Chapter 3 ANOVA RCBD
Dataset (Somasegaran and Hoben 1985)
gs4_deauth() giúp cho không cần xác thực API từ googlesheet.
library(googlesheets4)
gs4_deauth()
data_rcbd <- read_sheet('1dFmKOhpYABrPR_e5MF27W2LSbaAGdt5dFVV3zc1H47I')## v Reading from "raw_data".## v Range 'Sheet1'.print(data_rcbd, n = Inf)## # A tibble: 48 x 3
## block treatment yield
## <chr> <chr> <dbl>
## 1 block1 TAL102 9.66
## 2 block1 TAL379 9.36
## 3 block1 TAL206 8.41
## 4 block1 TAL435 8.61
## 5 block1 TAL411 9.2
## 6 block1 ALLEN527 8.11
## 7 block1 TAL211 8.83
## 8 block1 TAL487 6.27
## 9 block1 CB1795 6.79
## 10 block1 TAL650 6.95
## 11 block1 TAL649 6.55
## 12 block1 TAL860 6
## 13 block1 TAL183 6.11
## 14 block1 TAL378 5.39
## 15 block1 CONTROL1 1.53
## 16 block1 CONTROL2 6.41
## 17 block2 TAL102 10.6
## 18 block2 TAL379 9
## 19 block2 TAL206 9.44
## 20 block2 TAL435 9.23
## 21 block2 TAL411 8.19
## 22 block2 ALLEN527 8.82
## 23 block2 TAL211 6.32
## 24 block2 TAL487 8.67
## 25 block2 CB1795 8.17
## 26 block2 TAL650 5.83
## 27 block2 TAL649 4.82
## 28 block2 TAL860 4.83
## 29 block2 TAL183 3.46
## 30 block2 TAL378 4.46
## 31 block2 CONTROL1 1.3
## 32 block2 CONTROL2 7.83
## 33 block3 TAL102 10.8
## 34 block3 TAL379 10.5
## 35 block3 TAL206 10.2
## 36 block3 TAL435 8.22
## 37 block3 TAL411 8.46
## 38 block3 ALLEN527 8.62
## 39 block3 TAL211 9.14
## 40 block3 TAL487 8.35
## 41 block3 CB1795 5.7
## 42 block3 TAL650 6.83
## 43 block3 TAL649 8.1
## 44 block3 TAL860 6.54
## 45 block3 TAL183 5.51
## 46 block3 TAL378 5.07
## 47 block3 CONTROL1 1.8
## 48 block3 CONTROL2 5.83ANOVA
library(agricolae)##
## Attaching package: 'agricolae'## The following object is masked from 'package:ape':
##
## consensusoutAOV <- aov(yield ~ block + treatment, data = data_rcbd)
outAOV## Call:
## aov(formula = yield ~ block + treatment, data = data_rcbd)
##
## Terms:
## block treatment Residuals
## Sum of Squares 2.42538 217.47868 27.94669
## Deg. of Freedom 2 15 30
##
## Residual standard error: 0.9651716
## Estimated effects may be unbalancedCheck assumptions
plot(fitted(outAOV), residuals(outAOV))
hist(residuals(outAOV))
lines(density(residuals(outAOV)))
hist(residuals(outAOV), prob = TRUE)
lines(density(residuals(outAOV)))
library(ggpubr)##
## Attaching package: 'ggpubr'## The following object is masked from 'package:ape':
##
## rotateggqqplot(residuals(outAOV))
ANOVA table
anova(outAOV)## Analysis of Variance Table
##
## Response: yield
## Df Sum Sq Mean Sq F value Pr(>F)
## block 2 2.425 1.2127 1.3018 0.287
## treatment 15 217.479 14.4986 15.5638 3.284e-10 ***
## Residuals 30 27.947 0.9316
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1t-test LSD
outFactorial <-LSD.test (outAOV, c("treatment"), main = "yield ~ block + treatment",console=TRUE)##
## Study: yield ~ block + treatment
##
## LSD t Test for yield
##
## Mean Square Error: 0.9315563
##
## treatment, means and individual ( 95 %) CI
##
## yield std r LCL UCL Min Max
## ALLEN527 8.516667 0.3661056 3 7.3786265 9.654707 8.11 8.82
## CB1795 6.886667 1.2378341 3 5.7486265 8.024707 5.70 8.17
## CONTROL1 1.543333 0.2502665 3 0.4052932 2.681374 1.30 1.80
## CONTROL2 6.690000 1.0289801 3 5.5519598 7.828040 5.83 7.83
## TAL102 10.363333 0.6198656 3 9.2252932 11.501374 9.66 10.83
## TAL183 5.026667 1.3895443 3 3.8886265 6.164707 3.46 6.11
## TAL206 9.346667 0.8936629 3 8.2086265 10.484707 8.41 10.19
## TAL211 8.096667 1.5464260 3 6.9586265 9.234707 6.32 9.14
## TAL378 4.973333 0.4724757 3 3.8352932 6.111374 4.46 5.39
## TAL379 9.616667 0.7774531 3 8.4786265 10.754707 9.00 10.49
## TAL411 8.616667 0.5229085 3 7.4786265 9.754707 8.19 9.20
## TAL435 8.686667 0.5093460 3 7.5486265 9.824707 8.22 9.23
## TAL487 7.763333 1.3031245 3 6.6252932 8.901374 6.27 8.67
## TAL649 6.490000 1.6408230 3 5.3519598 7.628040 4.82 8.10
## TAL650 6.536667 0.6149255 3 5.3986265 7.674707 5.83 6.95
## TAL860 5.790000 0.8741281 3 4.6519598 6.928040 4.83 6.54
##
## Alpha: 0.05 ; DF Error: 30
## Critical Value of t: 2.042272
##
## least Significant Difference: 1.609432
##
## Treatments with the same letter are not significantly different.
##
## yield groups
## TAL102 10.363333 a
## TAL379 9.616667 ab
## TAL206 9.346667 abc
## TAL435 8.686667 bc
## TAL411 8.616667 bc
## ALLEN527 8.516667 bc
## TAL211 8.096667 bcd
## TAL487 7.763333 cd
## CB1795 6.886667 de
## CONTROL2 6.690000 de
## TAL650 6.536667 def
## TAL649 6.490000 def
## TAL860 5.790000 ef
## TAL183 5.026667 f
## TAL378 4.973333 f
## CONTROL1 1.543333 gReferences
Somasegaran, P., and H. J. Hoben. 1985. “Methods in Legume-Rhizobium Technology.”