我有一个具有一个时标和多个时间序列的数据帧。我想以动态滚动的方式将其子集化，并将窗口的宽度设置为例如10年由于未等距采样时间序列，因此窗口沿数据帧滚动时，其行数将发生变化。 计算应基于时间值而不是观察次数。
例如，在以下data.frame中：
time var1 var2
5262 -8.981 -0.011
5263.2 -8.993 -0.012
5264.4 -8.978 0.015
5265.6 -9.169 -0.191
5266.8 -8.897 0.272
5268 -9.024 -0.127
5269.2 -8.996 0.028
5270.46 -8.979 0.017
5271.84 -9.004 -0.025
5273.22 -9.01 -0.006
5274.6 -9.106 -0.096
5275.98 -8.971 0.135
5277.36 -8.996 -0.025
5278.74 -8.956 0.04
5280.12 -8.981 -0.025
5281.5 -8.982 -0.001
5282.88 -9.042 -0.06
5284.26 -9.091 -0.049
5285.64 -9.066 0.025
5287.02 -9.03 0.036
5288.4 -9.031 -0.001
5289.78 -9.028 0.003
5291.16 -9.164 -0.136
5294.72 -9.034 0.13
5297.3 -9.296 -0.262
5299.88 -9.097 0.199
5302.46 -8.995 0.102
5305.04 -9.084 -0.089
5307.62 -9.047 0.037
5310.2 -9.066 -0.019
5312.78 -9.07 -0.004
5315.36 -9 0.07
5317.94 -9.057 -0.057
5320.52 -9.219 -0.162
5323.1 -9.084 0.135
5325.68 -9.034 0.05
5328.26 -9.147 -0.113
5330.84 -9.169 -0.022
5333.42 -9.143 0.026
5336 -9.211 -0.068
5338.58 -9.061 0.15
5341.16 -9.1 -0.039
5343.74 -9.094 0.006
5346.32 -9.104 -0.01
5348.9 -9.089 0.015
5351.48 -9.127 -0.038
5354.06 -8.973 0.154
5356.64 -9.009 -0.036
5359.22 -8.966 0.043
5361.8 -8.996 -0.03
5364.38 -8.877 0.119
5366.96 -8.962 -0.085
5369.54 -8.902 0.06
5372.12 -8.915 -0.013
5374.7 -8.913 0.002
5377.28 -8.834 0.079
5379.86 -8.91 -0.076
5382.44 -8.742 0.168
5385.02 -8.877 -0.135
5387.6 -8.743 0.134
5390.18 -8.898 -0.155
5392.76 -8.77 0.128
5395.34 -8.97 -0.2
5397.92 -8.849 0.121
5400.5 -8.846 0.003
5403.08 -8.865 -0.019
5405.66 -8.865 0
5408.24 -8.876 -0.011
5410.82 -8.775 0.101
5413.4 -8.842 -0.067
5415.98 -8.821 0.021
5418.56 -8.85 -0.029
我之前所做的是将df子集化，但是通过使用以下代码引用行号并执行线性回归。
data.column=2
time.column=1
length=dim(data)[1]
window=10
adj_r_sqr=matrix(0,nrow=length,length(window_vekt))
colnames(adj_r_sqr)=window
for(i in 1:(length-window)){
x=data[i:(i+window),time.column]
y=data[i:(i+window),data.column]
lmodel=lm(y~x)
adj_r_sqr[i+floor(window/2)-1),which(window_vekt==window)]=summary(lmodel)$adj.r.squared}
但这不会解释时间间隔的变化。
我需要做的是一种调整，该调整基于第一列筛选数据帧，并将其子集滚动显示，以便子集覆盖所选窗口，并且如果该窗口中的行数小于5则给出NA。 。 另一个问题可能是对数据进行子集化，而不是滚动地进行，而是再次使用时间变量以拼接的方式进行。
以前，我设法不仅提取了adj。 r2，但p值和其他以及斜率也可以使用：
RMSE=sqrt(mean((summary(lmodel)$residuals)^2))
p_val_y=summary(lmodel)$coefficients[2,4]
p_val_intercept=summary(lmodel)$coefficients[1,4]
slope=coeff[i+summary(lmodel)$coefficients[2,1]
但是使用旧的窗口，不幸的是，由于我的能力不足，我无法在@Uwe建议的查询中实现这些功能。
可以在以下链接上找到测试数据集： test_data.csv
答案 0 :(得分：3)
这可以通过聚集非等额联接来解决，该联接聚集涵盖给定时间段的不同数量的行。
library(data.table)
