Exam6.2 {eda4treeR} R Documentation

## Example 6.2 from Experimental Design & 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)

2. Sami Ullah (samiullahuos@gmail.com)

### References

1. Williams, E.R., Matheson, A.C. and Harwood, C.E. (2002).Experimental Design and Analysis for Tree Improvement. CSIRO Publishing.

DataExam6.2

### Examples

data(DataExam6.2)
library(tidyverse)
library(lme4)

DataExam6.2.1 <- DataExam6.2[DataExam6.2$Province=="PNG",] fm6.3 <- lm(formula = Dbh.mean ~ Replication+Family ,data = DataExam6.2.1 #, subset #, weights #, na.action , method = "qr" , model = TRUE , x = FALSE , y = FALSE , qr = TRUE , singular.ok = TRUE , contrasts = NULL ) b <- anova(fm6.3) print(b) HM <- function(x){length(x)/sum(1/x)} w <- HM(DataExam6.2.1$Dbh.count)
S2      <- b[["Mean Sq"]][length(b[["Mean Sq"]])]
Sigma2t <- mean(DataExam6.2.1$Dbh.variance) sigma2m <- S2-(Sigma2t/w) fm6.3.1<- lmer(formula= Dbh.mean ~ 1+Replication+(1|Family) ,data = DataExam6.2.1 ,REML = TRUE ,control = lmerControl() ,start = NULL , verbose = 0L #, subset #, weights #, na.action #,offset , contrasts = NULL , devFunOnly = FALSE) summary(fm6.3.1) print(VarCorr(fm6.3.1),comp=c("Variance")) sigma2f <- 0.2584 h2 <- (sigma2f/(0.3))/(Sigma2t+sigma2m+sigma2f) cbind(w,Sigma2t,sigma2m,sigma2f,h2) print("Dbh Heritability for all the Provinces") fm6.4 <- lm(formula = Dbh.mean ~ Replication+Family ,data = DataExam6.2 #, subset #, weights #, na.action , method = "qr" , model = TRUE , x = FALSE , y = FALSE , qr = TRUE , singular.ok = TRUE , contrasts = NULL ) b <- anova(fm6.4) print(b) HM <- function(x){length(x)/sum(1/x)} w <- HM(DataExam6.2$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
,control = lmerControl()
,start = NULL
, verbose = 0L
#, subset
#, weights
#, na.action
#,offset
, contrasts = NULL
, devFunOnly = FALSE)
summary(fm6.4.1)
print(VarCorr(fm6.4.1),comp=c("Variance"))
sigma2f <- 0.3514
h2 <- (sigma2f/(0.3))/(Sigma2t+sigma2m+sigma2f)
cbind(w,Sigma2t,sigma2m,sigma2f,h2)
print("Genetic Correlation Between Dbh and Height for PNG Province")
fm6.7.1<- lmer(formula=
Dbh.mean ~ 1+Replication+(1|Family)
,data = DataExam6.2.1
,REML = TRUE
,control = lmerControl()
,start = NULL
, verbose = 0L
#, subset
#, weights
#, na.action
#,offset
, contrasts = NULL
, devFunOnly = FALSE)
summary(fm6.7.1)
print(VarCorr(fm6.7.1),comp=c("Variance"))
sigma2f[1] <- 0.2584

fm6.7.2<- lmer(formula=
Ht.mean ~ 1+Replication+(1|Family)
,data = DataExam6.2.1
,REML = TRUE
,control = lmerControl()
,start = NULL
, verbose = 0L
#, subset
#, weights
#, na.action
#,offset
, contrasts = NULL
, devFunOnly = FALSE)
summary(fm6.7.2)
print(VarCorr(fm6.7.2),comp=c("Variance"))
sigma2f[2] <- 0.2711

fm6.7.3<- lmer(formula=
Sum.means ~ 1+Replication+(1|Family)
,data = DataExam6.2.1
,REML = TRUE
,control = lmerControl()
,start = NULL
, verbose = 0L
#, subset
#, weights
#, na.action
#,offset
, contrasts = NULL
, devFunOnly = FALSE)
summary(fm6.7.3)
print(VarCorr(fm6.7.3),comp=c("Variance"))
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)


[Package eda4treeR version 0.3.0 Index]