Help for package eda4treeR

Type:

Package

Title:

Experimental Design and Analysis for Tree Improvement

Version:

1.1.0

Maintainer:

Muhammad Yaseen <myaseen208@gmail.com>

Description:

Provides data sets and R Codes for E.R. Williams, C.E. Harwood and A.C. Matheson (2023). Experimental Design and Analysis for Tree Improvement, CSIRO Publishing.

Depends:

R (≥ 4.1.0)

Imports:

car, dae, dplyr, emmeans, ggplot2, lmerTest, magrittr, predictmeans, stats, supernova

License:

GPL-3

URL:

https://github.com/MYaseen208/eda4treeR https://CRAN.R-project.org/package=eda4treeR https://myaseen208.com/eda4treeR/ https://myaseen208.com/EDATR/

BugReports:

https://github.com/myaseen208/eda4treeR/issues

LazyData:

TRUE

RoxygenNote:

7.3.2

Encoding:

UTF-8

Suggests:

testthat

Note:

1. Asian Development Bank (ADB), Islamabad, Pakistan. 2. Benazir Income Support Programme (BISP), Islamabad, Pakistan. 3. Department of Mathematics and Statistics, University of Agriculture Faisalabad, Pakistan.

NeedsCompilation:

Packaged:

2024-09-13 21:21:45 UTC; myaseen208

Author:

Muhammad Yaseen

[aut, cre, cph], Sami Ullah [aut, ctb], Kent Eskridge [aut, ctb], Emlyn Williams [aut, ctb]

Repository:

CRAN

Date/Publication:

2024-09-13 21:50:02 UTC

Data for Example 2.1 from Experimental Design and Analysis for Tree Improvement

Description

Exam2.1 is used to compare two seed lots by using single factor ANOVA.

Usage

data(DataExam2.1)

Format

A data.frame with 16 rows and 2 variables.

seedlot: Two Seedlots Seed Orchad (SO) and routin plantation (P)
dbh: Diameter at breast height

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam2.1)

Data for Example 2.2 from Experimental Design and Analysis for Tree Improvement

Description

Exam2.2 is used to compare two seed lots by using ANOVA under RCB Design.

Usage

data(DataExam2.2)

Format

A data.frame with 16 rows and 2 variables.

repl: repl
block: block
Seedlot: Two Seedlots Seed Orchad (SO) and routin plantation (P)
dbh: Diameter at breast height

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam2.2)

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

Description

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

Usage

data(DataExam3.1)

Format

A data.frame with 80 rows and 6 variables.

repl: Replication number of different Seedlots
PlotNo: Plot No of differnt Trees
seedlot: Seed Lot number
TreeNo: Tree number of Seedlots
ht: Height in meter
dgl: Diameter at ground level

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam3.1)

Data for Example 3.1.1 from Experimental Design and Analysis for Tree Improvement

Description

Exam3.1.1 is part of data from Australian Centre for Agricultural Research (ACIAR) in Queensland, Australia (Experiment 309).

Usage

data(DataExam3.1.1)

Format

A data.frame with 10 rows and 6 variables.

repl: Replication number of different Seedlots
PlotNo: Plot No of differnt Trees
seedlot: Seed Lot number
TreeNo: Tree number of Seedlots
ht: Height in meter
Var: Var
TreeCount: TreeCount

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam3.1.1)

Data for 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.

Usage

data(DataExam4.3)

Format

A data.frame with 72 rows and 8 variables.

rep: Replication number of Treatment
row: Row number of different Seedlots
column: Column number of differnt Trees
seedlot: Seed lot number
treat: Treatment types
count: Number of germinated seeds out of 25
percent: Germination Percentage
contcomp: Control or Trated Plot

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam4.3)

Data for Example 4.3.1 from Experimental Design and Analysis for Tree Improvement

Description

Exam4.3.1 presents the germination count data for 4 Pre-Treatments and 6 Seedlots.

Usage

data(DataExam4.3.1)

Format

A data.frame with 72 rows and 8 variables.