# define parameters
time_1 <- -10
time_2 <- 10
n_min <- 5L
# create helper columns
setDT(dat)[, `:=`(join = time, start = time + time_1, end = time + time_2)][
# non-equi join and aggregate
dat, on = .(join >= start, join <= end), by = .EACHI, {
lmodel <- lm(var1 ~ time)
lsumm <- summary(lmodel)
.(time = i.time,
N = .N,
adj_r_sqr = lsumm$adj.r.squared,
RMSE = sqrt(mean(lsumm$residuals^2)),
p_val_y = if (.N > 1) lsumm$coefficients[2,4] else NA_real_,
p_val_intercept = lsumm$coefficients[1,4],
slope = coef(lmodel)[2]
)
}]
join join time N adj_r_sqr RMSE p_val_y p_val_intercept slope 1: 5252.00 5272.00 5262.00 9 -1.412484e-01 0.06749996 0.923658051 0.76541424 8.050483e-04 2: 5253.20 5273.20 5263.20 9 -1.412484e-01 0.06749996 0.923658051 0.76541424 8.050483e-04 3: 5254.40 5274.40 5264.40 10 -1.248329e-01 0.06411770 0.973340896 0.77035143 2.202740e-04 4: 5255.60 5275.60 5265.60 11 -5.914522e-02 0.06631161 0.523022100 0.72831553 -3.713582e-03 5: 5256.80 5276.80 5266.80 12 -8.934954e-02 0.06570860 0.760946792 0.96376799 -1.488205e-03 6: 5258.00 5278.00 5268.00 13 -8.696209e-02 0.06341811 0.845238030 0.82437256 -7.998616e-04 7: 5259.20 5279.20 5269.20 14 -8.098718e-02 0.06260179 0.874476744 0.52663574 5.619086e-04 8: 5260.46 5280.46 5270.46 15 -6.931305e-02 0.06060738 0.765814583 0.39825585 9.125789e-04 9: 5261.84 5281.84 5271.84 16 -5.793624e-02 0.05873142 0.679041724 0.29974544 1.102731e-03 10: 5263.22 5283.22 5273.22 15 -6.443192e-02 0.06113970 0.702430340 0.34981142 1.151509e-03 11: 5264.60 5284.60 5274.60 15 -7.684134e-02 0.06444578 0.975417917 0.56686522 9.651049e-05 12: 5265.98 5285.98 5275.98 15 1.462585e-01 0.04608930 0.088169168 0.30888780 -4.011513e-03 13: 5267.36 5287.36 5277.36 15 1.964299e-02 0.04086278 0.278246773 0.81455430 -2.166657e-03 14: 5268.74 5288.74 5278.74 15 6.215962e-02 0.04008533 0.188319558 0.64301698 -2.594692e-03 15: 5270.12 5290.12 5280.12 15 4.288832e-02 0.04025402 0.224394742 0.72203401 -2.388716e-03 16: 5271.50 5291.50 5281.50 15 1.512386e-01 0.04851035 0.084258193 0.28641439 -4.218427e-03 17: 5272.88 5292.88 5282.88 14 1.381176e-01 0.05002305 0.104568029 0.29409267 -4.558051e-03 18: 5274.26 5294.26 5284.26 13 1.327825e-01 0.05157048 0.120223456 0.28797520 -5.072464e-03 19: 5275.64 5295.64 5285.64 13 3.596767e-01 0.04138389 0.017834204 0.06667444 -6.361901e-03 20: 5277.02 5297.02 5287.02 12 2.873660e-01 0.04305408 0.041945918 0.12120605 -6.247512e-03 21: 5278.40 5298.40 5288.40 12 5.028991e-01 0.05961926 0.005895787 0.01376478 -1.191625e-02 22: 5279.78 5299.78 5289.78 