MultiFAM: Functional Additive Models with Multiple Predictor Processes

In hadjipantelis/tPACE: Functional Data Analysis and Empirical Dynamics

Functional Additive Models with Multiple Predictor Processes

Description

Smooth backfitting procedure for functional additive models with multiple predictor processes

Usage

MultiFAM(
  Y,
  X,
  ker = "epan",
  nEval = 51,
  XTest = NULL,
  bwMethod = 0,
  alpha = 0.7,
  supp = c(-2, 2),
  optnsList = NULL
)

Arguments

Y	An n-dimensional vector whose elements consist of scalar responses.
X	A d-dimensional list whose components consist of two lists of Ly and Lt containing observation times and functional covariate values for each predictor component, respectively. For details of Ly and Lt, see FPCA for detail.
ker	A function object representing the base kernel to be used in the smooth backfitting algorithm (default is 'epan' which is the only option supported currently).
nEval	The number of evaluation grid points for kernel smoothing (default is 51. If it is specified as 0, then estimated FPC scores in the training set are used for evaluation grid instead of equal grid).
XTest	A d-dimensional list for test set of functional predictors (default is NULL). If XTest is specified, then estimated FPC scores in the test set are used for evaluation grid.
bwMethod	The method of initial bandwidth selection for kernel smoothing, a positive value for designating K-fold cross-validtaion and zero for GCV (default is 0)
alpha	The shrinkage factor (positive number) for bandwidth selection. See Han et al. (2016) (default is 0.7).
supp	The lower and upper limits of kernel smoothing domain for studentized FPC scores, which FPC scores are divided by the square roots of eigenvalues (default is [-2,2]).
optnsList	A d-dimensional list whose components consist of optns for each predictor component, respectively. (default is NULL which assigns the same default optns for all components as in FPCA).

Details

MultiFAM fits functional additive models for a scalar response and multiple predictor processes and implements the smooth backfitting algorithm provided in Han, K., Müller, H.G., Park, B.U. (2018). Smooth backfitting for additive modeling with small errors-in-variables, with an application to additive functional regression for multiple predictor functions. Bernoulli 24, 1233–1265.

It is based on the model

E(Y | \mathbf{X}) = ∑_{j=1}^d ∑_{k=1}^{K_j} g_{jk}(ξ_{jk}),

where ξ_{jk} stand for the k-th FPC scores of the j-th predictor process. MultiFAM only is for the multiple predictor processes case. For a univariate predictor use FAM, the functional additive model (Müller and Yao 2008). It is necessary to designate an estimation support for the additive component functions where the additive modeling is only allowed over restricted intervals (see Han et al., 2018).

Value

A list containing the following fields:

mu	A scalar for the centered regression model.
SBFit	An N by (K_1 + ... + K_d) matrix whose column vectors consist of the smooth backfitting component function estimators at the given N estimation points.
xi	An N by (K_1 + ... + K_d) matrix whose column vectors consist of the FPC score grid vectors at which each additive component function is evaluated.
bw	A (K_1 + ... + K_d)-dimensional bandwidth vector.
lambda	A (K_1 + ... + K_d)-dimensional vector containing eigenvalues.
phi	A d-dimensional list whose components consist of an nWorkGrid by K_j matrix containing eigenfunctions, supported by WorkGrid. See FPCA.
workGrid	A d-dimensional list whose components consist of an nWorkGrid by K_j working grid, a regular grid on which the eigenanalysis is carried out See FPCA.

References

Mammen, E., Linton, O. and Nielsen, J. (1999), "The existence and asymptotic properties of a backfitting projection algorithm under weak conditions", Annals of Statistics, Vol.27, No.5, p.1443-1490.

Mammen, E. and Park, B. U. (2006), "A simple smooth backfitting method for additive models", Annals of Statistics, Vol.34, No.5, p.2252-2271.

Müller, H.-G. and Yao, F. (2008), "Functional additive models", Journal of the American Statistical Association, Vol.103, No.484, p.1534-1544.

Han, K., Müller, H.-G. and Park, B. U. (2016), "Smooth backfitting for additive modeling with small errors-in-variables, with an application to additive functional regression for multiple predictor functions", Bernoulli (accepted).

Examples

set.seed(1000)

library(MASS)

f11 <- function(t) t
f12 <- function(t) 2*cos(2*pi*t/4)
f21 <- function(t) 1.5*sin(2*pi*t/4)
f22 <- function(t) 1.5*atan(2*pi*t/4)

n<-100
N<-200

sig <- matrix(c(2.0, 0.0, 0.5, -.2,
                0.0, 1.2, -.2, 0.3,
                0.5, -.2, 1.7, 0.0,
                -.2, 0.3, 0.0, 1.0),
              nrow=4,ncol=4)

scoreX <- mvrnorm(n,mu=rep(0,4),Sigma=sig)
scoreXTest <- mvrnorm(N,mu=rep(0,4),Sigma=sig)

