library(corrplot)
library(lattice)
library(ggplot2)
library(plotly)
library(RVAideMemoire)
library(vegan)

# Ejemplo reproducible
set.seed(123)
df <- data.frame(
  Tratamiento = factor(rep(c("T1", "T2", "T3"), each = 10)),
  Grasas = rnorm(30, 10, 2),
  Humedad = rnorm(30, 20, 3),
  Proteina = rnorm(30, 15, 2),
  Cenizas = rnorm(30, 5, 1)
)

# Matriz de correlación
matriz_cor <- cor(df[, -1], method = "pearson")
corrplot(matriz_cor, method = "color", type = "upper", order = "hclust",
         addCoef.col = "black", tl.col = "black", tl.srt = 45, diag = FALSE)

# Boxplot
bwplot(Proteina ~ Tratamiento, data = df)

# Scatter plot
ggplot(df, aes(x = Proteina, y = Cenizas, color = Tratamiento)) +
  geom_point(size = 4, alpha = 0.7) +
  theme_minimal()

# ANOVA univariado
modelo_anova <- aov(Cenizas ~ Tratamiento, data = df)
summary(modelo_anova)
shapiro.test(residuals(modelo_anova))
bartlett.test(Cenizas ~ Tratamiento, data = df)

# Tukey
TukeyHSD(modelo_anova)

# MANOVA
variables_respuesta <- cbind(df$Grasas, df$Humedad, df$Proteina, df$Cenizas)
modelo_manova <- manova(variables_respuesta ~ Tratamiento, data = df)
summary(modelo_manova)
summary.aov(modelo_manova)

# PERMANOVA
modelo_permanova <- adonis2(df[, c("Grasas", "Humedad", "Proteina", "Cenizas")] ~ Tratamiento,
                            data = df,
                            method = "euclidean",
                            permutations = 999)
print(modelo_permanova)

matriz_distancias <- vegdist(df[, c("Grasas", "Humedad", "Proteina", "Cenizas")], method = "euclidean")
pairwise.perm.manova(matriz_distancias, df$Tratamiento, nperm = 999, p.method = "holm")