Question

我的数据框看起来像这样（dput在下面）：

Date        SiteSub       HeatingDegreeDay MCnt  MCnt_lag7  MCnt_lag9
2009-11-01  EC_BC.Z_Z     0.00             0     0          0
2009-11-02  EC_BC.Z_Z     0.00             0     0          0
2009-11-03  EC_BC.Z_Z     0.00             0     0          0
2009-11-04  EC_BC.Z_Z     0.00             0     0          0
2009-11-05  EC_BC.Z_Z     0.00             0     0          0
2009-11-06  EC_BC.Z_Z     0.00             0     0          0
2009-11-07  EC_BC.Z_Z     0.00             1     0          0

我正在尝试计算此数据框中HeatingDegreeDay，MCnt，MCnt_lag7，MCnt_lag9的移动总和或宽度7的平均值。要考虑的数据的一些特征是：NA向量中缺少日期和HeatingDegreeDay值的不规则时间序列。

一旦我计算了7天移动总和或平均值，我需要计算相关系数，以帮助我确定哪个滞后（7天或9天）最适合HeatingDegreeDay向量。

问题：移动总和或平均值计算是否可以与相同代码中的相关系数计算相结合，还是需要分步进行？如果是这样，怎么样？

问题：在计算移动总和或平均值时，我一直遇到麻烦。首先，使用rollapply，我无法将多个向量传递给rollapply，因为它似乎是单变量的。其次，使用TTR＆＃39; SMA，我得到了一个＆＃34;不正确的维数＆＃34;错误。我无法使用rollmean，因为我的数据有NA个。

我看过： R: How to apply moving averages to subset of columns in a data frame?和 Conditional rolling mean (moving average) on irregular time series

我试过了：

#Calculate moving average 
Lag0910_79 <- as.numeric(Lag0910_79$HeatingDegreeDay, Lag0910_79$MCnt7, Lag0910_79$MCnt9)
Lagzoo <- as.zoo(Lag0910_79)
Lagzoo_7 <- rollapply(Lagzoo, width=7, mean, na.rm=TRUE)
Lagzoo_7 <- as.data.frame(Lagzoo_7)

结果：

dput(head(Lagzoo_7, 15))

structure(list(Lagzoo_7 = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1)), .Names = "Lagzoo_7", row.names = c("4", "5", "6", 
"7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", 
"18"), class = "data.frame")`

和：

Lagzoo.ttr <- SMA(Lag0910_79[, "HeatingDegreeDay"], 7)

Lag0910_79 [，＆＃34; HeatingDegreeDay＆＃34;]出错：维度数不正确

我该如何使这项工作？显然，我没有做对。谢谢你的帮助！

我的数据结构如下：

structure(list(Date = structure(c(14549, 14550, 14551, 14552, 
14553, 14554, 14555, 14556, 14557, 14558, 14559, 14560, 14561, 
14562, 14563, 14564, 14565, 14566, 14567, 14568, 14569, 14570, 
14571, 14572, 14573, 14574, 14575, 14576, 14577, 14578, 14579, 
14580, 14581, 14582, 14583, 14584, 14585, 14586, 14587, 14588, 
14589, 14590, 14591, 14592, 14593, 14594, 14595, 14596, 14597, 
14598, 14599, 14600, 14601, 14602, 14603, 14604, 14605, 14606, 
14607, 14608, 14609, 14610, 14611, 14612, 14613, 14614, 14615, 
14616, 14617, 14618, 14619, 14620, 14620, 14620, 14621, 14622, 
14622, 14623, 14624, 14625, 14626, 14627, 14628, 14629, 14629, 
14629, 14629, 14629, 14630, 14631, 14631, 14631, 14632, 14632, 
14632, 14632, 14632, 14632, 14632, 14633, 14633, 14633, 14634, 
14634, 14634, 14634, 14635, 14635, 14635, 14635, 14636, 14636, 
14636, 14636, 14636, 14636, 14637, 14637, 14637, 14638, 14638, 
14638, 14639, 14639, 14640, 14641, 14642, 14643, 14643, 14644, 
14645, 14646, 14647, 14648, 14649, 14650, 14651, 14652, 14653, 
14654, 14655, 14656, 14657, 14658, 14659, 14660, 14661, 14661, 
14662, 14663, 14663, 14664, 14665, 14666, 14667, 14668, 14669, 
14669, 14670, 14671, 14672, 14673, 14674, 14675, 14675, 14676, 
14677, 14678, 14678, 14679, 14680, 14681, 14681, 14681, 14682, 
14682, 14682, 14683, 14684, 14685, 14686, 14687, 14688, 14689, 
14689, 14690, 14691, 14692, 14693, 14694, 14694, 14694, 14695, 
14696, 14697, 14698, 14699, 14700, 14701, 14702, 14703, 14703, 
14703, 14703, 14704, 14704, 14705, 14706), class = "Date"), SiteSub = structure(c(1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "EC_BC.Z_Z", class = "factor"), 
    HeatingDegreeDay = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, NA, 1L, 1L, 3L, 4L, 9L, 11L, 
    14L, 15L, 12L, 13L, 17L, 16L, 16L, 16L, 10L, 8L, 8L, 7L, 
    6L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 5L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("0.00", 
    "0.02", "0.05", "0.14", "0.32", "0.50", "0.89", "0.96", "0.98", 
    "1.02", "1.04", "1.30", "1.40", "1.49", "1.50", "1.58", "1.86"
    ), class = "factor"), MCnt = structure(c(1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 
    2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 
    1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 
    2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 1L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("0", "1"), class = "factor"), 
    MCnt_lag7 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 
    2L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 
    2L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 
    2L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 
    2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 2L, 2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, NA, NA, 
    NA, NA, NA, NA), .Label = c("0", "1"), class = "factor"), 
    MCnt_lag9 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 
    1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 
    1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 1L, 
    1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 
    2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 
    2L, 2L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 2L, 
    1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, NA, NA, NA, NA, NA, 
    NA, NA, NA, NA), .Label = c("0", "1"), class = "factor")), .Names = c("Date", 
"SiteSub", "HeatingDegreeDay", "MCnt", "MCnt_lag7", "MCnt_lag9"
), row.names = c("1", "2", "3", "4", "5", "6", "7", "8", "9", 
"10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", 
"21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", 
"32", "33", "34", "35", "36", "37", "38", "39", "40", "41", "42", 
"43", "44", "45", "46", "47", "48", "49", "50", "51", "52", "53", 
"54", "55", "56", "57", "58", "59", "60", "61", "62", "63", "64", 
"65", "66", "67", "68", "69", "70", "71", "72", "73", "74", "75", 
"76", "77", "78", "79", "80", "81", "82", "83", "84", "85", "86", 
"87", "88", "89", "90", "91", "92", "93", "94", "95", "96", "97", 
"98", "99", "100", "101", "102", "103", "104", "105", "106", 
"107", "108", "109", "110", "111", "112", "113", "114", "115", 
"116", "117", "118", "119", "120", "121", "122", "123", "124", 
"125", "126", "127", "128", "129", "130", "131", "132", "133", 
"134", "135", "136", "137", "138", "139", "140", "141", "142", 
"143", "144", "145", "146", "147", "148", "149", "150", "151", 
"152", "153", "154", "155", "156", "157", "158", "159", "160", 
"161", "162", "163", "164", "165", "166", "167", "168", "169", 
"170", "171", "172", "173", "174", "175", "176", "177", "178", 
"179", "180", "181", "182", "183", "184", "185", "186", "187", 
"188", "189", "190", "191", "192", "193", "194", "195", "196", 
"197", "198", "199", "200", "201", "202", "203", "204", "205", 
"206", "207", "208"), class = "data.frame")

