Message-ID: <CACk-te1ougD-7va8+sLktoA15my5L4U0BkpmYF-wa74QJPBaAA@mail.gmail.com>
Date: 2011-12-04T16:01:43Z
From: Bert Gunter
Subject: Complex multiple t tests in a data frame with several id factors
In-Reply-To: <CAOHtMfXgiFrN6LJu5ABr8sLVTXHWdw_nfzRS9qF=-zyUSDfQ4Q@mail.gmail.com>
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
>
> ______________________________________________
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> 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