[R-sig-Geo] Spatial autocorrelation help

[Dechen Lham]

Hi Patrick

Thank you for your quick response and i went through your thesis and its very useful information. One thing that i was wondering was, you could potentially also use quadratic terms of the predictors which may have non-linear relation with the response variable right? rather than to use GAMM.

Besides i need to still figure out how to check the SAC correctly in my data as there is the global morans I and a local morans I right? Further need to figure out how to plot them correctly to see the patterns. I did make a correlogram of the raw data and from the residuals of the best model but both looked very similar and also after accounting for SAC, the morans I was significant and SAC was not accounted for. So it would be great if you can see I am doing something wrong while accepting for the SAC below…please


regards


> On 10 Jul 2018, at 8:38 PM, Patrick Schratz <patrick.schratz using gmail.com> wrote:
> 
> Hi Dechen,
> 
> it is very important to account for SAC in any model. This can be done in various ways. In log.reg it is common to include spatial autocorrelation structures that describe the underlying SAC. To do so, you can use mixed models, e.g. MASS::glmmPQL().
> 
> Also have a look at Wood (2017) Generalized Additive Models in R.
> 
> I did account for it in my master thesis.Even though the code is not attached, it may help you: https://zenodo.org/record/814262 <https://zenodo.org/record/814262>
> Cheers, Patrick
> On Jul 10 2018, at 7:46 pm, Dechen Lham <dechen.lham using ieu.uzh.ch> wrote:
> 
> Hello all,
> 
> I would like some help in my problem below:
> 
> I am running a logistic regression and my best model residuals has spatial autocorrelation (SAC) when checked as below and also on the raw data of the response type. My response is binary 0 and 1 (type of prey and to be predicted by several predictors). These type of prey are obtained from a total of 200 locations (where the faecal samples are collected from). In order to account for this SAC , I used the auto_covdist function from spdep package. But when i use this as a new predictor in my model, and then check for spatial autocorrelation in the residues of the model, there is still spatial autocorrelation,…..could u see if i am doing something wrong please?
> 
> #account for SAC in the model using weights
> # auto_covariate is a distance weighted covariate
> data$response <- as.numeric(data$response)
> auto_weight <- autocov_dist(data$prey.type, xy=coords, nbs=1, type="inverse", zero.policy = TRUE,style="W", longlat = TRUE)
> 
> m5_auto <- glm(response ~ predictor1 + predictor2 + predictor3 + predictor4 + predictor1:predictor4, weight=auto_weight, family=quasibinomial("logit"), data=data)
> 
> # check spatial autocorrelation - first convert data to spatial points dataframe
> dat <- SpatialPointsDataFrame(cbind(data$long, data$lat), data)
> lstw <- nb2listw(knn2nb(knearneigh(dat, k = 2)))
> 
> # check SAC in model residuals
> moran.test(residuals.glm(m5_auto), lstw) # and gives the below:
> 
> Moran I test under randomisation
> 
> data: residuals.glm(m5)
> weights: lstw
> 
> Moran I statistic standard deviate = 1.9194, p-value = 0.02747
> alternative hypothesis: greater
> sample estimates:
> Moran I statistic Expectation Variance
> 0.160824328 -0.004608295 0.007428642
> 
> -Someone said its stupid to account for spatial autocorrelation in a logistic regression when you have a significant SAC using moran’s I. So i am now wondering how this can be solved? or does a SAC in a logistic regression be just ignored?
> 
> I am new to spatial statistics and now idea how to move with such. I only know that my data has spatial
> autocorrelation (which i hope to have checked correctly using morans I as above) and now need to account for this in my analysis. Some advice would be greatly appreciated by people who have used to account for SAC in their logistic models. Is a logistic mixed models an option to consider?especially if your covariates are spatial in nature,…i read somewhere that if you cant account for SAC in glm then you can move to mixed models esp if your covariates are spatial which is expected to digest the SAC.
> 
> Help and advice would be greatly appreciated.
> 
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