Row: Row number of different Seedlots
Column: Column number of differnt Trees
Replication: Replication number of Treatment
Contcomp: Control or Trated Plot
Pretreatment: Treatment types
SeedLot: Seed lot number
GerminationCount: Number of germinated seeds out of 25
Percent: Germination Percentage

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam4.3.1)

Data for Example 4.4 from Experimental Design and Analysis for Tree Improvement

Description

Exam4.4 presents the height means for 4 seedlots under factorial arrangement for two levels of Fertilizer and two levels of Irrigation.

Usage

data(DataExam4.4)

Format

A data.frame with 32 rows and 5 variables.

repl: Replication number
irrig: Irrigation type
fert: Fertilizer type
seedlot: Seed Lot number
height: Height of the plants

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam4.4)

Data for Example 5.1 from Experimental Design and Analysis for Tree Improvement

Description

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

Usage

data(DataExam5.1)

Format

A data.frame with 108 rows and 4 variables.

site: Sites for the experiment
seedlot: Seed lot number
ht: Height of the plants
sitemean: Mean Height of Each Site

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam5.1)

Data for Example 5.2 from Experimental Design and Analysis for Tree Improvement

Description

Exam5.2 presents the height of 37 seedlots from 6 sites.

Usage

data(DataExam5.2)

Format

A data.frame with 108 rows and 4 variables.

site: Sites for the experiment
seedlot: Seed lot number
ht: Height of the plants
sitemean: Mean Height of Each Site

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam5.2)

Data for Example 6.2 from Experimental Design and Analysis for Tree Improvement

Description

Exam 6.2 Dbh mean, Dbh varince and number of trees per plot from 3 provinces("PNG","Sabah","Queensland") with 4 replicationsof 48 families.

Usage

data(DataExam6.2)

Format

A data.frame with 192 rows and 7 variables.

Replication: Replication number of different Families
Plot.number: Plot number of differnt Trees
Family: Family Numuber
Province: Province of family
Dbh.mean: Average Diameter at breast height of trees within plot
Dbh.variance: Variance of Diameter at breast height of trees within plot
Dbh.count: Number of trees within plot

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam6.2)

Data for Example 8.1 from Experimental Design and Analysis for Tree Improvement

Description

Exam8.1 presents the Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries.

Usage

data(DataExam8.1)

Format

A data.frame with 236 rows and 8 variables.

repl: There are 4 replication for the design
row: Experiment is conducted under 6 rows

col: Experiment is conducted under 4 columns
inoc: Seedling were inoculated for 2 different time periods half for one week and half for seven weeks
prov: provenance
Country: Data for different seedlots was collected from 18 countries
Dbh: Diameter at breast height
Country.1: Recoded Country lables

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam8.1)

Data for Example 8.2 from Experimental Design and Analysis for Tree Improvement

Description

Exam8.2 presents the Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries.

Usage

data(DataExam8.2)

Format

A data.frame with 236 rows and 8 variables.

repl: There are 4 replication for the design
row: Experiment is conducted under 6 rows

column: Experiment is conducted under 4 columns
clonenum: Clonenum
contcompf: Contcompf
standard: Standard
clone: Clone
dbh: dbhmean
dbhvar: dbhvariance
ht: htmean
htvar: htvariance
count: count
contcompv: Contcompv

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

data(DataExam8.2)

Example 2.1 from Experimental Design and Analysis for Tree Improvement

Description

Exam2.1 is used to compare two seed lots by using single factor ANOVA.

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam2.1)
# Pg. 22
fmtab2.3  <- lm(formula = dbh ~ seedlot, data = DataExam2.1)
# Pg. 23
anova(fmtab2.3)

# Pg. 23
emmeans(object = fmtab2.3, specs = ~ seedlot)
emmip(object = fmtab2.3, formula = ~ seedlot) +
  theme_classic()

Example 2.2 from Experimental Design and Analysis for Tree Improvement

Description

Exam2.2 is used to compare two seed lots by using ANOVA under RCB Design.

