我正在尝试匹配rms包功能＆＃39; smean.cl.boot＆＃39;使用dplyr引导置信区间（方法1）。但是，我无法在dplyr调用中引导单列。有人可以告诉我如何重新采样单个列，获取每个组的均值，并最终估计该列的分位数？
请考虑这个小数据集。我在使用plyr包来估计分位数之前总结了分组数据，但由于某种原因，我得到的结果与rms包不同。
require(rms)
require(dplyr)
require(plyr)
fish <- structure(list(wk = c(1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5,
5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8,
8, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10), pd = c(317.308439683869,
0, 126.719553152898, NA, NA, NA, NA, 2671.6, 3540.6976744186,
1270.35740604274, 1067.69362430466, 688.099646524154, 317.444499806234,
420.941879550524, 280.475476696762, 250.681324772507, 159.048160622895,
258.125109208457, 450.868907331836, 0, 120.83949704142, 244.794377928162,
0, 226.610029158717, 0, NA, NA, NA, 0, 0, 776.419523429887, 0,
0, 5572.7956254272, NA, 0, 235.711495898971, 0, 0, 0, 0, 0, 0,
158.796322731685, 0, 0, 0, 278.669954021457, 0, 0, 0, 0, 0, 0,
0, NA, 623.451776649746, 0, 440.704258124564, 0, 69.0758191406588,
0, 0, 51.2873010185801, 26.8224496254879, 104.366153205662, 0,
71.1744651415584, 0, 0)), .Names = c("wk", "pd"), row.names = c(NA,
70L), class = "data.frame")
fish
# Method 1
fish <- na.omit(fish)
x <- data.frame(boot=1:1000) %>%
group_by(boot) %>%
do(sample_n(fish, nrow(fish), replace=TRUE)) %>%
group_by(boot,wk)
plyr::ddply(x,'wk',summarise,Mean=mean(pd),lower=quantile(pd,prob=0.025),upper=quantile(pd,prob=0.975))
wk Mean Lower Upper
1 1 148.00933 0.000000 317.30844
2 2 1425.26210 643.274777 2322.42315
3 3 217.14835 125.537283 304.37517
4 4 117.85110 0.000000 235.70220
5 5 1058.20252 0.000000 2915.80107
6 6 33.67307 0.000000 101.01921
7 7 62.49518 0.000000 142.11517
8 8 0.00000 0.000000 0.00000
9 9 161.89026 9.867974 356.25816
10 10 36.23577 11.133767 66.07178
#Method 2
boots <- fish %>%
group_by(wk) %>%
do(data.frame(rbind(smean.cl.boot(.$pd))))
data.frame(boots)
wk Mean lower upper
1 1 145.71624 0.0000 317.3084
2 2 1490.79383 317.4445 3540.6977
3 3 215.44592 0.0000 450.8689
4 4 124.15618 0.0000 244.7944
5 5 976.88218 0.0000 5572.7956
6 6 27.88334 0.0000 235.7115
7 7 52.79893 0.0000 278.6700
8 8 0.00000 0.0000 0.0000
9 9 165.98724 0.0000 623.4518
10 10 35.66937 0.0000 104.3662
我错过了方法1的一步吗？