0

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. When I try to use existing libraries and functions, such as `spdep`

's `lagsarlm`

, `glmmfields`

, `spatialreg`

, `gstat`

, `geoRglm`

, 'spatialprobit' 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 independent 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.