Question

我正尝试通过一项采用分割图设计的实验以及随着时间的推移采用的几种测量方法来拟合重复测量方差分析。

实验设计如下：

我在该字段中有 9个区块。在每个块中，我有两个表示分割因子的子图（命名为 trt ，“有”或“没有”特定处理）。在每个子图中（有或没有），我都有另一个具有两个四边形的分割因子（分别命名为 sp ，“物种1”，“物种2”）。在每个样方中，我在研究中每种物种都有两棵幼苗（每棵幼苗都有唯一的ID标识）。最终，我在实验中对每棵幼苗的给定响应变量进行了四个时间（周）重复测量。

因此，我有9个块，每个块内有2种处理，每个处理内有2种，每个种有2种幼苗。监测4周。

我想了解时间* trt * sp是否影响我的响应变量。

考虑到我的实验设计，以下代码是否正确地适用于aov重复测量拆分-分裂-绘图模型的误差项？

fit <- aov(response ~ time * sp * trt + Error(block/trt/sp/id), data = d3)
summary(fit)

Error: block
          Df Sum Sq Mean Sq F value Pr(>F)
Residuals  1  11.29   11.29               

Error: block:trt
    Df Sum Sq Mean Sq
trt  1  0.114   0.114

Error: block:trt:sp
       Df Sum Sq Mean Sq
sp      1  61.14   61.14
sp:trt  1  10.27   10.27

Error: block:trt:sp:id
          Df Sum Sq Mean Sq F value Pr(>F)
sp         1   7.16   7.159   2.299  0.141
trt        1   1.07   1.072   0.344  0.562
sp:trt     1   2.18   2.181   0.701  0.410
Residuals 28  87.17   3.113               

Error: Within
             Df Sum Sq Mean Sq F value Pr(>F)
time          1   0.38  0.3781   0.237  0.627
time:sp       1   0.93  0.9317   0.585  0.446
time:trt      1   1.91  1.9117   1.201  0.276
time:sp:trt   1   2.73  2.7257   1.712  0.194
Residuals   104 165.59  1.5922

此代码会导致以下警告消息：

Warning message:
In aov(response ~ time * sp * trt + Error(block/trt/sp/id), data = d3) :
  Error() model is singular

