## ----include = FALSE---------------------------------------------------------- knitr::opts_chunk$set( collapse = TRUE, comment = "#>", eval = FALSE ) ## ----------------------------------------------------------------------------- # library(hbsaems) # # # Load data # data("data_fhnorm") # data <- data_fhnorm # head(data) # # # Load adjacency matrix # data("adjacency_matrix_car") # adjacency_matrix_car ## ----------------------------------------------------------------------------- # model_car <- hbm( # formula = bf(y ~ x1 + x2 + x3), # Formula model # hb_sampling = "gaussian", # Gaussian family for continuous outcomes # hb_link = "identity", # Identity link function (no transformation) # re = ~(1|group), # sre = "sre", # Spatial random effect variable # sre_type = "car", # car_type = "icar", # M = adjacency_matrix_car, # data = data) # Dataset # # summary(model_car) ## ----------------------------------------------------------------------------- # # Load data # data("data_betalogitnorm") # head(data_betalogitnorm) # # model_car_beta <- hbm_betalogitnorm(response = "y", # predictors = c("x1", "x2", "x3"), # sre = "sre", # sre_type = "car", # car_type = "icar", # M = adjacency_matrix_car, # data = data_betalogitnorm) # summary(model_car_beta) ## ----------------------------------------------------------------------------- # library(hbsaems) # # # Load data # data("data_fhnorm") # data <- data_fhnorm # head(data) # # # Load adjacency matrix # data("spatial_weight_sar") # spatial_weight_sar ## ----------------------------------------------------------------------------- # model_sar <- hbm( # formula = bf(y ~ x1 + x2 + x3), # Formula model # hb_sampling = "gaussian", # Gaussian family for continuous outcomes # hb_link = "identity", # Identity link function (no transformation) # re = ~(1|group), # sre_type = "sar", # sar_type = "lag", # M = spatial_weight_sar, # data = data) # Dataset # # summary(model_sar)