在R中重复按列计算，并从列表中保存输出

我不经常在这里问问题，因为我通常尝试自己解决它（使用许多stackoverflow线程）。然而，我现在陷入困境，我希望能得到一些帮助

目标：

我想使用分类单元丰度表（丰度）进行分析：

以及2）环境参数文件（因子）：

但是，原始环境参数文件包含>80个因素，我们希望单独测试它们

通常，mvabund分析分为三个功能：

library(mvabund)    
mva <- mvabund(abundance)
mod <- manyglm(mva ~ factor(factors$Mutualistic), family="negative.binominal")
aov <- anova(mod, p.uni="adjusted")

第一个解决方案：

我的第一个目标是通过使用一个循环或类似的东西，逐列执行所有三个测试（即互惠、共栖和寄生）。这是我没有太多经验的事情，但我通过以下思路解决了它：

这是我采用的sapply功能，它可以工作：

sapply(1:ncol(factors), function(i) {
  m1 <- anova((manyglm(mva ~ factor(factors[,i]), family="negative.binominal")), p.uni="adjusted")
  list(multip=m1$table[c(3,4)])
  })

$multip
                          Dev Pr(>Dev)
(Intercept)                NA       NA
factor(factors[, i]) 7.983104    0.112

$multip
                          Dev Pr(>Dev)
(Intercept)                NA       NA
factor(factors[, i]) 1.862846    0.702

$multip
                          Dev Pr(>Dev)
(Intercept)                NA       NA
factor(factors[, i]) 5.655806    0.228

问题：

然而，我也想得到每个物种和每个因子的单变量结果。而这正是我目前奋斗的地方

方差分析输出的结构如下：

Multivariate test:
        Res.Df  Df.diff Dev Pr(>Dev)
(Intercept) 7                       
factor(factors$Salinity)    6   1   7.983   0.101

Univariate Tests:
        GoodBacteria    BadBacteria UnknownBacteria SomeBacteria       
        Dev Pr(>Dev)    Dev Pr(>Dev)    Dev Pr(>Dev)    Dev Pr(>Dev)
(Intercept)
factor(factors$Salinity)    5.489 0.090 2.41 0.259  0.085 0.770 0 1.000   

    Length Class      Mode     
    family       1      -none-     character
    p.uni        1      -none-     character
    test         1      -none-     character
    cor.type     1      -none-     character
    resamp       1      -none-     character
    nBoot        1      -none-     numeric  
    shrink.param 2      -none-     numeric  
    n.bootsdone  1      -none-     numeric  
    table        4      data.frame list     
    uni.p        8      -none-     numeric  
    uni.test     8      -none-     numeric 

uni.p contains the p values and species names (e.g., GoodBacteria) and uni.test the Dev values and species names. But I still don't understand how I can extract these values together with the sapply function above in order to store everything in one output or dataframe.

任何帮助都将不胜感激

更新

我把剧本改了一点

    sapply(1:ncol(factors), function(i) {
      m1 <- anova((manyglm(mva ~ factor(factors[,i]), family="negative.binominal")), p.uni="adjusted")
      unlist(data.frame(dev_p=m1$table[c(3,4)], uni_p=m1$uni.p, uni_dev=m1$uni.test))
    })

    Length Class      Mode     
    family       1      -none-     character
    p.uni        1      -none-     character
    test         1      -none-     character
    cor.type     1      -none-     character
    resamp       1      -none-     character
    nBoot        1      -none-     numeric  
    shrink.param 2      -none-     numeric  
    n.bootsdone  1      -none-     numeric  
    table        4      data.frame list     
    uni.p        8      -none-     numeric  
    uni.test     8      -none-     numeric 

uni.p contains the p values and species names (e.g., GoodBacteria) and uni.test the Dev values and species names. But I still don't understand how I can extract these values together with the sapply function above in order to store everything in one output or dataframe.

    sapply(1:ncol(factors), function(i) {
      m1 <- anova((manyglm(mva ~ factor(factors[,i]), family="negative.binominal")), p.uni="adjusted")
      unlist(data.frame(dev_p=m1$table[c(3,4)], uni_p=m1$uni.p, uni_dev=m1$uni.test))
    })

                              [,1]       [,2]         [,3]
d.Dev1                          NA         NA           NA
d.Dev2                  7.98310400 1.86284590 5.6558056350
d.Pr..Dev.1                     NA         NA           NA
d.Pr..Dev.2             0.08200000 0.64200000 0.2260000000
uni.GoodBacteria1               NA         NA           NA
uni.GoodBacteria2       0.08600000 0.62000000 0.2850000000
uni.BadBacteria1                NA         NA           NA
uni.BadBacteria2        0.25500000 0.88200000 0.9900000000
uni.UnknownBacteria1            NA         NA           NA
uni.UnknownBacteria2    0.78500000 0.78800000 0.2440000000
uni.SomeBacteria1               NA         NA           NA
uni.SomeBacteria2       1.00000000 1.00000000 1.0000000000
unidev.GoodBacteria1            NA         NA           NA
unidev.GoodBacteria2    5.48864799 1.44833012 2.4419358477
unidev.BadBacteria1             NA         NA           NA
unidev.BadBacteria2     2.40968141 0.03306757 0.0001129865
unidev.UnknownBacteria1         NA         NA           NA
unidev.UnknownBacteria2 0.08477459 0.38144821 3.2137568008
unidev.SomeBacteria1            NA         NA           NA
unidev.SomeBacteria2    0.00000000 0.00000000 0.0000000000