emulator (version 1.2-20)

regressor.basis: Regressor basis function

Description

Creates a regressor basis for a vector.

Usage

regressor.basis(x)
regressor.multi(x.df,func=regressor.basis)

Arguments

x

vector of coordinates

x.df

Matrix whose rows are coordinates of points

func

Regressor basis function to use; defaults to regressor.basis

Value

Returns simple regressor basis for vectors or matrices.

Details

The regressor basis specified by regressor.basis() is just the addition of a constant term, which is conventionally placed in the first position. This is a very common choice for a set of bases, although it is important to investigate both simpler and more sophisticated alternatives. Tony would recommend simpler functions (perhaps as simple as function(x){1}, that is, nothing but a constant), and Jonty would recommend more complicated bespoke functions that reflect prior beliefs.

Function regressor.multi() is just a wrapper for regressor.basis() that works for matrices. This is used internally and the user should not need to change it.

Note that the user is free to define and use functions other than this one when using, for example, corr().

Examples

# NOT RUN {
regressor.basis(rep(5,6))
m <- matrix(1:27,9,3)
regressor.multi(m)
regressor.multi(m,func=function(x){c(a=88,x,x^2,x[1]^4)})


# and now a little example where we can choose the basis functions
# explicitly and see the effect it has.  Note particularly the poor
# performance of func2() in extrapolation:


func1 <- function(x){
  out <- c(1,cos(x))
  names(out) <- letters[1:length(x)]
  return(out)
}

func2 <- function(x){
  out <- c(1,cos(x),cos(2*x),cos(3*x))
  names(out) <- letters[1:length(x)]
  return(out)
}

func3 <- function(x){out <- c(1,x)
names(out)[1] <- "const"
return(out)
}

func.chosen <- func1


toy <- sort(c(seq(from=0,to=1,len=9),0.2))
toy <- as.matrix(toy)
colnames(toy) <- "a"
rownames(toy) <- paste("obs",1:nrow(toy),sep=".")

d.noisy <- as.vector(toy>0.5)+rnorm(length(toy))/40

fish <- 100
x <- seq(from=-1,to=2,len=1000)
A <- corr.matrix(toy,scales=fish)
Ainv <- solve(A)

 ## Now the interpolation.  Change func.chosen() from func1() to func2()
 ## and see the difference!

jj <- interpolant.quick(as.matrix(x), d.noisy, toy, scales=fish,
                        func=func.chosen, 
                         Ainv=Ainv,g=TRUE)

plot(x,jj$mstar.star,xlim=range(x),type="l",col="black",lwd=3)
lines(x,jj$prior,col="green",type="l")
lines(x,jj$mstar.star+jj$Z,type="l",col="red",lty=2)
lines(x,jj$mstar.star-jj$Z,type="l",col="red",lty=2)
points(toy,d.noisy,pch=16,cex=2)
legend("topright",lty=c(1,2,1,0),
    col=c("black","red","green","black"),pch=c(NA,NA,NA,16),
    legend=c("best estimate","+/-1 sd","prior","training set"))


  ## Now we will use O&O 2002.

## First, some simulated design points:
xdash <- as.matrix(c(-0.5, -0.1, -0.2, 1.1, 1.15))

## create an augmented design set:
design.augmented <- rbind(toy,xdash)

## And calculate the correlation matrix of the augmented dataset:
A.augmented <- corr.matrix(design.augmented, scales=fish)
Ainv.augmented <- solve(A.augmented)

## Now, define a function that samples from the
## appropriate posterior t-distribution, adds these random
## variables to the dataset, then calculates a new
## etahat and evaluates and plots it:

f <- function(...){

ddash <- cond.sample(n=1, x=xdash, xold=toy, d=d.noisy, A=A,
                           Ainv=Ainv, scales=fish, func=func.chosen)

        jj.aug <-
          interpolant.quick(x      = as.matrix(x),
                            d      = c(d.noisy,as.vector(ddash)),
                            xold   = design.augmented,
                            Ainv   = Ainv.augmented,
                            scales = fish,func=func.chosen)
points(xdash,ddash,type="p",pch=16,col="gray")
points(x, jj.aug, type="l", col="gray")
}


## Now execute the function a few times to assess the uncertainty in eta:
f()
f()
f()

# }