search for: spatial_regress

...t 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. C...

...if there is a document or paper that explains how the spatial correlation structure (such as "corExp" or "corGaus") works. Let me use the example and data posted on UCLA's R FAQ webpage to explain my problems. The link for the webpage is: http://www.ats.ucla.edu/stat/r/faq/spatial_regression.htm install.packages("nlme") library(nlme) spdata <- read.table("http://www.ats.ucla.edu/stat/R/faq/thick.csv", header = T, sep = ",") dummy <- rep(1, 75) spdata <- cbind(spdata, dummy) ### estimate the null model ### soil.model <- lme(fixed = thick ~...

...covariance error matrix. The combined goal of these two parallel adjustments (I believe) would be to maximize overall model likelihood. I have been looking for an example of R code that uses a nonlinear mixed model in this way. I've only found this so far. http://www.ats.ucla.edu/stat/r/faq/spatial_regression.htm It seems that in the example given in this link, the incorporated correlation structure is not specifically on the error term but instead on the reponse itself. Therefore, it seems that the effect of the explanatory variable is diluted by this approach. For instance, if you had a 'tru...