Exam4.3 {eda4treeR}

Example 4.3 from Experimental Design and Analysis for Tree Improvement

Description

Exam4.3 presents the germination count data for 4 Pre-Treatments and 6 Seedlots.

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

DataExam4.3

Examples

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

data(DataExam4.3)

 # Pg. 50
 fm4.2    <-
   aov(
       formula = Percent ~ Repl + Contcomp + SeedLot +
                           Treat/Contcomp + Contcomp /SeedLot +
                           Treat/ Contcomp/SeedLot
      , data   = DataExam4.3
     )

 # Pg. 54
 anova(fm4.2)

 # Pg. 54
 model.tables(x = fm4.2, type = "means")

 emmeans(object = fm4.2, specs = ~ Contcomp)
 emmeans(object = fm4.2, specs = ~ SeedLot)
 emmeans(object = fm4.2, specs = ~ Contcomp + Treat)
 emmeans(object = fm4.2, specs = ~ Contcomp + SeedLot)
 emmeans(object = fm4.2, specs = ~ Contcomp + Treat + SeedLot)

 DataExam4.3 %>%
   dplyr::group_by(Treat, Contcomp, SeedLot) %>%
   dplyr::summarize(Mean=mean(Percent))
   RESFIT <- data.frame(residualvalue=residuals(fm4.2),fittedvalue=fitted.values(fm4.2))
   ggplot(mapping = aes(x = fitted.values(fm4.2), y = residuals(fm4.2)))+
   geom_point(size = 2)+
   labs(
       x = "Fitted Values"
     , y = "Residuals"
     ) +
     theme_classic()