Writing a function for the Cramer Von Mises test - r

Question

The cvm.test() from dgof package provides a way of doing the one-sample Cramer-von Mises test on discrete distributions, my goal is to develop a function that does the test for continuous distributions as well (like the Kolmogorov-Smirnov ks.test() from the stats package).
Note:this post is concerned only with fully specified df null hypothesis, so please no bootstraping or Monte Carlo Simulation here
> cvm.test
function (x, y, type = c("W2", "U2", "A2"), simulate.p.value = FALSE,
B = 2000, tol = 1e-08)
{
cvm.pval.disc <- function(STAT, lambda) {
x <- STAT
theta <- function(u) {
VAL <- 0
for (i in 1:length(lambda)) {
VAL <- VAL + 0.5 * atan(lambda[i] * u)
}
return(VAL - 0.5 * x * u)
}
rho <- function(u) {
VAL <- 0
for (i in 1:length(lambda)) {
VAL <- VAL + log(1 + lambda[i]^2 * u^2)
}
VAL <- exp(VAL * 0.25)
return(VAL)
}
fun <- function(u) return(sin(theta(u))/(u * rho(u)))
pval <- 0
try(pval <- 0.5 + integrate(fun, 0, Inf, subdivisions = 1e+06)$value/pi,
silent = TRUE)
if (pval > 0.001)
return(pval)
if (pval <= 0.001) {
df <- sum(lambda != 0)
est1 <- dchisq(STAT/max(lambda), df)
logf <- function(t) {
ans <- -t * STAT
ans <- ans - 0.5 * sum(log(1 - 2 * t * lambda))
return(ans)
}
est2 <- 1
try(est2 <- exp(nlm(logf, 1/(4 * max(lambda)))$minimum),
silent = TRUE)
return(min(est1, est2))
}
}
cvm.stat.disc <- function(x, y, type = c("W2", "U2", "A2")) {
type <- match.arg(type)
I <- knots(y)
N <- length(x)
e <- diff(c(0, N * y(I)))
obs <- rep(0, length(I))
for (j in 1:length(I)) {
obs[j] <- length(which(x == I[j]))
}
S <- cumsum(obs)
T <- cumsum(e)
H <- T/N
p <- e/N
t <- (p + p[c(2:length(p), 1)])/2
Z <- S - T
Zbar <- sum(Z * t)
S0 <- diag(p) - p %*% t(p)
A <- matrix(1, length(p), length(p))
A <- apply(row(A) >= col(A), 2, as.numeric)
E <- diag(t)
One <- rep(1, nrow(E))
K <- diag(0, length(H))
diag(K)[-length(H)] <- 1/(H[-length(H)] * (1 - H[-length(H)]))
Sy <- A %*% S0 %*% t(A)
M <- switch(type, W2 = E, U2 = (diag(1, nrow(E)) - E %*%
One %*% t(One)) %*% E %*% (diag(1, nrow(E)) - One %*%
t(One) %*% E), A2 = E %*% K)
lambda <- eigen(M %*% Sy)$values
STAT <- switch(type, W2 = sum(Z^2 * t)/N, U2 = sum((Z -
Zbar)^2 * t)/N, A2 = sum((Z^2 * t/(H * (1 - H)))[-length(I)])/N)
return(c(STAT, lambda))
}
cvm.pval.disc.sim <- function(STATISTIC, lambda, y, type,
tol, B) {
knots.y <- knots(y)
fknots.y <- y(knots.y)
u <- runif(B * length(x))
u <- sapply(u, function(a) return(knots.y[sum(a > fknots.y) +
1]))
dim(u) <- c(B, length(x))
s <- apply(u, 1, cvm.stat.disc, y, type)
s <- s[1, ]
return(sum(s >= STATISTIC - tol)/B)
}
type <- match.arg(type)
DNAME <- deparse(substitute(x))
if (is.stepfun(y)) {
if (length(setdiff(x, knots(y))) != 0) {
stop("Data are incompatable with null distribution; ",
"Note: This function is meant only for discrete distributions ",
"you may be receiving this error because y is continuous.")
}
tempout <- cvm.stat.disc(x, y, type = type)
STAT <- tempout[1]
lambda <- tempout[2:length(tempout)]
if (!simulate.p.value) {
PVAL <- cvm.pval.disc(STAT, lambda)
}
else {
PVAL <- cvm.pval.disc.sim(STAT, lambda, y, type,
tol, B)
}
METHOD <- paste("Cramer-von Mises -", type)
names(STAT) <- as.character(type)
RVAL <- list(statistic = STAT, p.value = PVAL, alternative = "Two.sided",
method = METHOD, data.name = DNAME)
}
else {
stop("Null distribution must be a discrete.")
