Hi John,
You are right. With more than two groups, the type 1 error rate should be a
problem.
A.K.
----- Original Message -----
From: John Kane <jrkrid...@inbox.com>
To: arun <smartpink...@yahoo.com>
Cc:
Sent: Thursday, October 4, 2012 8:40 AM
Subject: Re: [R] t-test
My stats are lousy but isnt Nico doing some multiple t-tests when an anova with
some post hoc comparisons complete with a Tukey or Bonferroni correction looks
more suitable?
Of course I have no idea of the topic area and maybe he has already done thi.
John Kane
Kingston ON Canada
> -----Original Message-----
> From: smartpink...@yahoo.com
> Sent: Thu, 4 Oct 2012 05:31:55 -0700 (PDT)
> To: nicome...@gmail.com
> Subject: Re: [R] t-test
>
> HI,
> Try this:
> sapply(split(dat,dat$Name),function(x) t.test(x[,2],dat[,2])$p.value)
> # CTK100 CTK103 CTK121
> #0.86330310 0.32706859 0.02023357
> A.K.
>
>
>
> ----- Original Message -----
> From: Nico Met <nicome...@gmail.com>
> To: Rui Barradas <ruipbarra...@sapo.pt>
> Cc: r-help@r-project.org; r-help <r-h...@stat.math.ethz.ch>
> Sent: Thursday, October 4, 2012 6:37 AM
> Subject: Re: [R] t-test
>
> Dear Rui,
>
> Many thanks for help.
>
> "mean for CTK and all" = comparison between mean of all groups ( which
> means second col) vs. each groups like CTK100, CTK121 etc.
>
> Regards
>
> Nico
>
> On Thu, Oct 4, 2012 at 12:28 PM, Rui Barradas <ruipbarra...@sapo.pt>
> wrote:
>
>> Hello,
>>
>> I'm not quite sure I understand, but something like this?
>>
>> tapply(dat$Score, dat$Name, FUN = mean)
>> sapply(unique(dat$Name), function(un){
> > with(dat, t.test(Score[Name == un], Score[Name != un])$p.value)})
>>
>> My doubt is in what you mean by "mean for CTK and all". The ?t.test
>> gives
>> a confidence interval for the difference in the means, so maybe you'll
>> have
>> to look there for what you want.
>>
>> Hope this helps,
>>
>> Rui Barradas
>> Em 04-10-2012 10:34, Nico Met escreveu:
>>
>>> Dear Group,
>>>
>>> I want to do a t-test calculation on a large data set.
>>>
>>> I am pasting some part of it
>>>
>>> structure(list(Name = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 3L,
>>> 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
>>> 2L), .Label = c("CTK100", "CTK103", "CTK121"), class = "factor"),
> >> Score = c(236.9726, 207.0055, 237.3464, 224.4774, 236.5034,
> >> 206.7382, 233.94, 240.31, 240.9, 235.15, 223.36, 248.67,
> >> 249.25, 201.4051, 244.1689, 182.2756, 229.001, 241.3211,
> >> 196.0453, 232.6055, 225.0783, 196.0453, 232.6055, 225.0783
> >> )), .Names = c("Name", "Score"), class = "data.frame", row.names
> =
>>> c(NA,
>>> 24L))
>>>
>>>
>>> I want to compare groups with CTK100 and with all the groups and want
>>> to
>>> save the p-values and mean for each of that particular group (for
>>> example:
>>> mean for CTK and all)
>>> Similarly, for other groups like that CTK121 etc...
>>>
>>> Is there any way to automate this process?
>>>
>>> Thanks for your advice !!!!!
>>>
>>> Nico
>>>
> >> [[alternative HTML version deleted]]
>>>
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>>
>>
>
> [[alternative HTML version deleted]]
>
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