Nothing
powt <- function (rwl, rescale = FALSE)
{
# add a check for negative nums
if(any(rwl <0, na.rm = TRUE)) {
stop("'rwl' values must be greater than zero")
}
if (!is.data.frame(rwl))
stop("'rwl' must be a data.frame")
if (!is.logical(rescale))
stop("'rescale' must be either FALSE (the default) or TRUE")
# used to set min numb to zeros.
getprec <- function(rwl) {
rwl.num <- as.numeric(as.matrix(rwl))
rwl.num <- rwl.num[!is.na(rwl.num) & rwl.num != 0]
if (length(rwl.num) == 0) {
NA_real_
}
else {
rwl.char <- format(rwl.num, scientific = FALSE)
if (grepl(".", rwl.char[1], fixed = TRUE)) {
maxdig <- nchar(sub("^[^.]*\\.", "", rwl.char[1]))
}
else {
rm.trail.zeros <- sub("0*$", "", rwl.char)
n.trail.zeros <- nchar(rwl.char) - nchar(rm.trail.zeros)
maxdig <- -min(n.trail.zeros)
}
10^-maxdig
}
}
fit.lm <- function(series) {
n <- length(series)
drop.1 <- series[-1]
drop.n <- series[-n]
runn.M <- (drop.1 + drop.n)/2
runn.S <- abs(drop.1 - drop.n)
runn.S[runn.S == 0] <- prec
runn.M[runn.M == 0] <- prec
mod <- lm.fit(cbind(1, log(runn.M)), log(runn.S))
b <- mod[["coefficients"]][2]
1 - b
}
transf <- function(x) {
Xt <- x
X.nna <- which(!is.na(x))
X <- na.omit(x)
p <- abs(fit.lm(X))
X2 <- X^p
Xt[X.nna] <- X2
Xt
}
prec <- getprec(rwl)
xt <- lapply(rwl, FUN = transf)
if (rescale) {
sds <- lapply(rwl, FUN = function(x) sd(x, na.rm = TRUE))
means <- lapply(rwl, FUN = function(x) mean(x, na.rm = TRUE))
rescale_ <- function(x, .sd, .mean) {
scale(x) * .sd + .mean
}
xt <- mapply(rescale_, xt, sds, means)
}
res <- data.frame(xt, row.names = row.names(rwl), check.names = FALSE)
class(res) <- c("rwl", "data.frame")
res
}
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