}
class(RVAL) <- "htest"
return(RVAL)
}
<environment: namespace:dgof>
Kolmogorov-Smirnov ks.test() from stats package for comparison (note that this function does both the one-sample and two-sample tests):
> ks.test
function (x, y, ..., alternative = c("two.sided", "less", "greater"),
exact = NULL, tol = 1e-08, simulate.p.value = FALSE, B = 2000)
{
pkolmogorov1x <- function(x, n) {
if (x <= 0)
return(0)
if (x >= 1)
return(1)
j <- seq.int(from = 0, to = floor(n * (1 - x)))
1 - x * sum(exp(lchoose(n, j) + (n - j) * log(1 - x -
j/n) + (j - 1) * log(x + j/n)))
}
exact.pval <- function(alternative, STATISTIC, x, n, y, knots.y,
tol) {
ts.pval <- function(S, x, n, y, knots.y, tol) {
f_n <- ecdf(x)
eps <- min(tol, min(diff(knots.y)) * tol)
eps2 <- min(tol, min(diff(y(knots.y))) * tol)
a <- rep(0, n)
b <- a
f_a <- a
for (i in 1:n) {
a[i] <- min(c(knots.y[which(y(knots.y) + S >=
i/n + eps2)[1]], Inf), na.rm = TRUE)
b[i] <- min(c(knots.y[which(y(knots.y) - S >
(i - 1)/n - eps2)[1]], Inf), na.rm = TRUE)
f_a[i] <- ifelse(!(a[i] %in% knots.y), y(a[i]),
y(a[i] - eps))
}
f_b <- y(b)
p <- rep(1, n + 1)
for (i in 1:n) {
tmp <- 0
for (k in 0:(i - 1)) {
tmp <- tmp + choose(i, k) * (-1)^(i - k - 1) *
max(f_b[k + 1] - f_a[i], 0)^(i - k) * p[k +
1]
}
p[i + 1] <- tmp
}
p <- max(0, 1 - p[n + 1])
if (p > 1) {
warning("numerical instability in p-value calculation.")
p <- 1
}
return(p)
}
less.pval <- function(S, n, H, z, tol) {
m <- ceiling(n * (1 - S))
c <- S + (1:m - 1)/n
CDFVAL <- H(sort(z))
for (j in 1:length(c)) {
ifelse((min(abs(c[j] - CDFVAL)) < tol), c[j] <- 1 -
c[j], c[j] <- 1 - CDFVAL[which(order(c(c[j],
CDFVAL)) == 1)])
}
b <- rep(0, m)
b[1] <- 1
for (k in 1:(m - 1)) b[k + 1] <- 1 - sum(choose(k,
1:k - 1) * c[1:k]^(k - 1:k + 1) * b[1:k])
p <- sum(choose(n, 0:(m - 1)) * c^(n - 0:(m - 1)) *
b)
return(p)
}
greater.pval <- function(S, n, H, z, tol) {
m <- ceiling(n * (1 - S))
c <- 1 - (S + (1:m - 1)/n)
CDFVAL <- c(0, H(sort(z)))
for (j in 1:length(c)) {
if (!(min(abs(c[j] - CDFVAL)) < tol))
c[j] <- CDFVAL[which(order(c(c[j], CDFVAL)) ==
1) - 1]
}
b <- rep(0, m)
b[1] <- 1
for (k in 1:(m - 1)) b[k + 1] <- 1 - sum(choose(k,
1:k - 1) * c[1:k]^(k - 1:k + 1) * b[1:k])
p <- sum(choose(n, 0:(m - 1)) * c^(n - 0:(m - 1)) *
b)
return(p)
}
p <- switch(alternative, two.sided = ts.pval(STATISTIC,
x, n, y, knots.y, tol), less = less.pval(STATISTIC,
n, y, knots.y, tol), greater = greater.pval(STATISTIC,
n, y, knots.y, tol))
return(p)
}
sim.pval <- function(alternative, STATISTIC, x, n, y, knots.y,
tol, B) {
fknots.y <- y(knots.y)
u <- runif(B * length(x))
u <- sapply(u, function(a) return(knots.y[sum(a > fknots.y) +
1]))
dim(u) <- c(B, length(x))
getks <- function(a, knots.y, fknots.y) {
dev <- c(0, ecdf(a)(knots.y) - fknots.y)
STATISTIC <- switch(alternative, two.sided = max(abs(dev)),
greater = max(dev), less = max(-dev))
return(STATISTIC)
}
s <- apply(u, 1, getks, knots.y, fknots.y)
return(sum(s >= STATISTIC - tol)/B)
}
alternative <- match.arg(alternative)
DNAME <- deparse(substitute(x))
x <- x[!is.na(x)]
n <- length(x)
if (n < 1L)
stop("not enough 'x' data")
PVAL <- NULL
if (is.numeric(y)) {
DNAME <- paste(DNAME, "and", deparse(substitute(y)))
y <- y[!is.na(y)]
n.x <- as.double(n)
