fcrd3fact: Analysis of Factorial Completely Randomized Design for 3...
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
fact.A
vector containing levels of first factor
fact.B
vector containing levels of second factor
fact.C
vector containing levels of third factor
Multiple.comparison.test
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 for both the factors as well as interaction.
Examples
1
2
3
4
data(factorialdata)
#FCRD analysis along with dunccan test for two dependent var.
fcrd3fact(factorialdata[5:6],factorialdata$Nitrogen,
factorialdata$Phosphorus,factorialdata$Potassium,2)
Example output
$Yield
$Yield[[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
fact.A 1 24.00 24.00 0.1469 0.70656
fact.B 1 112.67 112.67 0.6896 0.41851
fact.C 1 0.17 0.17 0.0010 0.97492
fact.A:fact.B 1 42.67 42.67 0.2612 0.61630
fact.A:fact.C 1 620.17 620.17 3.7960 0.06914 .
fact.B:fact.C 1 48.17 48.17 0.2948 0.59463
fact.A:fact.B:fact.C 1 181.50 181.50 1.1109 0.30753
Residuals 16 2614.00 163.37
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Yield[[2]]
[1] "R Square 0.283"
$Yield[[3]]
[1] "SEm of A: 3.69 , SEd of A: 5.218 , SEm of B: 3.69 , SEd of B 5.218 , SEm of C: 3.69 , SEd of C: 5.218 , SEm of AB: 5.218 , SEd of AB: 7.38 , SEm of AC: 5.218 , SEd of AC: 7.38 , SEm of BC: 5.218 , SEd of BC: 7.38 , SEm of ABC: 7.38 , SEd of ABC: 10.436"
$Yield[[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.93419, p-value = 0.121
$Yield[[5]]
[1] "Normality assumption is not violated"
$Yield[[6]]
[1] "All the factor A level means are same so dont go for any multiple comparison test"
$Yield[[7]]
$Yield[[7]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[7]][[2]]
Table CriticalRange
2 2.997999 11.062
$Yield[[7]][[3]]
dependent.var groups
n1 123.6667 a
n0 121.6667 a
$Yield[[8]]
[1] "All the factor B level means are same so dont go for any multiple comparison test"
$Yield[[9]]
$Yield[[9]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[9]][[2]]
Table CriticalRange
2 2.997999 11.062
$Yield[[9]][[3]]
dependent.var groups
p0 124.8333 a
p1 120.5000 a
$Yield[[10]]
[1] "All the factor C level means are same so dont go for any multiple comparison test"
$Yield[[11]]
$Yield[[11]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[11]][[2]]
Table CriticalRange
2 2.997999 11.062
$Yield[[11]][[3]]
dependent.var groups
k0 122.7500 a
k1 122.5833 a
$Yield[[12]]
[1] "The means of levels of interaction between A and B factors are same so dont go for any multiple comparison test"
$Yield[[13]]
$Yield[[13]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[13]][[2]]
Table CriticalRange
2 2.997999 15.64403
3 3.143802 16.40486
4 3.234945 16.88045
$Yield[[13]][[3]]
dependent.var groups
n1:p0 127.1667 a
n0:p0 122.5000 a
n0:p1 120.8333 a
n1:p1 120.1667 a
$Yield[[14]]
[1] "The means of levels of interaction between B and C factors are same so dont go for any multiple comparison test"
$Yield[[15]]
$Yield[[15]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[15]][[2]]
Table CriticalRange
2 2.997999 15.64403
3 3.143802 16.40486
