blocks|key|1710267|text|也许你会发现这很有用|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1710268|res+<-+sapply(split(Puromycin[,-3],++Puromycin$state),+t.test)[c(1:3,5),]
conf.level+<-+sapply(sapply(split(Puromycin[,-3],++Puromycin$state),+t.test)[4,+],+'[',+1:2)
res+<-+rbind(res,+conf.level.lower=conf.level[1,],+conf.level.upper=conf.level[2,])
res
+++++++++++++++++treated++++untreated+++
statistic++++++++4.297025+++4.206221++++
parameter++++++++23+++++++++21++++++++++
p.value++++++++++0.00026856+0.0003968191
estimate+++++++++70.96417+++55.50182++++
conf.level.lower+36.80086+++28.06095++++
conf.level.upper+105.1275+++82.94268++++|code-block|syntax|javascript|1710269|entityMap^0|0|0^^$0|@$1|2|3|4|5|6|7|I|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|J|8|@]|9|@]|A|$E|F]]|$1|G|3|-4|5|6|7|K|8|@]|9|@]|A|$]]]|H|$]]

Maybe you can find this useful

<pre><code>res &lt;- sapply(split(Puromycin[,-3], Puromycin$state), t.test)[c(1:3,5),]
conf.level &lt;- sapply(sapply(split(Puromycin[,-3], Puromycin$state), t.test)[4, ], '[', 1:2)
res &lt;- rbind(res, conf.level.lower=conf.level[1,], conf.level.upper=conf.level[2,])
res
 treated untreated 
statistic 4.297025 4.206221 
parameter 23 21 
p.value 0.00026856 0.0003968191
estimate 70.96417 55.50182 
conf.level.lower 36.80086 28.06095 
conf.level.upper 105.1275 82.94268 
</code></pre>

blocks|key|1923795|text|也许这会产生你想要的结果：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1923796|df+<-+read.table(text="Group+++var1++++var2++++var3++++var4++++var5
1+++++++++++3+++5+++7+++3+++7
1+++++++++++3+++7+++5+++9+++6
1+++++++++++5+++2+++6+++7+++6
1+++++++++++9+++5+++7+++0+++8
1+++++++++++2+++4+++5+++7+++8
1+++++++++++2+++3+++1+++6+++4
2+++++++++++4+++2+++7+++6+++5
2+++++++++++0+++8+++3+++7+++5
2+++++++++++1+++2+++3+++5+++9
2+++++++++++1+++5+++3+++8+++0
2+++++++++++2+++6+++9+++0+++7
2+++++++++++3+++6+++7+++8+++8
2+++++++++++10++6+++3+++8+++0",+header+=+TRUE)


t(sapply(df[-1],+function(x)+
+++++unlist(t.test(x~df$Group)[c("estimate","p.value","statistic","conf.int")])))|code-block|syntax|javascript|1923797|结果是：|1923798|+++++estimate.mean+in+group+1+estimate.mean+in+group+2+++p.value+statistic.t+conf.int1+conf.int2
var1+++++++++++++++++4.000000+++++++++++++++++3.000000+0.5635410+++0.5955919+-2.696975++4.696975
var2+++++++++++++++++4.333333+++++++++++++++++5.000000+0.5592911++-0.6022411+-3.104788++1.771454
var3+++++++++++++++++5.166667+++++++++++++++++5.000000+0.9028444+++0.1249164+-2.770103++3.103436
var4+++++++++++++++++5.333333+++++++++++++++++6.000000+0.7067827++-0.3869530+-4.497927++3.164593
var5+++++++++++++++++6.500000+++++++++++++++++4.857143+0.3053172+++1.0925986+-1.803808++5.089522|1923799|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

Maybe this produces the result you are looking for:

