[R-sig-eco] anova.cca question / missing data in constraining matrix

[Jari Oksanen]

Dear Colleen, 
On 03/06/2013, at 22:32 PM, ckellogg wrote:

> Hello Jari,
> Thank you for your help with this.  The solution you suggested in your
> second post worked quite well.  However, i think another subset of my data
> is too 'holey', because when I run CCA on this set of environmental
> variables (or the a CCA with the previous environmental variables and the
> additional ones), I get an error: 
> 
>> toolik250.cca2
>> <-cca(toolikotus250.ra~logtemp+conductivity+pH+logBacProd+DIC+logDCO2+sqrtDCH4+logDOC+sqrtPhosphate+sqrtNitrate+sqrtTDN+sqrtTDP+logPC+logPN+Ca+Mg+logNa+logK+SO4+logChloride+Silica,toolikenv.s,
>> na.action=na.exclude)
> Error in predict.cca(x, newdata = excluded, type = "wa", model = "CA") : 
>  model “CA” has rank 0
> 
> The CCA runs if I use na.action=na.omit, but then when I run the anovas,
> there is apparently no residual component.  For example,
> No residual component
> 
> Model: cca(formula = toolikotus250.ra ~ logtemp + conductivity + pH +
> logBacProd + DIC + logDCO2 + sqrtDCH4 + logDOC + sqrtPhosphate + sqrtNitrate
> + sqrtTDN + sqrtTDP + logPC + logPN + Ca + Mg + logNa + logK + SO4 +
> logChloride + Silica, data = toolikenv.s, na.action = na.omit, subset =
> -toolik250.cca2$na.action)
>         Df  Chisq F N.Perm Pr(>F)
> Model    12 5.3003        0       
> Residual  0 0.0000   
> 
Yes, probably too many holes. You have no residual variation which indicates that the number
of predictor variables (constraints) is higher than the number of remaining observations. 

Cheers, Jari Oksanen

> So, I am thinking that examining the relationship between the microbial
> community and this subset of environmental variables might not be possible
> without my first manually curating which samples and variables should be
> included, correct?
> 
> Thank you,
> Colleen
> 
> 
> 
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-- 
Jari Oksanen, Dept Biology, Univ Oulu, 90014 Finland
jari.oksanen at oulu.fi, Ph. +358 400 408593, http://cc.oulu.fi/~jarioksa