Y <- f11(scoreX[,1]) + f12(scoreX[,2]) + f21(scoreX[,3]) + f22(scoreX[,4]) + rnorm(n,0,0.5)
YTest <- f11(scoreXTest[,1]) + f12(scoreXTest[,2]) + 
f21(scoreXTest[,3]) + f22(scoreXTest[,4]) + rnorm(N,0,0.5)

phi11 <- function(t) sqrt(2)*sin(2*pi*t)
phi12 <- function(t) sqrt(2)*sin(4*pi*t)
phi21 <- function(t) sqrt(2)*cos(2*pi*t)
phi22 <- function(t) sqrt(2)*cos(4*pi*t)

grid <- seq(0,1,length.out=21)
Lt <- Lx1 <- Lx2 <- list()
for (i in 1:n) {
  Lt[[i]] <- grid
  Lx1[[i]] <- scoreX[i,1]*phi11(grid) + scoreX[i,2]*phi12(grid) + rnorm(1,0,0.01)
  Lx2[[i]] <- scoreX[i,3]*phi21(grid) + scoreX[i,4]*phi22(grid) + rnorm(1,0,0.01)
}

LtTest <- Lx1Test <- Lx2Test <- list()
for (i in 1:N) {
  LtTest[[i]] <- grid
  Lx1Test[[i]] <- scoreXTest[i,1]*phi11(grid) + scoreXTest[i,2]*phi12(grid) + rnorm(1,0,0.01)
  Lx2Test[[i]] <- scoreXTest[i,3]*phi21(grid) + scoreXTest[i,4]*phi22(grid) + rnorm(1,0,0.01)
}

X1 <- list(Ly=Lx1, Lt=Lt)
X2 <- list(Ly=Lx2, Lt=Lt)

X1Test <- list(Ly=Lx1Test, Lt=LtTest)
X2Test <- list(Ly=Lx2Test, Lt=LtTest)

X <- list(X1, X2)
XTest <- list(X1Test, X2Test)

# estimation
sbf <- MultiFAM(Y=Y,X=X)

xi <- sbf$xi

par(mfrow=c(2,2))
j <- 1
p0 <- trapzRcpp(sort(xi[,j]),dnorm(sort(xi[,j]),0,sqrt(sig[j,j])))
g11 <- f11(sort(xi[,j])) - 
trapzRcpp(sort(xi[,j]),f11(sort(xi[,j]))*dnorm(sort(xi[,j]),0,sqrt(sig[j,j])))/p0
tmpSgn <- sign(sum(g11*sbf$SBFit[,j]))
plot(sort(xi[,j]),g11,type='l',col=2,ylim=c(-2.5,2.5),xlab='xi11')
points(sort(xi[,j]),tmpSgn*sbf$SBFit[order(xi[,j]),j],type='l')
legend('top',c('true','SBF'),col=c(2,1),lwd=2,bty='n',horiz=TRUE)

j <- 2
p0 <- trapzRcpp(sort(xi[,j]),dnorm(sort(xi[,j]),0,sqrt(sig[j,j])))
g12 <- f12(sort(xi[,j])) - 
trapzRcpp(sort(xi[,j]),f12(sort(xi[,j]))*dnorm(sort(xi[,j]),0,sqrt(sig[j,j])))/p0
tmpSgn <- sign(sum(g12*sbf$SBFit[,j]))
plot(sort(xi[,j]),g12,type='l',col=2,ylim=c(-2.5,2.5),xlab='xi12')
points(sort(xi[,j]),tmpSgn*sbf$SBFit[order(xi[,j]),j],type='l')
legend('top',c('true','SBF'),col=c(2,1),lwd=2,bty='n',horiz=TRUE)

j <- 3
p0 <- trapzRcpp(sort(xi[,j]),dnorm(sort(xi[,j]),0,sqrt(sig[j,j])))
g21 <- f21(sort(xi[,j])) - 
trapzRcpp(sort(xi[,j]),f21(sort(xi[,j]))*dnorm(sort(xi[,j]),0,sqrt(sig[j,j])))/p0
tmpSgn <- sign(sum(g21*sbf$SBFit[,j]))
plot(sort(xi[,j]),g21,type='l',col=2,ylim=c(-2.5,2.5),xlab='xi21')
points(sort(xi[,j]),tmpSgn*sbf$SBFit[order(xi[,j]),j],type='l')
legend('top',c('true','SBF'),col=c(2,1),lwd=2,bty='n',horiz=TRUE)

j <- 4
p0 <- trapzRcpp(sort(xi[,j]),dnorm(sort(xi[,j]),0,sqrt(sig[j,j])))
g22 <- f22(sort(xi[,j])) - 
trapzRcpp(sort(xi[,j]),f22(sort(xi[,j]))*dnorm(sort(xi[,j]),0,sqrt(sig[j,j])))/p0
tmpSgn <- sign(sum(g22*sbf$SBFit[,j]))
plot(sort(xi[,j]),g22,type='l',col=2,ylim=c(-2.5,2.5),xlab='xi22')
points(sort(xi[,j]),tmpSgn*sbf$SBFit[order(xi[,j]),j],type='l')
legend('top',c('true','SBF'),col=c(2,1),lwd=2,bty='n',horiz=TRUE)


# fitting
sbf <- MultiFAM(Y=Y,X=X,nEval=0)
yHat <- sbf$mu+apply(sbf$SBFit,1,'sum')
plot(yHat,Y)
abline(coef=c(0,1),col=2)


# R^2
R2 <- 1-sum((Y-yHat)^2)/sum((Y-mean(Y))^2)
R2


# prediction
sbf <- MultiFAM(Y=Y,X=X,XTest=XTest)
yHat <- sbf$mu+apply(sbf$SBFit,1,'sum')
plot(yHat,YTest)
abline(coef=c(0,1),col=2)

hadjipantelis/tPACE documentation built on Aug. 16, 2022, 10:45 a.m.