predict.m2skreg: Prediction for Manifold-to-Scalar Kernel Regression

In Riemann: Learning with Data on Riemannian Manifolds

Prediction for Manifold-to-Scalar Kernel Regression

Description

Given new observations X_1, X_2, …, X_M \in \mathcal{M}, plug in the data with respect to the fitted model for prediction.

Usage

## S3 method for class 'm2skreg'
predict(object, newdata, geometry = c("intrinsic", "extrinsic"), ...)

Arguments

object	an object of m2skreg class. See riem.m2skreg for more details.
newdata	a S3 "riemdata" class for manifold-valued data corresponding to X_1,…,X_M.
geometry	(case-insensitive) name of geometry; either geodesic ("intrinsic") or embedded ("extrinsic") geometry.
...	further arguments passed to or from other methods.

Value

a length-M vector of predictted values.

See Also

riem.m2skreg

Examples

#-------------------------------------------------------------------
#                    Example on Sphere S^2
#
#  X : equi-spaced points from (0,0,1) to (0,1,0)
#  y : sin(x) with perturbation
#
#  Our goal is to check whether the predict function works well
#  by comparing the originally predicted values vs. those of the same data.
#-------------------------------------------------------------------
# GENERATE DATA
npts = 100
nlev = 0.25
thetas = seq(from=0, to=pi/2, length.out=npts)
Xstack = cbind(rep(0,npts), sin(thetas), cos(thetas))

Xriem  = wrap.sphere(Xstack)
ytrue  = sin(seq(from=0, to=2*pi, length.out=npts))
ynoise = ytrue + rnorm(npts, sd=nlev)

# FIT & PREDICT
obj_fit   = riem.m2skreg(Xriem, ynoise, bandwidth=0.01)
yval_fits = obj_fit$ypred
yval_pred = predict(obj_fit, Xriem)

# VISUALIZE
xgrd <- 1:npts
opar <- par(no.readonly=TRUE)
par(mfrow=c(1,2))
plot(xgrd, yval_fits, pch=19, cex=0.5, "b", xlab="", ylim=c(-2,2), main="original fit")
lines(xgrd, ytrue, col="red", lwd=1.5)
plot(xgrd, yval_pred, pch=19, cex=0.5, "b", xlab="", ylim=c(-2,2), main="from 'predict'")
lines(xgrd, ytrue, col="red", lwd=1.5)
par(opar)

Riemann documentation built on March 18, 2022, 7:55 p.m.