stripplot: Analysis of Strip plot design
In doebioresearch: Analysis of Design of Experiments for Biological Research

Description Usage Arguments Value Examples

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

1	stripplot(data, block, column, row, mean.comparison.test)

`data`	dependent variables
`block`	vector containing replications
`column`	vector containing column strip levels
`row`	vector containing row strip levels
`mean.comparison.test`	0 for no test, 1 for LSD test, 2 for Duncan test and 3 for HSD test

ANOVA, interpretation of ANOVA, R-square, normality test result, SEm, SEd and multiple comparison test result

data(splitdata)
#Split data is used for sake of demonstration
#Using Date of sowing as Column factor and varieties as Row factor and using LSD test for Yield only
stripplot(splitdata[4],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)
#Using Date of sowing as Column factor and varieties as Row factor and using LSD test for both var.
stripplot(splitdata[4:5],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)

$Yield
$Yield[[1]]
$Yield[[1]][[1]]
            Df      Sum Sq     Mean Sq   F value    Pr(>F)
block        2  61557.0556  30778.5278 0.8915086 0.4402683
Column       1    186.7778    186.7778 0.0010000 0.9776000
Ea           2 328175.7222 164087.8611        NA        NA
Row          5 510277.2222 102055.4444 3.0280000 0.0640000
Eb          10 337066.2778  33706.6278        NA        NA
Interaction  5 125446.5556  25089.3111 0.7267189 0.6190384
Ec          10 345240.9444  34524.0944        NA        NA

$Yield[[1]][[2]]
[1] "CV(a): 8.063 , CV(b) : 3.655 , CV(c) : 3.699"

$Yield[[1]][[3]]
[1] "R Square 0.798"

$Yield[[1]][[4]]

	Shapiro-Wilk normality test

data:  model$residuals
W = 0.98651, p-value = 0.931


$Yield[[1]][[5]]
[1] "Normality assumption is not violated"

$Yield[[1]][[6]]
[1] "All the column 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] "All the row factor factor level means are same so dont go for any multiple comparison test"

$Yield[[1]][[9]]
$Yield[[1]][[9]][[1]]
   MSerror Df     Mean       CV  t.value      LSD
  33706.63 10 5023.611 3.654615 2.228139 236.1779

$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
  34524.09 10 5023.611 3.698666 2.228139 338.032

$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]]
            Df      Sum Sq     Mean Sq   F value    Pr(>F)
block        2  61557.0556  30778.5278 0.8915086 0.4402683
Column       1    186.7778    186.7778 0.0010000 0.9776000
Ea           2 328175.7222 164087.8611        NA        NA
Row          5 510277.2222 102055.4444 3.0280000 0.0640000
Eb          10 337066.2778  33706.6278        NA        NA
Interaction  5 125446.5556  25089.3111 0.7267189 0.6190384
Ec          10 345240.9444  34524.0944        NA        NA

$Yield[[1]][[2]]
[1] "CV(a): 8.063 , CV(b) : 3.655 , CV(c) : 3.699"

$Yield[[1]][[3]]
[1] "R Square 0.798"

$Yield[[1]][[4]]

	Shapiro-Wilk normality test

data:  model$residuals
W = 0.98651, p-value = 0.931


$Yield[[1]][[5]]
[1] "Normality assumption is not violated"

$Yield[[1]][[6]]
[1] "All the column 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] "All the row factor factor level means are same so dont go for any multiple comparison test"

$Yield[[1]][[9]]
$Yield[[1]][[9]][[1]]
   MSerror Df     Mean       CV  t.value      LSD
  33706.63 10 5023.611 3.654615 2.228139 236.1779

$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
  34524.09 10 5023.611 3.698666 2.228139 338.032

$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]]
            Df       Sum Sq      Mean Sq    F value      Pr(>F)
block        2 3.022056e+03 1.511028e+03 10.3286750 0.003692555
Column       1 2.777778e-02 2.777778e-02  0.0000000 1.000000000
Ea           2 4.500556e+02 2.250278e+02         NA          NA
Row          5 1.167139e+03 2.334278e+02  1.8430000 0.192000000
Eb          10 1.266278e+03 1.266278e+02         NA          NA
Interaction  5 2.924722e+02 5.849444e+01  0.3998405 0.838271837
Ec          10 1.462944e+03 1.462944e+02         NA          NA

$Plant_Height[[1]][[2]]
[1] "CV(a): 13.104 , CV(b) : 9.83 , CV(c) : 10.566"

$Plant_Height[[1]][[3]]
[1] "R Square 0.809"

$Plant_Height[[1]][[4]]

	Shapiro-Wilk normality test

data:  model$residuals
W = 0.98999, p-value = 0.9825


$Plant_Height[[1]][[5]]
[1] "Normality assumption is not violated"

$Plant_Height[[1]][[6]]
[1] "All the column 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 row 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
  126.6278 10 114.4722 9.830246 2.228139 14.47592

$Plant_Height[[1]][[9]][[2]]
  dependent.var groups
6      122.8333      a
5      121.6667      a
1      112.5000      a
2      112.1667      a
4      109.0000      a
3      108.6667      a


$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
  146.2944 10 114.4722 10.56608 2.228139 22.00445

$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