splitplot: Analysis of Split plot design
In doebioresearch: Analysis of Design of Experiments for Biological Research
Description
Usage
Arguments
Value
Examples
Description
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.
Usage
1
Arguments
data
dependent variables
block
vector containing replications
main.plot
vector containing main-plot levels
sub.plot
vector containing sub-plot levels
mean.comparison.test
0 for no test, 1 for LSD test, 2 for Dunccan test and 3 for HSD test
Value
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result
Examples
1
2
3
4
5
6
data(splitdata)
#Using Date of sowing as Main-plot factor and varieties as sub-plot factor and using LSD test
#Split plot analysis with LSD test for Yield
splitplot(splitdata[4],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)
#Split plot analysis with LSD test for both Yield and Plant Height
splitplot(splitdata[4:5],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)
Example output
$Yield
$Yield[[1]]
$Yield[[1]][[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
block 2 61557 30779 0.1876 0.84205
main.plot 1 187 187 0.0011 0.97615
Ea 2 328176 164088
sub.plot 5 510277 102055 2.9915 0.03556 *
main.plot:sub.plot 5 125447 25089 0.7354 0.60557
Eb 20 682307 34115
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Yield[[1]][[2]]
[1] "CV(a): 8.063 , CV(b) : 3.677"
$Yield[[1]][[3]]
[1] "R Square 0.601"
$Yield[[1]][[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.94087, p-value = 0.05406
$Yield[[1]][[5]]
[1] "Normality assumption is not violated"
$Yield[[1]][[6]]
[1] "All the main plot factor level means are same so dont go for any multiple comparison test"
$Yield[[1]][[7]]
$Yield[[1]][[7]][[1]]
MSerror Df Mean CV t.value LSD
164087.9 2 5023.611 8.063474 4.302653 580.9694
$Yield[[1]][[7]][[2]]
dependent.var groups
1 5025.889 a
2 5021.333 a
$Yield[[1]][[8]]
[1] "The means of one or more levels of sub plot factor are not same, so go for multiple comparison test"
$Yield[[1]][[9]]
$Yield[[1]][[9]][[1]]
MSerror Df Mean CV t.value LSD
34115.36 20 5023.611 3.676707 2.085963 222.4442
$Yield[[1]][[9]][[2]]
dependent.var groups
6 5154.333 a
5 5075.833 a
4 5070.000 a
3 5053.833 a
2 5013.167 a
1 4774.500 b
$Yield[[1]][[10]]
[1] "All the interaction level means are same so dont go for any multiple comparison test"
$Yield[[1]][[11]]
$Yield[[1]][[11]][[1]]
MSerror Df Mean CV t.value LSD
34115.36 20 5023.611 3.676707 2.085963 314.5836
$Yield[[1]][[11]][[2]]
dependent.var groups
2:6 5173.333 a
1:6 5135.333 a
2:5 5095.000 a
2:3 5074.333 a
1:4 5070.000 a
2:4 5070.000 a
2:2 5069.667 a
1:5 5056.667 a
1:3 5033.333 a
1:2 4956.667 ab
1:1 4903.333 ab
2:1 4645.667 b
$Yield
$Yield[[1]]
$Yield[[1]][[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
block 2 61557 30779 0.1876 0.84205
main.plot 1 187 187 0.0011 0.97615
Ea 2 328176 164088
sub.plot 5 510277 102055 2.9915 0.03556 *
main.plot:sub.plot 5 125447 25089 0.7354 0.60557
Eb 20 682307 34115
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Yield[[1]][[2]]
[1] "CV(a): 8.063 , CV(b) : 3.677"
$Yield[[1]][[3]]
[1] "R Square 0.601"