11 4.293597e-01 0.06222915 0.017042382 0.03427994 -1.172318e-02 23: 5281.16 5301.16 5291.16 11 2.515343e-01 0.06779510 0.066374606 0.13205602 -8.296797e-03 24: 5284.72 5304.72 5294.72 9 -1.061158e-01 0.08743787 0.644376920 0.85394202 -2.841506e-03 25: 5287.30 5307.30 5297.30 8 -1.646716e-01 0.09175885 0.922552618 0.87761939 -6.640669e-04 26: 5289.88 5309.88 5299.88 7 1.448103e-02 0.08454602 0.344665870 0.25432610 7.335472e-03 27: 5292.46 5312.46 5302.46 7 -7.071101e-02 0.08530518 0.472291173 0.35934829 5.744740e-03 28: 5295.04 5315.04 5305.04 7 1.737202e-01 0.07307195 0.192955618 0.13615980 9.523810e-03 29: 5297.62 5317.62 5307.62 7 -8.646937e-02 0.03513349 0.502173104 0.25578453 2.200997e-03 30: 5300.20 5320.20 5310.20 7 -1.900736e-01 0.03206618 0.846235515 0.69966581 -5.675526e-04 31: 5302.78 5322.78 5312.78 7 5.536354e-05 0.05732965 0.363144502 0.54063521 -4.969546e-03 32: 5305.36 5325.36 5315.36 7 5.335182e-02 0.05578091 0.299620417 0.45814873 -5.592470e-03 33: 5307.94 5327.94 5317.94 7 -1.657721e-01 0.06294568 0.717357983 0.94666422 -2.090255e-03 34: 5310.52 5330.52 5320.52 7 -6.028327e-02 0.06414699 0.453853207 0.63535624 -4.512735e-03 35: 5313.10 5333.10 5323.10 7 8.740895e-02 0.06389452 0.264987111 0.38753689 -6.949059e-03 36: 5315.68 5335.68 5325.68 7 -1.197917e-01 0.05898249 0.575617471 0.80276014 -3.059247e-03 37: 5318.26 5338.26 5328.26 7 -1.150258e-01 0.05925244 0.564069556 0.78843841 -3.169989e-03 38: 5320.84 5340.84 5330.84 7 -5.959161e-02 0.05513800 0.452664410 0.66756091 -3.889812e-03 39: 5323.42 5343.42 5333.42 7 -1.914314e-01 0.05727449 0.857057704 0.88289558 -9.413068e-04 40: 5326.00 5346.00 5336.00 7 1.980666e-01 0.03842399 0.175981115 0.09097818 5.246401e-03 41: 5328.58 5348.58 5338.58 7 2.435057e-01 0.03768830 0.147555108 0.07564568 5.592470e-03 42: 5331.16 5351.16 5341.16 7 1.506886e-01 0.03812754 0.210258699 0.10821693 4.748062e-03 43: 5333.74 5353.74 5343.74 7 -8.457396e-02 0.04203885 0.498438355 0.28478388 2.657807e-03 44: 5336.32 5356.32 5346.32 7 -7.010584e-02 0.04407872 0.471193766 0.27563851 2.976190e-03 45: 5338.90 5358.90 5348.90 7 3.356662e-01 0.03913660 0.100918300 0.05396996 6.810631e-03 46: 5341.48 5361.48 5351.48 7 5.571886e-01 0.03769020 0.032860686 0.01839957 9.551495e-03 47: 5344.06 5364.06 5354.06 7 5.543731e-01 0.03777052 0.033425890 0.01873703 9.523810e-03 48: 5346.64 5366.64 5356.64 7 6.568462e-01 0.04090850 0.016679391 0.01024635 1.252769e-02 49: 5349.22 5369.22 5359.22 7 4.263635e-01 0.04784925 0.066663521 0.04074626 9.689922e-03 