Message-ID: <CAOHtMfVh_f0L=GxhLyZCYmQyXXw2F3T4WR-8=jaxWFEz0pc4DA@mail.gmail.com>
Date: 2011-12-04T16:06:41Z
From: Kaiyin Zhong
Subject: Complex multiple t tests in a data frame with several id factors
In-Reply-To: <CACk-te1ougD-7va8+sLktoA15my5L4U0BkpmYF-wa74QJPBaAA@mail.gmail.com>
Okay, thank you.
On Mon, Dec 5, 2011 at 12:01 AM, Bert Gunter <gunter.berton at gene.com> wrote:
> The concentrations of the different metals within an animal are
> correlated, so that doing as you suggest will almost certainly result
> in nonsense P values. So I suggest you seek local statistical help or,
> failing that, post on a statistical forum like stats.stackexchange.com
> ?.
>
> There are ?various multivariate packages -- check e.g. the ChemPhys
> and Multivariate task views -- that may be pertinent, but your post
> suggests that you probably need some help to use them. Ergo my
> suggestion above.
>
> Cheers,
> Bert
>
> On Sun, Dec 4, 2011 at 7:36 AM, Kaiyin Zhong <kindlychung at gmail.com> wrote:
>> I have assayed the concentrations of various metal elements in
>> different anatomic regions of two strains of mice. Now, for each
>> element, in each region, I want to do a t test to find whether there
>> is any difference between the two strains.
>>
>> Here is what I did (using simulated data as an example):
>>
>> # create the data frame
>>> elemconc = data.frame(expand.grid(id=1:3, geno=c('exp', 'wt'), region=c('brain', 'spine'), elem=c('fe', 'cu', 'zn')), conc=rnorm(36, 10))
>>> elemconc
>> ? id geno region elem ? ? ?conc
>> 1 ? 1 ?exp ?brain ? fe ?8.497498
>> 2 ? 2 ?exp ?brain ? fe ?9.280944
>> 3 ? 3 ?exp ?brain ? fe ?9.726271
>> 4 ? 1 ? wt ?brain ? fe 11.556397
>> 5 ? 2 ? wt ?brain ? fe 10.992550
>> 6 ? 3 ? wt ?brain ? fe ?9.711200
>> 7 ? 1 ?exp ?spine ? fe 11.168603
>> 8 ? 2 ?exp ?spine ? fe ?9.331127
>> 9 ? 3 ?exp ?spine ? fe 11.048226
>> 10 ?1 ? wt ?spine ? fe ?8.480867
>> 11 ?2 ? wt ?spine ? fe ?8.887062
>> 12 ?3 ? wt ?spine ? fe ?8.329797
>> 13 ?1 ?exp ?brain ? cu 10.242652
>> 14 ?2 ?exp ?brain ? cu ?9.865984
>> 15 ?3 ?exp ?brain ? cu ?9.163728
>> 16 ?1 ? wt ?brain ? cu 11.099385
>> 17 ?2 ? wt ?brain ? cu ?9.364261
>> 18 ?3 ? wt ?brain ? cu ?9.718322
>> 19 ?1 ?exp ?spine ? cu 10.720157
>> 20 ?2 ?exp ?spine ? cu 11.505430
>> 21 ?3 ?exp ?spine ? cu ?9.499359
>> 22 ?1 ? wt ?spine ? cu ?9.855950
>> 23 ?2 ? wt ?spine ? cu 10.120489
>> 24 ?3 ? wt ?spine ? cu ?9.526252
>> 25 ?1 ?exp ?brain ? zn ?9.736196
>> 26 ?2 ?exp ?brain ? zn 11.938710
>> 27 ?3 ?exp ?brain ? zn ?9.668625
>> 28 ?1 ? wt ?brain ? zn ?9.961574
>> 29 ?2 ? wt ?brain ? zn 10.461621
>> 30 ?3 ? wt ?brain ? zn ?9.873667
>> 31 ?1 ?exp ?spine ? zn ?9.708067
>> 32 ?2 ?exp ?spine ? zn 10.109309
>> 33 ?3 ?exp ?spine ? zn 10.973387
>> 34 ?1 ? wt ?spine ? zn ?8.406536
>> 35 ?2 ? wt ?spine ? zn ?7.797746
>> 36 ?3 ? wt ?spine ? zn 11.127984
>>
>> # use tapply to aggregate
>>> tapply(elemconc$conc, elemconc[c('elem', 'region')], function(x) x)
>> ? ?region
>> elem brain ? ? spine
>> ?fe Numeric,6 Numeric,6
>> ?cu Numeric,6 Numeric,6
>> ?zn Numeric,6 Numeric,6
>>
>> # check whether the order of data has been preserved after aggregation
>>> x['fe', 'brain']
>> [[1]]
>> [1] ?8.497498 ?9.280944 ?9.726271 11.556397 10.992550 ?9.711200
>>
>> # create an external factor for strain grouping
>>> tmpgeno = rep(c('exp', 'wt'), each=3)
>>> tmpgeno
>> [1] "exp" "exp" "exp" "wt" ?"wt" ?"wt"
>>
>> # do the t test using the grouping factor
>>> x = tapply(elemconc$conc, elemconc[c('elem', 'region')], function(x) t.test(x~tmpgeno) )
>>> x
>> ? ?region
>> elem brain ?spine
>> ?fe List,9 List,9
>> ?cu List,9 List,9
>> ?zn List,9 List,9
>>
>> I believe I have made no mistakes so far, but I wonder is there a
>> better way of doing this?
>>
>>
>> --
>> Kaiyin Zhong
>> ------------------------------------------------------------------------------------------------------------------
>> Neuroscience Research Institute, Peking University, Beijing, P.R.China 100038
>>
>> ______________________________________________
>> R-help at r-project.org mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
>
>
> --
>
> Bert Gunter
> Genentech Nonclinical Biostatistics
>
> Internal Contact Info:
> Phone: 467-7374
> Website:
> http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
--
Kaiyin Zhong
------------------------------------------------------------------------------------------------------------------
Neuroscience Research Institute, Peking University, Beijing, P.R.China 100038