rcbd: Analysis of Randomized Complete Block 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
treatmentvector
vector containing treatments
replicationvector
vector containing replications
MultipleComparisonTest
0 for no test, 1 for LSD test, 2 for Duncan 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
7
8
9
data<-data.frame(GFY=c(16,13,14,16,16,17,16,17,16,16,17,16,15,15,15,13,15,14,
16,14,15,14,15,17,18,15,15,15,14,14,14,14,15,15,13,15,14,14,13,13,13,12,15,12,15),
DMY=c(5,5,6,5,6,7,6,8,6,9,8,7,5,5,5,4,6,5,8,5,5,5,4,6,6,5,5,6,6,6,5,5,5,5,5,6,5,5,5,4,5,4,5,5,5),
Rep=rep(c("R1","R2","R3"),each=15),
Trt=rep(c("T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12","T13","T14","T15"),3))
#' #RCBD analysis with duncan test for GFY only
rcbd(data[1],data$Trt,data$Rep,2)
#RCBD analysis with duncan test for both GFY and DMY
rcbd(data[1:2],data$Trt,data$Rep,2)
Example output
$GFY
$GFY[[1]]
Analysis of Variance Table
Response: data2
Df Sum Sq Mean Sq F value Pr(>F)
replication 2 26.533 13.2667 9.4122 0.0007475 ***
trt 14 17.200 1.2286 0.8716 0.5944508
Residuals 28 39.467 1.4095
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$GFY[[2]]
[1] "R Square 0.526"
$GFY[[3]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.98487, p-value = 0.8148
$GFY[[4]]
[1] "Normality assumption is not violated"
$GFY[[5]]
[1] "SEm 0.6854 , SEd 0.9694"
$GFY[[6]]
[1] "All the treatment means are same so dont go for any multiple comparison test"
$GFY[[7]]
$GFY[[7]][[1]]
MSerror Df Mean CV
1.409524 28 14.8 8.021849
$GFY[[7]][[2]]
Table CriticalRange
2 2.896885 1.985669
3 3.043847 2.086404
4 3.138859 2.151530
5 3.206478 2.197879
6 3.257369 2.232763
7 3.297090 2.259989
8 3.328885 2.281783
9 3.354805 2.299550
10 3.376223 2.314231
11 3.394100 2.326485
12 3.409132 2.336788
13 3.421839 2.345499
14 3.432619 2.352887
15 3.441780 2.359167
$GFY[[7]][[3]]
data2 groups
T10 15.66667 a
T4 15.66667 a
T6 15.66667 a
T8 15.33333 a
T9 15.33333 a
T11 15.00000 a
T13 15.00000 a
T15 14.66667 a
T7 14.66667 a
T1 14.33333 a
T12 14.33333 a
T3 14.33333 a
T5 14.33333 a
T2 14.00000 a
T14 13.66667 a
$GFY
$GFY[[1]]
Analysis of Variance Table
Response: data2
Df Sum Sq Mean Sq F value Pr(>F)
replication 2 26.533 13.2667 9.4122 0.0007475 ***
trt 14 17.200 1.2286 0.8716 0.5944508
Residuals 28 39.467 1.4095
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$GFY[[2]]
[1] "R Square 0.526"
$GFY[[3]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.98487, p-value = 0.8148
$GFY[[4]]
[1] "Normality assumption is not violated"
$GFY[[5]]
[1] "SEm 0.6854 , SEd 0.9694"
$GFY[[6]]
[1] "All the treatment means are same so dont go for any multiple comparison test"
$GFY[[7]]
$GFY[[7]][[1]]
MSerror Df Mean CV
1.409524 28 14.8 8.021849
$GFY[[7]][[2]]
Table CriticalRange
2 2.896885 1.985669
3 3.043847 2.086404
4 3.138859 2.151530
5 3.206478 2.197879
6 3.257369 2.232763
7 3.297090 2.259989
8 3.328885 2.281783
9 3.354805 2.299550
10 3.376223 2.314231
11 3.394100 2.326485
12 3.409132 2.336788
13 3.421839 2.345499
14 3.432619 2.352887
15 3.441780 2.359167
$GFY[[7]][[3]]
data2 groups
T10 15.66667 a
