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#!/usr/bin/env Rscript
# https://www.r-bloggers.com/2015/09/how-to-perform-a-logistic-regression-in-r/
data <- read.csv("survey.csv")
str(data)
head(data)
data$price20 <- ifelse(data$Price == 20, 1, 0)
data$price30 <- ifelse(data$Price == 30, 1, 0)
head(data)
model <- glm(
MYDEPV ~ Income + Age + price20 + price30,
family = binomial(link = "logit"),
data = data
)
summary(model)
coef(model)
plot(data$Income, data$MYDEPV)
test_dat <- data.frame(Income = seq(20, 100, 1), Age = 20, price20 = 1, price30 = 0)
pred <- predict(model, newdata = test_dat, type = "response")
lines(test_dat$Income, pred, col = "blue", lwd = 2)
new_data3 <- data.frame(
Income = c(58),
Age = c(25),
price20 = c(1),
price30 = c(0)
)
predicted <- predict(model, newdata = new_data3)
print(1 / (1 + exp(-predicted)))
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