Thin plate spline regression
Fits a thin plate spline surface to irregularly spaced data. The smoothing parameter is chosen by generalized cross-validation. The assumed model is additive Y = f(X) +e where f(X) is a d dimensional surface. This function also works for just a single dimension and is a special case of a spatial process estimate (Kriging). A "fast" version of this function uses a compactly supported Wendland covariance and computes the estimate for a fixed smoothing parameter.
Tps(x, Y, m = NULL, p = NULL, scale.type = "range", lon.lat = FALSE,
miles = TRUE, method = "GCV", GCV = TRUE, ...)
fastTps(x, Y, m = NULL, p = NULL, theta, lon.lat=FALSE,
find.trA = TRUE, lambda=0, ...)x |
Matrix of independent variables. Each row is a location or a set of independent covariates. |
Y |
Vector of dependent variables. |
m |
A polynomial function of degree (m-1) will be included in the model as the drift (or spatial trend) component. Default is the value such that 2m-d is greater than zero where d is the dimension of x. |
p |
Polynomial power for Wendland radial basis functions. Default is 2m-d where d is the dimension of x. |
scale.type |
The independent variables and knots are scaled to the specified scale.type. By default the scale type is "range", whereby the locations are transformed to the interval (0,1) by forming (x-min(x))/range(x) for each x. Scale type of "user" allows specification of an x.center and x.scale by the user. The default for "user" is mean 0 and standard deviation 1. Scale type of "unscaled" does not scale the data. |
theta |
The tapering range that is passed to the Wendland compactly supported covariance. The covariance (i.e. the radial basis function) is zero beyond range theta. The larger theta the closer this model will approximate the standard thin plate spline. |
lon.lat |
If TRUE locations are interpreted as lognitude and
latitude and great circle distance is used to find distances among
locations. The theta scale parameter for |
method |
Determines what "smoothing" parameter should be used. The default is to estimate standard GCV Other choices are: GCV.model, GCV.one, RMSE, pure error and REML. The differences are explained in the Krig help file. |
GCV |
If TRUE the decompositions are done to efficiently evaluate the estimate, GCV function and likelihood at multiple values of lambda. |
miles |
If TRUE great circle distances are in miles if FALSE distances are in kilometers |
lambda |
Smoothing parameter the ratio of error variance to process variance, default is zero which corresponds to interpolation. See fastTpsMLE to estimate this paramter from the data. |
find.trA |
If TRUE will estimate the effective degrees of freedom
using a simple Monte Carlo method. This will add to the computational
burden by approximately |
... |
For For Arguments for Tps:
|
Both of these functions are special cases of using the
Krig and mKrig functions. See the help on each of these
for more information on the calling arguments and what is returned.
Tps makes use of the stable computations via eigen decompositions in Krig. fastTps follows the more standard computations for spatial statistics centered around the Cholesky decomposition in mKrig.
A thin plate spline is the result of minimizing the residual sum of squares subject to a constraint that the function have a certain level of smoothness (or roughness penalty). Roughness is quantified by the integral of squared m-th order derivatives. For one dimension and m=2 the roughness penalty is the integrated square of the second derivative of the function. For two dimensions the roughness penalty is the integral of
(Dxx(f))**22 + 2(Dxy(f))**2 + (Dyy(f))**22
(where Duv denotes the second partial derivative with respect to u and v.) Besides controlling the order of the derivatives, the value of m also determines the base polynomial that is fit to the data. The degree of this polynomial is (m-1).
The smoothing parameter controls the amount that the data is smoothed. In the usual form this is denoted by lambda, the Lagrange multiplier of the minimization problem. Although this is an awkward scale, lambda =0 corresponds to no smoothness constraints and the data is interpolated. lambda=infinity corresponds to just fitting the polynomial base model by ordinary least squares.
This estimator is implemented by passing the right generalized covariance function based on radial basis functions to the more general function Krig. One advantage of this implementation is that once a Tps/Krig object is created the estimator can be found rapidly for other data and smoothing parameters provided the locations remain unchanged. This makes simulation within R efficient (see example below). Tps does not currently support the knots argument where one can use a reduced set of basis functions. This is mainly to simplify the code and a good alternative using knots would be to use a valid covariance from the Matern family and a large range parameter.
CAUTION about lon.lat=TRUE: The option to use great circle distance
to define the radial basis functions (lon.lat=TRUE) is very useful
for small geographic domains where the spherical geometry is well approximated by a plane. However, for large domains the spherical distortion be large enough that the basis function no longer define a positive definite system and Tps will report a numerical error. An alternative is to switch to a three
dimensional thin plate spline the locations being the direction cosines. This will
give approximate great circle distances for locations that are close and also the numerical methods will always have a positive definite matrices.
