Data for Example 3.1 from Experimental Design and Analysis for Tree Improvement

Source: R/Exam3.1.R

Exam3.1 is part of data from Australian Centre for Agricultural Research (ACIAR) in Queensland, Australia (Experiment 309).

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

DataExam3.1

Author

  1. Muhammad Yaseen (myaseen208@gmail.com)

  2. Sami Ullah (samiullahuos@gmail.com)

Examples

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

data(DataExam3.1)

# Pg. 28
fmtab3.3 <- lm(formula = Ht ~ Repl*SeedLot, data = DataExam3.1)

fmtab3.3ANOVA1 <-
  anova(fmtab3.3) %>%
  mutate(
  "F value" = c(anova(fmtab3.3)[1:2, 3]/anova(fmtab3.3)[3, 3]
               , anova(fmtab3.3)[4, 3]
               , NA)
               )

 # Pg. 33 (Table 3.3)
fmtab3.3ANOVA1 %>%
  mutate(
  "Pr(>F)"  = pf(q = fmtab3.3ANOVA1[ ,4]
               , df1 = fmtab3.3ANOVA1[ ,1]
               , df2 = fmtab3.3ANOVA1[4,1], lower.tail = FALSE)
               )
#>              Df Sum Sq Mean Sq F value   Pr(>F)    
#> Repl          1  20.30  20.301  3.4197  0.06864 .  
#> SeedLot       4 505.87 126.467 21.3035 1.59e-11 ***
#> Repl:SeedLot  4  23.75   5.936  2.5088  0.04955 *  
#> Residuals    70 175.61   2.509                     
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

 # Pg. 33  (Table 3.3)
 emmeans(object  = fmtab3.3, specs = ~ SeedLot)
#> NOTE: Results may be misleading due to involvement in interactions
#>  SeedLot      emmean    SE df lower.CL upper.CL
#>  Acacia        10.29 0.396 70     9.50    11.08
#>  Angophora      7.10 0.396 70     6.31     7.89
#>  Casuarina      5.51 0.396 70     4.72     6.30
#>  Melaleuca      4.94 0.396 70     4.15     5.73
#>  Petalostigma   2.73 0.396 70     1.94     3.52
#> 
#> Results are averaged over the levels of: Repl 
#> Confidence level used: 0.95 

 # Pg. 34  (Figure 3.2)
 ggplot(mapping = aes(x = fitted.values(fmtab3.3), y = residuals(fmtab3.3)))+
 geom_point(size = 2) +
 labs(
    x = "Fitted Values"
  , y = "Residual"
   ) +
 theme_classic()



# Pg. 33 (Table 3.4)
DataExam3.1m <- DataExam3.1
DataExam3.1m[c(28, 51, 76), 5] <- NA
DataExam3.1m[c(28, 51, 76), 6] <- NA


fmtab3.4 <- lm(formula = Ht ~ Repl*SeedLot, data = DataExam3.1m)

fmtab3.4ANOVA1 <-
  anova(fmtab3.4) %>%
  mutate(
      "F value" = c(anova(fmtab3.4)[1:2, 3]/anova(fmtab3.4)[3, 3]
                   , anova(fmtab3.4)[4, 3], NA))

# Pg. 33 (Table 3.4)
fmtab3.4ANOVA1 %>%
  mutate(
        "Pr(>F)"  = pf(q = fmtab3.4ANOVA1[ ,4]
                     , df1 = fmtab3.4ANOVA1[ ,1]
                     , df2 = fmtab3.4ANOVA1[4,1], lower.tail = FALSE)
                     )
#>              Df Sum Sq Mean Sq F value    Pr(>F)    
#> Repl          1  18.88  18.877 10.4201  0.001931 ** 
#> SeedLot       4 588.68 147.169 81.2367 < 2.2e-16 ***
#> Repl:SeedLot  4   7.25   1.812  0.7497  0.561661    
#> Residuals    67  50.23   0.750                      
#> ---
#> Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

 # Pg. 33  (Table 3.4)
 emmeans(object  = fmtab3.4, specs = ~ SeedLot)
#> NOTE: Results may be misleading due to involvement in interactions
#>  SeedLot      emmean    SE df lower.CL upper.CL
#>  Acacia        10.87 0.224 67    10.42    11.31
#>  Angophora      7.76 0.231 67     7.30     8.22
#>  Casuarina      5.51 0.216 67     5.08     5.94
#>  Melaleuca      4.94 0.216 67     4.51     5.38
#>  Petalostigma   2.73 0.216 67     2.30     3.16
#> 
#> Results are averaged over the levels of: Repl 
#> Confidence level used: 0.95