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 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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