4 3.234945 16.88045
$Yield[[15]][[3]]
dependent.var groups
p0:k1 126.1667 a
p0:k0 123.5000 a
p1:k0 122.0000 a
p1:k1 119.0000 a
$Yield[[16]]
[1] "The means of levels of interaction between A and C factors are same so dont go for any multiple comparison test"
$Yield[[17]]
$Yield[[17]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[17]][[2]]
Table CriticalRange
2 2.997999 15.64403
3 3.143802 16.40486
4 3.234945 16.88045
$Yield[[17]][[3]]
dependent.var groups
n1:k0 128.8333 a
n0:k1 126.6667 a
n1:k1 118.5000 a
n0:k0 116.6667 a
$Yield[[18]]
[1] "The means of levels of interaction between all the three factors ABC are same so dont go for any multiple comparison test"
$Yield[[19]]
$Yield[[19]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[19]][[2]]
Table CriticalRange
2 2.997999 22.12400
3 3.143802 23.19997
4 3.234945 23.87256
5 3.297445 24.33379
6 3.342599 24.66701
7 3.376283 24.91558
8 3.401918 25.10475
$Yield[[19]][[3]]
dependent.var groups
n1:p0:k0 133.6667 a
n0:p0:k1 131.6667 a
n1:p1:k0 124.0000 a
n0:p1:k1 121.6667 a
n1:p0:k1 120.6667 a
n0:p1:k0 120.0000 a
n1:p1:k1 116.3333 a
n0:p0:k0 113.3333 a
$Plant_Height
$Plant_Height[[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
fact.A 1 10.667 10.6667 1.3617 0.26034
fact.B 1 2.667 2.6667 0.3404 0.56772
fact.C 1 24.000 24.0000 3.0638 0.09920 .
fact.A:fact.B 1 28.167 28.1667 3.5957 0.07613 .
fact.A:fact.C 1 8.167 8.1667 1.0426 0.32242
fact.B:fact.C 1 0.167 0.1667 0.0213 0.88585
fact.A:fact.B:fact.C 1 10.667 10.6667 1.3617 0.26034
Residuals 16 125.333 7.8333
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Plant_Height[[2]]
[1] "R Square 0.403"
$Plant_Height[[3]]
[1] "SEm of A: 0.808 , SEd of A: 1.143 , SEm of B: 0.808 , SEd of B 1.143 , SEm of C: 0.808 , SEd of C: 1.143 , SEm of AB: 1.143 , SEd of AB: 1.616 , SEm of AC: 1.143 , SEd of AC: 1.616 , SEm of BC: 1.143 , SEd of BC: 1.616 , SEm of ABC: 1.616 , SEd of ABC: 2.285"
$Plant_Height[[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.96559, p-value = 0.5605
$Plant_Height[[5]]
[1] "Normality assumption is not violated"
$Plant_Height[[6]]
[1] "All the factor A level means are same so dont go for any multiple comparison test"
$Plant_Height[[7]]
$Plant_Height[[7]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[7]][[2]]
Table CriticalRange
2 2.997999 2.422223
$Plant_Height[[7]][[3]]
dependent.var groups
n1 13.08333 a
n0 11.75000 a
$Plant_Height[[8]]
[1] "All the factor B level means are same so dont go for any multiple comparison test"
$Plant_Height[[9]]
$Plant_Height[[9]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[9]][[2]]
Table CriticalRange
2 2.997999 2.422223
$Plant_Height[[9]][[3]]
dependent.var groups
p0 12.75000 a
p1 12.08333 a
$Plant_Height[[10]]
[1] "All the factor C level means are same so dont go for any multiple comparison test"
$Plant_Height[[11]]
$Plant_Height[[11]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[11]][[2]]
Table CriticalRange
2 2.997999 2.422223
$Plant_Height[[11]][[3]]
dependent.var groups
k1 13.41667 a
k0 11.41667 a
$Plant_Height[[12]]