$Yield[[1]][[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.94087, p-value = 0.05406
$Yield[[1]][[5]]
[1] "Normality assumption is not violated"
$Yield[[1]][[6]]
[1] "All the main plot factor level means are same so dont go for any multiple comparison test"
$Yield[[1]][[7]]
$Yield[[1]][[7]][[1]]
MSerror Df Mean CV t.value LSD
164087.9 2 5023.611 8.063474 4.302653 580.9694
$Yield[[1]][[7]][[2]]
dependent.var groups
1 5025.889 a
2 5021.333 a
$Yield[[1]][[8]]
[1] "The means of one or more levels of sub plot factor are not same, so go for multiple comparison test"
$Yield[[1]][[9]]
$Yield[[1]][[9]][[1]]
MSerror Df Mean CV t.value LSD
34115.36 20 5023.611 3.676707 2.085963 222.4442
$Yield[[1]][[9]][[2]]
dependent.var groups
6 5154.333 a
5 5075.833 a
4 5070.000 a
3 5053.833 a
2 5013.167 a
1 4774.500 b
$Yield[[1]][[10]]
[1] "All the interaction level means are same so dont go for any multiple comparison test"
$Yield[[1]][[11]]
$Yield[[1]][[11]][[1]]
MSerror Df Mean CV t.value LSD
34115.36 20 5023.611 3.676707 2.085963 314.5836
$Yield[[1]][[11]][[2]]
dependent.var groups
2:6 5173.333 a
1:6 5135.333 a
2:5 5095.000 a
2:3 5074.333 a
1:4 5070.000 a
2:4 5070.000 a
2:2 5069.667 a
1:5 5056.667 a
1:3 5033.333 a
1:2 4956.667 ab
1:1 4903.333 ab
2:1 4645.667 b
$Plant_Height
$Plant_Height[[1]]
$Plant_Height[[1]][[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
block 2 3022.06 1511.03 6.7149 0.1296
main.plot 1 0.03 0.03 0.0001 0.9921
Ea 2 450.06 225.03
sub.plot 5 1167.14 233.43 1.7106 0.1782
main.plot:sub.plot 5 292.47 58.49 0.4287 0.8232
Eb 20 2729.22 136.46
$Plant_Height[[1]][[2]]
[1] "CV(a): 13.104 , CV(b) : 10.205"
$Plant_Height[[1]][[3]]
[1] "R Square 0.644"
$Plant_Height[[1]][[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.96789, p-value = 0.3707
$Plant_Height[[1]][[5]]
[1] "Normality assumption is not violated"
$Plant_Height[[1]][[6]]
[1] "All the main plot factor level means are same so dont go for any multiple comparison test"
$Plant_Height[[1]][[7]]
$Plant_Height[[1]][[7]][[1]]
MSerror Df Mean CV t.value LSD
225.0278 2 114.4722 13.10442 4.302653 21.51459
$Plant_Height[[1]][[7]][[2]]
dependent.var groups
2 114.5000 a
1 114.4444 a
$Plant_Height[[1]][[8]]
[1] "All the sub plot factor factor level means are same so dont go for any multiple comparison test"
$Plant_Height[[1]][[9]]
$Plant_Height[[1]][[9]][[1]]
MSerror Df Mean CV t.value LSD
136.4611 20 114.4722 10.2048 2.085963 14.06859
$Plant_Height[[1]][[9]][[2]]
dependent.var groups
6 122.8333 a
5 121.6667 ab
1 112.5000 ab
2 112.1667 ab
4 109.0000 ab
3 108.6667 b
$Plant_Height[[1]][[10]]
[1] "All the interaction level means are same so dont go for any multiple comparison test"
$Plant_Height[[1]][[11]]
$Plant_Height[[1]][[11]][[1]]
MSerror Df Mean CV t.value LSD
136.4611 20 114.4722 10.2048 2.085963 19.89599
$Plant_Height[[1]][[11]][[2]]
dependent.var groups
2:6 123.6667 a
1:5 123.0000 a
1:6 122.0000 a
2:5 120.3333 a
2:2 118.0000 a
1:1 115.6667 a
1:3 110.0000 a
1:4 109.6667 a
2:1 109.3333 a
2:4 108.3333 a
2:3 107.3333 a
1:2 106.3333 a
doebioresearch documentation built on July 8, 2020, 7:18 p.m.