我非常感谢您在此问题上的任何帮助。非常感谢你！如果需要，我很乐意提供任何进一步的细节。

数据和图

说明结果

可重复性数据显示如下（打印输出）：

编辑1（更新的数据集）：

structure(list(block = c(1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 
3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 
7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 1L, 1L, 1L, 1L, 2L, 
2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 
6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 1L, 
1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 
5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 
9L, 9L, 9L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 
4L, 4L, 4L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 8L, 
8L, 8L, 8L, 9L, 9L, 9L, 9L), trt = c("without", "without", "with", 
"with", "with", "with", "without", "without", "with", "with", 
"without", "without", "with", "with", "without", "without", "with", 
"with", "without", "without", "with", "with", "without", "without", 
"without", "without", "with", "with", "with", "with", "without", 
"without", "without", "without", "with", "with", "without", "without", 
"with", "with", "with", "with", "without", "without", "with", 
"with", "without", "without", "with", "with", "without", "without", 
"with", "with", "without", "without", "with", "with", "without", 
"without", "without", "without", "with", "with", "with", "with", 
"without", "without", "without", "without", "with", "with", "without", 
"without", "with", "with", "with", "with", "without", "without", 
"with", "with", "without", "without", "with", "with", "without", 
"without", "with", "with", "without", "without", "with", "with", 
"without", "without", "without", "without", "with", "with", "with", 
"with", "without", "without", "without", "without", "with", "with", 
"without", "without", "with", "with", "with", "with", "without", 
"without", "with", "with", "without", "without", "with", "with", 
"without", "without", "with", "with", "without", "without", "with", 
"with", "without", "without", "without", "without", "with", "with", 
"with", "with", "without", "without", "without", "without", "with", 
"with"), sp = c("species 1", "species 2", "species 2", "species 1", 
"species 2", "species 1", "species 2", "species 1", "species 1", 
"species 2", "species 2", "species 1", "species 2", "species 1", 
"species 1", "species 2", "species 1", "species 2", "species 2", 
"species 1", "species 2", "species 1", "species 1", "species 2", 
"species 1", "species 2", "species 1", "species 2", "species 1", 
"species 2", "species 2", "species 1", "species 2", "species 1", 
"species 2", "species 1", "species 1", "species 2", "species 2", 
"species 1", "species 2", "species 1", "species 2", "species 1", 
"species 1", "species 2", "species 2", "species 1", "species 2", 
"species 1", "species 1", "species 2", "species 1", "species 2", 
"species 2", "species 1", "species 2", "species 1", "species 1", 
"species 2", "species 1", "species 2", "species 1", "species 2", 
"species 1", "species 2", "species 2", "species 1", "species 2", 
"species 1", "species 2", "species 1", "species 1", "species 2", 
"species 2", "species 1", "species 2", "species 1", "species 2", 
"species 1", "species 1", "species 2", "species 2", "species 1", 
"species 2", "species 1", "species 1", "species 2", "species 1", 
"species 2", "species 2", "species 1", "species 2", "species 1", 
"species 1", "species 2", "species 1", "species 2", "species 1", 
"species 2", "species 1", "species 2", "species 2", "species 1", 
"species 2", "species 1", "species 2", "species 1", "species 1", 
"species 2", "species 2", "species 1", "species 2", "species 1", 
"species 2", "species 1", "species 1", "species 2", "species 2", 
"species 1", "species 2", "species 1", "species 1", "species 2", 
"species 1", "species 2", "species 2", "species 1", "species 2", 
"species 1", "species 1", "species 2", "species 1", "species 2", 
"species 1", "species 2", "species 1", "species 2", "species 2", 
"species 1", "species 2", "species 1", "species 2", "species 1"
), id = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 
24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 
28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 36L, 1L, 2L, 3L, 4L, 
5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
19L, 20L, 21L, 22L, 23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 
32L, 33L, 34L, 35L, 36L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 
10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
23L, 24L, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 
36L), .Label = 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"), class = "factor"), time = 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, 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, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L), 
    response = c(3.1, 5.7, 4.8, 2.9, 5, 3.9, 5.7, 4.2, 3.6, 4.4, 
    3.9, 2.9, 3.2, 7.5, 4.3, 4.8, 3, 4.9, 5.6, 3.9, 4.1, 4.2, 
    2.8, 3.9, 3.9, 7.5, 4, 4.3, 3.1, 7.1, 5.8, 2.5, 6.4, 4.5, 
    5, 3.6, 3.1, 5.2, 3.6, 2.9, 5.2, 4.6, 4.7, 4.3, 3.9, 4.4, 
    4.2, 3.6, 3.2, 2.7, 3.4, 5.6, 2.8, 6, 5.1, 3.7, 4.1, 3.4, 
    3, 4.1, 3.2, 6.7, 3.1, 3.8, 2.9, 6.9, 5.6, 2.1, 5.6, 4.8, 
    4.8, 2.7, 3, 5.5, 3.4, 3.1, 5.1, 5, 5, 4.8, 4, 4, 4, 2.6, 
    3, 3, 3.9, 6, 3, 7, 5, 3.5, 4, 4, 3, 4, 3, 6.5, 4, 5, 4, 
    8, 6, 2.2, 5.9, 4, 6, 3, 3, 5, 3.5, 3, 5, 4, 4, 2, 6.5, 4, 
    5, 2, 3, 3, 3, 5.5, 2, 5, 6, 2.5, 5, 2.5, 3, 5, 3, 5.5, 3, 
    2, 3, 6, 5, 5, 5, 3, 4, 15)), row.names = c(NA, -144L), class = "data.frame")

R中的重复测量方差分析（分割图设计）

数据和图

0 个答案: