[英]Calculate moving recency-weighted mean in R
I would like to calculate the moving recency-weighted mean finishing positions of a horse given the times (day) and finishing positions (pos) for a sequence of races in which the horse participated. 我想计算一匹马的移动新近加权平均完成位置给出马匹参与的一系列比赛的时间(日)和结束位置(pos)。 Such statistics are useful in handicapping .
这些统计数据在设定障碍方面很有用。
Currently, I am using a "loop-inside-a-loop" approach. 目前,我正在使用“loop-inside-a-loop”方法。 Is there a faster or more elegant R-language approach to this problem?
是否有更快或更优雅的R语言方法来解决这个问题?
#
# Test data
#
day <- c(0, 6, 10, 17, 21, 26, 29, 31, 34, 38, 41, 47, 48, 51, 61)
pos <- c(3, 5, 6, 1, 1, 3, 4, 1, 2, 2, 2, 6, 4, 5, 6)
testdata <- data.frame(id = 1, day = day, pos = pos, wt.pos = NA)
#
# No weight is given to observations earlier than cutoff
#
cutoff <- 30
#
# Rolling recency-weighted mean (wt.pos)
#
for(i in 2:nrow(testdata)) {
wt <- numeric(i-1)
for(j in 1:(i-1))
wt[j] <- max(0, cutoff - day[i] + day[j] + 1)
if (sum(wt) > 0)
testdata$wt.pos[i] <- sum(pos[1:j] * wt) / sum(wt)
}
> testdata
id day pos wt.pos
1 1 0 3 NA
2 1 6 5 3.000000
3 1 10 6 4.125000
4 1 17 1 4.931034
5 1 21 1 3.520548
6 1 26 3 2.632911
7 1 29 4 2.652174
8 1 31 1 2.954128
9 1 34 2 2.436975
10 1 38 2 2.226891
11 1 41 2 2.119048
12 1 47 6 2.137615
13 1 48 4 3.030534
14 1 51 5 3.303704
15 1 61 6 4.075000
I'd go for 我会去的
# Calculate `wt` for all values of `i` in one go
wt <- lapply(2:nrow(testdata), function(i)
pmax(0, cutoff - day[i] + day[1:(i-1)] + 1))
# Fill in the column
testdata$wt.pos[-1] <- mapply(
function(i, w) if(sum(w) > 0) sum(pos[1:i]*w)/sum(w) else NA,
1:(nrow(testdata)-1), wt)
Note that by calculating the second argument to max
for all values of j
at the same time we have vectorized the computation, which improves the speed by many orders of magnitude. 请注意,通过计算同时
j
所有值的第二个参数max
,我们已经对计算进行了矢量化,从而将速度提高了许多个数量级。
I found no easy way to vectorize the outer loop and the if
case though (apart from rewriting it in C which seems like overkill), but lapply
, mapply
and similar are still faster than for
loops. 我发现没有简单的方法来矢量化外部循环和
if
情况(除了在C中重写它看起来像是矫枉过正),但是lapply
, mapply
和类似仍然比for
循环更快。
This version demonstrates how to calculate moving recency-weighted means for 1 or more variables (eg, finishing position, speed rating, etc.) and 1 or more subjects (horses). 此版本演示了如何计算1个或多个变量(例如,完成位置,速度等级)和1个或更多个主题(马)的移动新近度加权平均值。
library(plyr)
day <- c(0, 6, 10, 17, 21, 26, 29, 31, 34, 38, 41, 47, 48, 51, 61)
pos <- c(3, 5, 6, 1, 1, 3, 4, 1, 2, 2, 2, 6, 4, 5, 6)
dis <- 100 + 0.5 * (pos - 1)
testdata1 <- data.frame(id = 1, day = day, pos = pos, dis = dis)
day <- c(0, 4, 7, 14, 22, 23, 31, 38, 42, 47, 52, 59, 68, 69, 79)
pos <- c(1, 3, 2, 6, 4, 5, 2, 1, 4, 5, 2, 1, 5, 5, 2)
dis <- 100 + 0.5 * (pos - 1)
testdata2 <- data.frame(id = 2, day = day, pos = pos, dis = dis)
testdata <- rbind(testdata1, testdata2)
# Moving recency-weighted mean
rollmean <- function(day, obs, cutoff = 90) {
obs <- as.matrix(obs)
wt <- lapply(2:nrow(obs), function(i)
pmax(0, cutoff - day[i] + day[1:(i-1)] + 1))
wt.obs <- lapply(1:(nrow(obs)-1), FUN =
function(i)
if(sum(wt[[i]]) > 0) {
apply(obs[1:i, , drop = F] * wt[[i]], 2, sum) / sum(wt[[i]])
} else {
rep(NA, ncol(obs))
}
)
answer <- rbind(rep(NA, ncol(obs)), do.call(rbind, wt.obs))
if (!is.null(dimnames(answer)))
dimnames(answer)[[2]] <- paste("wt", dimnames(answer)[[2]], sep = ".")
return(answer)
}
x <- dlply(testdata, .(id), .fun =
function(DF) rollmean(DF$day, DF[, c("pos", "dis"), drop = F])
)
y <- do.call(rbind, x)
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