[R] Complex multiple t tests in a data frame with several id factors

[Kaiyin Zhong]

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
>>
>> ______________________________________________
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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



-- 
Kaiyin Zhong
------------------------------------------------------------------------------------------------------------------
Neuroscience Research Institute, Peking University, Beijing, P.R.China 100038