frbd2fact: Analysis of Factorial Randomized Block Design for 2 factors

In doebioresearch: Analysis of Design of Experiments for Biological Research

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

frbd2fact(data, replicationvector, fact.A, fact.B, Multiple.comparison.test)

Arguments

data	dependent variables
replicationvector	vector containing replications
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 results for both the factors as well as interaction.

Examples

data(factorialdata)
#FRBD analysis along with dunccan test for two dependent var.
frbd2fact(factorialdata[5:6],factorialdata$Replication,
factorialdata$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)
replicationvector  2  700.08  350.04  2.2796 0.1311
fact.A             1   24.00   24.00  0.1563 0.6972
fact.B             1  112.67  112.67  0.7337 0.4029
fact.A:fact.B      1   42.67   42.67  0.2779 0.6045
Residuals         18 2763.92  153.55               

$Yield[[2]]
[1] "R Square 0.241"

$Yield[[3]]
[1] "SEm of A: 5.059 , SEd of A: 7.154 , SEm of B: 5.059 , SEd of B 7.154 , SEm of AB: 7.154 , SEd of AB: 10.118"

$Yield[[4]]

	Shapiro-Wilk normality test

data:  model$residuals
W = 0.94271, p-value = 0.1874


$Yield[[5]]
[1] "Normality assumption is not 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
  153.5509 18 122.6667 10.10182

$Yield[[7]][[2]]
     Table CriticalRange
2 2.971152      10.62822

$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
  153.5509 18 122.6667 10.10182

$Yield[[9]][[2]]
     Table CriticalRange
2 2.971152      10.62822

$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
  153.5509 18 122.6667 10.10182

$Yield[[11]][[2]]
     Table CriticalRange
2 2.971152      15.03057
3 3.117384      15.77034
4 3.209655      16.23712

$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)    
replicationvector  2 92.333  46.167 10.9342 0.0007795 ***
fact.A             1 10.667  10.667  2.5263 0.1293712    
fact.B             1  2.667   2.667  0.6316 0.4371305    
fact.A:fact.B      1 28.167  28.167  6.6711 0.0187637 *  
Residuals         18 76.000   4.222                      
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

$Plant_Height[[2]]
[1] "R Square 0.638"

$Plant_Height[[3]]
[1] "SEm of A: 0.839 , SEd of A: 1.186 , SEm of B: 0.839 , SEd of B 1.186 , SEm of AB: 1.186 , SEd of AB: 1.678"

$Plant_Height[[4]]

	Shapiro-Wilk normality test

data:  model$residuals
W = 0.92997, p-value = 0.09734


$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
  4.222222 18 12.41667 16.54876

$Plant_Height[[7]][[2]]
     Table CriticalRange
2 2.971152      1.762401

$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
  4.222222 18 12.41667 16.54876

$Plant_Height[[9]][[2]]
     Table CriticalRange
2 2.971152      1.762401

$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 not same, so go for multiple comparison test"

$Plant_Height[[11]]
$Plant_Height[[11]][[1]]
   MSerror Df     Mean       CV
  4.222222 18 12.41667 16.54876

$Plant_Height[[11]][[2]]
     Table CriticalRange
2 2.971152      2.492412
3 3.117384      2.615082
4 3.209655      2.692485

$Plant_Height[[11]][[3]]
      dependent.var groups
n1:p0      14.50000      a
n0:p1      12.50000     ab
n1:p1      11.66667      b
n0:p0      11.00000      b

doebioresearch documentation built on July 8, 2020, 7:18 p.m.