LDRTools (version 0.2-1)

AOP: Function to Average Orthogonal Projection Matrices

Description

The function computes the average of orthogonal projection matrices and estimates the average rank.

Usage

AOP(x, weights = "constant")

Value

A list containing the following components:

P

The estimated average orthogonal projection matrix.

O

An orthogonal matrix on which P is based upon.

k

The rank of the average orthogonal projection matrix.

Arguments

x

List of orthogonal projection matrices, can have different ranks.

weights

The weight function used for the individual ranks. Possible inputs are constant, inverse and sq.inverse (see details).

Author

Eero Liski and Klaus Nordhausen

Details

The AOP maximizes the function $D(P)=w(k)tr({\overline{P}}_{w}P)-\frac{1}{2}{w}^2(k)k$, where ${\overline{P}}_w=\frac{1}{m}\sum _{i=1}^{m}w(k_i)P_i$ is a regular average of weighted orthogonal projection matrices, $m$ is the number of orthogonal projection matrices averaged, $w(k)$is the weight function and $k$ is the rank of $P$. The possible weights are defined as constant: $w(k)=1$, inverse: $w(k)=1/k$ and sq.inverse: $w(k)=1/\sqrt{k}$. The constant weight corresponds to the so called Crone & Crosby distance. Orthogonal projection matrices of zero rank are also possible inputs for the function. In such a case, the function prints a warning giving the number of orthogonal projection matrices with zero rank.

References

Crone, L. J., and Crosby, D. S. (1995), Statistical Applications of a Metric on Subspaces to Satellite Meteorology, Technometrics 37, 324-328.

Liski E., Nordhausen K., Oja H., and Ruiz-Gazen A. (2016), Combining Linear Dimension Reduction Subspaces. In: Agostinelli C., Basu A., Filzmoser P., Mukherjee D. (eds) Recent Advances in Robust Statistics: Theory and Applications. tools:::Rd_expr_doi("10.1007/978-81-322-3643-6_7").

See Also

Pdist

Examples

## Ex.1
##
library(dr)
# Australian athletes data with 202 observations
data(ais)
# 10 explanatory variables
X <- as.matrix(ais[,c(2:3,5:12)])
colnames(X) <- names(ais[,c(2:3,5:12)])
p <- dim(X)[2]
# Response variable lean body mass (LBM)
y <- ais$LBM
# Significance level 
alpha <- 0.05


# SIR
s0.sir <- dr(y ~ X, method="sir")
# Estimate of k 
k.sir <- sum(dr.test(s0.sir, numdir=4)[,3] < alpha)
# List of transformation matrices corresponding to 
# k.sir and fixed k=1, respectively
B.sir.list <- list(B1=s0.sir$evectors[,1:k.sir], B2=s0.sir$evectors[,1:1])
# List of orthogonal projectors corresponding to 
# k.sir, fixed k=1 and fixed k=0, respectively
P.sir.list <- list(P1=O2P(B.sir.list$B1), P2=O2P(B.sir.list$B2), 
P3=diag(0,p))


# SAVE
s0.save <- dr(y ~ X, method="save")
# Estimate of k 
k.save <- sum(dr.test(s0.save, numdir=4)[,3] < alpha)
# List of transformation matrices corresponding to 
# k.save and fixed k=1, respectively
B.save.list <- list(B1=s0.save$evectors[,1:k.save], 
B2=s0.save$evectors[,1:1])
# List of orthogonal projectors corresponding to 
# k.save, fixed k=1 and fixed k=0, respectively
P.save.list <- list(P1=O2P(B.save.list$B1), P2=O2P(B.save.list$B2), 
P3=diag(0,p))


# DR k-estimates
dr.k <- c(k.sir, k.save)
names(dr.k) <- c("SIR","SAVE")
dr.k


# List of individually estimated projectors
proj.list.a <- list(P.sir.list$P1, P.save.list$P1)
# List of fixed projectors
proj.list.b <- list(P.sir.list$P2, P.save.list$P2)
# List of zero projectors
proj.list.c <- list(P.sir.list$P3, P.save.list$P3)
# List of zero-rank SIR-projector and 
# other individually estimated projectors
proj.list.d <- list(P.sir.list$P3, P.save.list$P1)


# AOP (constant) object corresponding to the first projector list
AOP.const.a <- AOP(proj.list.a, weights="constant")

# AOP (inverse) objects corresponding to three projector lists
AOP.inv.a <- AOP(proj.list.a, weights="inverse")
AOP.inv.b <- AOP(proj.list.b, weights="inverse")
AOP.inv.c <- AOP(proj.list.c, weights="inverse")

# AOP (sq.inverse) objects corresponding to three projector lists
AOP.sqinv.a <- AOP(proj.list.a, weights="sq.inverse")
AOP.sqinv.c <- AOP(proj.list.c, weights="sq.inverse")
AOP.sqinv.d <- AOP(proj.list.d, weights="sq.inverse")


# k-estimates of the AOP's
AOP.a <- c(AOP.const.a$k, AOP.inv.a$k, AOP.sqinv.a$k)
names(AOP.a) <- c("const","inv","sqinv")
AOP.a

AOP.c <- AOP.inv.c$k
names(AOP.c) <- c("inv")
AOP.c

AOP.d <- AOP.sqinv.d$k
names(AOP.d) <- c("sqinv")
AOP.d


# Scatter plots between the response and the transformed data 
# corresponding to the different AOP transformation matrices

# AOP.inverse
newdata.inv.AOPa <- cbind(y,X %*% AOP.inv.a$O)
pairs(newdata.inv.AOPa)

newdata.inv.AOPb <- cbind(y,X %*% AOP.inv.b$O)
pairs(newdata.inv.AOPb)


# AOP.sq.inverse
newdata.sqinv.AOPc <- cbind(y,X %*% AOP.sqinv.c$O)
pairs(newdata.sqinv.AOPc)

newdata.sqinv.AOPd <- cbind(y,X %*% AOP.sqinv.d$O)
pairs(newdata.sqinv.AOPd)






###################################
## Ex.2
##
a <- c(1,1,rep(0,8))
A <- diag(a)
B <- diag(0,10)
B[3,1] <- 1
P.A <- O2P(A[,1:2])
P.B <- O2P(B[,1])
zero.mat <- diag(0,10)
# True projector, k=3
P.C <- P.A + P.B

# Average P.A and P.B
proj.list <- list(P.A, P.B)
AOP.const <- AOP(proj.list, weights="constant")
AOP.inv <- AOP(proj.list, weights="inverse")
AOP.sqinv <- AOP(proj.list, weights="sq.inverse")
k.list <- c(AOP.const$k, AOP.inv$k, AOP.sqinv$k)
names(k.list) <- c("const","inv","sqinv")
k.list

# Average P.A, P.B and three zero rank matrices
proj.list <- list(P.A, P.B, zero.mat, zero.mat, zero.mat)
AOP.const <- AOP(proj.list, weights="constant")
AOP.inv <- AOP(proj.list, weights="inverse")
AOP.sqinv <- AOP(proj.list, weights="sq.inverse")
k.list <- c(AOP.const$k, AOP.inv$k, AOP.sqinv$k)
names(k.list) <- c("const","inv","sqinv")
k.list