Hi Julie, Mixed effects models are typically used to allow for correlations between observations of the outcome variable---the fact that you are trying to model dA ~ dCOM rather assumes you expect some sort of association between the two. It is not exactly clear what you want to deal with using a correlation structure, but it seems to me your model is this:
dA = a * (dA + dB + dC + dE + dF) + b which is a little circular because dA is explaining itself. Without knowing more, I would think about something like: dA = a1 * dB + a2 * dC + a3 * dE + a4 * dF + b if you want a single slope for all a1 - a4 effects, you could move to a structural equation modelling framework and impose the constraint that: a1 = a2 = a3 = a4 in the parameter estimation. Cheers, Josh On Sun, Jan 22, 2012 at 12:31 PM, JulieV <sharkette...@hotmail.com> wrote: > Hi, > I have a Community (COM) composed of 6 species: A, B, C, D, E & F. > The density of my Community is thus (Eq.1): dCOM = dA + dB + dC + dE + dF > > I would like to calculate and plot a linear regression between the density > of each of my species and the density of the whole community (illustrating > how the density of each species varies with variations of the whole > community). > For example, I would like to plot dA = a * dCOM + b, with a and b the slope > and intercept of the regression. > > The problem is that dA and dCOM are correlated because dA contributes to > values of dCOM (see Eq.1 above), and thus I'm probably not allowed to use a > "simple" linear regression (because parametric statistics do not allow for > correlated observations). > > >From what I red (eg, www.ats.ucla.edu/stat/r/faq/spatial_regression.htm), > Linear Mixed Models allow for correlated observations by adding a correction > to the values. The webpage also says that we can use the correlation option > in the lme function (nlme package) to find the type of correction to be > used, but I can’t figure out how to do this for my dataset. > > Can someone help me please ? > You will find an example of my dataset below (density of species A [dA] and > density of the whole community [dCOM]) and my R script. > > > Example from my dataset: > dA dCOM > 0.611 0.73 > 0.474 0.669 > 0.203 0.388 > 0.011 0.213 > 0.407 0.722 > 0.148 0.437 > 0.084 0.281 > 0 0.054 > 0.402 0.93 > 0.044 0.285 > 0.011 0.147 > 0 0.091 > 0.547 0.767 > 0.559 0.699 > 0.321 0.441 > 0.084 0.262 > 0.428 0.761 > 0.234 0.398 > 0.019 0.191 > 0 0.053 > 0.302 0.509 > 0.06 0.213 > 0.045 0.184 > 0.033 0.118 > 0.374 0.585 > 0.256 0.5 > 0.22 0.401 > 0.136 0.275 > 0.503 0.684 > 0.48 0.617 > 0.257 0.387 > 0.038 0.159 > 0.382 0.611 > 0.192 0.393 > 0.047 0.243 > 0.036 0.188 > > R script for this example: > > dummy <- rep(1,36) # grouping variable in our data > model1 <- lme(fixed = dA ~ dCOM, data = bb1, random = ~ 1 | dCOM/dummy, > correlation= ?,method = "ML") > > Thank you very much. > > Julie. > > > -- > View this message in context: > http://r.789695.n4.nabble.com/Calculating-plotting-a-linear-regression-between-two-correlated-variables-tp4319051p4319051.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > 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. -- Joshua Wiley Ph.D. Student, Health Psychology Programmer Analyst II, Statistical Consulting Group University of California, Los Angeles https://joshuawiley.com/ ______________________________________________ R-help@r-project.org mailing list 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.