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam2.2)

# Pg. 24
fmtab2.5 <-
          lm(
             formula  = dbh ~ block + seedlot
           , data     = DataExam2.2
           )

# Pg. 26
anova(fmtab2.5)

# Pg. 26
emmeans(object = fmtab2.5, specs = ~ seedlot)
emmip(object = fmtab2.5, formula = ~ seedlot) +
  theme_classic()

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

Description

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

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

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)[3, 4]
         , NA
         )
          )

 # Pg. 33 (Table 3.3)
fmtab3.3ANOVA1 %>%
  mutate(
  "Pr(>F)"  =
       c(
         NA
       , pf(
            q   = fmtab3.3ANOVA1[2, 4]
          , df1 = fmtab3.3ANOVA1[2, 1]
          , df2 = fmtab3.3ANOVA1[3, 1], lower.tail = FALSE
          )
       , NA
       , NA
       )
       )

 # Pg. 33  (Table 3.3)
 emmeans(object  = fmtab3.3, specs = ~ seedlot)

 # 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)[3, 4]
             , NA
             )
             )

# Pg. 33 (Table 3.4)
fmtab3.4ANOVA1 %>%
  mutate(
  "Pr(>F)"  =
       c(
         NA
       , pf(
            q   = fmtab3.4ANOVA1[2, 4]
          , df1 = fmtab3.4ANOVA1[2, 1]
          , df2 = fmtab3.4ANOVA1[3, 1], lower.tail = FALSE
          )
       , NA
       , NA
       )
       )

 # Pg. 33  (Table 3.4)
 emmeans(object  = fmtab3.4, specs = ~ seedlot)

Example 3.1.1 from Experimental Design and Analysis for Tree Improvement

Description

Exam3.1.1 is part of data from Australian Centre for Agricultural Research (ACIAR) in Queensland, Australia (Experiment 309).

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam3.1.1)

# Pg. 36
fm3.8 <-
      lm(
         formula = ht ~ repl + seedlot
       , data    = DataExam3.1.1
       )

# Pg. 40
anova(fm3.8)

# Pg. 40
emmeans(object = fm3.8, specs  = ~seedlot)
emmip(object = fm3.8, formula  = ~seedlot) +
 theme_classic()

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)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

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()

Example 4.3.1 from Experimental Design and Analysis for Tree Improvement

Description

Exam4.3.1 presents the germination count data for 4 Pre-Treatments and 6 Seedlots.

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam4.3)

# Pg. 57
fm4.4    <-
  aov(
      formula = percent ~ repl + treat*seedlot
    , data    = DataExam4.3 %>%
                 filter(treat != "control")
     )

 # Pg. 57
 anova(fm4.4)
 model.tables(x = fm4.4, type = "means", se = TRUE)

 emmeans(object = fm4.4, specs = ~ treat)
 emmeans(object = fm4.4, specs = ~ seedlot)
 emmeans(object = fm4.4, specs = ~ treat * seedlot)

Example 4.4 from Experimental Design and Analysis for Tree Improvement

Description

Exam4.4 presents the height means for 4 seedlots under factorial arrangement for two levels of Fertilizer and two levels of Irrigation.

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam4.4)

# Pg. 58
fm4.6    <-
  aov(
      formula = height ~ repl + irrig*fert*seedlot +
                         Error(repl/irrig:fert)
    , data    = DataExam4.4
    )

# Pg. 61
 summary(fm4.6)

# Pg. 61
model.tables(x = fm4.6, type = "means")

# Pg. 61
emmeans(object = fm4.6, specs = ~ irrig)
emmip(object = fm4.6, formula  = ~ irrig) +
    theme_classic()

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)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

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

Example 5.2 from Experimental Design and Analysis for Tree Improvement

Description

Exam5.2 presents the height of 37 seedlots from 6 sites.

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam5.2)

# Pg. 75
fm5.7 <-
 lm(
     formula = ht ~ site*seedlot
   , data    = DataExam5.2
   )

# Pg. 77
anova(fm5.7)

fm5.9 <-
 lm(
     formula = ht ~ site*seedlot
   , data    = DataExam5.2
   )
# Pg. 77
anova(fm5.9)

ANOVAfm5.9 <- anova(fm5.9)

ANOVAfm5.9[4, 1:3] <- c(384, 384*964, 964)
ANOVAfm5.9[3, 4]   <- ANOVAfm5.9[3, 3]/ANOVAfm5.9[4, 3]
ANOVAfm5.9[3, 5]   <-
    pf(
        q = ANOVAfm5.9[3, 4]
    , df1 = ANOVAfm5.9[3, 1]
    , df2 = ANOVAfm5.9[4, 1]
    , lower.tail = FALSE
    )
# Pg. 77
ANOVAfm5.9