Here is an example using this idea for RMprecip and also some
examples of building grids and evaluating the Tps results on them:
# a useful function:
dircos<- function(x1){
coslat1 <- cos((x1[, 2] * pi)/180)
sinlat1 <- sin((x1[, 2] * pi)/180)
coslon1 <- cos((x1[, 1] * pi)/180)
sinlon1 <- sin((x1[, 1] * pi)/180)
cbind(coslon1*coslat1, sinlon1*coslat1, sinlat1)}
# fit in 3-d to direction cosines
out<- Tps(dircos(RMprecip$x),RMprecip$y)
xg<-make.surface.grid(fields.x.to.grid(RMprecip$x))
fhat<- predict( out, dircos(xg))
# coerce to image format from prediction vector and grid points.
out.p<- as.surface( xg, fhat)
surface( out.p)
# compare to the automatic
out0<- Tps(RMprecip$x,RMprecip$y, lon.lat=TRUE)
surface(out0)The function fastTps is really a convenient wrapper function that
calls mKrig with the Wendland covariance function. This is
experimental and some care needs to exercised in specifying the taper
range and power ( p) which describes the polynomial behavior of
the Wendland at the origin. Note that unlike Tps the locations are not
scaled to unit range and this can cause havoc in smoothing problems with
variables in very different units. So rescaling the locations x<- scale(x)
is a good idea for putting the variables on a common scale for smoothing.
This function does have the potential to approximate estimates of Tps
for very large spatial data sets. See wendland.cov and help on
the SPAM package for more background.
Also, the function predictSurface.fastTps has been made more efficient for the
case of k=2 and m=2. Also see the handy function fastTpsMLE to estimate lambda by maximum likelihood.
See also the mKrig function for handling larger data sets and also for an example of combining Tps and mKrig for evaluation on a huge grid.
A list of class Krig. This includes the fitted values, the predicted surface evaluated at the observation locations, and the residuals. The results of the grid search minimizing the generalized cross validation function are returned in gcv.grid. Note that the GCV/REML optimization is done even if lambda or df is given. Please see the documentation on Krig for details of the returned arguments.
See "Nonparametric Regression and Generalized Linear Models" by Green and Silverman. See "Additive Models" by Hastie and Tibshirani.
#2-d example
fit<- Tps(ChicagoO3$x, ChicagoO3$y) # fits a surface to ozone measurements.
set.panel(2,2)
plot(fit) # four diagnostic plots of fit and residuals.
set.panel()
# summary of fit and estiamtes of lambda the smoothing parameter
summary(fit)
surface( fit) # Quick image/contour plot of GCV surface.
# NOTE: the predict function is quite flexible:
look<- predict( fit, lambda=2.0)
# evaluates the estimate at lambda =2.0 _not_ the GCV estimate
# it does so very efficiently from the Krig fit object.
look<- predict( fit, df=7.5)
# evaluates the estimate at the lambda values such that
# the effective degrees of freedom is 7.5
# compare this to fitting a thin plate spline with
# lambda chosen so that there are 7.5 effective
# degrees of freedom in estimate
# Note that the GCV function is still computed and minimized
# but the lambda values used correpsonds to 7.5 df.
fit1<- Tps(ChicagoO3$x, ChicagoO3$y,df=7.5)
set.panel(2,2)
plot(fit1) # four diagnostic plots of fit and residuals.
# GCV function (lower left) has vertical line at 7.5 df.
set.panel()
