dat <- read.delim("clipboard", dec=","); attach(dat)

names(dat)
head(dat)
str(dat)
dat$Especie=as.factor(dat$Especie)
attach(dat)
levels(Especie)
str(dat)

model.results = glm(Muerte ~ Especie*Concentracion, binomial(link = "probit"))
summary(model.results)

127/116

par(mfrow=c(2,2))
plot(model.results)

install.packages("DHARMa")

library(DHARMa)
residh <- simulateResiduals(model.results)

plot(residh)

anova(model.results, test="Chisq")

#Elaborar Ho y Ha

model.results1 = glm(Muerte ~ Especie+Concentracion, binomial(link = "probit"))
summary(model.results1)
residh1 <- simulateResiduals(model.results1)
plot(residh1)
anova(model.results1, test="Chisq")

model.results2 = glm(Muerte ~ Concentracion, binomial(link = "probit"))
summary(model.results2)
residh2 <- simulateResiduals(model.results2)
plot(residh2)
anova(model.results2, test="Chisq")
anova(model.results,model.results2, test="Chisq")


#Qué pasos siguen?

library(ggplot2)
library(sjPlot)



p <- plot_model(model.results1, type = "pred", terms = c("Concentracion[n=300]","Especie"),
                digits=1, line.size=0.5, show.data=F,
                colors = viridisLite::magma(begin = 0.2, end = 0.8, n = 3) # Color blind friendly.
)
p


p <- plot_model(model.results2, type = "pred", terms = c("Concentracion[n=300]"),
                digits=1, line.size=0.5, show.data=F,
                colors = viridisLite::magma(begin = 0.2, end = 0.8, n = 3) # Color blind friendly.
)
p