[1] "The means of levels of interaction between A and B factors are same so dont go for any multiple comparison test"
$Plant_Height[[13]]
$Plant_Height[[13]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[13]][[2]]
Table CriticalRange
2 2.997999 3.425541
3 3.143802 3.592137
4 3.234945 3.696277
$Plant_Height[[13]][[3]]
dependent.var groups
n1:p0 14.50000 a
n0:p1 12.50000 a
n1:p1 11.66667 a
n0:p0 11.00000 a
$Plant_Height[[14]]
[1] "The means of levels of interaction between B and C factors are same so dont go for any multiple comparison test"
$Plant_Height[[15]]
$Plant_Height[[15]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[15]][[2]]
Table CriticalRange
2 2.997999 3.425541
3 3.143802 3.592137
4 3.234945 3.696277
$Plant_Height[[15]][[3]]
dependent.var groups
p0:k1 13.83333 a
p1:k1 13.00000 a
p0:k0 11.66667 a
p1:k0 11.16667 a
$Plant_Height[[16]]
[1] "The means of levels of interaction between A and C factors are same so dont go for any multiple comparison test"
$Plant_Height[[17]]
$Plant_Height[[17]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[17]][[2]]
Table CriticalRange
2 2.997999 3.425541
3 3.143802 3.592137
4 3.234945 3.696277
$Plant_Height[[17]][[3]]
dependent.var groups
n1:k1 13.50000 a
n0:k1 13.33333 a
n1:k0 12.66667 a
n0:k0 10.16667 a
$Plant_Height[[18]]
[1] "The means of levels of interaction between all the three factors ABC are same so dont go for any multiple comparison test"
$Plant_Height[[19]]
$Plant_Height[[19]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[19]][[2]]
Table CriticalRange
2 2.997999 4.844446
3 3.143802 5.080049
4 3.234945 5.227325
5 3.297445 5.328318
6 3.342599 5.401283
7 3.376283 5.455713
8 3.401918 5.497136
$Plant_Height[[19]][[3]]
dependent.var groups
n1:p0:k1 15.66667 a
n0:p1:k1 14.66667 ab
n1:p0:k0 13.33333 ab
n0:p0:k1 12.00000 ab
n1:p1:k0 12.00000 ab
n1:p1:k1 11.33333 ab
n0:p1:k0 10.33333 ab
n0:p0:k0 10.00000 b
doebioresearch documentation built on July 8, 2020, 7:18 p.m.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Examples
Description
The function gives ANOVA, R-square of the model, normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means.
Usage
1 |
Arguments
data |
dependent variables |
fact.A |
vector containing levels of first factor |
fact.B |
vector containing levels of second factor |
fact.C |
vector containing levels of third factor |
Multiple.comparison.test |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
Value
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result for both the factors as well as interaction.
Examples
1 2 3 4 | data(factorialdata)
#FCRD analysis along with dunccan test for two dependent var.
fcrd3fact(factorialdata[5:6],factorialdata$Nitrogen,
factorialdata$Phosphorus,factorialdata$Potassium,2)
|
Example output
$Yield
$Yield[[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
fact.A 1 24.00 24.00 0.1469 0.70656
fact.B 1 112.67 112.67 0.6896 0.41851
fact.C 1 0.17 0.17 0.0010 0.97492
fact.A:fact.B 1 42.67 42.67 0.2612 0.61630
fact.A:fact.C 1 620.17 620.17 3.7960 0.06914 .
