fcrd2fact: Analysis of Factorial Completely Randomized Design for 2...
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
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
5
data(factorialdata)
#Analysis of Factorial Completely Randomized design along with Dunccan test for Yield only
fcrd2fact(factorialdata[5],factorialdata$Nitrogen,factorialdata$Phosphorus,2)
#Analysis of Factorial Completely Randomized design along with Dunccan test for Yield & Plant Height
fcrd2fact(factorialdata[5:6],factorialdata$Nitrogen,factorialdata$Phosphorus,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 24.0 24.000 0.1386 0.7136
fact.B 1 112.7 112.667 0.6505 0.4294
fact.A:fact.B 1 42.7 42.667 0.2463 0.6251
Residuals 20 3464.0 173.200
$Yield[[2]]
[1] "R Square 0.049"
$Yield[[3]]
[1] "SEm of A: 3.799 , SEd of A: 5.373 , SEm of B: 3.799 , SEd of B: 5.373 , SEm of AB: 5.373 , SEd of AB 7.598"
$Yield[[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.85265, p-value = 0.002449
$Yield[[5]]
[1] "Normality assumption is violated"
$Yield[[6]]
[1] "All the first factor level means are same so dont go for any multiple comparison test"
$Yield[[7]]
$Yield[[7]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[7]][[2]]
Table CriticalRange
2 2.949998 11.2074
$Yield[[7]][[3]]
dependent.var groups
n1 123.6667 a
n0 121.6667 a
$Yield[[8]]
[1] "All the second factor level means are same so dont go for any multiple comparison test"
$Yield[[9]]
$Yield[[9]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[9]][[2]]
Table CriticalRange
2 2.949998 11.2074
$Yield[[9]][[3]]
dependent.var groups
p0 124.8333 a
p1 120.5000 a
$Yield[[10]]
[1] "The means of levels of interaction between two factors are same so dont go for any multiple comparison test"
$Yield[[11]]
$Yield[[11]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[11]][[2]]
Table CriticalRange
2 2.949998 15.84966
3 3.096506 16.63682
4 3.189616 17.13708
$Yield[[11]][[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
$Yield[[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
fact.A 1 24.0 24.000 0.1386 0.7136
fact.B 1 112.7 112.667 0.6505 0.4294
fact.A:fact.B 1 42.7 42.667 0.2463 0.6251
Residuals 20 3464.0 173.200
$Yield[[2]]
[1] "R Square 0.049"
$Yield[[3]]
[1] "SEm of A: 3.799 , SEd of A: 5.373 , SEm of B: 3.799 , SEd of B: 5.373 , SEm of AB: 5.373 , SEd of AB 7.598"
$Yield[[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.85265, p-value = 0.002449
$Yield[[5]]
[1] "Normality assumption is violated"
$Yield[[6]]
[1] "All the first factor level means are same so dont go for any multiple comparison test"
$Yield[[7]]
$Yield[[7]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[7]][[2]]
Table CriticalRange
2 2.949998 11.2074
$Yield[[7]][[3]]
dependent.var groups
n1 123.6667 a
n0 121.6667 a
$Yield[[8]]
[1] "All the second factor level means are same so dont go for any multiple comparison test"
$Yield[[9]]
$Yield[[9]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[9]][[2]]
Table CriticalRange
2 2.949998 11.2074
$Yield[[9]][[3]]
dependent.var groups
p0 124.8333 a
p1 120.5000 a
$Yield[[10]]
[1] "The means of levels of interaction between two factors are same so dont go for any multiple comparison test"
$Yield[[11]]
$Yield[[11]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[11]][[2]]
Table CriticalRange
2 2.949998 15.84966
3 3.096506 16.63682
4 3.189616 17.13708
$Yield[[11]][[3]]
dependent.var groups
n1:p0 127.1667 a
n0:p0 122.5000 a
n0:p1 120.8333 a
n1:p1 120.1667 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 1.2673 0.2736
fact.B 1 2.667 2.6667 0.3168 0.5798
fact.A:fact.B 1 28.167 28.1667 3.3465 0.0823 .