Answer 1

如果问题的dput输出中显示的数据框是DF，那么这会将第3：6列转换为数字，执行生成rmean的rollmean计算，这是一个滚动矩阵手段。然后，它使用corNA生成滚动关联的向量rcor，并将所有内容放入一个数据框DF3：

library(zoo)

DF2 <- DF
DF2[3:6] <- lapply(DF2[3:6], function(x) as.numeric(as.character(x)))
m <- as.matrix(DF2[3:6])
rmean <- rollapplyr(m, 7, mean, na.rm = TRUE, fill = NA) # mean matrix

corNA <- function(x) {
    x <- na.omit(x[, 1:2])
    if (nrow(x) < 2 || sd(x[,1]) == 0 || sd(x[,2]) == 0) return(NA)
    cor(x[, 1], x[,2])
}

rcor <- rollapplyr(m, 7, corNA, by.column = FALSE, fill = NA) # vector of cors

DF3 <- data.frame(DF2, rmean, rcor) # put it all together

动物园版本在这里。由于动物园需要唯一的日期，我们会聚合具有相同日期的行：

z <- read.zoo(DF2[-2], aggregate = mean) # can omit aggregate=mean if dates are unique

zmean <- rollapplyr(z, 7, mean, na.rm = TRUE, fill = NA) # means
zcor <- rollapplyr(z, 7, corNA, by.column = FALSE, fill = NA) # cors

z2 <- merge(z, zmean, zcor) # omit this if separate objects are ok

Answer 2

这是你想要的滚动意味着什么？

# Convert dates to days
aa = as.Date(x$Date)
x$Date = as.numeric(aa - aa[1])

# I think it's easier to get rid of the factors
factor2number = function(x) as.numeric(as.character(x))
x[,3:6] = apply(x[,3:6],2,factor2number)

# A rolling mean function
rollmean_r = function(x,y,width) {
  out = numeric(length(x))
  for( i in seq_along(x) ) {
    window = x >= (x[i]-width) & x <= (x[i]+width)
    out[i] = .Internal(mean( y[window] ))
  }
  return(out)
}

# Calculate the rolling means
x[,3:6] = apply(x[3:6], 2, function(y) rollmean_r(x$Date,y,7) )
x
#    Date   SiteSub HeatingDegreeDay       MCnt MCnt_lag7  MCnt_lag9
# 1     0 EC_BC.Z_Z                0 0.12500000         0         0
# 2     1 EC_BC.Z_Z                0 0.11111111         0         0
# 3     2 EC_BC.Z_Z                0 0.10000000         0         0
# 4     3 EC_BC.Z_Z                0 0.09090909         0         0
# 5     4 EC_BC.Z_Z                0 0.08333333         0         0
# 6     5 EC_BC.Z_Z                0 0.07692308         0         0
# 7     6 EC_BC.Z_Z                0 0.07142857         0         0
# 8     7 EC_BC.Z_Z                0 0.06666667         0         0
# 9     8 EC_BC.Z_Z                0 0.07142857         0         0
# 10    9 EC_BC.Z_Z                0 0.07692308         0         0
# 11   10 EC_BC.Z_Z                0 0.08333333         0         0
# 12   11 EC_BC.Z_Z                0 0.09090909         0         0
# 13   12 EC_BC.Z_Z                0 0.10000000         0         0
# 14   13 EC_BC.Z_Z                0 0.11111111         0         0
# 15   14 EC_BC.Z_Z                0 0.00000000         0         0

不规则时间序列中多个向量的移动平均或总和计算

2 个答案: