Dear Bruce,
Besides any other problems I can see that you consider FA as a factor and
that could be a problem with your data set.
Manuel
2014-07-30 7:10 GMT-06:00 Bruce Miller <batsnc...@gmail.com>:
> Hi all,
>
> Sorry this is a bit long, but the explanation of what I want to do needs
> to be clear to avoid issues such as this quote..."It is impossible to
> speak in such a way that you cannot be misunderstood." Karl Popper.
>
> I am running linear regression models, but I am getting expected results.
> I wonder what else I might try to derive an estimated value of bat
> echolocation parameters based on forearm measurements. It is known that
> the size of the bat is negatively related to the characteristic
> frequency (Fc) of their echolocation calls (decades of my field work) .
> So in general larger guys have lower frequency calls and smaller guys
> have higher frequency calls.
>
> I have run the regressions based on the FA (valid forearm measurements)
> and the known and valid Fc ranges for a dozen species or so and using
> the lm models to "predict" Fc values for a few species that have FA
> values but have not yet been recorded. Hence there are no valid
> echolocation call parameters. R Code used is below discussion.
>
> I have valid ranges for the known species FA (forearm measurements) and
> Fc(minimum) and Fc (maximum). So I do two separate runs with the data
> using the lm model one with FA~Fcmin and one FA~Fcmax.
>
> The goal is to provide the predicted (estimated) values for the species
> with known FA values but w/o verified Fc value ranges.
>
> My concern is that the predicted values returned are much lower than the
> true values for the verified species. Therefore I am not confident the
> predicted values for those w/o verified Fc ranges are useful.
>
> One very helpful person looked at one simple data set I sent and showed
> that the statistical differences between the true values and predicted
> were not significant.
>
> However Krebs' admonishment to students eons ago "Do not confuse
> statistical significance with ecological significance" is true here.
> The values of the predicted ranges are far lower than reality so the few
> species that do not have field recorded Fc values are suspect. These
> differences in predicted values from a true range will will make a
> difference for potentially IDing the unknown calls. A difference of
> 10kHz Fc generally suggests a different species, albeit some are much
> closer and may only have a 5 kHz difference.
>
> I am looking at acoustic data sets of calls from South America and there
> are many "sonospecies."
> These are clearly separate species based on echolocation call parameters
> that have yet had "faces & voices" matched. We know that call
> parameters are diagnostic for families and genera even when the species
> is unknown. It is then the Fc values that assist in identifying the
> species within a cluster of calls from the same genus.
>
> Sample of R code used:
>
> Bats <- dget('C:/=Bat data working/Acoustic
> Parameters/_Working/=Vespertilionidae/Bats.robj')
>
> model.lm <- lm(formula=Fc ~ as.factor(FA),data=Bats,na.action=na.omit)
> > Anova(model.lm,type='II') Error in solve.default(L %*% V %*% t(L)) :
> system is computationally singular: reciprocal condition number = 0
> > summary(model.lm)
>
> Call:
> lm(formula = Fc ~ as.factor(FA), data = Bats, na.action = na.omit)
>
> Residuals:
> ALL 5 residuals are 0: no residual degrees of freedom!
>
> Coefficients:
> Estimate Std. Error t value Pr(>|t|)
> (Intercept) 53.3 NA NA NA
> as.factor(FA)34.4 -4.6 NA NA NA
> as.factor(FA)35.4 2.3 NA NA NA
> as.factor(FA)35.5 9.0 NA NA NA
> as.factor(FA)40.5 -7.3 NA NA NA
>
> Residual standard error: NaN on 0 degrees of freedom
> (2 observations deleted due to missingness)
> Multiple R-squared: 1, Adjusted R-squared: NaN
> F-statistic: NaN on 4 and 0 DF, p-value: NA
>
> > tmp<-predict(model.lm)
> > Bats[names(tmp),"predicted"]<-tmp
> > rm('tmp')
> > rm('model.lm')
> >
>
> >model.lm <- lm(formula= Fc~ FA,data=Bats,na.action=na.omit)
>
> >Anova(model.lm,type='II')
>
> >summary(model.lm)
>
> >tmp<-predict(model.lm,Bats)
>
> >Bats[names(tmp),"Predicted.Fc"]<-tmp
>
> >rm('tmp')
>
> >rm('model.lm')
>
> With the results it can be seen that the predicted Fc values on right
> are not close to the true Fc values on left and then make me hesitant to
> accept the 2 with NA predicted values. FYI Species are simple 6 letter
> coded for genus and species.
> Species FA Fcmin Fcmax FcMinpredic FcMaxpredic
> Myoalb 35.3 45.7 48.7 51.73 55.26
> Myoata 37 NA NA 49.52 52.59
> Myokea 33.7 57.8 61.3 53.80 57.77
> Myonig 34.5 51.6 55.7 52.77 56.52
> Myooxy 40.5 45.7 47.6 44.98 47.09
> Myorip 36 53.3 57.5 50.82 54.16
> Myosim 38 NA NA 48.23 51.02
>
>
> Perhaps simple linear regression is not the method to use?
> Thanks for any additional suggestions.
>
> Bruce
>
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>
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--
*Manuel SpÃnola, Ph.D.*
Instituto Internacional en Conservación y Manejo de Vida Silvestre
Universidad Nacional
Apartado 1350-3000
Heredia
COSTA RICA
mspin...@una.ac.cr
mspinol...@gmail.com
Teléfono: (506) 2277-3598
Fax: (506) 2237-7036
Personal website: Lobito de rÃo <https://sites.google.com/site/lobitoderio/>
Institutional website: ICOMVIS <http://www.icomvis.una.ac.cr/>
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