Residuals 20 168.333 8.4167
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Plant_Height[[2]]
[1] "R Square 0.198"
$Plant_Height[[3]]
[1] "SEm of A: 0.837 , SEd of A: 1.184 , SEm of B: 0.837 , SEd of B: 1.184 , SEm of AB: 1.184 , SEd of AB 1.675"
$Plant_Height[[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.9604, p-value = 0.4465
$Plant_Height[[5]]
[1] "Normality assumption is not violated"
$Plant_Height[[6]]
[1] "All the first factor level means are same so dont go for any multiple comparison test"
$Plant_Height[[7]]
$Plant_Height[[7]][[1]]
MSerror Df Mean CV
8.416667 20 12.41667 23.36496
$Plant_Height[[7]][[2]]
Table CriticalRange
2 2.949998 2.470592
$Plant_Height[[7]][[3]]
dependent.var groups
n1 13.08333 a
n0 11.75000 a
$Plant_Height[[8]]
[1] "All the second factor level means are same so dont go for any multiple comparison test"
$Plant_Height[[9]]
$Plant_Height[[9]][[1]]
MSerror Df Mean CV
8.416667 20 12.41667 23.36496
$Plant_Height[[9]][[2]]
Table CriticalRange
2 2.949998 2.470592
$Plant_Height[[9]][[3]]
dependent.var groups
p0 12.75000 a
p1 12.08333 a
$Plant_Height[[10]]
[1] "The means of levels of interaction between two factors are same so dont go for any multiple comparison test"
$Plant_Height[[11]]
$Plant_Height[[11]][[1]]
MSerror Df Mean CV
8.416667 20 12.41667 23.36496
$Plant_Height[[11]][[2]]
Table CriticalRange
2 2.949998 3.493945
3 3.096506 3.667469
4 3.189616 3.777747
$Plant_Height[[11]][[3]]
dependent.var groups
n1:p0 14.50000 a
n0:p1 12.50000 a
n1:p1 11.66667 a
n0:p0 11.00000 a
doebioresearch documentation built on July 8, 2020, 7:18 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Value Examples
Description
The function gives ANOVA, R-square of the model, Normality testing of residuals, SEm (standard error of mean), SEd (standard error of difference), interpretation of ANOVA results and multiple comparison test for means
Usage
1 |
Arguments
data |
dependent variables |
fact.A |
vector containing levels of first factor |
fact.B |
vector containing levels of second factor |
Multiple.comparison.test |
0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test |
Value
ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result for both the factors as well as interaction.
Examples
1 2 3 4 5 | data(factorialdata)
#Analysis of Factorial Completely Randomized design along with Dunccan test for Yield only
fcrd2fact(factorialdata[5],factorialdata$Nitrogen,factorialdata$Phosphorus,2)
#Analysis of Factorial Completely Randomized design along with Dunccan test for Yield & Plant Height
fcrd2fact(factorialdata[5:6],factorialdata$Nitrogen,factorialdata$Phosphorus,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 24.0 24.000 0.1386 0.7136
fact.B 1 112.7 112.667 0.6505 0.4294
fact.A:fact.B 1 42.7 42.667 0.2463 0.6251
Residuals 20 3464.0 173.200
$Yield[[2]]
[1] "R Square 0.049"
$Yield[[3]]
[1] "SEm of A: 3.799 , SEd of A: 5.373 , SEm of B: 3.799 , SEd of B: 5.373 , SEm of AB: 5.373 , SEd of AB 7.598"
$Yield[[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.85265, p-value = 0.002449
$Yield[[5]]
[1] "Normality assumption is violated"
$Yield[[6]]
[1] "All the first factor level means are same so dont go for any multiple comparison test"
$Yield[[7]]
$Yield[[7]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[7]][[2]]
Table CriticalRange
2 2.949998 11.2074
$Yield[[7]][[3]]
dependent.var groups
n1 123.6667 a
n0 121.6667 a
$Yield[[8]]
[1] "All the second factor level means are same so dont go for any multiple comparison test"
$Yield[[9]]
$Yield[[9]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[9]][[2]]
Table CriticalRange
2 2.949998 11.2074
$Yield[[9]][[3]]
dependent.var groups