T4 15.66667 a
T6 15.66667 a
T8 15.33333 a
T9 15.33333 a
T11 15.00000 a
T13 15.00000 a
T15 14.66667 a
T7 14.66667 a
T1 14.33333 a
T12 14.33333 a
T3 14.33333 a
T5 14.33333 a
T2 14.00000 a
T14 13.66667 a
$DMY
$DMY[[1]]
Analysis of Variance Table
Response: data2
Df Sum Sq Mean Sq F value Pr(>F)
replication 2 12.133 6.0667 5.0157 0.01375 *
trt 14 7.200 0.5143 0.4252 0.95260
Residuals 28 33.867 1.2095
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$DMY[[2]]
[1] "R Square 0.363"
$DMY[[3]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.97393, p-value = 0.3985
$DMY[[4]]
[1] "Normality assumption is not violated"
$DMY[[5]]
[1] "SEm 0.635 , SEd 0.898"
$DMY[[6]]
[1] "All the treatment means are same so dont go for any multiple comparison test"
$DMY[[7]]
$DMY[[7]][[1]]
MSerror Df Mean CV
1.209524 28 5.533333 19.87561
$DMY[[7]][[2]]
Table CriticalRange
2 2.896885 1.839407
3 3.043847 1.932722
4 3.138859 1.993051
5 3.206478 2.035986
6 3.257369 2.068300
7 3.297090 2.093521
8 3.328885 2.113710
9 3.354805 2.130168
10 3.376223 2.143768
11 3.394100 2.155119
12 3.409132 2.164663
13 3.421839 2.172732
14 3.432619 2.179577
15 3.441780 2.185394
$DMY[[7]][[3]]
data2 groups
T10 6.333333 a
T11 6.000000 a
T4 6.000000 a
T6 6.000000 a
T8 5.666667 a
T9 5.666667 a
T12 5.333333 a
T13 5.333333 a
T14 5.333333 a
T15 5.333333 a
T2 5.333333 a
T3 5.333333 a
T5 5.333333 a
T7 5.333333 a
T1 4.666667 a
doebioresearch documentation built on July 8, 2020, 7:18 p.m.
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Description Usage Arguments Value Examples
Description
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.
Usage
1 |
Arguments
data |
dependent variables |
treatmentvector |
vector containing treatments |
replicationvector |
vector containing replications |
MultipleComparisonTest |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
Value
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result
Examples
1 2 3 4 5 6 7 8 9 | data<-data.frame(GFY=c(16,13,14,16,16,17,16,17,16,16,17,16,15,15,15,13,15,14,
16,14,15,14,15,17,18,15,15,15,14,14,14,14,15,15,13,15,14,14,13,13,13,12,15,12,15),
DMY=c(5,5,6,5,6,7,6,8,6,9,8,7,5,5,5,4,6,5,8,5,5,5,4,6,6,5,5,6,6,6,5,5,5,5,5,6,5,5,5,4,5,4,5,5,5),
Rep=rep(c("R1","R2","R3"),each=15),
Trt=rep(c("T1","T2","T3","T4","T5","T6","T7","T8","T9","T10","T11","T12","T13","T14","T15"),3))
#' #RCBD analysis with duncan test for GFY only
rcbd(data[1],data$Trt,data$Rep,2)
#RCBD analysis with duncan test for both GFY and DMY
rcbd(data[1:2],data$Trt,data$Rep,2)
|
Example output
$GFY
$GFY[[1]]
Analysis of Variance Table
Response: data2
Df Sum Sq Mean Sq F value Pr(>F)
replication 2 26.533 13.2667 9.4122 0.0007475 ***
trt 14 17.200 1.2286 0.8716 0.5944508
Residuals 28 39.467 1.4095
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$GFY[[2]]
[1] "R Square 0.526"
$GFY[[3]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.98487, p-value = 0.8148
$GFY[[4]]
[1] "Normality assumption is not violated"
$GFY[[5]]
[1] "SEm 0.6854 , SEd 0.9694"
$GFY[[6]]
[1] "All the treatment means are same so dont go for any multiple comparison test"