n.y <- length(y)
if (n.y < 1L)
stop("not enough 'y' data")
if (is.null(exact))
exact <- (n.x * n.y < 10000)
METHOD <- "Two-sample Kolmogorov-Smirnov test"
TIES <- FALSE
n <- n.x * n.y/(n.x + n.y)
w <- c(x, y)
z <- cumsum(ifelse(order(w) <= n.x, 1/n.x, -1/n.y))
if (length(unique(w)) < (n.x + n.y)) {
warning("cannot compute correct p-values with ties")
z <- z[c(which(diff(sort(w)) != 0), n.x + n.y)]
TIES <- TRUE
}
STATISTIC <- switch(alternative, two.sided = max(abs(z)),
greater = max(z), less = -min(z))
nm_alternative <- switch(alternative, two.sided = "two-sided",
less = "the CDF of x lies below that of y", greater = "the CDF of x lies above that of y")
if (exact && (alternative == "two.sided") && !TIES)
PVAL <- 1 - .C("psmirnov2x", p = as.double(STATISTIC),
as.integer(n.x), as.integer(n.y), PACKAGE = "dgof")$p
}
else if (is.stepfun(y)) {
z <- knots(y)
if (is.null(exact))
exact <- (n <= 30)
if (exact && n > 30) {
warning("numerical instability may affect p-value")
}
METHOD <- "One-sample Kolmogorov-Smirnov test"
dev <- c(0, ecdf(x)(z) - y(z))
STATISTIC <- switch(alternative, two.sided = max(abs(dev)),
greater = max(dev), less = max(-dev))
if (simulate.p.value) {
PVAL <- sim.pval(alternative, STATISTIC, x, n, y,
z, tol, B)
}
else {
PVAL <- switch(exact, `TRUE` = exact.pval(alternative,
STATISTIC, x, n, y, z, tol), `FALSE` = NULL)
}
nm_alternative <- switch(alternative, two.sided = "two-sided",
less = "the CDF of x lies below the null hypothesis",
greater = "the CDF of x lies above the null hypothesis")
}
else {
if (is.character(y))
y <- get(y, mode = "function")
if (mode(y) != "function")
stop("'y' must be numeric or a string naming a valid function")
if (is.null(exact))
exact <- (n < 100)
METHOD <- "One-sample Kolmogorov-Smirnov test"
TIES <- FALSE
if (length(unique(x)) < n) {
warning(paste("default ks.test() cannot compute correct p-values with ties;\n",
"see help page for one-sample Kolmogorov test for discrete distributions."))
TIES <- TRUE
}
x <- y(sort(x), ...) - (0:(n - 1))/n
STATISTIC <- switch(alternative, two.sided = max(c(x,
1/n - x)), greater = max(1/n - x), less = max(x))
if (exact && !TIES) {
PVAL <- if (alternative == "two.sided")
1 - .C("pkolmogorov2x", p = as.double(STATISTIC),
as.integer(n), PACKAGE = "dgof")$p
else 1 - pkolmogorov1x(STATISTIC, n)
}
nm_alternative <- switch(alternative, two.sided = "two-sided",
less = "the CDF of x lies below the null hypothesis",
greater = "the CDF of x lies above the null hypothesis")
}
names(STATISTIC) <- switch(alternative, two.sided = "D",
greater = "D^+", less = "D^-")
pkstwo <- function(x, tol = 1e-06) {
if (is.numeric(x))
x <- as.vector(x)
else stop("argument 'x' must be numeric")
p <- rep(0, length(x))
p[is.na(x)] <- NA
IND <- which(!is.na(x) & (x > 0))
if (length(IND)) {
p[IND] <- .C("pkstwo", as.integer(length(x[IND])),
p = as.double(x[IND]), as.double(tol), PACKAGE = "dgof")$p
}
return(p)
}
if (is.null(PVAL)) {
PVAL <- ifelse(alternative == "two.sided", 1 - pkstwo(sqrt(n) *
STATISTIC), exp(-2 * n * STATISTIC^2))
}
RVAL <- list(statistic = STATISTIC, p.value = PVAL, alternative = nm_alternative,
method = METHOD, data.name = DNAME)
class(RVAL) <- "htest"
return(RVAL)
}
<environment: namespace:dgof>