Revision 616fe292ea4c66f8db35d04c9fa045fea56eea02 authored by Roger Koenker on 04 September 2016, 13:02:15 UTC, committed by cran-robot on 04 September 2016, 13:02:15 UTC
1 parent db6345a
boot.R
``````"boot.rq"<-
function (x, y, tau = 0.5, R = 200, bsmethod = "xy", mofn = length(y),
cluster = NULL, U = NULL, ...)
{
n <- length(y)
if(class(x) != "matrix.csr") x <- as.matrix(x)
p <- ncol(x)
B <- matrix(0, R, p)
if(tau <= 0 || tau >= 1) stop("tau outside (0,1) not allowed")
if(length(cluster)) bsmethod <- "cluster"
if (bsmethod == "xy") {
if(!length(U)){
if(mofn < p || mofn > n) stop("mofn is out of range")
U <- matrix(sample(n, mofn * R, replace = TRUE), mofn, R)
}
B <- sqrt(mofn/n)*boot.rq.xy(x, y, U, tau)
}
else if (bsmethod == "wxy") {
if(!length(U)) U <- matrix(rexp(n * R,1), n, R)
B <- boot.rq.wxy(x, y, U, tau)
}
else if (bsmethod == "cluster"){ # Hagemann wild gradient bootstrap
if(!length(U)){
if(length(cluster) != n) stop("cluster is wrong length")
u <- unique(cluster)
m <- length(u)
h <- c(-(sqrt(5) - 1)/2, (sqrt(5) + 1)/2)
hp <- c((sqrt(5) + 1)/sqrt(20), (sqrt(5) - 1)/sqrt(20))
U <- matrix(sample(h, R * m, prob = hp, replace = TRUE), m, R)
U <- apply(U, 2, function(v) v[match(cluster,u)])
}
r <- c(rq.fit(x, y, tau)\$resid)
psi <- (r < 0) - tau
W <- as.matrix(t(x) %*% (U * psi))
if(class(x) == "matrix.csr")
B <- boot.rq.spwy(W, x, y, tau)
else
B <- boot.rq.pwy(W, x, y, tau)
}
else if (bsmethod == "jack"){ # Portnoy proposal
if(!length(U)){
if(missing(mofn)) mofn <- n - ceiling(2*sqrt(n))
if(mofn < p || mofn > n) stop("mofn is out of range")
U <- matrix(0, mofn, R)
U <- apply(U, 2, function(x) sample(n, mofn))
}
B <- (mofn - p + 1)/sqrt(n * (n - mofn)) * boot.rq.xy(x, y, U, tau)
}
else if (bsmethod == "pwy") {
if(!length(U)){
U <- matrix(runif(n * R), n, R)
}
W <- t(x) %*% ((U < tau) - tau)
B <- boot.rq.pwy(W, x, y, tau)
}
else if (bsmethod == "mcmb") {
B <- boot.rq.mcmb(x, y, tau = tau, R = R)
}
else if (bsmethod == "wild") {
n <- length(y)
fit <- rq.fit(x, y, tau = tau)
S <- sample(c(-2*tau,2*(1-tau)),prob = c(tau,1-tau),size = n * R, replace = TRUE)
W <- matrix(S,n,R)
r <- c(fit\$resid)
f0 <- akj(r,z=0)\$dens
r <- r + hat(x) * (tau - I(r < 0))/f0
Y <- c(fitted(fit)) + W * abs(r)
B <- rqs.fit(x,Y,tau = tau)
}
else stop("your chosen bootstrap method is not allowed")
#cat(paste("Bootstrap standard errors based on ",R," replications"))
list(B = B, U = U)
}
"boot.rq.mcmb" <-
function (x, y, tau = 0.5, R = 200)
{
n <- length(y)
p <- ncol(x)
if(n < 2000)
fit <- rq(y~x - 1, tau = tau, ci = FALSE)
else
fit <- rq(y~x - 1, tau = tau, method = "fn")
coef <- fit\$coef
svdx <- svd(x)
condx <- svdx\$d[1]/svdx\$d[p]
Ainv <- svdx\$v %*% diag(svdx\$d) %*% t(svdx\$v)
coefTilda <-  Ainv %*% coef
A <- svdx\$v %*% diag(1/svdx\$d) %*% t(svdx\$v)
r <- fit\$resid
psi <- signr <- sign(r)
psi[signr > 0] <- tau
psi[signr < 0] <- tau - 1
psimat <- matrix(psi, nrow = n, ncol = p, byrow = FALSE)
x <- x %*% A
ZTilda <- x * psimat
sumxij <- apply(x, 2, sum)
sumabsxij <- apply(abs(x), 2, sum)
zstar <- .C("bootnp", as.double(t(x)), as.double(y),
as.double(tau), as.double(coefTilda),
as.double(t(A)), as.double(ZTilda),
as.double(sumxij), as.double(sumabsxij),
as.integer(n), as.integer(p), success = as.integer(1),
