最近在学习tidyverse，批量方差分析之前都是用for循环，然后用formula处理模型，再把结果保存为list的形式，现在学习了tidyverse的操作，可以用pivot_longer将所有性状进行长数据转化，然后用group_by和nest变为列表，最后用map进行批量建模，用tidy进行结果的整理，更加行云流水。下面我们通过代码来看一下。
1，数据描述
> library(learnasreml)
> data(fm)
> str(fm)
'data.frame': 827 obs. of 13 variables:
$ TreeID : Factor w/ 827 levels "80001","80002",..: 1 2 3 4 5 6 7 8 9 10 ...
$ Spacing: Factor w/ 2 levels "2","3": 2 2 2 2 2 2 2 2 2 2 ...
$ Rep : Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
$ Fam : Factor w/ 55 levels "70001","70002",..: 44 44 44 15 15 2 2 10 10 10 ...
$ Plot : Factor w/ 4 levels "1","2","3","4": 1 2 4 1 4 2 4 1 2 3 ...
$ dj : num 0.334 0.348 0.354 0.335 0.322 0.359 0.368 0.358 0.323 0.298 ...
$ dm : num 0.405 0.393 0.429 0.408 0.372 0.45 0.509 0.381 0.393 0.361 ...
$ wd : num 0.358 0.365 0.379 0.363 0.332 0.392 0.388 0.369 0.347 0.324 ...
$ h1 : int 29 24 19 46 33 30 37 32 34 28 ...
$ h2 : int 130 107 82 168 135 132 124 126 153 127 ...
$ h3 : int 239 242 180 301 271 258 238 290 251 243 ...
$ h4 : int 420 410 300 510 470 390 380 460 430 410 ...
$ h5 : int 630 600 500 700 670 570 530 660 600 630 ...
数据共有827行数据，相对Fam进行方差分析。
比如对dj进行方差分析：可以看到Fam之间达到极显著水平。
问题来了，如果相对dj，dm……h5这些性状都进行方差分析，应该如何处理呢？当然可以一个性状做一个模型，我们更想批量处理一些。
2. For循环的解决方案
# for
nn = names(fm)[-c(1:5)]
nn
re = NULL
for(i in seq_along(nn)){
# i = 1
mod = aov(formula(paste0(nn[i],"~Fam + Rep")),data=fm)
re[[i]] = summary(mod)
}
re
names(re) = nn
re
结果：
> re
$dj
# A tibble: 3 x 6
term df sumsq meansq statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Fam 54 0.0912 0.00169 3.52 9.85e-15
2 Rep 4 0.0319 0.00797 16.6 4.60e-13
3 Residuals 767 0.368 0.000480 NA NA
$dm
# A tibble: 3 x 6
term df sumsq meansq statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Fam 54 0.214 0.00396 2.12 0.00000996
2 Rep 4 0.0279 0.00696 3.73 0.00515
3 Residuals 766 1.43 0.00187 NA NA
$wd
# A tibble: 3 x 6
term df sumsq meansq statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Fam 54 0.123 0.00227 3.86 3.83e-17
2 Rep 4 0.0469 0.0117 19.9 1.29e-15
3 Residuals 768 0.452 0.000588 NA NA
$h1
# A tibble: 3 x 6
term df sumsq meansq statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Fam 54 13444. 249. 4.71 4.35e-23
2 Rep 4 4623. 1156. 21.9 4.06e-17
3 Residuals 768 40572. 52.8 NA NA
$h2
# A tibble: 3 x 6
term df sumsq meansq statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Fam 54 82699. 1531. 2.05 2.31e- 5
2 Rep 4 65677. 16419. 22.0 3.11e-17
3 Residuals 768 572403. 745. NA NA
$h3
# A tibble: 3 x 6
term df sumsq meansq statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Fam 54 183935. 3406. 1.88 2.12e- 4
2 Rep 4 108005. 27001. 14.9 1.01e-11
3 Residuals 768 1393118. 1814. NA NA
$h4
# A tibble: 3 x 6
term df sumsq meansq statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Fam 54 382898. 7091. 1.17 1.97e- 1
2 Rep 4 454090. 113523. 18.7 1.12e-14
3 Residuals 765 4644446. 6071. NA NA
$h5
# A tibble: 3 x 6
term df sumsq meansq statistic p.value
<chr> <dbl> <dbl> <dbl> <dbl> <dbl>
1 Fam 54 676396. 12526. 1.58 5.79e- 3
2 Rep 4 682404. 170601. 21.6 7.01e-17
3 Residuals 765 6049952. 7908. NA NA
3. tidyverse的map解决方案
head(fm)
fm1 = fm %>% pivot_longer(-c(1:5),names_to = "trait",values_to = "y")
head(fm1)
fm1 %>% group_by(trait) %>% nest %>%
mutate(model = map(data,~aov(y ~ Spacing + Rep, data=.))) %>%
mutate(result = map(model,~tidy(.))) %>%
unnest(result)
第一步：将数据转化为长数据
第二步：将数据group_by，然后nest形成列表
第三步：使用map进行批量方差分析
第四步：使用map进行结果整理
