r - 如何解决重复测量方差分析中使用 ezANOVA 函数的错误？

Question

我正在尝试通过四年（“年”）的处理（“C”和“N”添加）来研究草原上莎草植物的苔藓（“Bi”）。

每个处理有三个级别（“C0”“C1”“C2”和“N0”“N1”“N2”）。我尝试多次使用 R 中的 ezANOVA 函数通过重复测量 ANOVA 方法分析我的数据。但它总是失败，我找不到哪里出错了。

这是有关我的数据的一些信息。

 C  N   Year    Bi
C0  N0  2011    44.5
C0  N0  2011    59.5
C0  N0  2011    78.4
C0  N0  2011    60.4
C0  N1  2011    102.8
C0  N1  2011    107.7
C0  N1  2011    100.2
C0  N1  2011    80.6
C0  N2  2011    127
C0  N2  2011    151.6
C0  N2  2011    139.6
C0  N2  2011    175.2
C1  N0  2011    142.4
C1  N0  2011    135.2
C1  N0  2011    138.8
C1  N0  2011    132.8
C1  N1  2011    123.4
C1  N1  2011    99.4
C1  N1  2011    107.3
C1  N1  2011    137.26
C1  N2  2011    100.2
C1  N2  2011    112
C1  N2  2011    114.4
C1  N2  2011    108.4
C2  N0  2011    119
C2  N0  2011    136.08
C2  N0  2011    169.6
C2  N0  2011    122.8
C2  N1  2011    105.3
C2  N1  2011    97.6
C2  N1  2011    99.2
C2  N1  2011    94.2
C2  N2  2011    88.2
C2  N2  2011    97.6
C2  N2  2011    112.2
C2  N2  2011    116.8
C0  N0  2012    44.5
C0  N0  2012    59.5
C0  N0  2012    78.4
C0  N0  2012    60.4
C0  N1  2012    102.8
C0  N1  2012    107.7
C0  N1  2012    100.2
C0  N1  2012    80.6
C0  N2  2012    127
C0  N2  2012    151.6
C0  N2  2012    139.6
C0  N2  2012    175.2
C1  N0  2012    142.4
C1  N0  2012    135.2
C1  N0  2012    138.8
C1  N0  2012    132.8
C1  N1  2012    123.4
C1  N1  2012    99.4
C1  N1  2012    107.3
C1  N1  2012    137.26
C1  N2  2012    100.2
C1  N2  2012    112
C1  N2  2012    114.4
C1  N2  2012    108.4
C2  N0  2012    119
C2  N0  2012    136.08
C2  N0  2012    169.6
C2  N0  2012    122.8
C2  N1  2012    105.3
C2  N1  2012    97.6
C2  N1  2012    99.2
C2  N1  2012    94.2
C2  N2  2012    88.2
C2  N2  2012    97.6
C2  N2  2012    112.2
C2  N2  2012    116.8
C0  N0  2013    44.5
C0  N0  2013    59.5
C0  N0  2013    78.4
C0  N0  2013    60.4
C0  N1  2013    102.8
C0  N1  2013    107.7
C0  N1  2013    100.2
C0  N1  2013    80.6
C0  N2  2013    127
C0  N2  2013    151.6
C0  N2  2013    139.6
C0  N2  2013    175.2
C1  N0  2013    142.4
C1  N0  2013    135.2
C1  N0  2013    138.8
C1  N0  2013    132.8
C1  N1  2013    123.4
C1  N1  2013    99.4
C1  N1  2013    107.3
C1  N1  2013    137.26
C1  N2  2013    100.2
C1  N2  2013    112
C1  N2  2013    114.4
C1  N2  2013    108.4
C2  N0  2013    119
C2  N0  2013    136.08
C2  N0  2013    169.6
C2  N0  2013    122.8
C2  N1  2013    105.3
C2  N1  2013    97.6
C2  N1  2013    99.2
C2  N1  2013    94.2
C2  N2  2013    88.2
C2  N2  2013    97.6
C2  N2  2013    112.2
C2  N2  2013    116.8
C0  N0  2014    44.5
C0  N0  2014    59.5
C0  N0  2014    78.4
C0  N0  2014    60.4
C0  N1  2014    102.8
C0  N1  2014    107.7
C0  N1  2014    100.2
C0  N1  2014    80.6
C0  N2  2014    127
C0  N2  2014    151.6
C0  N2  2014    139.6
C0  N2  2014    175.2
C1  N0  2014    142.4
C1  N0  2014    135.2
C1  N0  2014    138.8
C1  N0  2014    132.8
C1  N1  2014    123.4
C1  N1  2014    99.4
C1  N1  2014    107.3
C1  N1  2014    137.26
C1  N2  2014    100.2
C1  N2  2014    112
C1  N2  2014    114.4
C1  N2  2014    108.4
C2  N0  2014    119
C2  N0  2014    136.08
C2  N0  2014    169.6
C2  N0  2014    122.8
C2  N1  2014    105.3
C2  N1  2014    97.6
C2  N1  2014    99.2
C2  N1  2014    94.2
C2  N2  2014    88.2
C2  N2  2014    97.6
C2  N2  2014    112.2
C2  N2  2014    116.8

代码：

dataLong1 <- data.frame(a1=c(data3$C), a2=c(data3$N), a3=c(data3$Year), 
                        a4=c(data3$Bi))
dataLongLong <- reshape(dataLong1, varying=list(c("a1", "a2","a3")), 
                       v.names="Treatment", times=c(1,2,3), direction="long")
dataLong1$a1 <- factor(dataLong1$a1)
colnames(dataLongLong)
dataLong1$a3 <- factor(dataLong1$a3)
dataLongLong$C <- gl(3, 12, length=144, labels=c("C0", "C1", "C2"), ordered=FALSE)
dataLongLong$N <- gl(3, 4, length=3*48, labels=c("N0", "N1", "N2"), ordered=FALSE)
dataLongLong$Year <- gl(4, 36, labels=c("2011", "2012","2013","2014"), ordered=FALSE)

library(ez)
ezANOVA(dataLongLong, dv=.(a4), wid=.(id), within=.(C, N, Year), detailed=TRUE)

而且我也尝试了很多次并在函数中更改了许多参数，但都失败了。

Warning: Collapsing data to cell means. *IF* the requested effects are a 
subset of the full design, you must use the "within_full" argument, else 
results may be inaccurate.

Error in ezANOVA_main(data = data, dv = dv, wid = wid, within = within,  : 
  One or more cells is missing data. Try using ezDesign() to check your 
data.

标签： r