MODELOS LA UNIÓN

datos1 <- LA_UNION_1


head(datos1)

library(tidyverse)
library(ISLR)
default1 <-LA_UNION_1$Xanthomonas
balance1<- LA_UNION_1$Northing + LA_UNION_1$Easting

modelo_logistico1 <- glm(default1 ~ balance1, data = datos1, family = "binomial")

ggplot(data = datos1, aes(x = balance1, y = default1)) +
  geom_point(aes(color = as.factor(default1)), shape = 1) + 
  stat_function(fun = function(x){predict(modelo_logistico1,
                                          newdata = data.frame(balance1 = x),
                                          type = "response")}) +
  theme_bw() +
  labs(title = "Regresión logística LA UNIÓN 1",
       y = "Probabilidad default") +
  theme(legend.position = "none")

datos2 <- LA_UNION_2


head(datos2)

default2 <- LA_UNION_2$Xanthomonas
balance2<- LA_UNION_2$Northing + LA_UNION_2$Easting

modelo_logistico2 <- glm(default2 ~ balance2, data = datos2, family = "binomial")

ggplot(data = datos2, aes(x = balance2, y = default2)) +
  geom_point(aes(color = as.factor(default2)), shape = 1) + 
  stat_function(fun = function(x){predict(modelo_logistico2,
                                          newdata = data.frame(balance2 = x),
                                          type = "response")}) +
  theme_bw() +
  labs(title = "Regresión logística LA UNIÓN 2",
       y = "Probabilidad default") +
  theme(legend.position = "none")

datos3 <-LA_UNION_3


head(datos3)

default3 <- LA_UNION_3$Xanthomonas
balance3<- LA_UNION_3$Northing +LA_UNION_3$Easting

modelo_logistico3 <- glm(default3 ~ balance3, data = datos3, family = "binomial")

ggplot(data = datos3, aes(x = balance3, y = default3)) +
  geom_point(aes(color = as.factor(default3)), shape = 1) + 
  stat_function(fun = function(x){predict(modelo_logistico3,
                                          newdata = data.frame(balance3 = x),
                                          type = "response")}) +
  theme_bw() +
  labs(title = "Regresión logística LA UNIÓN 3",
       y = "Probabilidad default") +
  theme(legend.position = "none")