p0 124.8333 a
p1 120.5000 a
$Yield[[10]]
[1] "The means of levels of interaction between two factors are same so dont go for any multiple comparison test"
$Yield[[11]]
$Yield[[11]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[11]][[2]]
Table CriticalRange
2 2.949998 15.84966
3 3.096506 16.63682
4 3.189616 17.13708
$Yield[[11]][[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
$Yield[[1]]
Analysis of Variance Table
Response: dependent.var
Df Sum Sq Mean Sq F value Pr(>F)
fact.A 1 24.0 24.000 0.1386 0.7136
fact.B 1 112.7 112.667 0.6505 0.4294
fact.A:fact.B 1 42.7 42.667 0.2463 0.6251
Residuals 20 3464.0 173.200
$Yield[[2]]
[1] "R Square 0.049"
$Yield[[3]]
[1] "SEm of A: 3.799 , SEd of A: 5.373 , SEm of B: 3.799 , SEd of B: 5.373 , SEm of AB: 5.373 , SEd of AB 7.598"
$Yield[[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.85265, p-value = 0.002449
$Yield[[5]]
[1] "Normality assumption is violated"
$Yield[[6]]
[1] "All the first factor level means are same so dont go for any multiple comparison test"
$Yield[[7]]
$Yield[[7]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[7]][[2]]
Table CriticalRange
2 2.949998 11.2074
$Yield[[7]][[3]]
dependent.var groups
n1 123.6667 a
n0 121.6667 a
$Yield[[8]]
[1] "All the second factor level means are same so dont go for any multiple comparison test"
$Yield[[9]]
$Yield[[9]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[9]][[2]]
Table CriticalRange
2 2.949998 11.2074
$Yield[[9]][[3]]
dependent.var groups
p0 124.8333 a
p1 120.5000 a
$Yield[[10]]
[1] "The means of levels of interaction between two factors are same so dont go for any multiple comparison test"
$Yield[[11]]
$Yield[[11]][[1]]
MSerror Df Mean CV
173.2 20 122.6667 10.72871
$Yield[[11]][[2]]
Table CriticalRange
2 2.949998 15.84966
3 3.096506 16.63682
4 3.189616 17.13708
$Yield[[11]][[3]]
dependent.var groups
n1:p0 127.1667 a
n0:p0 122.5000 a
n0:p1 120.8333 a
n1:p1 120.1667 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 1.2673 0.2736
fact.B 1 2.667 2.6667 0.3168 0.5798
fact.A:fact.B 1 28.167 28.1667 3.3465 0.0823 .
Residuals 20 168.333 8.4167
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
$Plant_Height[[2]]
[1] "R Square 0.198"
$Plant_Height[[3]]
[1] "SEm of A: 0.837 , SEd of A: 1.184 , SEm of B: 0.837 , SEd of B: 1.184 , SEm of AB: 1.184 , SEd of AB 1.675"
$Plant_Height[[4]]
Shapiro-Wilk normality test
data: model$residuals
W = 0.9604, p-value = 0.4465
$Plant_Height[[5]]
[1] "Normality assumption is not violated"
$Plant_Height[[6]]
[1] "All the first factor level means are same so dont go for any multiple comparison test"
$Plant_Height[[7]]
$Plant_Height[[7]][[1]]
MSerror Df Mean CV
8.416667 20 12.41667 23.36496
$Plant_Height[[7]][[2]]
Table CriticalRange
2 2.949998 2.470592
$Plant_Height[[7]][[3]]
dependent.var groups
n1 13.08333 a
n0 11.75000 a
$Plant_Height[[8]]
[1] "All the second factor level means are same so dont go for any multiple comparison test"
$Plant_Height[[9]]
$Plant_Height[[9]][[1]]
MSerror Df Mean CV
8.416667 20 12.41667 23.36496
$Plant_Height[[9]][[2]]
Table CriticalRange
2 2.949998 2.470592
$Plant_Height[[9]][[3]]
dependent.var groups
p0 12.75000 a
p1 12.08333 a
$Plant_Height[[10]]
[1] "The means of levels of interaction between two factors are same so dont go for any multiple comparison test"
$Plant_Height[[11]]
$Plant_Height[[11]][[1]]
MSerror Df Mean CV
8.416667 20 12.41667 23.36496
$Plant_Height[[11]][[2]]
Table CriticalRange
2 2.949998 3.493945
3 3.096506 3.667469
4 3.189616 3.777747
$Plant_Height[[11]][[3]]
dependent.var groups
n1:p0 14.50000 a
n0:p1 12.50000 a
n1:p1 11.66667 a
n0:p0 11.00000 a
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.