datos1 <- ENTRE_RIOS_1
head(datos1)
library(tidyverse)
library(ISLR)
default1 <- ENTRE_RIOS_1$Xanthomonas
balance1<- ENTRE_RIOS_1$Northing + ENTRE_RIOS_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 ENTRE RIOS 1",
y = "Probabilidad default") +
theme(legend.position = "none")

datos2 <- ENTRE_RIOS_2
head(datos2)
default2 <- ENTRE_RIOS_2$Xanthomonas
balance2<- ENTRE_RIOS_2$Northing + ENTRE_RIOS_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 ENTRE RIOS 2",
y = "Probabilidad default") +
theme(legend.position = "none")

datos3 <- ENTRE_RIOS_3
head(datos3)
default3 <- ENTRE_RIOS_3$Xanthomonas
balance3<- ENTRE_RIOS_3$Northing + ENTRE_RIOS_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 ENTRE RIOS 3",
y = "Probabilidad default") +
theme(legend.position = "none")

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