我在一个数据框内跨多个组运行简单的单向ANOVA .
数据框可在此处获取:https://www.dropbox.com/s/6nsjk4l1pgiwal3/cut1.csv?dl=0
>download.file('https://www.dropbox.com/s/6nsjk4l1pgiwal3/cut1.csv?raw=1', destfile = "cut1.csv", method = "auto")
> data <- read.csv("cut1.csv")
> cut1 <- data %>% mutate(Plot = as.factor(Plot), Block = as.factor(Block), Cut = as.factor(Cut))
> str(cut1)
'data.frame': 160 obs. of 6 variables:
$ Plot : Factor w/ 16 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Block : Factor w/ 4 levels "1","2","3","4": 1 1 1 1 2 2 2 2 3 3 ...
$ Treatment : Factor w/ 4 levels "AN","C","IU",..: 4 2 3 1 1 3 4 2 3 1 ...
$ Cut : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
$ Measurement: Factor w/ 10 levels "ADF","Ash","Crude_Protein",..: 5 5 5 5 5 5 5 5 5 5 ...
$ Value : num 956 965 961 963 955 ...
我使用了this SO question中的一些代码来启用aov函数以应用于 Measurement
因子的每个级别:
anova_1<- sapply(unique(as.character(cut1$Measurement)),
function(meas)aov(Value~Treatment+Block,cut1,subset=(Measurement==meas)),
simplify=FALSE,USE.NAMES=TRUE)
summary_1 <- lapply(anova_1, summary)
我可以通过 summary_1
手动查看,但理想情况下我想要做的是将 Measurement
因子的每个级别的p值提取到一个数据帧中,然后我可以进行过滤,这样我只能看到哪些<0.5 . 然后我会在这些上运行 TukeyHSD
.
summary_1
看起来像这样(只显示前2个列表):
> str(summary_1)
List of 10
$ Dry_matter :List of 1
..$ :Classes ‘anova’ and 'data.frame': 3 obs. of 5 variables:
.. ..$ Df : num [1:3] 3 3 9
.. ..$ Sum Sq : num [1:3] 359 167 612
.. ..$ Mean Sq: num [1:3] 119.8 55.5 68
.. ..$ F value: num [1:3] 1.761 0.816 NA
.. ..$ Pr(>F) : num [1:3] 0.224 0.517 NA
..- attr(*, "class")= chr [1:2] "summary.aov" "listof"
$ Crude_Protein:List of 1
..$ :Classes ‘anova’ and 'data.frame': 3 obs. of 5 variables:
.. ..$ Df : num [1:3] 3 3 9
.. ..$ Sum Sq : num [1:3] 306 721 1606
.. ..$ Mean Sq: num [1:3] 102 240 178
.. ..$ F value: num [1:3] 0.572 1.347 NA
.. ..$ Pr(>F) : num [1:3] 0.647 0.319 NA
..- attr(*, "class")= chr [1:2] "summary.aov" "listof"
我可以从 summary_1
中的一个列表中提取p值,如下所示:
> summary_1$OAH[[1]][,5][1]
[1] 0.4734992
但是,我不知道如何从所有嵌套列表中提取并放置在数据框中 .
非常需要任何帮助 .
2 回答
您可以将包
broom
与dplyr
结合使用Anova
来应用Anova
,并以整齐的格式将输出分配给data.frame
.这是香草R的解决方案:
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