datos1 <- SAN_VICENTE_1
head(datos1)
library(tidyverse)
library(ISLR)
default1 <- SAN_VICENTE_1$Xanthomonas
balance1<- SAN_VICENTE_1$Northing + SAN_VICENTE_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 SAN VICENTE 1",
y = "Probabilidad default") +
theme(legend.position = "none")

datos2 <- SAN_VICENTE_2
head(datos2)
default2 <- SAN_VICENTE_2$Xanthomonas
balance2<- SAN_VICENTE_2$Northing + SAN_VICENTE_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 SAN VICENTE 2",
y = "Probabilidad default") +
theme(legend.position = "none")

datos3 <- SAN_VICENTE_3
head(datos3)
default3 <- SAN_VICENTE_3$Xanthomonas
balance3<- SAN_VICENTE_3$Northing + SAN_VICENTE_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 SAN VICENTE 3",
y = "Probabilidad default") +
theme(legend.position = "none")

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