MODELOS JARDÍN

datos1 <- JARDIN_1


head(datos1)

library(tidyverse)
library(ISLR)
default1 <- JARDIN_1$Xanthomonas
balance1<- JARDIN_1$Northing + JARDIN_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 JARDÍN 1",
       y = "Probabilidad default") +
  theme(legend.position = "none")

datos2 <- JARDIN_2


head(datos2)

default2 <- JARDIN_2$Xanthomonas
balance2<- JARDIN_2$Northing + JARDIN_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 JARDÍN 2",
       y = "Probabilidad default") +
  theme(legend.position = "none")

datos3 <- JARDIN_3


head(datos3)

default3 <- JARDIN_3$Xanthomonas
balance3<- JARDIN_3$Northing + JARDIN_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 JARDÍN 3",
       y = "Probabilidad default") +
  theme(legend.position = "none")

datos4 <- JARDIN_4


head(datos4)

default4 <- JARDIN_4$Xanthomonas
balance4<- JARDIN_4$Northing + JARDIN_4$Easting

modelo_logistico4 <- glm(default4 ~ balance4, data = datos4, family = "binomial")

ggplot(data = datos4, aes(x = balance4, y = default4)) +
  geom_point(aes(color = as.factor(default4)), shape = 1) + 
  stat_function(fun = function(x){predict(modelo_logistico4,
                                          newdata = data.frame(balance4 = x),
                                          type = "response")}) +
  theme_bw() +
  labs(title = "Regresión logística JARDÍN 4",
       y = "Probabilidad default") +
  theme(legend.position = "none")