mixure models

Plot table proportions by predicted class {1, 2} for each outcome variable and demographic variable

Prorportion of class membership prediction

• From the posterior distribution we get the probability for each respondent of being a member of a class.

survey %>% select(outcome_variables) %>%  get_label() 

##                                                                           ABINSPAY 
##               "Should people be able to use health insurance for abortion - Ver Y" 
##                                                                          ABMEDGOV1 
##     "Woman and doctor or Govt should decide what info needed for abortion - Ver X" 
##                                                                            ABHELP1 
##                                      "R would help with arrangements for abortion" 
##                                                                            ABHELP2 
##                                            "R would help with paying for abortion" 
##                                                                            ABHELP3 
##                        "R would help with paying for abortion-related other costs" 
##                                                                            ABHELP4 
##                                 "R would help with emotional support for abortion" 
##                                                                            ABMORAL 
##                                             "R has a moral opposition to abortion" 
##                                                                           ABSTATE1 
##                                    "Difficulty of obtaining abortion in R's state" 
##                                                                           ABSTATE2 
## "Change abortion laws in R's state to make it easier or harder to get an abortion"

breakdown of class membership predictions

df %>% group_by(class_prediction) %>%  
  count() %>%  
  ungroup() %>% 
  mutate(proportion = n/sum(n)) %>% 
  datatable()

df %>% group_by(class_prediction) %>%  
  count_(outcome_variables) %>%  
  mutate(proportion = n/sum(n)) %>% 
  arrange(desc(proportion)) %>%  
  datatable()

demographic variables value codes

survey %>% select(demogrpahic_variables) %>%  get_labels() 

## $SEX
## [1] "MALE"   "FEMALE"
## 
## $RACE
## [1] "IAP"   "WHITE" "BLACK" "OTHER"
## 
## $CLASS
## [1] "IAP"           "LOWER CLASS"   "WORKING CLASS" "MIDDLE CLASS" 
## [5] "UPPER CLASS"   "NO CLASS"      "DK"            "NA"           
## 
## $PARTYID
##  [1] "STRONG DEMOCRAT"    "NOT STR DEMOCRAT"   "IND,NEAR DEM"      
##  [4] "INDEPENDENT"        "IND,NEAR REP"       "NOT STR REPUBLICAN"
##  [7] "STRONG REPUBLICAN"  "OTHER PARTY"        "DK"                
## [10] "NA"

predicted class memberhip by gender

df %>% 
  group_by(class_prediction) %>% 
  count(SEX) %>% 
  ungroup() %>% 
  mutate(proportion = n/sum(n)) %>% 
  plot_ly(x = ~SEX, y = ~proportion, type = 'bar', colors=~class_prediction, name = ~class_prediction) %>% 
  layout(yaxis = list(title = 'Proportion', range=c(0,1)), barmode = 'group')

predicted class memberhip by RACE

df %>% 
  group_by(class_prediction) %>% 
  count(RACE) %>% 
  ungroup() %>% 
  mutate(proportion = n/sum(n)) %>% 
  plot_ly(x = ~RACE, y = ~proportion, type = 'bar', colors=~class_prediction, name = ~class_prediction) %>% 
  layout(yaxis = list(title = 'Proportion', range=c(0,1)), barmode = 'group')

predicted class memberhip by CLASS

df %>% 
  group_by(class_prediction) %>% 
  count(CLASS) %>% 
  ungroup() %>% 
  mutate(proportion = n/sum(n)) %>% 
  plot_ly(x = ~CLASS, y = ~proportion, type = 'bar', colors=~class_prediction, name = ~class_prediction) %>% 
  layout(yaxis = list(title = 'Proportion', range=c(0,1)), barmode = 'group')

predicted class memberhip by PARTYID

df %>% 
  group_by(class_prediction) %>% 
  count(PARTYID) %>% 
  ungroup() %>% 
  mutate(proportion = n/sum(n)) %>% 
  plot_ly(x = ~PARTYID, y = ~proportion, type = 'bar', colors=~class_prediction, name = ~class_prediction) %>% 
  layout(yaxis = list(title = 'Proportion', range=c(0,1)), barmode = 'stack')