```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```

```{r}
library(tidyverse)
library(agridat)
library(emmeans)
library(multcomp)
```


```{r}
data("omer.sorghum")
df <- omer.sorghum %>% filter(env == "E3")
```

```{r}
n_distinct(df$gen)
n_distinct(df$rep)

df %>% 
  ggplot(aes(gen, yield))+
  geom_point()

```

```{r}
lm(yield ~ 1 + gen + rep, data = df)

options(contrasts = c("contr.sum", "contr.poly"))

m1 <- lm(yield ~ 1 + gen + rep, data = df)

summary(m1)

# glmmTMB(yield ~ 1 + gen + rep, data = df,
#         family = gaussian())
```

```{r}
# constant variance
plot(x = m1$fitted.values, y = abs(m1$residuals))

summary(lm(abs(m1$residuals)  ~ m1$fitted.values))
```

```{r}
car::qqPlot(m1)
```

```{r}
car::Anova(m1, type = 2)
```

```{r}
sqrt(1306996/51)

summary(m1)$sigma
```

```{r}
m2 <- lm(yield ~ gen , data = df)
car::Anova(m2)
```

```{r}
sqrt(1540802/54)
summary(m2)$sigma
```

```{r}
means_1<-emmeans(m1, ~gen)
means_2<-emmeans(m2, ~gen)

cld(means_1, method = "sidak", Letters = letters)
cld(means_2, method = "sidak", Letters = letters)
```