# The basic matrix decompositions are the same for
# both fit and fit1 objects.
# predict( fit1) is the same as predict( fit, df=7.5)
# predict( fit1, lambda= fit$lambda) is the same as predict(fit)
# predict onto a grid that matches the ranges of the data.
out.p<-predictSurface( fit)
image( out.p)
# the surface function (e.g. surface( fit)) essentially combines
# the two steps above
# predict at different effective
# number of parameters
out.p<-predictSurface( fit,df=10)
## Not run:
# predicting on a grid along with a covariate
data( COmonthlyMet)
# predicting average daily minimum temps for spring in Colorado
# NOTE to create an 4km elevation grid:
# data(PRISMelevation); CO.elev1 <- crop.image(PRISMelevation, CO.loc )
# then use same grid for the predictions: CO.Grid1<- CO.elev1[c("x","y")]
obj<- Tps( CO.loc, CO.tmin.MAM.climate, Z= CO.elev)
out.p<-predictSurface( obj,
grid.list=CO.Grid, ZGrid= CO.elevGrid)
image.plot( out.p)
US(add=TRUE, col="grey")
contour( CO.elevGrid, add=TRUE, levels=c(2000), col="black")
## End(Not run)
## Not run:
#A 1-d example with confidence intervals
out<-Tps( rat.diet$t, rat.diet$trt) # lambda found by GCV
out
plot( out$x, out$y)
xgrid<- seq( min( out$x), max( out$x),,100)
fhat<- predict( out,xgrid)
lines( xgrid, fhat,)
SE<- predictSE( out, xgrid)
lines( xgrid,fhat + 1.96* SE, col="red", lty=2)
lines(xgrid, fhat - 1.96*SE, col="red", lty=2)
#
# compare to the ( much faster) B spline algorithm
# sreg(rat.diet$t, rat.diet$trt)
# Here is a 1-d example with 95 percent CIs where sreg would not
# work:
# sreg would give the right estimate here but not the right CI's
x<- seq( 0,1,,8)
y<- sin(3*x)
out<-Tps( x, y) # lambda found by GCV
plot( out$x, out$y)
xgrid<- seq( min( out$x), max( out$x),,100)
fhat<- predict( out,xgrid)
lines( xgrid, fhat, lwd=2)
SE<- predictSE( out, xgrid)
lines( xgrid,fhat + 1.96* SE, col="red", lty=2)
lines(xgrid, fhat - 1.96*SE, col="red", lty=2)
## End(Not run)
# More involved example adding a covariate to the fixed part of model
## Not run:
set.panel( 1,3)
# without elevation covariate
out0<-Tps( RMprecip$x,RMprecip$y)
surface( out0)
US( add=TRUE, col="grey")
# with elevation covariate
out<- Tps( RMprecip$x,RMprecip$y, Z=RMprecip$elev)
# NOTE: out$d[4] is the estimated elevation coefficient
# it is easy to get the smooth surface separate from the elevation.
out.p<-predictSurface( out, drop.Z=TRUE)
surface( out.p)
US( add=TRUE, col="grey")
# and if the estimate is of high resolution and you get by with
# a simple discretizing -- does not work in this case!
quilt.plot( out$x, out$fitted.values)
#
# the exact way to do this is evaluate the estimate
# on a grid where you also have elevations
# An elevation DEM from the PRISM climate data product (4km resolution)
data(RMelevation)
grid.list<- list( x=RMelevation$x, y= RMelevation$y)
fit.full<- predictSurface( out, grid.list, ZGrid= RMelevation)
# this is the linear fixed part of the second spatial model:
# lon,lat and elevation
fit.fixed<- predictSurface( out, grid.list, just.fixed=TRUE, ZGrid= RMelevation)
# This is the smooth part but also with the linear lon lat terms.
fit.smooth<-predictSurface( out, grid.list, drop.Z=TRUE)
#
set.panel( 3,1)
fit0<- predictSurface( out0, grid.list)
image.plot( fit0)
title(" first spatial model (w/o elevation)")
image.plot( fit.fixed)
title(" fixed part of second model (lon,lat,elev linear model)")
US( add=TRUE)
image.plot( fit.full)
title("full prediction second model")
set.panel()
## End(Not run)
###
### fast Tps
# m=2 p= 2m-d= 2
#
# Note: theta =3 degrees is a very generous taper range.
# Use some trial theta value with rdist.nearest to determine a
# a useful taper. Some empirical studies suggest that in the
# interpolation case in 2 d the taper should be large enough to
# about 20 non zero nearest neighbors for every location.
fastTps( RMprecip$x,RMprecip$y,m=2,lambda= 1e-2, theta=3.0) -> out2
# note that fastTps produces an mKrig object so one can use all the
# the overloaded functions that are defined for the mKrig class.
# summary of what happened note estimate of effective degrees of
# freedom
print( out2)
## Not run:
set.panel( 1,2)
surface( out2)
#
# now use great circle distance for this smooth
# Here "theta" for the taper support is the great circle distance in degrees latitude.
# Typically for data analysis it more convenient to think in degrees. A degree of
# latitude is about 68 miles (111 km).
#
fastTps( RMprecip$x,RMprecip$y,m=2,lambda= 1e-2,lon.lat=TRUE, theta= 3.0) -> out3
print( out3) # note the effective degrees of freedom is different.
surface(out3)
set.panel()
## End(Not run)
## Not run:
#
# simulation reusing Tps/Krig object
#
fit<- Tps( rat.diet$t, rat.diet$trt)
true<- fit$fitted.values
N<- length( fit$y)
temp<- matrix( NA, ncol=50, nrow=N)
sigma<- fit$shat.GCV
for ( k in 1:50){
ysim<- true + sigma* rnorm(N)
temp[,k]<- predict(fit, y= ysim)
}
matplot( fit$x, temp, type="l")
## End(Not run)
#
#4-d example
fit<- Tps(BD[,1:4],BD$lnya,scale.type="range")
# plots fitted surface and contours
# default is to hold 3rd and 4th fixed at median values
surface(fit)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.