Tab5.14 <-
  DataExam5.2 %>%
  summarise(
       Mean = round(mean(ht, na.rm = TRUE), 0)
     , .by  = seedlot
     ) %>%
   left_join(
      DataExam5.2 %>%
      nest_by(seedlot) %>%
      mutate(fm2 = list(lm(ht ~ sitemean, data = data))) %>%
      summarise(Slope = round(coef(fm2)[2], 2))
    , by = "seedlot"
     ) %>%
  as.data.frame()

# Pg. 81
Tab5.14


DevSS2 <-
  DataExam5.2 %>%
  nest_by(seedlot) %>%
  mutate(fm2 = list(lm(ht ~ sitemean, data = data))) %>%
  summarise(SSE = anova(fm2)[2, 2]) %>%
  ungroup() %>%
  summarise(Dev = sum(SSE)) %>%
  as.numeric()


ANOVAfm5.9.1 <-
  rbind(
     ANOVAfm5.9[1:3, ]
   , c(
        ANOVAfm5.9[2, 1]
      , ANOVAfm5.9[3, 2] - DevSS2
      , (ANOVAfm5.9[3, 2] - DevSS2)/ANOVAfm5.9[2, 1]
      , NA
      , NA
      )
   , c(
        ANOVAfm5.9[3, 1]-ANOVAfm5.9[2, 1]
      , DevSS2
      , DevSS2/(ANOVAfm5.9[3, 1]-ANOVAfm5.9[2, 1])
      , DevSS2/(ANOVAfm5.9[3, 1]-ANOVAfm5.9[2, 1])/ANOVAfm5.9[4, 3]
      , pf(
              q = DevSS2/(ANOVAfm5.9[3, 1]-ANOVAfm5.9[2, 1])/ANOVAfm5.9[4, 3]
          , df1 = ANOVAfm5.9[3, 1]-ANOVAfm5.9[2, 1]
          , df2 = ANOVAfm5.9[4, 1]
          , lower.tail = FALSE
          )
      )
   , ANOVAfm5.9[4, ]
  )
rownames(ANOVAfm5.9.1) <-
  c(
     "site"
   , "seedlot"
   , "site:seedlot"
   , "  regressions"
   , "  deviations"
   , "Residuals"
   )

# Pg. 82
ANOVAfm5.9.1

Code <-
 c(
   "a","a","a","a","b","b","b","b"
 , "c","d","d","d","d","e","f","g"
 , "h","h","i","i","j","k","l","m"
 ,"n","n","n","o","p","p","q","r"
 , "s","t","t","u","v"
 )

Tab5.14$Code <- Code

ggplot(
   data = Tab5.14
 , mapping = aes(x = Mean, y = Slope)
 ) +
 geom_point(size = 2) +
 geom_text(
    mapping = aes(label = Code)
  , hjust   = -0.5
  , vjust   = -0.5
 ) +
 theme_bw() +
 labs(
     x = "SeedLot Mean"
   , y = "Regression Coefficient"
   )

Example 6.2 from Experimental Design and Analysis for Tree Improvement

Description

Exam 6.2 Dbh mean, Dbh varince and number of trees per plot from 3 provinces("PNG","Sabah","Queensland") with 4 replications of 48 families.

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam6.2)

DataExam6.2.1 <-
    DataExam6.2 %>%
    filter(Province == "PNG")

# Pg. 94
fm6.3 <-
     lm(
          formula = Dbh.mean ~ Replication + Family
        , data    = DataExam6.2.1
       )

b    <- anova(fm6.3)


HM      <- function(x){length(x)/sum(1/x)}
w       <- HM(DataExam6.2.1$Dbh.count)
S2      <- b[["Mean Sq"]][length(b[["Mean Sq"]])]
Sigma2t <- mean(DataExam6.2.1$Dbh.variance)
sigma2m <- S2-(Sigma2t/w)

fm6.3.1 <-
  lmer(
      formula = Dbh.mean ~ 1 + Replication + (1|Family)
    , data    = DataExam6.2.1
    , REML    = TRUE
    )

