Hello Roger,

Once again thanks for your help. About the alpha level. Do I have a problem
of increasing it to a 0.5 value?

Thank you


2013/6/11 Roger Bivand <Roger.Bivand@nhh.no>

> On Tue, 11 Jun 2013, Rodrigo Tardin wrote:
>
>  Hello Roger,
>>
>> Thanks a lot for your help. I fit a model as you said but with a binomial
>> response and I did ok! However, in ME model, I had to increase the alpha
>> value to 0.5.
>>
>
> You can only use SpatialFiltering when you could use lm(), not glm() to
> fit the model, its results are only valid in that case. If your response is
> binomial, only the results of ME are valid. In addition, only ME() handles
> the offset correctly as far as I know. Do not use SpatialFiltering when
> themodel cannot not be fitted with lm().
>
> Roger
>
>
>
>> Is it bad to have such a "high" alpha value?
>>
>>
>> The results for ME and Spatial Filtering models were a little bit
>> different:
>> anova(glmerr12,test="Chisq")
>>
>> Analysis of Deviance Table
>>
>> Model: binomial, link: logit
>>
>> Response: OCC_BIN
>>
>> Terms added sequentially (first to last)
>>
>>
>>                Df Deviance Resid. Df Resid. Dev Pr(>Chi)
>> NULL                               81     984.43
>> DEPTH            1   322.27        80     662.17   <2e-16 ***
>> DISTCOAST        1   114.42        79     547.74   <2e-16 ***
>> SLOPE            1     0.00        78    2739.32        1
>> fitted(errcol2)  1   360.44        77    2378.88   <2e-16 ***
>>
>>
>> anova(glmerr1,test="Chisq")
>> Analysis of Deviance Table
>>
>> Model: gaussian, link: identity
>>
>> Response: OCC_BIN
>>
>> Terms added sequentially (first to last)
>>
>>
>>                Df Deviance Resid. Df Resid. Dev  Pr(>Chi)
>> NULL                               81      20189
>> DEPTH            1   1916.3        80      18272 0.0006276 ***
>> DISTCOAST        1   5093.0        79      13179 2.484e-08 ***
>> SLOPE            1    557.4        78      12622 0.0651683 .
>> fitted(errcol1)  1      1.7        77      12620 0.9180173
>> ---
>> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>>
>>
>> Both models showed significant relationship with DEPTH and DISTANCE TO
>> COAST and not with SLOPE. However, one model was significant for fitted
>> spatial autocorrelation (ME model) and the other did not.
>>
>>
>> When I tried to compare the results via anova (as done in spdep manual -
>> pag.118) for these binomial models, I could not obtain the p level. My
>> code:
>> ##Anova comparing both models
>> anova(glmerr12,glmerr1,test="**Chisq")
>> Analysis of Deviance Table
>>
>> Model 1: OCC_BIN ~ DEPTH + DISTCOAST + SLOPE + fitted(errcol2)
>> Model 2: OCC_BIN ~ DEPTH + DISTCOAST + SLOPE + fitted(errcol1)
>>  Resid. Df Resid. Dev Df Deviance Pr(>Chi)
>> 1        77     2378.9
>> 2        77    12620.3  0   -10241
>>
>> ##glmerr 12 is the ME model
>> ##glmerr1 is the Spatial Filtering model
>>
>>
>> Can you help me (or others in this list) one more time?
>>
>> Once again thanks a lot for everyone who can help me here.
>>
>> Rodrigo
>>
>>
>> 2013/6/10 Roger Bivand <Roger.Bivand@nhh.no>
>>
>>  On Mon, 10 Jun 2013, Rodrigo Tardin wrote:
>>>
>>>  Dear list,
>>>
>>>>
>>>> I am new to this list and I am trying to fit a Spatial Eigenvector GLM
>>>> to
>>>> count data.
>>>>
>>>> I am trying to fit a ME model in spdep package to investigate
>>>> environmental
>>>> drivers on Bryde's whale distribution. I have 3 environmental variables
>>>> (depth, distance to coast, slope), my response variable is the
>>>> occurrence
>>>> (counts) of whales in 82 km2 grids.
>>>>
>>>> I used the dnearneigh function to build .nb data using the coordinates
