[R-sig-Geo] eigenvectors increase spatial autocorrelation in ols regression

[Vinicius Maia]

It is worth sending this question to r-sig-mixed-models using r-project.org.



Em qua., 22 de jul. de 2020 às 11:45, Spencer Graves <
spencer.graves using prodsyse.com> escreveu:

>        Have you considered the "corSpher" and "corSpatial" functions in
> the nlme package?
>
>
>        Spencer Graves
>
>
> On 2020-07-22 05:09, Peter B. Pearman wrote:
> >
> > Dear Roger and list members,
> >
> > I have a ols regression and want to remove spatial autocorrelation
> > (SAC) from the residuals, in order to avoid its potential effects of
> > SAC on the hypothesis tests (and the reviewers/editor).  I have
> > generated spatial eigenvectors with SpatialFiltering(), and added the
> > generated vectors to the regression.  Surprisingly, SAC appears to
> > become more pronounced.  I also tried ME(), but many more vectors are
> > produced and SAC is also not removed.  Isn't including the vectors
> > from SpatialFiltering() supposed to reduce SAC?
> >
> > Can you please enlighten me as to what's going on, what I am doing
> > wrong, or what I should try?
> >
> > Thanks in advance for you time.
> >
> > Peter
> >
> > The data are here:
> >
> >
> https://drive.google.com/file/d/1Z3FIGIAbYvXqGETn0EWLjTOY8MYpZi_S/view?usp=sharing
> >
> > The analysis goes like this:
> >
> > library(spatialreg)
> > library(spdep)
> > library(tidyverse)
> > library(car)
> > library(ncf)
> >
> >
> > data <- read_csv("for_RAR.csv")
> > set.seed(12345)
> > x <- data$ses.mntd
> > y <- log(data$RAR)
> > ols_for_RAR <- lm(y ~ x)
> > ## qqplot() and shapiro.test() show residuals are nicely distributed
> > # x is significant and R-square about 0.2, demonstrated here
> > car::Anova(ols_for_RAR,type="III")
> > summary(ols_for_RAR)
> >
> > # the following appears to make an acceptable neighbor network
> > c1<-c(data$LONG)
> > c2<-c(data$LAT)
> > cbindForests<-cbind(c1,c2)
> > # a value of 0.7, below, is sufficient to join all the points.
> > # qualitatively the results aren't affected, as far as I see by
> > setting this higher
> > # However, the number of eigenvectors generated by SpatialFiltering()
> > varies a lot
> > nbnear4 <- dnearneigh(cbindForests, 0, 0.7)
> > plot(nbnear4, cbindForests, col = "red", pch = 20)
> >
> > # SAC appears significant at short distances (<10km), which is what I
> > want to remove
> > cor_for <- correlog(c1, c2, residuals(ols_for_RAR), increment = 1,
> > resamp = 1000, latlon=TRUE, na.rm = TRUE)
> > plot(cor_for$correlation[1:20],type="s")
> > # p-values
> > print(cor_for$p[which(cor_for$p < 0.05)])
> > # Moran's I values
> > cor_for$correlation[which(cor_for$p < 0.05)]
> >
> > # Generate optimized spatial eigenvectors using SpatialFiltering() and
> > use them
> > # Several vectors are generated depending on values in dnearneigh()
> > spfilt_mntd_RAR<- spatialreg::SpatialFiltering(y ~ x, nb=nbnear4,style
> > = "W", tol=0.0001, ExactEV = TRUE)
> > new_mod <- lm(y ~ x + fitted(spfilt_mntd_RAR))
> > car::Anova(new_mod, type="III")
> > summary(new_mod)
> >
> > # Plot Moran's I at distances under 20km
> > cor_for_1c <- correlog(c1, c2, residuals(new_mod), increment = 1,
> > resamp = 1000, latlon=TRUE, na.rm = TRUE)
> > plot(cor_for_1c$correlation[1:20],type="s")
> >
> > # Extract significant values of Moran's I
> > cor_for_1c$p[which(cor_for_1c$p < 0.05)]
> > cor_for_1c$correlation[which(cor_for_1c$p < 0.05)]
> >
> > # The result is that Moran's I is significant at additional short
> > distances
> > --
> >
> > _+_+_+_+_+_+_+_+_
> >
> > Peter B. Pearman
> > Ikerbasque Research Professor
> >
> > Laboratory for Computational Ecology and Evolution
> > Departamento de Biología Vegetal y Ecología
> > Facultad de Ciencias y Tecnología
> > Ap. 644
> > Universidad del País Vasco/ Euskal Herriko Unibertsitatea
> >
> > Barrio Sarriena s/n
> > 48940 Leioa, Bizkaia
> >
> > SPAIN
> >
> > Tel. +34 94 601 8030
> >
> > Fax  +34 94 601 3500
> >
> > www.ehu.eus/es/web/bgppermp <http://www.ehu.es/peter.pearman>
> >
> > When you believe in things that you don’t understand
> >
> > Then you suffer
> >
> > -- Stevie Wonder
> >
> >
> > Download my public encryption key here: https://pgp.mit.edu/
> >
> > and please use it when you send me e-mail.
> >
> >
> > _______________________________________________
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>
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