cols)) # skew-symm part with(dat1, image(arg1, arg2, predskew, asp = 1, main = "Skew-symmetric part of y", col = cols)) with(dat1, image(arg1, arg2, asp = 1, main = "Fit via symm.smooth", matrix(dat2$yskew, nrow = length(arg1)),
col = cols))
par(opar)

stopifnot(all.equal(dat1$predskew, - t(dat1$predskew)))
stopifnot(all.equal(dat1$predsymm, t(dat1$predsymm)))

# cyclic (skew-)symmetric splines ---------------------------------------

# fit the above example with cyclic smooths
modpac <- gam( I(yskew + ysymm) ~ s(arg1, arg2, bs = "symm",
xt = list(skew = TRUE, cyclic = TRUE)) +
s(arg1, arg2, bs = "symm", xt = list(skew = FALSE, cyclic = TRUE)),
knots = list(arg1 = c(1, 50), arg2 = c(1,50)),
# specify arg range to specify 'wavelength'!
data = dat2)
plot(modpac, asp = 1, se = FALSE, pages = 1)

predsc <- predict(modpac, type = "terms")
dat1$predskewc <- matrix(predsc[,1], nrow = length(dat1$arg1))
dat1$predsymmc <- matrix(predsc[,2], nrow = length(dat1$arg1))

# check cyclic margins
opar <- par(mfrow = c(1,2))
with(dat1, matplot(arg1, predsymmc[, c(1,10, 40)], t = "l",
main = "symmetric smooth"))
abline(h = dat1$predsymmc[1, c(1,10, 40)], col = "darkgrey") abline(v = c(1,50), col = "darkgrey") with(dat1, matplot(arg1, predskewc[, c(1,10, 40)], t = "l", main = "skew-symmetric smooth")) abline(h = dat1$predskewc[1, c(1,10, 40)], col = "darkgrey")
abline(v = c(1,50), col = "darkgrey")
par(opar)

# 1D point symmetric B-splines --------------------------------------------

# generate toy data
dat <- data.frame( x = 1:100 )
ps_obj <- with(dat, s(x, bs = "ps"))
B <- Predict.matrix(smooth.construct(ps_obj, dat, NULL), dat)
set.seed(3904)
dat$y <- B \%*\% rnorm(ncol(B)) plot(dat, t = "l") # fit skew-symmetric spline mod0 <- gam( y ~ s(x, bs = "symm", xt = list(skew = TRUE)), knots = list(x = c(0,100)), # specify x range to determine inversion point dat = dat ) lines(dat$x, predict(mod0), col = "cornflowerblue", lty = "dashed")

# or a symmetric spline to first part only
mod1 <- gam( y ~ s(x, bs = "symm"),
knots = list(x=c(0,50)),
dat = dat[1:50, ])
lines(dat[1:50, ]\$x, predict(mod1), col = "darkred", lty = "dashed")
}
\references{
Cederbaum, Scheipl, Greven (2016): Fast symmetric additive covariance smoothing.
Submitted on arXiv.
}
\seealso{
\code{\link[mgcv]{smooth.construct}} and \code{\link[mgcv]{smoothCon}} for details on constructors
}
\author{
Jona Cederbaum, Almond Stoecker
}