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<TITLE>Help with interpreting correlog results, package ncf</TITLE>
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<P><FONT SIZE=2 FACE="Arial">Dear Roger Bivand and list members,</FONT>
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<P><FONT SIZE=2 FACE="Arial">I am looking for advice on how to interpret a correlogram of species richness data collected from photographic transects of the seabed. I am an amateur in R, and am completely new to spatial analysis, so please forgive the naivety of my issue.</FONT></P>
<P><FONT SIZE=2 FACE="Arial">I have four photographic transects consisting of photos at varying distances apart. Photos within transects are not at equal distances apart, and the transects themselves also vary in distance from one another. Three of these transects are relatively close together (within a 26 km diameter), while the fourth transect is roughly 100 km away from the first three. The total distance between the two farthest photos is 138 km. </FONT></P>
<P><FONT SIZE=2 FACE="Arial">Using glm I have fit a poisson distribution with log link to determine the influence of four environmental variables on species richness. This was fit using all photos across all transects. I am interested in determining whether there is spatial autocorrelation present in the residuals of this model. I used correlog( ) in package 'ncf' to create a Moran's I correlogram. The code I used is: </FONT></P>
<P><FONT SIZE=2 FACE="Arial">cor1<-correlog(data$Longitude, data$Latitude, residuals(glm1), increment=1, latlon=TRUE, resamp=1000). </FONT>
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<P><FONT SIZE=2 FACE="Arial">Latlon was set to true as my coordinates are geographical. I set increment=1 as I understood each distance class would represent 1 km. I then plotted the correlogram using plot(cor1$correlation[1:30]). There are 30 distance classes. The correlogram is attached to this email. I am confused on how to interpret the results of the correlogram. I have 30 distance classes, although the scale of my data is roughly 138 km, therefore each distance class does not equal 1 km as I expected. The output of cor1 (attached) shows 30 distance classes, although they are labelled 1-8, 19-26, 111-119, and 134-138. I'm assuming the distances classes are broken up like this because the photos are not contiguous? Should the autocorrelation therefore be assessed within transects instead of across them? </FONT></P>
<P><FONT SIZE=2 FACE="Arial">My second question relates to significance testing. I set resamp to 1000 and generated the p values for each distance class. Is this a suitable method for testing the significance of the autocorrelation at each class? In this case my first distance class is significant (p=0.028). From reading other list postings there doesn't appear to be a method to test for significant autocorrelation in glm fits (although there is for lm fits). </FONT></P>
<P><FONT SIZE=2 FACE="Arial">Thanks for any advice you can provide.</FONT>
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<P><FONT SIZE=2 FACE="Arial">Cheers,</FONT>
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<P><FONT SIZE=2 FACE="Arial">Lindsay</FONT>
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<P><FONT FACE="Arial" SIZE=2 COLOR="#000000"> <<Correlogram_SpeciesRichness.pdf>> </FONT><FONT FACE="Arial" SIZE=2 COLOR="#000000"> <<Cor1.txt>> </FONT>
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