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 <batsncats@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 Spínola, Ph.D.*
Instituto Internacional en Conservación y Manejo de Vida Silvestre
Universidad Nacional
Apartado 1350-3000
Heredia
COSTA RICA
mspinola@una.ac.cr
mspinola10@gmail.com
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