Exam3.1 | R Documentation |
Exam3.1 is part of data from Australian Centre for Agricultural Research (ACIAR) in Queensland, Australia (Experiment 309).
Muhammad Yaseen (myaseen208@gmail.com)
Sami Ullah (samiullahuos@gmail.com)
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/).
DataExam3.1
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)
)
# 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)[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)
)
# Pg. 33 (Table 3.4)
emmeans(object = fmtab3.4, specs = ~ SeedLot)
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