Thank you for your help....

Yes i wanted to do the t test for all columns except for the grouping
column.....


2011/9/12 Uwe Ligges <lig...@statistik.tu-dortmund.de>

>
>
> On 12.09.2011 13:16, Raphael Saldanha wrote:
>
>> Hi!
>>
>> Try something like this:
>>
>> subset(example, disease==TRUE)
>> subset(example, disease==FALSE)
>>
>
>
> Hmmm, I think the actual answer to the question is something along this
> line:
>
> sapply(example[names(example)!**="disease"],
>       function(x) t.test(x ~ example[["disease"]])[[3]])
>
>
> Uwe Ligges
>
>
>
>
>
>> On Mon, Sep 12, 2011 at 4:54 AM, C.H.<chainsawti...@gmail.com>  wrote:
>>
>>  Dear R experts,
>>>
>>> Suppose I have an data frame likes this:
>>>
>>>  example<- data.frame(age=c(1,2,3, 4,5,6),
>>>>
>>> height=c(100,110,120,130,140,**150), disease=c(TRUE, TRUE, TRUE, FALSE,
>>> FALSE,
>>> FALSE))
>>>
>>>  example
>>>>
>>>  age height disease
>>> 1   1    100    TRUE
>>> 2   2    110    TRUE
>>> 3   3    120    TRUE
>>> 4   4    130   FALSE
>>> 5   5    140   FALSE
>>> 6   6    150   FALSE
>>>
>>> Is there anyway to compare the age and height between those with
>>> disease=TRUE and disease=FALSE using t.test and extract the p-values
>>> quickly?
>>>
>>> I can do this individually
>>>
>>> t.test(example$age~example$**disease)[3]
>>>
>>> But when the number of variable grow to something like 200 it is not
>>> easy any more.
>>>
>>> Thanks!
>>>
>>> Regards,
>>>
>>> CH
>>>
>>> --
>>> CH Chan
>>>
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>>>
>>>
>>
>>
>>
> ______________________________**________________
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