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Description Usage Arguments Value Examples
Description
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.
Usage
1 |
Arguments
data |
dependent variables |
block |
vector containing replications |
main.plot |
vector containing main-plot levels |
sub.plot |
vector containing sub-plot levels |
mean.comparison.test |
0 for no test, 1 for LSD test, 2 for Dunccan test and 3 for HSD test |
Value
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result
Examples
1 2 3 4 5 6 | data(splitdata)
#Using Date of sowing as Main-plot factor and varieties as sub-plot factor and using LSD test
#Split plot analysis with LSD test for Yield
splitplot(splitdata[4],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)
#Split plot analysis with LSD test for both Yield and Plant Height
splitplot(splitdata[4:5],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)
|
Example output
$Yield
$Yield[[1]]
$Yield[[1]][[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
block 2 61557 30779 0.1876 0.84205
main.plot 1 187 187 0.0011 0.97615
Ea 2 328176 164088
sub.plot 5 510277 102055 2.9915 0.03556 *
main.plot:sub.plot 5 125447 25089 0.7354 0.60557
Eb 20 682307 34115
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Yield[[1]][[2]]
[1] "CV(a): 8.063 , CV(b) : 3.677"
$Yield[[1]][[3]]
[1] "R Square 0.601"
$Yield[[1]][[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.94087, p-value = 0.05406
$Yield[[1]][[5]]
[1] "Normality assumption is not violated"
$Yield[[1]][[6]]
[1] "All the main plot factor level means are same so dont go for any multiple comparison test"
$Yield[[1]][[7]]
$Yield[[1]][[7]][[1]]
MSerror Df Mean CV t.value LSD
164087.9 2 5023.611 8.063474 4.302653 580.9694
$Yield[[1]][[7]][[2]]
dependent.var groups
1 5025.889 a
2 5021.333 a
$Yield[[1]][[8]]
[1] "The means of one or more levels of sub plot factor are not same, so go for multiple comparison test"
$Yield[[1]][[9]]
$Yield[[1]][[9]][[1]]
MSerror Df Mean CV t.value LSD
34115.36 20 5023.611 3.676707 2.085963 222.4442
$Yield[[1]][[9]][[2]]
dependent.var groups
6 5154.333 a
5 5075.833 a
4 5070.000 a
3 5053.833 a
2 5013.167 a
1 4774.500 b
$Yield[[1]][[10]]
[1] "All the interaction level means are same so dont go for any multiple comparison test"
$Yield[[1]][[11]]
$Yield[[1]][[11]][[1]]
MSerror Df Mean CV t.value LSD
34115.36 20 5023.611 3.676707 2.085963 314.5836
$Yield[[1]][[11]][[2]]
dependent.var groups
2:6 5173.333 a
1:6 5135.333 a
2:5 5095.000 a
2:3 5074.333 a
1:4 5070.000 a
2:4 5070.000 a
2:2 5069.667 a
1:5 5056.667 a
1:3 5033.333 a
1:2 4956.667 ab
1:1 4903.333 ab
2:1 4645.667 b
$Yield
$Yield[[1]]
$Yield[[1]][[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
block 2 61557 30779 0.1876 0.84205
main.plot 1 187 187 0.0011 0.97615
Ea 2 328176 164088
sub.plot 5 510277 102055 2.9915 0.03556 *
main.plot:sub.plot 5 125447 25089 0.7354 0.60557
Eb 20 682307 34115
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Yield[[1]][[2]]
[1] "CV(a): 8.063 , CV(b) : 3.677"
$Yield[[1]][[3]]
[1] "R Square 0.601"