$GFY[[7]]
$GFY[[7]][[1]]
MSerror Df Mean CV
1.409524 28 14.8 8.021849
$GFY[[7]][[2]]
Table CriticalRange
2 2.896885 1.985669
3 3.043847 2.086404
4 3.138859 2.151530
5 3.206478 2.197879
6 3.257369 2.232763
7 3.297090 2.259989
8 3.328885 2.281783
9 3.354805 2.299550
10 3.376223 2.314231
11 3.394100 2.326485
12 3.409132 2.336788
13 3.421839 2.345499
14 3.432619 2.352887
15 3.441780 2.359167
$GFY[[7]][[3]]
data2 groups
T10 15.66667 a
T4 15.66667 a
T6 15.66667 a
T8 15.33333 a
T9 15.33333 a
T11 15.00000 a
T13 15.00000 a
T15 14.66667 a
T7 14.66667 a
T1 14.33333 a
T12 14.33333 a
T3 14.33333 a
T5 14.33333 a
T2 14.00000 a
T14 13.66667 a
$GFY
$GFY[[1]]
Analysis of Variance Table
Response: data2
Df Sum Sq Mean Sq F value Pr(>F)
replication 2 26.533 13.2667 9.4122 0.0007475 ***
trt 14 17.200 1.2286 0.8716 0.5944508
Residuals 28 39.467 1.4095
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$GFY[[2]]
[1] "R Square 0.526"
$GFY[[3]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.98487, p-value = 0.8148
$GFY[[4]]
[1] "Normality assumption is not violated"
$GFY[[5]]
[1] "SEm 0.6854 , SEd 0.9694"
$GFY[[6]]
[1] "All the treatment means are same so dont go for any multiple comparison test"
$GFY[[7]]
$GFY[[7]][[1]]
MSerror Df Mean CV
1.409524 28 14.8 8.021849
$GFY[[7]][[2]]
Table CriticalRange
2 2.896885 1.985669
3 3.043847 2.086404
4 3.138859 2.151530
5 3.206478 2.197879
6 3.257369 2.232763
7 3.297090 2.259989
8 3.328885 2.281783
9 3.354805 2.299550
10 3.376223 2.314231
11 3.394100 2.326485
12 3.409132 2.336788
13 3.421839 2.345499
14 3.432619 2.352887
15 3.441780 2.359167
$GFY[[7]][[3]]
data2 groups
T10 15.66667 a
T4 15.66667 a
T6 15.66667 a
T8 15.33333 a
T9 15.33333 a
T11 15.00000 a
T13 15.00000 a
T15 14.66667 a
T7 14.66667 a
T1 14.33333 a
T12 14.33333 a
T3 14.33333 a
T5 14.33333 a
T2 14.00000 a
T14 13.66667 a
$DMY
$DMY[[1]]
Analysis of Variance Table
Response: data2
Df Sum Sq Mean Sq F value Pr(>F)
replication 2 12.133 6.0667 5.0157 0.01375 *
trt 14 7.200 0.5143 0.4252 0.95260
Residuals 28 33.867 1.2095
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$DMY[[2]]
[1] "R Square 0.363"
$DMY[[3]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.97393, p-value = 0.3985
$DMY[[4]]
[1] "Normality assumption is not violated"
$DMY[[5]]
[1] "SEm 0.635 , SEd 0.898"
$DMY[[6]]
[1] "All the treatment means are same so dont go for any multiple comparison test"
$DMY[[7]]
$DMY[[7]][[1]]
MSerror Df Mean CV
1.209524 28 5.533333 19.87561
$DMY[[7]][[2]]
Table CriticalRange
2 2.896885 1.839407
3 3.043847 1.932722
4 3.138859 1.993051
5 3.206478 2.035986
6 3.257369 2.068300
7 3.297090 2.093521
8 3.328885 2.113710
9 3.354805 2.130168
10 3.376223 2.143768
11 3.394100 2.155119
12 3.409132 2.164663
13 3.421839 2.172732
14 3.432619 2.179577
15 3.441780 2.185394
$DMY[[7]][[3]]
data2 groups
T10 6.333333 a
T11 6.000000 a
T4 6.000000 a
T6 6.000000 a
T8 5.666667 a
T9 5.666667 a
T12 5.333333 a
T13 5.333333 a
T14 5.333333 a
T15 5.333333 a
T2 5.333333 a
T3 5.333333 a
T5 5.333333 a
T7 5.333333 a
T1 4.666667 a
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