我想通过Tuckey HSD的结果合并因子的水平。
因子有16个级别：
levels(aov_dat_mssubclass$MSSubClass)
[1] "20" "30" "40" "45" "50" "60" "70" "75" "80" "85" "90" "120"
[13] "150" "160" "180" "190"
如果执行Tukey HSD，则会得到以下结果：
aov_mssubclass = aov(SalePrice~MSSubClass, aov_dat_mssubclass)
thsd_mssubclass = TukeyHSD(aov_mssubclass)
library(knitr)
kable(head(thsd_mssubclass))
| | diff| lwr| upr| p adj|
|:-------|---------:|----------:|---------:|---------:|
|180-30 | 0.0679407| -0.3096859| 0.4455673| 0.9999992|
|45-30 | 0.1452240| -0.2038366| 0.4942847| 0.9861887|
|190-30 | 0.3010442| 0.0569789| 0.5451094| 0.0027207|
|90-30 | 0.3471111| 0.1421648| 0.5520574| 0.0000011|
|160-30 | 0.3733815| 0.1789057| 0.5678574| 0.0000000|
|50-30 | 0.3807276| 0.2173256| 0.5441297| 0.0000000|
现在，我想结合那些基于0.01显着性水平而没有不同的水平。例如，我将合并前两行（将“ 180”，“ 45”和“ 30”合并为“ A”）。问题在于，不仅对必须在显着性水平0.01上匹配（180 -30/45 -30），而且其他因子水平也要匹配（180和45）。如果因子水平不错，要找到这些组合将非常困难。
在r中有没有办法解决这个问题？
as.data.frame(thsd_mssubclass$MSSubClass) %>% dput()
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"60-75", "60-120"))