library(readr)
library(AER)
library(dplyr)
Titanic_2 <- read_csv("https://stanford.io/2O9RUCF")[,-3]
colnames(Titanic_2) = c("Survived", "Class", "Sex", "Age", "Siblings", "Parents", "Fare")
Titanic_2$Class <- as.factor(Titanic_2$Class)
passengers <- cbind("Class" = as.factor(1:3), as.data.frame(Titanic_2 %>% select(-Sex,-Survived,-Class) %>% summarise_all(funs(mean)) %>% mutate(Sex = "male")))
# fit the Probit model and assign it to `Pogit_mod`
# obtain a robust summary of the model coefficients
# predict the probability of survival for the three passengers
# fit the Probit model, assign it to `Probit_mod`
Probit_mod <- glm(Survived ~ ., family = binomial(link = "probit"), data = Titanic_2)
# obtain a robust summary of the model coefficients
coeftest(Probit_mod, vcovHC)
# predict the probability of survival for the hypothecial individuals
predict(Probit_mod, newdata = passengers, type = "response")
test_predefined_objects("Titanic_2")
test_object("Probit_mod")
test_predefined_objects("passengers")
test_output_contains("predict(Probit_mod, newdata = passengers, type = 'response')")
success_msg("Nicely done! Note that ")