>>>> data.
>>>>
>>>>     nb1  <-  dnearneigh(xy,  0,  10)  ----
>>>>>
>>>>>  and: lw1  <-  nb2listw(nb1,  style="W")
>>>>
>>>> x - is latitude coordinates
>>>> y - is longitude coordinates
>>>>
>>>>
>>>> Using the glm and Spatial filtering function (as done in spdep manual) I
>>>> had no problems. However, when I used the 'ME' it says: base correlation
>>>> larger than alpha. My alpha is 0.05.
>>>>
>>>> Here it is my code:
>>>> ##This part works well
>>>> glmbase1  <-  glm(OCC  ~  DEPTH  +  DISTCOAST+SLOPE,  offset=RT,
>>>> data=bryde3)
>>>>
>>>> lagcol1  <-  SpatialFiltering(OCC_BIN  ~  1,  ~  DEPTH  +  DISTCOAST+
>>>> SLOPE, data=bryde3,  style="W",  nb=nb1, alpha=0.05,  verbose=TRUE)
>>>>
>>>> glmlag1  <-  glm(OCC ~  DEPTH  +  DISTCOAST+SLOPE +  fitted(lagcol1),
>>>> offset=RT,data=bryde3)
>>>>
>>>>
>>> These are not the same models - lagcol1 is as you know the process in the
>>> response (here OCC - RT??), not in the error. In ME, the process is in
>>> the
>>> error, and it looks as though the covariates mop up much of the spatial
>>> dependence (it is also a Poisson model with a default log link).
>>> SpatialFiltering() does not handle an offset (as you have realised, I
>>> think), so again, this is a possible source of difference. ME is testing
>>> slightly diffferently too. Do:
>>>
>>> errcol1  <-  SpatialFiltering(I(OCC-RT)  ~  DEPTH  +  DISTCOAST +
>>>
>>>  SLOPE, data=bryde3,  style="W",  nb=nb1, alpha=0.05,  verbose=TRUE)
>>> glmerr1  <-  glm(OCC ~  DEPTH  +  DISTCOAST+SLOPE +  fitted(errcol1),
>>>  offset=RT, data=bryde3)
>>>
>>> and
>>>
>>> errcol2  <-  ME(OCC ~  DEPTH  +  DISTCOAST+SLOPE,  offset=RT,
>>> data=bryde3,
>>> family="gaussian", listw=lw1,  alpha=0.05,  verbose=TRUE)
>>> glmerr12  <-  glm(OCC ~  DEPTH  +  DISTCOAST+SLOPE +  fitted(errcol2),
>>>  offset=RT, data=bryde3)
>>>
>>> give similar results?
>>>
>>> Hope this helps,
>>>
>>> Roger
>>>
>>>
>>>
>>>
>>>
>>>  anova(glmlag1)
>>>> anova(glmbase1,  glmlag1)
>>>> set.seed(123)
>>>>
>>>> ##From here on, it does not
>>>> lagcol1  <-  ME(OCC ~  DEPTH  +  DISTCOAST+SLOPE,  offset=RT,
>>>> data=bryde3,
>>>> family="poisson",
>>>> listw=lw1,  alpha=0.05,  verbose=TRUE)
>>>>
>>>> Error in ME(OCC ~ DEPTH + DISTCOAST + SLOPE, data = bryde3, family =
>>>> "poisson",  :
>>>>  base correlation larger than alpha
>>>>
>>>> When I increase alpha to 0.6 (lower values keep saying the same error),
>>>> this error message does not appear. However, when I try to rerun GLM
>>>> with
>>>> eigenvectors included as covariates (as done in spdep manual - page
>>>> 118) I
>>>> have the following error message:
>>>>
>>>> Error in glm.fit(x = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
>>>>  :
>>>>  NA/NaN/Inf in 'x'
>>>> warning message: step size truncated due to divergence
>>>>
>>>> Can anyone please help me?
>>>> Other question, if I may, is there a strong difference between Spatial
>>>> Filtering and ME?
>>>>
>>>> Many thanks in advance,
>>>> Rodrigo
>>>>
>>>>
>>>>  --
>>> Roger Bivand
>>> Department of Economics, NHH Norwegian School of Economics,
>>> Helleveien 30, N-5045 Bergen, Norway.
>>> voice: +47 55 95 93 55; fax +47 55 95 95 43
>>> e-mail: Roger.Bivand@nhh.no
>>>
>>>
>>>
>>
>>
>>
> --
> Roger Bivand
> Department of Economics, NHH Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; fax +47 55 95 95 43
> e-mail: Roger.Bivand@nhh.no
>



-- 
Rodrigo Tardin

Doutorando em Ecologia e Conservação - IBRAG - UERJ
Mestre em Biologia Animal - PPGBA - UFRRJ
Especialista em Docência do Ensino Superior - IAVM
Laboratório de Bioacústica e Ecologia de Cetáceos - UFRRJ/ IF/ DCA

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