【发布时间】:2017-05-02 23:15:31
【问题描述】:
我正在尝试使用我用 ddply 子集的数据集(通过变量“站点”和“类别”)对变量“大小”运行 Shapiro Wilks 测试,但我不断收到错误消息.
这是我的数据集 (d) 的示例。我有 9 个类别和 13 个站点的 4237 个观察结果:
Site Genus Size Category
Arn01 ACR 4 ACR
Arn01 ACR 7 ACR
Arn02 ACR 3 ACR
我为 Shapiro Wilks 创建了一个函数:
shap.w <- function(input){ #shapiro wilk test function
if(sum(!is.na(input$Size)) > 3 & sum(!is.na(input$Size)) < 5000){
p <- shapiro.test(input$Size)$p.value
return(p)}else{return(NA)} }
然后,我尝试使用 ddply 将该函数应用于我的数据子集:
sw_test <- ddply(d, .(Site, Category), .fun = shap.w)
但是当我这样做时,我收到一条错误消息:
Error in shapiro.test(input$Size) : all 'x' values are identical
即使他们显然不是。任何帮助/建议将不胜感激。
ETA 输出
dput(d[1:20,]):
> dput(d[1:20,])
structure(list(Site = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("Arn01n",
"Arn02n", "Arn03n", "Arn04n", "Arn05n", "Arn06n", "Arn07n", "Arn08n",
"Arn09n", "Arn10n", "Arn11n", "Arn12n", "Arn13n"), class = "factor"),
Genus = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 30L, 30L, 30L, 30L), .Label = c("ACA",
"ACR", "AST", "COS", "CYP", "ECH", "FUN", "FVA", "FVT", "GAR",
"GON", "HEL", "HYD", "ISO", "LEA", "LEO", "LEP", "LOB", "MER",
"MNT", "MST", "MYC", "PAV", "PBR", "PLA", "PLAT", "POC",
"POD", "PRE", "PRM", "PRS", "PSA", "SAR", "STY"), class = "factor"),
Size = c(4, 2, 4, 4, 3, 5, 5, 4, 4, 4, 4, 3, 6, 3, 4, 5,
2, 3, 3, 6), Category = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 8L, 8L, 8L, 8L), .Label = c("ACR",
"FAV", "FUN", "HEL", "ISO", "MNT", "POC", "PRM", "PRS"), class = "factor")),
.Names = c("Site",
"Genus", "Size", "Category"), row.names = c(NA, 20L), class = "data.frame")`
table(d$Size) 的 ETA 输出
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 22 23 24 25 26 27 28 29 30 31 33 35 36 37 38 39
14 271 525 548 521 424 201 206 50 357 23 95 36 7 171 11 14 30 4 145 11 21 5 46 4 1 5 1 95 1 2 31 3 1 2 1
40 41 42 43 44 45 46 48 50 51 53 55 56 57 60 62 63 65 66 70 72 75 76 80 82 83 85 88 90 94 95 100 105 110 120 125
80 1 9 3 4 22 1 4 42 1 1 4 1 3 64 3 5 9 4 13 1 2 1 20 2 2 2 1 5 1 2 17 1 2 6 2
128 130 143 150 155 160 180 200 230 300 890 920
1 1 1 1 1 1 1 2 1 1 1 1
【问题讨论】:
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评论不用于扩展讨论;这个对话是moved to chat。