# R/wtd.stats.s In harrelfe/Hmisc: Harrell Miscellaneous

#### Documented in num.denom.setupwtd.Ecdfwtd.loess.noiterwtd.meanwtd.quantilewtd.rankwtd.tablewtd.var

```## See stackoverflow.com/questions/10049402

wtd.mean <- function(x, weights=NULL, normwt='ignored', na.rm=TRUE)
{
if(! length(weights)) return(mean(x, na.rm=na.rm))
if(na.rm) {
s <- ! is.na(x + weights)
x <- x[s]
weights <- weights[s]
}

sum(weights * x) / sum(weights)
}

wtd.var <- function(x, weights=NULL, normwt=FALSE, na.rm=TRUE,
method = c('unbiased', 'ML'))
## By Benjamin Tyner <btyner@gmail.com> 2017-0-12
{
method <- match.arg(method)
if(! length(weights)) {
if(na.rm) x <- x[!is.na(x)]
return(var(x))
}

if(na.rm) {
s       <- !is.na(x + weights)
x       <- x[s]
weights <- weights[s]
}

if(normwt)
weights <- weights * length(x) / sum(weights)

if(normwt || method == 'ML')
return(as.numeric(stats::cov.wt(cbind(x), weights, method = method)\$cov))

# the remainder is for the special case of unbiased frequency weights
sw  <- sum(weights)
if(sw <= 1)
warning("only one effective observation; variance estimate undefined")

xbar <- sum(weights * x) / sw
sum(weights*((x - xbar)^2)) / (sw - 1)
}

wtd.quantile <- function(x, weights=NULL, probs=c(0, .25, .5, .75, 1),
type=c('quantile','(i-1)/(n-1)','i/(n+1)','i/n'),
normwt=FALSE, na.rm=TRUE)
{
if(! length(weights))
return(quantile(x, probs=probs, na.rm=na.rm))

type <- match.arg(type)
if(any(probs < 0 | probs > 1))
stop("Probabilities must be between 0 and 1 inclusive")

nams <- paste(format(round(probs * 100, if(length(probs) > 1)
2 - log10(diff(range(probs))) else 2)),
"%", sep = "")

i <- is.na(weights) | weights == 0
if(any(i)) {
x <- x[! i]
weights <- weights[! i]
}
if(type == 'quantile') {
w <- wtd.table(x, weights, na.rm=na.rm, normwt=normwt, type='list')
x     <- w\$x
wts   <- w\$sum.of.weights
n     <- sum(wts)
order <- 1 + (n - 1) * probs
low   <- pmax(floor(order), 1)
high  <- pmin(low + 1, n)
order <- order %% 1
## Find low and high order statistics
## These are minimum values of x such that the cum. freqs >= c(low,high)
allq <- approx(cumsum(wts), x, xout=c(low,high),
method='constant', f=1, rule=2)\$y
k <- length(probs)
quantiles <- (1 - order)*allq[1:k] + order*allq[-(1:k)]
names(quantiles) <- nams
return(quantiles)
}
w <- wtd.Ecdf(x, weights, na.rm=na.rm, type=type, normwt=normwt)
structure(approx(w\$ecdf, w\$x, xout=probs, rule=2)\$y,
names=nams)
}

wtd.Ecdf <- function(x, weights=NULL,
type=c('i/n','(i-1)/(n-1)','i/(n+1)'),
normwt=FALSE, na.rm=TRUE)
{
type <- match.arg(type)
switch(type,
'(i-1)/(n-1)'={a <- b <- -1},
'i/(n+1)'    ={a <- 0; b <- 1},
'i/n'        ={a <- b <- 0})

if(! length(weights)) {
##.Options\$digits <- 7  ## to get good resolution for names(table(x))
oldopt <- options('digits')
options(digits=7)
on.exit(options(oldopt))
cumu <- table(x)    ## R does not give names for cumsum
isdate <- testDateTime(x)  ## 31aug02
ax <- attributes(x)
ax\$names <- NULL
x <- as.numeric(names(cumu))
if(isdate) attributes(x) <- c(attributes(x),ax)
cumu <- cumsum(cumu)
cdf <- (cumu + a)/(cumu[length(cumu)] + b)
if(cdf[1]>0) {
x <- c(x[1], x);
cdf <- c(0,cdf)
}

return(list(x = x, ecdf=cdf))
}

w <- wtd.table(x, weights, normwt=normwt, na.rm=na.rm)
cumu <- cumsum(w\$sum.of.weights)
cdf <- (cumu + a)/(cumu[length(cumu)] + b)
list(x = c(if(cdf[1]>0) w\$x[1], w\$x), ecdf=c(if(cdf[1]>0)0, cdf))
}

wtd.table <- function(x, weights=NULL, type=c('list','table'),
normwt=FALSE, na.rm=TRUE)
{
type <- match.arg(type)
if(! length(weights))
weights <- rep(1, length(x))

isdate <- testDateTime(x)  ## 31aug02 + next 2
ax <- attributes(x)
ax\$names <- NULL

if(is.character(x)) x <- as.factor(x)
lev <- levels(x)
x <- unclass(x)

if(na.rm) {
s <- ! is.na(x + weights)
x <- x[s, drop=FALSE]    ## drop is for factor class
weights <- weights[s]
}

n <- length(x)
if(normwt)
weights <- weights * length(x) / sum(weights)

i <- order(x)  # R does not preserve levels here
x <- x[i]; weights <- weights[i]

if(anyDuplicated(x)) {  ## diff(x) == 0 faster but doesn't handle Inf
weights <- tapply(weights, x, sum)
if(length(lev)) {
levused <- lev[sort(unique(x))]
if((length(weights) > length(levused)) &&
any(is.na(weights)))
weights <- weights[! is.na(weights)]

if(length(weights) != length(levused))
stop('program logic error')

names(weights) <- levused
}

if(! length(names(weights)))
stop('program logic error')

if(type=='table')
return(weights)

x <- all.is.numeric(names(weights), 'vector')
if(isdate)
attributes(x) <- c(attributes(x),ax)

names(weights) <- NULL
return(list(x=x, sum.of.weights=weights))
}

xx <- x
if(isdate)
attributes(xx) <- c(attributes(xx),ax)

if(type=='list')
list(x=if(length(lev))lev[x]
else xx,
sum.of.weights=weights)
else {
names(weights) <- if(length(lev)) lev[x]
else xx
weights
}
}

wtd.rank <- function(x, weights=NULL, normwt=FALSE, na.rm=TRUE)
{
if(! length(weights))
return(rank(x, na.last=if(na.rm) NA else TRUE))

tab <- wtd.table(x, weights, normwt=normwt, na.rm=na.rm)

freqs <- tab\$sum.of.weights
## rank of x = # <= x - .5 (# = x, minus 1)
r <- cumsum(freqs) - .5*(freqs-1)
## Now r gives ranks for all unique x values.  Do table look-up
## to spread these ranks around for all x values.  r is in order of x
approx(tab\$x, r, xout=x)\$y
}

wtd.loess.noiter <- function(x, y, weights=rep(1,n),
span=2/3, degree=1, cell=.13333,
type=c('all','ordered all','evaluate'),
evaluation=100, na.rm=TRUE) {
type <- match.arg(type)
n <- length(y)
if(na.rm) {
s <- ! is.na(x + y + weights)
x <- x[s]; y <- y[s]; weights <- weights[s]; n <- length(y)
}

max.kd <- max(200, n)
# y <- stats:::simpleLoess(y, x, weights=weights, span=span,
#                          degree=degree, cell=cell)\$fitted
y <- fitted(loess(y ~ x, weights=weights, span=span, degree=degree,
control=loess.control(cell=cell, iterations=1)))

switch(type,
all=list(x=x, y=y),
'ordered all'={
i <- order(x);
list(x=x[i],y=y[i])
},
evaluate={
r <- range(x, na.rm=na.rm)
approx(x, y, xout=seq(r[1], r[2], length=evaluation))
})
}

num.denom.setup <- function(num, denom)
{
n <- length(num)
if(length(denom) != n)
stop('lengths of num and denom must match')

s <- (1:n)[! is.na(num + denom) & denom != 0]
num <- num[s];
denom <- denom[s]

subs <- s[num > 0]
y <- rep(1, length(subs))
wt <- num[num > 0]
other <- denom - num
subs <- c(subs, s[other > 0])
wt <- c(wt, other[other > 0])
y <- c(y, rep(0, sum(other>0)))
list(subs=subs, weights=wt, y=y)
}
```
harrelfe/Hmisc documentation built on May 19, 2024, 4:13 a.m.