# Pg. 104
# summary(fm6.3.1)
varcomp(fm6.3.1)
sigma2f <- 0.2584
h2 <- (sigma2f/(0.3))/(Sigma2t + sigma2m + sigma2f)
cbind(hmean = w, Sigma2t, sigma2m, sigma2f, h2)

fm6.4 <-
  lm(
      formula = Dbh.mean ~ Replication+Family
     , data   = DataExam6.2
     )

b    <- anova(fm6.4)
HM      <- function(x){length(x)/sum(1/x)}
w       <- HM(DataExam6.2$Dbh.count)
S2      <- b[["Mean Sq"]][length(b[["Mean Sq"]])]
Sigma2t <- mean(DataExam6.2$Dbh.variance)
sigma2m <- S2-(Sigma2t/w)

fm6.4.1 <-
 lmer(
   formula = Dbh.mean ~ 1 + Replication + Province + (1|Family)
 , data    = DataExam6.2
 , REML    = TRUE
    )

# Pg. 107
varcomp(fm6.4.1)
sigma2f <- 0.3514
h2 <- (sigma2f/(0.3))/(Sigma2t+sigma2m+sigma2f)
cbind(hmean = w, Sigma2t, sigma2m, sigma2f, h2)

fm6.7.1 <-
 lmer(
   formula = Dbh.mean ~ 1+Replication+(1|Family)
 , data    = DataExam6.2.1
 , REML = TRUE
 )

# Pg. 116
varcomp(fm6.7.1)
sigma2f[1] <- 0.2584

fm6.7.2<-
 lmer(
   formula = Ht.mean ~ 1 + Replication + (1|Family)
 , data    = DataExam6.2.1
 , REML    = TRUE
   )

# Pg. 116
varcomp(fm6.7.2)
sigma2f[2] <- 0.2711

fm6.7.3 <-
 lmer(
   formula = Sum.means ~ 1 + Replication + (1|Family)
 , data    = DataExam6.2.1
 , REML    = TRUE
 , control = lmerControl()
 )

# Pg. 116
varcomp(fm6.7.3)
sigma2f[3] <- 0.873
sigma2xy   <- 0.5*(sigma2f[3]-sigma2f[1]-sigma2f[2])
GenCorr <- sigma2xy/sqrt(sigma2f[1]*sigma2f[2])
cbind(
     S2x = sigma2f[1]
   , S2y = sigma2f[2]
   , S2.x.plus.y = sigma2f[3]
   , GenCorr
   )

Example 8.1 from Experimental Design and Analysis for Tree Improvement

Description

Exam8.1 presents the Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries.

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam8.1)

# Pg. 141
fm8.4 <-
  aov(
    formula = dbh ~ inoc + Error(repl/inoc) +
                    inoc*country*prov
  , data    = DataExam8.1
     )

# Pg. 150
summary(fm8.4)

# Pg. 150
model.tables(x = fm8.4, type = "means")

RESFit <-
    data.frame(
      fittedvalue   = fitted.aovlist(fm8.4)
    , residualvalue = proj(fm8.4)$Within[,"Residuals"]
    )

ggplot(
   data    =  RESFit
 , mapping = aes(x = fittedvalue, y = residualvalue)
 ) +
geom_point(size = 2) +
labs(
   x = "Residuals vs Fitted Values"
 , y = ""
 ) +
theme_bw()

# Pg. 153
fm8.6 <-
 aov(
   formula = terms(
                   dbh ~ inoc + repl + col +
                         repl:row + repl:col +
                         prov + inoc:prov
                   , keep.order = TRUE
                   )
 , data   = DataExam8.1
 )
summary(fm8.6)

Example 8.1.1 from Experimental Design and Analysis for Tree Improvement

Description

Exam8.1.1 presents the Mixed Effects Analysis of Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries given in Example 8.1.

