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 Dear list, I am currently working on modelling species distribution and try to account spatial autocorrelation. I use R and I successfully managed to incorporate moran's eigenvectors as predictor to my species distribution model using GLM (logit). I followed Dormann et al. (2007 ) and the appendix . I think I got corrected statistics in the moran.test() of model residuals as the p-value increases. Also the AIC score indicates a better spatial model. *Normal model glm:* Moran I test under randomisation data:  residuals(model) weights: priclus.listw Moran I statistic standard deviate = 7.5632, p-value = 1.966e-14 alternative hypothesis: greater sample estimates: Moran I statistic       Expectation          Variance       0.264335666      -0.007633588       0.001293086 *Spatial model:* Moran I test under randomisation data:  residuals(model) weights: priclus.listw Moran I statistic standard deviate = 1.5572, p-value = 0.05972 alternative hypothesis: greater sample estimates: Moran I statistic       Expectation          Variance       0.045968614      -0.007633588       0.001184932 *See the summary for both models below:* > summary(priclus8<- glm(pb_train ~ gesteine + schnee_tag_1 + rs_hospso, family= binomial(link="logit"), data=envtrain))         Call:         glm(formula = pb_train ~ gesteine + schnee_tag_1 + rs_hospso,             family = binomial(link = "logit"), data = envtrain)         Deviance Residuals:              Min        1Q    Median        3Q       Max         -2.76552  -0.18741  -0.00449   0.34032   2.03205         Coefficients:                      Estimate Std. Error z value Pr(>|z|)         (Intercept)  75.89890   20.21051   3.755 0.000173 ***         gesteine1     2.05287    0.55421   3.704 0.000212 ***         schnee_tag_1 -0.03328    0.01685  -1.975 0.048223 *         rs_hospso    -1.56444    0.37856  -4.133 3.59e-05 ***         ---         Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1         (Dispersion parameter for binomial family taken to be 1)             Null deviance: 387.96  on 284  degrees of freedom         Residual deviance: 129.56  on 281  degrees of freedom         AIC: 137.56         Number of Fisher Scoring iterations: 7 > sevm1 <- fitted(ME(pb_train ~ gesteine + schnee_tag_1 + rs_hospso, data=envtrain, family= binomial(link="logit"),listw=ME.listw)) > summary(priclus8_mem<- glm(pb_train ~ gesteine + schnee_tag_1 + rs_hospso+ I(sevm1), family= binomial(link="logit"), data=envtrain) )     Call:     glm(formula = pb_train ~ gesteine + schnee_tag_1 + rs_hospso +         I(sevm1), family = binomial(link = "logit"), data = envtrain)     Deviance Residuals:         Min       1Q   Median       3Q      Max     -3.6095  -0.1415  -0.0025   0.0784   2.5099     Coefficients:                    Estimate Std. Error z value Pr(>|z|)     (Intercept)   102.50545   28.27283   3.626 0.000288 ***     gesteine1       0.80346    0.79373   1.012 0.311417     schnee_tag_1   -0.06435    0.02448  -2.628 0.008586 **     rs_hospso      -1.99483    0.52879  -3.772 0.000162 ***     I(sevm1)vec8   35.86461    8.38806   4.276 1.91e-05 ***     I(sevm1)vec25 -46.33209    8.82448  -5.250 1.52e-07 ***     ---     Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1     (Dispersion parameter for binomial family taken to be 1)         Null deviance: 387.959  on 284  degrees of freedom     Residual deviance:  69.198  on 279  degrees of freedom     AIC: 81.198     Number of Fisher Scoring iterations: 8 Now I would like to compare the results of evaluate() and the predictions between the normal model and the spatial model. For the spatial model I get an error: "there are different length for the variables". I also want to plot the SAC-corrected predicted model to visualize the distribution. >e.priclus8_mem<-evaluate(test_pres_val, test_abs_val, priclus8_mem) #Error in model.frame.default(Terms, newdata, na.action = na.action, #xlev = object\$xlevels) :  Variablenlängen sind unterschiedlich #(gefunden für 'I(sevm1)') In addition: Warning message:'newdata' had #120 rows but variables found have 285 rows > plot(pclus8_mem<-predict(env_data, priclus8_mem, type="response"), main="GML priclu8_mem") #Error in model.frame.default(Terms, newdata, na.action = na.action, #xlev = object\$xlevels):Variablenlängen sind unterschiedlich (gefunden #für 'I(sevm1)') Is this error caused by points with no neighbour? I used zero.polycy=TRUE to accept no neighbours in the nb object. What else could be the problem? I also read Bivand et al. (2008): Applied Spatial Data Analysis with R and Borcard et al. (2011): Numerical Ecology with R. The given examples always refer to vector data but I am working with raster data… I am new in the matter and cannot get any further here. I have certainly overlooked something or misunderstood. Any help and further reading is appreciated! Many thanks, Patrick         [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-geo