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