$Yield[[1]][[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.94087, p-value = 0.05406
$Yield[[1]][[5]]
[1] "Normality assumption is not violated"
$Yield[[1]][[6]]
[1] "All the main plot factor level means are same so dont go for any multiple comparison test"
$Yield[[1]][[7]]
$Yield[[1]][[7]][[1]]
MSerror Df Mean CV t.value LSD
164087.9 2 5023.611 8.063474 4.302653 580.9694
$Yield[[1]][[7]][[2]]
dependent.var groups
1 5025.889 a
2 5021.333 a
$Yield[[1]][[8]]
[1] "The means of one or more levels of sub plot factor are not same, so go for multiple comparison test"
$Yield[[1]][[9]]
$Yield[[1]][[9]][[1]]
MSerror Df Mean CV t.value LSD
34115.36 20 5023.611 3.676707 2.085963 222.4442
$Yield[[1]][[9]][[2]]
dependent.var groups
6 5154.333 a
5 5075.833 a
4 5070.000 a
3 5053.833 a
2 5013.167 a
1 4774.500 b
$Yield[[1]][[10]]
[1] "All the interaction level means are same so dont go for any multiple comparison test"
$Yield[[1]][[11]]
$Yield[[1]][[11]][[1]]
MSerror Df Mean CV t.value LSD
34115.36 20 5023.611 3.676707 2.085963 314.5836
$Yield[[1]][[11]][[2]]
dependent.var groups
2:6 5173.333 a
1:6 5135.333 a
2:5 5095.000 a
2:3 5074.333 a
1:4 5070.000 a
2:4 5070.000 a
2:2 5069.667 a
1:5 5056.667 a
1:3 5033.333 a
1:2 4956.667 ab
1:1 4903.333 ab
2:1 4645.667 b
$Plant_Height
$Plant_Height[[1]]
$Plant_Height[[1]][[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
block 2 3022.06 1511.03 6.7149 0.1296
main.plot 1 0.03 0.03 0.0001 0.9921
Ea 2 450.06 225.03
sub.plot 5 1167.14 233.43 1.7106 0.1782
main.plot:sub.plot 5 292.47 58.49 0.4287 0.8232
Eb 20 2729.22 136.46
$Plant_Height[[1]][[2]]
[1] "CV(a): 13.104 , CV(b) : 10.205"
$Plant_Height[[1]][[3]]
[1] "R Square 0.644"
$Plant_Height[[1]][[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.96789, p-value = 0.3707
$Plant_Height[[1]][[5]]
[1] "Normality assumption is not violated"
$Plant_Height[[1]][[6]]
[1] "All the main plot factor level means are same so dont go for any multiple comparison test"
$Plant_Height[[1]][[7]]
$Plant_Height[[1]][[7]][[1]]
MSerror Df Mean CV t.value LSD
225.0278 2 114.4722 13.10442 4.302653 21.51459
$Plant_Height[[1]][[7]][[2]]
dependent.var groups
2 114.5000 a
1 114.4444 a
$Plant_Height[[1]][[8]]
[1] "All the sub plot factor factor level means are same so dont go for any multiple comparison test"
$Plant_Height[[1]][[9]]
$Plant_Height[[1]][[9]][[1]]
MSerror Df Mean CV t.value LSD
136.4611 20 114.4722 10.2048 2.085963 14.06859
$Plant_Height[[1]][[9]][[2]]
dependent.var groups
6 122.8333 a
5 121.6667 ab
1 112.5000 ab
2 112.1667 ab
4 109.0000 ab
3 108.6667 b
$Plant_Height[[1]][[10]]
[1] "All the interaction level means are same so dont go for any multiple comparison test"
$Plant_Height[[1]][[11]]
$Plant_Height[[1]][[11]][[1]]
MSerror Df Mean CV t.value LSD
136.4611 20 114.4722 10.2048 2.085963 19.89599
$Plant_Height[[1]][[11]][[2]]
dependent.var groups
2:6 123.6667 a
1:5 123.0000 a
1:6 122.0000 a
2:5 120.3333 a
2:2 118.0000 a
1:1 115.6667 a
1:3 110.0000 a
1:4 109.6667 a
2:1 109.3333 a
2:4 108.3333 a
2:3 107.3333 a
1:2 106.3333 a
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