fact.B:fact.C 1 48.17 48.17 0.2948 0.59463
fact.A:fact.B:fact.C 1 181.50 181.50 1.1109 0.30753
Residuals 16 2614.00 163.37
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Yield[[2]]
[1] "R Square 0.283"
$Yield[[3]]
[1] "SEm of A: 3.69 , SEd of A: 5.218 , SEm of B: 3.69 , SEd of B 5.218 , SEm of C: 3.69 , SEd of C: 5.218 , SEm of AB: 5.218 , SEd of AB: 7.38 , SEm of AC: 5.218 , SEd of AC: 7.38 , SEm of BC: 5.218 , SEd of BC: 7.38 , SEm of ABC: 7.38 , SEd of ABC: 10.436"
$Yield[[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.93419, p-value = 0.121
$Yield[[5]]
[1] "Normality assumption is not violated"
$Yield[[6]]
[1] "All the factor A level means are same so dont go for any multiple comparison test"
$Yield[[7]]
$Yield[[7]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[7]][[2]]
Table CriticalRange
2 2.997999 11.062
$Yield[[7]][[3]]
dependent.var groups
n1 123.6667 a
n0 121.6667 a
$Yield[[8]]
[1] "All the factor B level means are same so dont go for any multiple comparison test"
$Yield[[9]]
$Yield[[9]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[9]][[2]]
Table CriticalRange
2 2.997999 11.062
$Yield[[9]][[3]]
dependent.var groups
p0 124.8333 a
p1 120.5000 a
$Yield[[10]]
[1] "All the factor C level means are same so dont go for any multiple comparison test"
$Yield[[11]]
$Yield[[11]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[11]][[2]]
Table CriticalRange
2 2.997999 11.062
$Yield[[11]][[3]]
dependent.var groups
k0 122.7500 a
k1 122.5833 a
$Yield[[12]]
[1] "The means of levels of interaction between A and B factors are same so dont go for any multiple comparison test"
$Yield[[13]]
$Yield[[13]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[13]][[2]]
Table CriticalRange
2 2.997999 15.64403
3 3.143802 16.40486
4 3.234945 16.88045
$Yield[[13]][[3]]
dependent.var groups
n1:p0 127.1667 a
n0:p0 122.5000 a
n0:p1 120.8333 a
n1:p1 120.1667 a
$Yield[[14]]
[1] "The means of levels of interaction between B and C factors are same so dont go for any multiple comparison test"
$Yield[[15]]
$Yield[[15]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[15]][[2]]
Table CriticalRange
2 2.997999 15.64403
3 3.143802 16.40486
4 3.234945 16.88045
$Yield[[15]][[3]]
dependent.var groups
p0:k1 126.1667 a
p0:k0 123.5000 a
p1:k0 122.0000 a
p1:k1 119.0000 a
$Yield[[16]]
[1] "The means of levels of interaction between A and C factors are same so dont go for any multiple comparison test"
$Yield[[17]]
$Yield[[17]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[17]][[2]]
Table CriticalRange
2 2.997999 15.64403
3 3.143802 16.40486
4 3.234945 16.88045
$Yield[[17]][[3]]
dependent.var groups
n1:k0 128.8333 a
n0:k1 126.6667 a
n1:k1 118.5000 a
n0:k0 116.6667 a
$Yield[[18]]
[1] "The means of levels of interaction between all the three factors ABC are same so dont go for any multiple comparison test"
$Yield[[19]]
$Yield[[19]][[1]]
MSerror Df Mean CV
163.375 16 122.6667 10.41996
$Yield[[19]][[2]]
Table CriticalRange
2 2.997999 22.12400
3 3.143802 23.19997
4 3.234945 23.87256
5 3.297445 24.33379
6 3.342599 24.66701
7 3.376283 24.91558
8 3.401918 25.10475
$Yield[[19]][[3]]
dependent.var groups
n1:p0:k0 133.6667 a
n0:p0:k1 131.6667 a
n1:p1:k0 124.0000 a
n0:p1:k1 121.6667 a
n1:p0:k1 120.6667 a
n0:p1:k0 120.0000 a
n1:p1:k1 116.3333 a
n0:p0:k0 113.3333 a
$Plant_Height
$Plant_Height[[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
fact.A 1 10.667 10.6667 1.3617 0.26034
fact.B 1 2.667 2.6667 0.3404 0.56772
fact.C 1 24.000 24.0000 3.0638 0.09920 .
fact.A:fact.B 1 28.167 28.1667 3.5957 0.07613 .
