The code for the Holt-Winters function in R contains the following if clause:
if (!is.null(gamma) && is.logical(gamma) && !gamma)
Obviously the first means "if NOT gamma is Null". I am a bit confused by the meaning of the third - it looks like this reads, "if NOT gamma", but there is nothing following like an equals sign or an is.null, etc.
Forgive me if this is a somewhat basic question, but I am very new to R.
Full code:
{
x <- as.ts(x)
seasonal <- match.arg(seasonal)
f <- frequency(x)
if (!is.null(alpha) && (alpha == 0))
stop("cannot fit models without level ('alpha' must not be 0 or FALSE)")
if (!all(is.null(c(alpha, beta, gamma))) && any(c(alpha,
beta, gamma) < 0 || c(alpha, beta, gamma) > 1))
stop("'alpha', 'beta' and 'gamma' must be within the unit interval")
if ((is.null(gamma) || gamma > 0)) {
if (seasonal == "multiplicative" && any(x == 0))
stop("data must be non-zero for multiplicative Holt-Winters")
if (start.periods < 2)
stop("need at least 2 periods to compute seasonal start values")
}
if (!is.null(gamma) && is.logical(gamma) && !gamma) {
expsmooth <- !is.null(beta) && is.logical(beta) && !beta
if (is.null(l.start))
l.start <- if (expsmooth)
x[1L]
else x[2L]
if (is.null(b.start))
if (is.null(beta) || !is.logical(beta) || beta)
b.start <- x[2L] - x[1L]
start.time <- 3 - expsmooth
s.start <- 0
}
else {
start.time <- f + 1
wind <- start.periods * f
st <- decompose(ts(x[1L:wind], start = start(x), frequency = f),
seasonal)
if (is.null(l.start) || is.null(b.start)) {
dat <- na.omit(st$trend)
cf <- coef(.lm.fit(x = cbind(1, seq_along(dat)),
y = dat))
if (is.null(l.start))
l.start <- cf[1L]
if (is.null(b.start))
b.start <- cf[2L]
}
if (is.null(s.start))
s.start <- st$figure
}
lenx <- as.integer(length(x))
if (is.na(lenx))
stop("invalid length(x)")
len <- lenx - start.time + 1
hw <- function(alpha, beta, gamma) .C(C_HoltWinters, as.double(x),
lenx, as.double(max(min(alpha, 1), 0)), as.double(max(min(beta,
1), 0)), as.double(max(min(gamma, 1), 0)), as.integer(start.time),
as.integer(!+(seasonal == "multiplicative")), as.integer(f),
as.integer(!is.logical(beta) || beta), as.integer(!is.logical(gamma) ||
gamma), a = as.double(l.start), b = as.double(b.start),
s = as.double(s.start), SSE = as.double(0), level = double(len +
1L), trend = double(len + 1L), seasonal = double(len +
f))
if (is.null(gamma)) {
if (is.null(alpha)) {
if (is.null(beta)) {
error <- function(p) hw(p[1L], p[2L], p[3L])$SSE
sol <- optim(optim.start, error, method = "L-BFGS-B",
lower = c(0, 0, 0), upper = c(1, 1, 1), control = optim.control)
if (sol$convergence || any(sol$par < 0 | sol$par >
1)) {
if (sol$convergence > 50) {
warning(gettextf("optimization difficulties: %s",
sol$message), domain = NA)
}
else stop("optimization failure")
}
alpha <- sol$par[1L]
beta <- sol$par[2L]
gamma <- sol$par[3L]
}
else {
error <- function(p) hw(p[1L], beta, p[2L])$SSE
sol <- optim(c(optim.start["alpha"], optim.start["gamma"]),
error, method = "L-BFGS-B", lower = c(0, 0),
upper = c(1, 1), control = optim.control)
if (sol$convergence || any(sol$par < 0 | sol$par >
1)) {
if (sol$convergence > 50) {
warning(gettextf("optimization difficulties: %s",
sol$message), domain = NA)
}
else stop("optimization failure")
}
alpha <- sol$par[1L]
gamma <- sol$par[2L]
}
}
else {
if (is.null(beta)) {
error <- function(p) hw(alpha, p[1L], p[2L])$SSE
sol <- optim(c(optim.start["beta"], optim.start["gamma"]),
error, method = "L-BFGS-B", lower = c(0, 0),
upper = c(1, 1), control = optim.control)
if (sol$convergence || any(sol$par < 0 | sol$par >
1)) {
if (sol$convergence > 50) {
warning(gettextf("optimization difficulties: %s",
sol$message), domain = NA)
}
else stop("optimization failure")
}
beta <- sol$par[1L]
gamma <- sol$par[2L]
}
else {
error <- function(p) hw(alpha, beta, p)$SSE
gamma <- optimize(error, lower = 0, upper = 1)$minimum
}
}
}
else {
if (is.null(alpha)) {
if (is.null(beta)) {
error <- function(p) hw(p[1L], p[2L], gamma)$SSE
sol <- optim(c(optim.start["alpha"], optim.start["beta"]),
error, method = "L-BFGS-B", lower = c(0, 0),
upper = c(1, 1), control = optim.control)
if (sol$convergence || any(sol$par < 0 | sol$par >
1)) {
if (sol$convergence > 50) {
warning(gettextf("optimization difficulties: %s",
sol$message), domain = NA)
}
else stop("optimization failure")
}
alpha <- sol$par[1L]
beta <- sol$par[2L]
}
else {
error <- function(p) hw(p, beta, gamma)$SSE
alpha <- optimize(error, lower = 0, upper = 1)$minimum
}
}
else {
if (is.null(beta)) {
error <- function(p) hw(alpha, p, gamma)$SSE
beta <- optimize(error, lower = 0, upper = 1)$minimum
}
}
}
final.fit <- hw(alpha, beta, gamma)
fitted <- ts(cbind(xhat = final.fit$level[-len - 1], level = final.fit$level[-len -
1], trend = if (!is.logical(beta) || beta)
final.fit$trend[-len - 1], season = if (!is.logical(gamma) ||
gamma)
final.fit$seasonal[1L:len]), start = start(lag(x, k = 1 -
start.time)), frequency = frequency(x))
if (!is.logical(beta) || beta)
fitted[, 1] <- fitted[, 1] + fitted[, "trend"]
if (!is.logical(gamma) || gamma)
fitted[, 1] <- if (seasonal == "multiplicative")
fitted[, 1] * fitted[, "season"]
else fitted[, 1] + fitted[, "season"]
structure(list(fitted = fitted, x = x, alpha = alpha, beta = beta,
gamma = gamma, coefficients = c(a = final.fit$level[len +
1], b = if (!is.logical(beta) || beta) final.fit$trend[len +
1], s = if (!is.logical(gamma) || gamma) final.fit$seasonal[len +
1L:f]), seasonal = seasonal, SSE = final.fit$SSE,
call = match.call()), class = "HoltWinters")
}
