I am attempting to find the marginal effects for each covariate in the model: endorseFULL <- endorse(Y = Y, data = data_slvk, identical.lambda = FALSE, covariates = TRUE, formula.indiv = formula( ~ age + gender + education + is_capital + ideology + income + DemPolGrievence + PolicyPolGrievence + EUPolGrievence + EconGrievenceRetro + EconGrievenceProspInd + EconGrievenceProspAgg + NatPride + NativeRights + NativeJobs + DemonstrateNational + LawOrder + Chauvinism + ChristianSchool + DemonstrateTrad + GayNeighbor+ GayPartner+ ForNeighbor + ForPartner + Ukraine), hierarchical = FALSE). This is a Bayesian Model using an MCMC algorithm and it is being used to estimate the amount of support for militia groups. What we want to find is what the marginal effect on the support for the militia group would be if one of the covariates was tweaked. The covariates in question are: vars <- c(“id”, “gender”, “age”, “education”, “is_capital”, “income”, “ideology”, “DemPolGrievence”,”PolicyPolGrievence”,”EUPolGrievence”,”EconGrievenceRetro”,”EconGrievenceProspInd”,”EconGrievenceProspAgg”,”NatPride”,”NativeRights”,
“NativeJobs”,”LawOrder”,”Chauvinism”,”ChristianSchool”,”GayNeighbor”,”GayPartner”,”ForNeighbor”,”ForPartner”,”Ukraine”, “DemonstrateNational”, “DemonstrateTrad”).