basic multinomial logistic regression model added

code/2022_12_19.R

@@ -4,7 +4,7 @@ library(pacman)
 
 p_load(
   #--- Packages to Fit Models
-  MASS, logistf, survival, ISLR2, tree, randomForest, gbm, 
+  MASS, logistf, survival, ISLR2, tree, randomForest, gbm, nnet, caret,
   #--- Packages to Produce Tables
   gtsummary, flextable, janitor, broom, officer, kableExtra, reactable, 
   #--- Packages to Produce Figures
@@ -18,35 +18,41 @@ data <- read_csv(file.path(here(), "data", "created_data", "2022_12_02.csv"))
 
 ## categorising proportion based on #of times/year. 
 data <- data %>% 
-  mutate(prop_cat = factor(case_when(prop <= 3/12 ~ "stage1", 
-                                     prop <= 1 ~ "stage2"), 
-                           levels = c("stage1", "stage2")), 
+  mutate(prop_cat = factor(case_when(prop <= 0.1 ~ "stage1", 
+                                     (0.1 < prop & prop <= 0.30) ~ "stage2", 
+                                     (0.30 < prop & prop <= 1.00) ~ "stage3"),
+                           levels = c("stage1", "stage2", "stage3")), 
          sex = as.factor(sex), 
          n_br = as.factor(n_br), 
          n_dep = as.factor(n_dep), 
          name = as.factor(name))
 
-## v basic tree fitting
-tree.naive.model <- tree(prop_cat ~ age + sex + n_dep + n_br + rent + name, data = data)
-summary(tree.naive.model) #lmao this is dogshit
-
-## pruning the tree above
-cv.tree.naive.model <- cv.tree(tree.naive.model, FUN = prune.misclass)
-cv.tree.naive.model
+# ## v basic tree fitting
+# tree.naive.model <- tree(prop_cat ~ age + sex + n_dep + name, data = data)
+# summary(tree.naive.model)
+# 
+# ## pruning the tree above
+# cv.tree.naive.model <- cv.tree(tree.naive.model, FUN = prune.misclass)
+# cv.tree.naive.model
 
 ## random forest 
-rf.naive.model <- randomForest(prop_cat ~ age + sex + n_dep + n_br + rent + name, 
-                               data = data, 
-                               ntree = 30, 
-                               importance = TRUE)
-rf.naive.model  
-
+# rf.naive.model <- randomForest(prop_cat ~ age + sex + n_dep + name, 
+#                                data = data, 
+#                                ntree = 30, 
+#                                classwt = data %>% group_by(prop_cat) %>% summarise(n = n()) %>% pull(n), 
+#                                importance = TRUE)
+# rf.naive.model
 
 ## boosted trees
-data.boost <- data %>% mutate(prop_cat = case_when(prop_cat == "stage1" ~ 0, 
-                                                   prop_cat == "stage2" ~ 1))
-b.naive.model <- gbm(prop_cat ~ age + sex + n_dep + n_br + rent + name, 
-                     data = data.boost, 
-                     distribution = "bernoulli", 
-                     n.trees = 5000, interaction.depth = 3)
-summary(b.naive.model)
+# data.boost <- data %>% mutate(prop_cat = case_when(prop_cat == "stage1" ~ 0, 
+#                                                    prop_cat == "stage2" ~ 1))
+# b.naive.model <- gbm(prop_cat ~ age + sex + n_dep + name, 
+#                      data = data.boost, 
+#                      distribution = "bernoulli", 
+#                      n.trees = 5000, interaction.depth = 3)
+# summary(b.naive.model)
+
+confusionMatrix(predict(multinom(prop_cat ~ age + sex + n_dep + name, data = data),  data), 
+                data$prop_cat)
+
+