<pre><code>df &lt;- read.table(text="Group var1 var2 var3 var4 var5
1 3 5 7 3 7
1 3 7 5 9 6
1 5 2 6 7 6
1 9 5 7 0 8
1 2 4 5 7 8
1 2 3 1 6 4
2 4 2 7 6 5
2 0 8 3 7 5
2 1 2 3 5 9
2 1 5 3 8 0
2 2 6 9 0 7
2 3 6 7 8 8
2 10 6 3 8 0", header = TRUE)


t(sapply(df[-1], function(x) 
 unlist(t.test(x~df$Group)[c("estimate","p.value","statistic","conf.int")])))
</code></pre>

The result:

<pre><code> estimate.mean in group 1 estimate.mean in group 2 p.value statistic.t conf.int1 conf.int2
var1 4.000000 3.000000 0.5635410 0.5955919 -2.696975 4.696975
var2 4.333333 5.000000 0.5592911 -0.6022411 -3.104788 1.771454
var3 5.166667 5.000000 0.9028444 0.1249164 -2.770103 3.103436
var4 5.333333 6.000000 0.7067827 -0.3869530 -4.497927 3.164593
var5 6.500000 4.857143 0.3053172 1.0925986 -1.803808 5.089522
</code></pre>

blocks|key|1710353|text|您也可以使用定制的包matrixTests来实现这一点。下面是使用@Sven编写的data.frame的示例：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1710354|df+<-+read.table(text="Group+++var1++++var2++++var3++++var4++++var5
1+++++++++++3+++5+++7+++3+++7
1+++++++++++3+++7+++5+++9+++6
1+++++++++++5+++2+++6+++7+++6
1+++++++++++9+++5+++7+++0+++8
1+++++++++++2+++4+++5+++7+++8
1+++++++++++2+++3+++1+++6+++4
2+++++++++++4+++2+++7+++6+++5
2+++++++++++0+++8+++3+++7+++5
2+++++++++++1+++2+++3+++5+++9
2+++++++++++1+++5+++3+++8+++0
2+++++++++++2+++6+++9+++0+++7
2+++++++++++3+++6+++7+++8+++8
2+++++++++++10++6+++3+++8+++0",+header+=+TRUE)

library(matrixTests)

col_t_welch(df[df$Group==1,-1],+df[df$Group==2,-1])
+++++obs.x+obs.y+obs.tot+++mean.x+++mean.y++mean.diff+++++var.x+++++var.y+++stderr++++++++df++statistic++++pvalue++conf.low+conf.high+alternative+mean.null+conf.level
var1+++++6+++++7++++++13+4.000000+3.000000++1.0000000++7.200000+11.333333+1.679002+10.963146++0.5955919+0.5635410+-2.696975++4.696975+++two.sided+++++++++0+++++++0.95
var2+++++6+++++7++++++13+4.333333+5.000000+-0.6666667++3.066667++5.000000+1.106976+10.938135+-0.6022411+0.5592911+-3.104788++1.771454+++two.sided+++++++++0+++++++0.95
var3+++++6+++++7++++++13+5.166667+5.000000++0.1666667++4.966667++6.666667+1.334226+10.995151++0.1249164+0.9028444+-2.770103++3.103436+++two.sided+++++++++0+++++++0.95
var4+++++6+++++7++++++13+5.333333+6.000000+-0.6666667+10.666667++8.333333+1.722862+10.146824+-0.3869530+0.7067827+-4.497927++3.164593+++two.sided+++++++++0+++++++0.95
var5+++++6+++++7++++++13+6.500000+4.857143++1.6428571++2.300000+13.142857+1.503624++8.285649++1.0925986+0.3053172+-1.803808++5.089522+++two.sided+++++++++0+++++++0.95|code-block|syntax|javascript|1710355|entityMap^0|A|B|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|P|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|Q|8|@]|D|@]|E|$]]]|L|$]]

You can also use a custom made package <code>matrixTests</code> for this. Example using the data.frame prepared by @Sven below:

<pre><code>df &lt;- read.table(text="Group var1 var2 var3 var4 var5
1 3 5 7 3 7
1 3 7 5 9 6
1 5 2 6 7 6
1 9 5 7 0 8
1 2 4 5 7 8
1 2 3 1 6 4
2 4 2 7 6 5
2 0 8 3 7 5
2 1 2 3 5 9
2 1 5 3 8 0
2 2 6 9 0 7
2 3 6 7 8 8
2 10 6 3 8 0", header = TRUE)

library(matrixTests)

col_t_welch(df[df$Group==1,-1], df[df$Group==2,-1])
 obs.x obs.y obs.tot mean.x mean.y mean.diff var.x var.y stderr df statistic pvalue conf.low conf.high alternative mean.null conf.level
var1 6 7 13 4.000000 3.000000 1.0000000 7.200000 11.333333 1.679002 10.963146 0.5955919 0.5635410 -2.696975 4.696975 two.sided 0 0.95
var2 6 7 13 4.333333 5.000000 -0.6666667 3.066667 5.000000 1.106976 10.938135 -0.6022411 0.5592911 -3.104788 1.771454 two.sided 0 0.95
var3 6 7 13 5.166667 5.000000 0.1666667 4.966667 6.666667 1.334226 10.995151 0.1249164 0.9028444 -2.770103 3.103436 two.sided 0 0.95
var4 6 7 13 5.333333 6.000000 -0.6666667 10.666667 8.333333 1.722862 10.146824 -0.3869530 0.7067827 -4.497927 3.164593 two.sided 0 0.95
var5 6 7 13 6.500000 4.857143 1.6428571 2.300000 13.142857 1.503624 8.285649 1.0925986 0.3053172 -1.803808 5.089522 two.sided 0 0.95
</code></pre>

I have a dataframe with one factor column with two levels, and many numeric columns. I want to split the dataframe by the factor column and do t-test on the colunm pairs. 

Using the example dataset Puromycin I want the result to look something like this:

<pre><code>Variable Treated Untreated p-value Test-statistic CI of difference**** 
Conc 0.3450 0.2763 XXX T XX - XX
Rate 141.58 110.7272 xxx T XX - XX
</code></pre>

I think I am looking for a solution using PLYR that can an output the above results in a nice dataframe.

(The Puromycin only contains two numeric variables, but the solution I am looking for would work on a dataframe with many numeric variables)

UPDATE - I will try to clarify what i mean.

I would like to go from data that look like this:

<pre><code>Grouping variable var1 var2 var3 var4 var5
1 3 5 7 3 7
1 3 7 5 9 6
1 5 2 6 7 6
1 9 5 7 0 8
1 2 4 5 7 8
1 2 3 1 6 4
2 4 2 7 6 5
2 0 8 3 7 5
2 1 2 3 5 9
2 1 5 3 8 0
2 2 6 9 0 7
2 3 6 7 8 8
2 10 6 3 8 0
</code></pre>

To a result dataframe that look like this:

<pre><code>"Mean in group 1" "Mean in group 2" "P-value of difference" "N"

var1 ## ## ## ## 
var2 ## ## ## ## 
var3 ## ## ## ## 
var4 ## ## ## ## 
var5 ## ## ## ##
</code></pre>

Maybe it is something with mapply I am looking for because i want to split up my dataframe into dataframe1 and dataframe2 by a two-level factor, and apply a function( t-test) to the first parts of dataframe1 and dataframe2, and then a t-test on the second parts of dataframe1 and dataframe2, and then a t-test to the third parts of dataframe1 and dataframe2, and so on on all the column-pairs generated by the split by factor.

Apply t-test on many columns in a dataframe split by factor

我有一个数据帧，其中有一个具有两个级别的因子列，以及许多数值列。我想按factor列拆分数据帧，并对列对进行t测试。使用示例数据集Puromycin，我希望结果如下所示：Variable    Treated Untreated   p-value    Test-statistic CI of difference*...

问对按因子拆分的数据帧中的许多列应用t-test
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