50: 5351.80 5371.80 5361.80 7 2.805982e-01 0.03460683 0.127153338 0.06281590 5.481728e-03 51: 5354.38 5374.38 5364.38 7 3.716517e-01 0.03324983 0.086098144 0.04219813 6.146179e-03 52: 5356.96 5376.96 5366.96 7 1.502772e-01 0.03293035 0.210579418 0.09951647 4.097453e-03 53: 5359.54 5379.54 5369.54 7 3.375742e-01 0.03655294 0.100088760 0.05199627 6.381506e-03 54: 5362.12 5382.12 5372.12 7 -1.007327e-01 0.03472663 0.531670980 0.27614749 2.021041e-03 55: 5364.70 5384.70 5374.70 7 5.143262e-01 0.04270061 0.042184448 0.02514076 1.003599e-02 56: 5367.28 5387.28 5377.28 7 1.180375e-01 0.05043403 0.237081066 0.14603312 5.869324e-03 57: 5369.86 5389.86 5379.86 7 3.526957e-01 0.05256444 0.093690478 0.05958580 9.413068e-03 58: 5372.44 5392.44 5382.44 7 -1.143248e-01 0.06697669 0.562404900 0.40786204 3.599114e-03 59: 5375.02 5395.02 5385.02 7 -1.380467e-01 0.06582006 0.624143540 0.45527492 2.976190e-03 60: 5377.60 5397.60 5387.60 7 -1.437437e-01 0.08277209 0.640981907 0.80007275 -3.557586e-03 61: 5380.18 5400.18 5390.18 7 6.865351e-02 0.07101868 0.283553003 0.39287969 -7.392027e-03 62: 5382.76 5402.76 5392.76 7 -1.557062e-01 0.06968790 0.679826634 0.87463729 -2.643965e-03 63: 5385.34 5405.34 5395.34 7 -5.672857e-02 0.06614976 0.447786680 0.61411355 -4.720377e-03 64: 5387.92 5407.92 5397.92 7 -1.978539e-01 0.05568786 0.928270615 0.68172236 4.568106e-04 65: 5390.50 5410.50 5400.50 7 -1.682576e-01 0.05373328 0.727529980 0.98771567 -1.716501e-03 66: 5393.08 5413.08 5403.08 7 3.659413e-01 0.03870980 0.088336383 0.04816083 7.087486e-03 67: 5395.66 5415.66 5405.66 7 -5.189744e-02 0.02893483 0.439709327 0.19602199 2.104097e-03 68: 5398.24 5418.24 5408.24 7 6.657153e-02 0.02820253 0.285688876 0.12137978 2.920819e-03 69: 5400.82 5420.82 5410.82 7 -3.417829e-02 0.02978965 0.411610972 0.18697576 2.311739e-03 70: 5403.40 5423.40 5413.40 6 -1.689106e-01 0.03205016 0.626213128 0.38442443 1.915836e-03 71: 5405.98 5425.98 5415.98 5 -3.324952e-01 0.03383253 0.968080223 0.75067625 2.325581e-04 72: 5408.56 5428.56 5418.56 4 4.196229e-01 0.01811905 0.217004528 0.29310481 -7.906977e-03 join join time N adj_r_sqr RMSE p_val_y p_val_intercept slope
编辑：OP将link发布到另一个示例数据集，该数据集发生错误。原因是某些组的大小太小，仅包含一个数据点，因此线性模型没有斜率。
代码的更新版本可以解决此问题，并防止出现越界错误。
前两列显示了所涵盖的年份范围；如果不再需要它们，可以将其删除。
N是lm()计算中包含的行数。如果NA，OP已请求返回N < 5。此后也可以完成。
# define parameters
time_1 <- -10
time_2 <- 10
n_min <- 5L
# coerce to data.table
result <- setDT(dat)[
# create helper columns
, `:=`(join = time, start = time + time_1, end = time + time_2)][
# non-equi join and aggregate each interval
dat, on = .(join >= start, join <= end), by = .EACHI, {
# do computations within interval
lmodel <- lm(var1 ~ time)
lsumm <- summary(lmodel)
# create list of results, finally
.(time = i.time,
N = .N,
adj_r_sqr = lsumm$adj.r.squared,
RMSE = sqrt(mean(lsumm$residuals^2)),
p_val_y = if (.N > 1) lsumm$coefficients[2,4] else NA_real_,
p_val_intercept = lsumm$coefficients[1,4],
slope = coef(lmodel)[2]
)
}]
# clean-up result
computed_cols <- setdiff(names(result), c(names(dat), "N"))
result[
# remove join columns
, -(1:2)][
# put NA if too few data points
N < n_min, (computed_cols) := NA][]
time N adj_r_sqr RMSE p_val_y p_val_intercept slope 1: 5262.00 9 -1.412484e-01 0.06749996 0.923658051 0.76541424 8.050483e-04 2: 5263.20 9 -1.412484e-01 0.06749996 0.923658051 0.76541424 8.050483e-04 3: 5264.40 10 -1.248329e-01 0.06411770 0.973340896 0.77035143 2.202740e-04 ... 70: 5413.40 6 -1.689106e-01 0.03205016 0.626213128 0.38442443 1.915836e-03 71: 5415.98 5 -3.324952e-01 0.03383253 0.968080223 0.75067625 2.325581e-04 72: 5418.56 4 NA NA NA NA NA time N adj_r_sqr RMSE p_val_y p_val_intercept slope
OP也曾要求
另一个问题可能是对数据进行子集化，而不是 滚动，而不是再次使用时间变量进行拼接。
# define parameters
n_min <- 5L
t_len <- 20
# create "pretty" breaks
breaks <- setDT(dat)[, seq(floor(min(time)/t_len)*t_len, max(time) + t_len, t_len)]
dat[, {
lmodel <- lm(var1 ~ time)
lsumm <- summary(lmodel)
.(t_min = min(time),
t_max = max(time),
N = .N,
adj_r_sqr = lsumm$adj.r.squared,
RMSE = sqrt(mean(lsumm$residuals^2)),
p_val_y = if (.N > 1) lsumm$coefficients[2,4] else NA_real_,
p_val_intercept = lsumm$coefficients[1,4],
slope = coef(lmodel)[2]
)
}, by = .(cut(time, breaks))]
cut t_min t_max N adj_r_sqr RMSE p_val_y p_val_intercept slope 1: (5.26e+03,5.28e+03] 5262.00 5278.74 14 -0.08098718 0.06260179 0.87447674 0.52663574 0.0005619086 2: (5.28e+03,5.3e+03] 5280.12 5299.88 12 0.33144858 0.06512008 0.02934449 0.06866916 -0.0087040163 3: (5.3e+03,5.32e+03] 5302.46 5317.94 7 -0.19007362 0.03206618 0.84623551 0.69966581 -0.0005675526 4: (5.32e+03,5.34e+03] 5320.52 5338.58 8 -0.16348201 0.06360759 0.90221583 0.62280945 0.0005629384 5: (5.34e+03,5.36e+03] 5341.16 5359.22 8 0.54042068 0.03778046 0.02285248 0.01024298 0.0079272794 6: (5.36e+03,5.38e+03] 5361.80 5379.86 8 0.20369592 0.03803705 0.14585425 0.06090703 0.0043881506 7: (5.38e+03,5.4e+03] 5382.44 5397.92 7 0.06865351 0.07101868 0.28355300 0.39287969 -0.0073920266 8: (5.4e+03,5.42e+03] 5400.50 5418.56 8 -0.04065894 0.02837687 0.42672518 0.14267960 0.0016703581