Question

我有以下数据：

set.seed(10)
len =100
vnum=rnorm(len)
vint=sample(1:10,len,replace=TRUE)
vbin=sample(letters[1:2],len,replace=TRUE)
dd = data.frame(vnum, vint, vbin)
head(dd)
head(dd)
          vnum vint vbin
1:  0.01874617    3    a
2: -0.18425254    6    a
3: -1.37133055    7    a
4: -0.59916772    9    b
5:  0.29454513    2    b
6:  0.38979430    7    b

我需要通过t.test来比较vbin分组的vnum。我还需要对vint做同样的事情，并获得如下输出：需要输出：

        mean_a  sd_a  mean_b  sd_b  ttest_p.value
vnum
vint

我试过了：

aggregate(vnum~vbin, dd, function(x) mean(x) )
  vbin       vnum
1    a -0.1390285
2    b -0.1339682

t(aggregate(vnum~vbin, dd, mean ))
     [,1]         [,2]        
vbin "a"          "b"         
vnum "-0.1390285" "-0.1339682"

但无法在一个输出中获得所有功能。

Answer 1

定义自己的函数以生成所需的输出：

myt <- function(x, y, data) {
  z <- t.test(y ~ x, data)
  x <- as.character(substitute(x))
  y <- as.character(substitute(y))
  c(mean_a = z$estimate[1], 
    sd_a = sd(dd[[y]][dd[[x]]=="a"]), 
    mean_b = z$estimate[2], 
    sd_b = sd(dd[[y]][dd[[x]]=="b"]), 
    test_p.value = z$p.value)
}

然后你可以为你需要的每个变量运行它：

rbind(myt(vbin, vnum, dd), myt(vbin, vint, dd))
##      mean_a.mean in group a      sd_a mean_b.mean in group b     sd_b test_p.value
## [1,]             -0.1390285 0.8427401             -0.1339682 1.042787    0.9788149
## [2,]              6.4509804 2.8587670              5.7551020 3.003683    0.2385821

需要多功能输出

1 个答案: