r - 如何取 + 或 - SD 的平均值

Question

我有数据，其中 [1] 因变量取自受控和自变量 [2]，然后是自变量。和mean取自SD[1]。

(a) 这是以下结果SD：

   Year        Species Pop_Index                      
1  1994   Corn Bunting  2.082483                        
5  1998   Corn Bunting  2.048155                     
10 2004   Corn Bunting  2.061617                      
15 2009   Corn Bunting  2.497792                       
20 1994      Goldfinch  1.961236 
25 1999      Goldfinch  1.995600 
30 2005      Goldfinch  2.101403 
35 2010      Goldfinch  2.138496 
40 1995 Grey Partridge  2.162136

(b) 结果mean：

   Year        Species Pop_Index
1  1994   Corn Bunting  2.821668
5  1998   Corn Bunting  2.916975
10 2004   Corn Bunting  2.662797
15 2009   Corn Bunting  4.171538
20 1994      Goldfinch  3.226108
25 1999      Goldfinch  2.452807
30 2005      Goldfinch  2.954816
35 2010      Goldfinch  3.386772
40 1995 Grey Partridge  2.207708

(c) 这是以下代码SD：

structure(list(Year = c(1994L, 1998L, 2004L, 2009L, 1994L, 1999L, 
2005L, 2010L, 1995L), Species = structure(c(1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 3L), .Label = c("Corn Bunting", "Goldfinch", "Grey Partridge"
), class = "factor"), Pop_Index = c(2.0824833420524, 2.04815530904537, 
2.06161673349657, 2.49779159320587, 1.96123572400404, 1.99559986715288, 
2.10140285528351, 2.13849611018009, 2.1621364896722)), row.names = c(1L, 
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L), class = "data.frame")

(d) 这是代码mean：

structure(list(Year = c(1994L, 1998L, 2004L, 2009L, 1994L, 1999L, 
2005L, 2010L, 1995L), Species = structure(c(1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 3L), .Label = c("Corn Bunting", "Goldfinch", "Grey Partridge"
), class = "factor"), Pop_Index = c(2.82166841455814, 2.91697463618566, 
2.66279663056763, 4.17153795031277, 3.22610845074252, 2.45280743991572, 
2.95481600904799, 3.38677188055508, 2.20770835158744)), row.names = c(1L, 
5L, 10L, 15L, 20L, 25L, 30L, 35L, 40L), class = "data.frame")

(e) 这是多年来使用的mean代码mean Pop_Index：

df2 <- aggregate(Pop_Index ~ Year, df1, mean)

(f) 结果如下：

  Year Pop_Index
1 1994  3.023888
2 1995  2.207708
3 1998  2.916975
4 1999  2.452807
5 2004  2.662797
6 2005  2.954816
7 2009  4.171538
8 2010  3.386772

现在对我来说，用orSD做与以前相同的程序来取平均值是没有意义的。function meanSD

我在网上查了一下，发现有人用这个数据陷入了类似的困境：

月份：1 月
第 1 周平均值：67.3 标准。开发：0.8
第 2 周平均值：80.5 标准。开发：0.6
第 3 周平均值：82.4 标准。开发：0.8

并且响应：
“具有相同的样本量，这就是你所拥有的，你正在寻找的标准偏差是：
Sqrt [ (.64 + .36 + .64) / 3 ] = 0.739369”

我将如何在 R 中做到这一点，或者有另一种方法吗？因为我想绘制误差线，并且绘制的数据集类似于 (f) 的数据集，并且将 (a) 的 SD 与此相对绘制是荒谬的，因为矢量长度会有所不同。

---

原始样本中data.frame不包括几列和多行：

structure(list(GRIDREF = structure(c(1L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L), .Label = c("SP8816", "SP9212", "SP9322", 
"SP9326", "SP9440", "SP9513", "SP9632", "SP9939", "TF7133", "TF9437"
), class = "factor"), Lat = c(51.83568688, 51.83568688, 51.79908899, 
51.88880822, 51.92476157, 52.05042795, 51.80757645, 51.97818159, 
52.04057068, 52.86730817, 52.89542895), Long = c(-0.724233561, 
-0.724233561, -0.667258035, -0.650074995, -0.648996758, -0.630626734, 
-0.62349292, -0.603710436, -0.558026241, 0.538966197, 0.882597783
), Year = c(2006L, 2007L, 1999L, 2004L, 1995L, 2009L, 2011L, 
2007L, 2011L, 1996L, 2007L), Species = structure(c(4L, 7L, 5L, 
10L, 4L, 6L, 8L, 3L, 2L, 9L, 1L), .Label = c("Blue Tit", "Buzzard", 
"Canada Goose", "Collared Dove", "Greenfinch", "Jackdaw", "Linnet", 
"Meadow Pipit", "Robin", "Willow Warbler"), class = "factor"), 
    Pop_Index = c(0L, 0L, 2L, 0L, 1L, 0L, 1L, 4L, 0L, 0L, 8L)), row.names = c(1L, 
100L, 1000L, 2000L, 3000L, 4000L, 5000L, 6000L, 10000L, 20213L, 
30213L), class = "data.frame")

看看这个data.frame：

      GRIDREF      Lat       Long Year        Species Pop_Index TempJanuary
1      SP8816 51.83569 -0.7242336 2006  Collared Dove         0    2.128387
100    SP8816 51.83569 -0.7242336 2007         Linnet         0    4.233226
1000   SP9212 51.79909 -0.6672580 1999     Greenfinch         2    5.270968
2000   SP9322 51.88881 -0.6500750 2004 Willow Warbler         0    4.826452
3000   SP9326 51.92476 -0.6489968 1995  Collared Dove         1    4.390322
4000   SP9440 52.05043 -0.6306267 2009        Jackdaw         0    2.934516
5000   SP9513 51.80758 -0.6234929 2011   Meadow Pipit         1    3.841290
6000   SP9632 51.97818 -0.6037104 2007   Canada Goose         4    7.082580
10000  SP9939 52.04057 -0.5580262 2011        Buzzard         0    3.981290
20213  TF7133 52.86731  0.5389662 1996          Robin         0    3.532903
30213  TF9437 52.89543  0.8825978 2007       Blue Tit         8    7.028710

标签： rstatistics