Exam5.1 {eda4treeR}

Example 5.1 from Experimental Design and Analysis for Tree Improvement

Description

Exam5.1 presents the height of 27 seedlots from 4 sites.

Author(s)

1. Muhammad Yaseen ([e-mail])

2. Sami Ullah ([e-mail])

References

1. E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement. CSIRO Publishing (https://www.publish.csiro.au/book/3145/).

See Also

DataExam5.1

Examples

library(car)
library(dae)
library(dplyr)
library(emmeans)
library(ggplot2)
library(lmerTest)
library(magrittr)
library(predictmeans)
library(supernova)

data(DataExam5.1)

# Pg.68
fm5.4 <- lm(formula = Ht ~ Site*SeedLot, data = DataExam5.1)

# Pg. 73
anova(fm5.4)
# Pg. 73
emmeans(object = fm5.4, specs = ~ Site)
emmeans(object = fm5.4, specs = ~ SeedLot)

ANOVAfm5.4 <- anova(fm5.4)

ANOVAfm5.4[4, 1:3] <- c(208, 208*1040, 1040)
ANOVAfm5.4[3, 4]   <- ANOVAfm5.4[3, 3]/ANOVAfm5.4[4, 3]
ANOVAfm5.4[3, 5]   <- pf(
                            q = ANOVAfm5.4[3, 4]
                        , df1 = ANOVAfm5.4[3, 1]
                        , df2 = ANOVAfm5.4[4, 1]
                        , lower.tail = FALSE
                        )
# Pg. 73
ANOVAfm5.4

# Pg. 80
DataExam5.1 %>%
  filter(SeedLot %in% c("13653", "13871")) %>%
  ggplot(
    data = .
  , mapping = aes(x = SiteMean, y = Ht, color = SeedLot, shape = SeedLot)
  ) +
  geom_point() +
  geom_smooth(method = lm, se = FALSE, fullrange = TRUE)+
  theme_classic() +
  labs(
      x = "SiteMean"
    , y = "SeedLot Mean"
    )



Tab5.10 <-
  DataExam5.1 %>%
      summarise(Mean = mean(Ht), .by = SeedLot) %>%
    left_join(
      DataExam5.1 %>%
        nest_by(SeedLot) %>%
        mutate(fm1 = list(lm(Ht ~ SiteMean, data = data))) %>%
        summarise(Slope = coef(fm1)[2])
    , by = "SeedLot"
      )

# Pg. 81
Tab5.10

ggplot(data = Tab5.10, mapping = aes(x = Mean, y = Slope))+
 geom_point(size = 2) +
 theme_bw() +
 labs(
     x = "SeedLot Mean"
   , y = "Regression Coefficient"
   )

DevSS1 <-
        DataExam5.1 %>%
        nest_by(SeedLot) %>%
        mutate(fm1 = list(lm(Ht ~ SiteMean, data = data))) %>%
        summarise(SSE = anova(fm1)[2, 2]) %>%
        ungroup() %>%
        summarise(Dev = sum(SSE)) %>%
        as.numeric()

ANOVAfm5.4[2, 2]

length(levels(DataExam5.1$SeedLot))

ANOVAfm5.4.1 <-
  rbind(
     ANOVAfm5.4[1:3, ]
   , c(
        ANOVAfm5.4[2, 1]
      , ANOVAfm5.4[3, 2] - DevSS1
      , (ANOVAfm5.4[3, 2] - DevSS1)/ANOVAfm5.4[2, 1]
      , NA
      , NA
      )
   , c(
        ANOVAfm5.4[3, 1]-ANOVAfm5.4[2, 1]
      , DevSS1
      , DevSS1/(ANOVAfm5.4[3, 1]-ANOVAfm5.4[2, 1])
      , DevSS1/(ANOVAfm5.4[3, 1]-ANOVAfm5.4[2, 1])/ANOVAfm5.4[4, 3]
      , pf(
              q = DevSS1/(ANOVAfm5.4[3, 1]-ANOVAfm5.4[2, 1])/ANOVAfm5.4[4, 3]
          , df1 = ANOVAfm5.4[3, 1]-ANOVAfm5.4[2, 1]
          , df2 = ANOVAfm5.4[4, 1]
          , lower.tail = FALSE
          )
      )
   , ANOVAfm5.4[4, ]
  )
rownames(ANOVAfm5.4.1) <-
  c("Site", "SeedLot", "Site:SeedLot", "  regressions", "  deviations", "Residuals")
# Pg. 82
ANOVAfm5.4.1