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
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.