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam8.1)

# Pg. 155
fm8.8 <-
 lmerTest::lmer(
     formula = dbh ~ 1 + repl + col + prov +
                     (1|repl:row) + (1|repl:col)
   , data   = DataExam8.1
   , REML   = TRUE
   )

# Pg. 157
## Not run: 
varcomp(fm8.8)

## End(Not run)

anova(fm8.8)
anova(fm8.8, ddf = "Kenward-Roger")

predictmeans(model = fm8.8, modelterm = "repl")
predictmeans(model = fm8.8, modelterm = "col")
predictmeans(model = fm8.8, modelterm = "prov")

 # Pg. 161
  RCB1 <-
        aov(dbh ~ prov + repl, data = DataExam8.1)
  RCB  <-
        emmeans(RCB1,  specs = "prov") %>%
        as_tibble()

  Mixed <-
          emmeans(fm8.8, specs = "prov") %>%
          as_tibble()

  table8.9 <-
      left_join(
         x      = RCB
       , y      = Mixed
       , by     = "prov"
       , suffix = c(".RCBD", ".Mixed")
       )
  print(table8.9)

Example 8.1.2 from Experimental Design & Analysis for Tree Improvement

Description

Exam8.1.2 presents the Analysis of Nested Seedlot Structure of Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries given in Example 8.1.

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam8.1)

# Pg. 167
fm8.11 <-
  aov(
       formula = dbh ~ country + country:prov
     , data    = DataExam8.1
      )

  b <- anova(fm8.11)
  Res <- length(b[["Sum Sq"]])
  df  <- 119
  MSS <- 0.1951

  b[["Df"]][Res] <- df
  b[["Sum Sq"]][Res] <- MSS*df
  b[["Mean Sq"]][Res] <- b[["Sum Sq"]][Res]/b[["Df"]][Res]

  b[["F value"]][1:Res-1] <-
            b[["Mean Sq"]][1:Res-1]/b[["Mean Sq"]][Res]

  b[["Pr(>F)"]][Res-1] <-
     df(
       b[["F value"]][Res-1]
     , b[["Df"]][Res-1]
     , b[["Df"]][Res]
     )
  b

 emmeans(fm8.11, specs = "country")

Example 8.2 from Experimental Design and Analysis for Tree Improvement

Description

Exam8.2 presents the Diameter at breast height (Dbh) of 60 SeedLots under layout of row column design with 6 rows and 10 columns in 18 countries and 59 provinces of 18 selected countries.

Author(s)

Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)

References

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/).

Examples

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

data(DataExam8.2)

# Pg.
fm8.2  <-
  lmerTest::lmer(
    formula = dbh ~ repl + column +
                    contcompf + contcompf:standard +
                    (1|repl:row) + (1|repl:column) +
                    (1|contcompv:clone)
  , data    = DataExam8.2
    )
## Not run: 
varcomp(fm8.2)

## End(Not run)
anova(fm8.2)
Anova(fm8.2, type = "II", test.statistic = "Chisq")

predictmeans(model = fm8.2, modelterm = "repl")
predictmeans(model = fm8.2, modelterm = "column")

emmeans(object = fm8.2, specs = ~contcompf|standard)

Data for Example 2.1 from Experimental Design and Analysis for Tree Improvement

Description

Usage

Format

Author(s)

References

See Also

Examples

Data for Example 2.2 from Experimental Design and Analysis for Tree Improvement

Description

Usage

Format

Author(s)

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Examples

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

Description

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Examples

Data for Example 3.1.1 from Experimental Design and Analysis for Tree Improvement

Description

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Examples

Data for Example 4.3 from Experimental Design and Analysis for Tree Improvement

Description

Usage

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Examples

Data for Example 4.3.1 from Experimental Design and Analysis for Tree Improvement

Description

Usage

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Examples

Data for Example 4.4 from Experimental Design and Analysis for Tree Improvement

Description

Usage

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Examples

Data for Example 5.1 from Experimental Design and Analysis for Tree Improvement

Description

Usage

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Author(s)

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Examples

Data for Example 5.2 from Experimental Design and Analysis for Tree Improvement

Description

Usage

Format

Author(s)

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Examples

Data for Example 6.2 from Experimental Design and Analysis for Tree Improvement

Description

Usage

Format

Author(s)

References

Examples

Data for Example 8.1 from Experimental Design and Analysis for Tree Improvement