Exam6.2: Example 6.2 from Experimental Design and Analysis for Tree...

In MYaseen208/eda4treeR: Experimental Design and Analysis for Tree Improvement

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)

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

DataExam6.2

Examples

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

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)

MYaseen208/eda4treeR documentation built on May 8, 2023, 5:58 p.m.