R help - Jun 2020 - GLM model with spatialspillover on categorical variables

I did a regression analysis with categorical data with a glm model
approach, which worked fine. I have longitude and latitude coordinates for
each observation and I want to add their geographic spillover effect to the
model.

My sample data is structured:

Index DV IVI IVII IVIII IVIV Long Lat
 1  0  2  1  3  -12  -17.8  12
 2  0  1  1  6  112  11  -122
 3  1  3  6  1  91  57  53

with regression eq. DV ~ IVI + IVII + IVIII + IVIV

That mentioned, I assume that the nearer regions are, the more it may
influence my dependant variable. I found several approaches for spatial
regression models, but not for categorical data. I tried to use existing
libraries and functions, such as spdep's lagsarlm, glmmfields, spatialreg,
gstat, geoRglm and many more (I used this list as a reference:
https://cran.r-project.org/web/views/Spatial.html ). For numeric values, I
am able to do spatial regression, but for categorical values, I struggle.
The data structure is the following:

library(dplyr)
data <- data %>%
  mutate(
    DV = as.factor(DV),
    IVI = as.factor(IVI),
    IVII = as.factor(IVII),
    IVIII = as.factor(IVIII),
    IVIV = as.numeric(IVIV),
    longitude = as.numeric(longitude),
    latitude = as.numeric(latitude)
  )

My dependant variable (0|1) as well as my independant variables are
categorical and it would be no use to transform them, of course. I want to
have an other glm model in the end, but with spatial spillover effects
included. The libraries I tested so far can't handle categorical data. Any
leads/ideas would be greatly appreciated.

Thanks a lot.

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You should post on r-sig-geo, the list devoted to spatial data analysis,
rather than here.


Bert Gunter

"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )


On Thu, Jun 4, 2020 at 12:17 PM Lena Fehlhaber <sturmiechen at gmail.com>
wrote:
> I did a regression analysis with categorical data with a glm model
> approach, which worked fine. I have longitude and latitude coordinates for
> each observation and I want to add their geographic spillover effect to the
> model.
>
> My sample data is structured:
>
> Index DV IVI IVII IVIII IVIV Long Lat
>  1  0  2  1  3  -12  -17.8  12
>  2  0  1  1  6  112  11  -122
>  3  1  3  6  1  91  57  53
>
> with regression eq. DV ~ IVI + IVII + IVIII + IVIV
>
> That mentioned, I assume that the nearer regions are, the more it may
> influence my dependant variable. I found several approaches for spatial
> regression models, but not for categorical data. I tried to use existing
> libraries and functions, such as spdep's lagsarlm, glmmfields,
spatialreg,
> gstat, geoRglm and many more (I used this list as a reference:
> https://cran.r-project.org/web/views/Spatial.html ). For numeric values, I
> am able to do spatial regression, but for categorical values, I struggle.
> The data structure is the following:
>
> library(dplyr)
> data <- data %>%
>   mutate(
>     DV = as.factor(DV),
>     IVI = as.factor(IVI),
>     IVII = as.factor(IVII),
>     IVIII = as.factor(IVIII),
>     IVIV = as.numeric(IVIV),
>     longitude = as.numeric(longitude),
>     latitude = as.numeric(latitude)
>   )
>
> My dependant variable (0|1) as well as my independant variables are
> categorical and it would be no use to transform them, of course. I want to
> have an other glm model in the end, but with spatial spillover effects
> included. The libraries I tested so far can't handle categorical data.
Any
> leads/ideas would be greatly appreciated.
>
> Thanks a lot.
>
>         [[alternative HTML version deleted]]
>
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