theta = as.double(rep(0, R * p + p), as.integer(c(p, R + 1))),
as.integer(R), PACKAGE = "quantreg")
if (zstar\$success == 0)
return(list(success = 0))
else{
B <- matrix(zstar\$theta,p,R+1)[,-1]
B <- t(A %*% B)
}
B
}

"boot.rq.xy"<-
function(x, y, s, tau = 0.5, tol = 0.0001)
{
#function to compute xypairs bootstrap for regression quantiles
#stripped down for monte-carlo purposes
x <- as.matrix(x)
p <- ncol(x)
n <- nrow(x)
R <- ncol(s)
m <- nrow(s)
z <- .Fortran("xys",
as.integer(m),
as.integer(n),
as.integer(p),
as.integer(R),
as.integer(m + 5),
as.integer(p + 2),
as.double(x),
as.double(y),
as.double(tau),
as.double(tol),
flag = integer(R),
coef = double(p * R),
resid = double(m),
integer(m),
double((m + 5) * (p + 2)),
double(m),
xx = double(m * p),
yy = double(m),
as.integer(s),
PACKAGE = "quantreg")
if(sum(z\$flag)>0){
if(any(z\$flag)==2)
warning(paste(sum(z\$flag==2),"out of",R,
"BS replications have near singular design"))
if(any(z\$flag)==1)
warning(paste(sum(z\$flag==1),"out of",R,"may be nonuniqu"))
}
return(t(matrix(z\$coef, p, R)))
}
"boot.rq.wxy"<-
function(x, y, w, tau = 0.5, tol = 0.0001)
{
#function to compute weighted bootstrap a la Bose for regression quantiles
x <- as.matrix(x)
p <- ncol(x)
n <- nrow(x)
R <- ncol(w)
m <- nrow(w)
z <- .Fortran("wxy",
as.integer(n),
as.integer(p),
as.integer(R),
as.integer(m + 5),
as.integer(p + 2),
as.double(x),
as.double(y),
as.double(tau),
as.double(tol),
flag = integer(R),
coef = double(p * R),
resid = double(m),
integer(m),
double((m + 5) * (p + 2)),
double(m),
xx = double(m * p),
yy = double(m),
as.double(w),
PACKAGE = "quantreg")
if(sum(z\$flag)>0){
if(any(z\$flag)==2)
warning(paste(sum(z\$flag==2),"out of",R,
"BS replications have near singular design"))
if(any(z\$flag)==1)
warning(paste(sum(z\$flag==1),"out of",R,"may be nonunique"))
}
return(t(matrix(z\$coef, p, R)))
}

"boot.rq.pwy"<-
function(U,X, y, tau = 0.5, tol=1e-4)
{
#resampling method of parzen,wei,ying for quantile regression
#NB. x should be full design matrix including intercept
n <- length(y)
p <- ncol(X)
R <- ncol(U)
Y <- c(y,length(y)*max(abs(y)))
x <- rbind(X,0)
xu <- t(U)/tau
n <- n+1
z<-.Fortran("pwy",
as.integer(n),
as.integer(p),
as.integer(R),
as.integer(n + 5),
as.integer(p + 2),
as.double(xu),
as.double(x),
as.double(Y),
as.double(tau),
as.double(tol),
flag = as.integer(1),
coef = double(p * R),
resid = double(n),
integer(n),
double((n + 5) * (p + 2)),
double(n),
PACKAGE = "quantreg")
return(t(matrix(z\$coef, p, R)))
}

#################################################################
#	NB:  In an ideal world the loop would be in fortran
#################################################################

boot.rq.spwy <- function(W, a, y, tau=.5, control)
{
y <- c(y, length(y) * max(abs(y)))
n <- length(y)
m <- a@dimension[2]
a <- rbind(a, as.matrix.csr(t(rep(1,m))))
nra <- length(a@ra)
W <- W/tau
k <- ncol(W) # Number of resampling realizations
if(m != nrow(W))
stop("W row dimension not compatible with design matrix")
if(n != a@dimension[1])
stop("Dimensions of design matrix and the response vector not compatible")
for(i in 1:k){
a@ra[(nra - m + 1):nra] <- W[,i]
W[,i] <- rq.fit.sfn(a,y,tau = tau)\$coef
}
B <- t(W)
B
}

``````

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