fact.A:fact.C 1 8.167 8.1667 1.0426 0.32242
fact.B:fact.C 1 0.167 0.1667 0.0213 0.88585
fact.A:fact.B:fact.C 1 10.667 10.6667 1.3617 0.26034
Residuals 16 125.333 7.8333
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Plant_Height[[2]]
[1] "R Square 0.403"
$Plant_Height[[3]]
[1] "SEm of A: 0.808 , SEd of A: 1.143 , SEm of B: 0.808 , SEd of B 1.143 , SEm of C: 0.808 , SEd of C: 1.143 , SEm of AB: 1.143 , SEd of AB: 1.616 , SEm of AC: 1.143 , SEd of AC: 1.616 , SEm of BC: 1.143 , SEd of BC: 1.616 , SEm of ABC: 1.616 , SEd of ABC: 2.285"
$Plant_Height[[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.96559, p-value = 0.5605
$Plant_Height[[5]]
[1] "Normality assumption is not violated"
$Plant_Height[[6]]
[1] "All the factor A level means are same so dont go for any multiple comparison test"
$Plant_Height[[7]]
$Plant_Height[[7]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[7]][[2]]
Table CriticalRange
2 2.997999 2.422223
$Plant_Height[[7]][[3]]
dependent.var groups
n1 13.08333 a
n0 11.75000 a
$Plant_Height[[8]]
[1] "All the factor B level means are same so dont go for any multiple comparison test"
$Plant_Height[[9]]
$Plant_Height[[9]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[9]][[2]]
Table CriticalRange
2 2.997999 2.422223
$Plant_Height[[9]][[3]]
dependent.var groups
p0 12.75000 a
p1 12.08333 a
$Plant_Height[[10]]
[1] "All the factor C level means are same so dont go for any multiple comparison test"
$Plant_Height[[11]]
$Plant_Height[[11]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[11]][[2]]
Table CriticalRange
2 2.997999 2.422223
$Plant_Height[[11]][[3]]
dependent.var groups
k1 13.41667 a
k0 11.41667 a
$Plant_Height[[12]]
[1] "The means of levels of interaction between A and B factors are same so dont go for any multiple comparison test"
$Plant_Height[[13]]
$Plant_Height[[13]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[13]][[2]]
Table CriticalRange
2 2.997999 3.425541
3 3.143802 3.592137
4 3.234945 3.696277
$Plant_Height[[13]][[3]]
dependent.var groups
n1:p0 14.50000 a
n0:p1 12.50000 a
n1:p1 11.66667 a
n0:p0 11.00000 a
$Plant_Height[[14]]
[1] "The means of levels of interaction between B and C factors are same so dont go for any multiple comparison test"
$Plant_Height[[15]]
$Plant_Height[[15]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[15]][[2]]
Table CriticalRange
2 2.997999 3.425541
3 3.143802 3.592137
4 3.234945 3.696277
$Plant_Height[[15]][[3]]
dependent.var groups
p0:k1 13.83333 a
p1:k1 13.00000 a
p0:k0 11.66667 a
p1:k0 11.16667 a
$Plant_Height[[16]]
[1] "The means of levels of interaction between A and C factors are same so dont go for any multiple comparison test"
$Plant_Height[[17]]
$Plant_Height[[17]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[17]][[2]]
Table CriticalRange
2 2.997999 3.425541
3 3.143802 3.592137
4 3.234945 3.696277
$Plant_Height[[17]][[3]]
dependent.var groups
n1:k1 13.50000 a
n0:k1 13.33333 a
n1:k0 12.66667 a
n0:k0 10.16667 a
$Plant_Height[[18]]
[1] "The means of levels of interaction between all the three factors ABC are same so dont go for any multiple comparison test"
$Plant_Height[[19]]
$Plant_Height[[19]][[1]]
MSerror Df Mean CV
7.833333 16 12.41667 22.54075
$Plant_Height[[19]][[2]]
Table CriticalRange
2 2.997999 4.844446
3 3.143802 5.080049
4 3.234945 5.227325
5 3.297445 5.328318
6 3.342599 5.401283
7 3.376283 5.455713
8 3.401918 5.497136
$Plant_Height[[19]][[3]]
dependent.var groups
n1:p0:k1 15.66667 a
n0:p1:k1 14.66667 ab
n1:p0:k0 13.33333 ab
n0:p0:k1 12.00000 ab
n1:p1:k0 12.00000 ab
n1:p1:k1 11.33333 ab
n0:p1:k0 10.33333 ab
n0:p0:k0 10.00000 b
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