splitplot: Analysis of Split plot design

Description Usage Arguments Value Examples

View source: R/splitplot.R

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
splitplot(data, block, main.plot, sub.plot, mean.comparison.test)

Arguments

data

dependent variables

block

vector containing replications

main.plot

vector containing main-plot levels

sub.plot

vector containing sub-plot levels

mean.comparison.test

0 for no test, 1 for LSD test, 2 for Dunccan test and 3 for HSD test

Value

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

Examples

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data(splitdata)
#Using Date of sowing as Main-plot factor and varieties as sub-plot factor and using LSD test
#Split plot analysis with LSD test for Yield
splitplot(splitdata[4],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)
#Split plot analysis with LSD test for both Yield and Plant Height
splitplot(splitdata[4:5],splitdata$Replication,splitdata$Date_of_Sowing,splitdata$Varities,1)

Example output

$Yield
$Yield[[1]]
$Yield[[1]][[1]]
Analysis of Variance Table

Response: dependent.var
                   Df Sum Sq Mean Sq F value  Pr(>F)  
block               2  61557   30779  0.1876 0.84205  
main.plot           1    187     187  0.0011 0.97615  
Ea                  2 328176  164088                  
sub.plot            5 510277  102055  2.9915 0.03556 *
main.plot:sub.plot  5 125447   25089  0.7354 0.60557  
Eb                 20 682307   34115                  
---
Signif. codes:  0***0.001**0.01*0.05.’ 0.1 ‘ ’ 1

$Yield[[1]][[2]]
[1] "CV(a): 8.063 , CV(b) : 3.677"

$Yield[[1]][[3]]
[1] "R Square 0.601"

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

	Shapiro-Wilk normality test

data:  model$residuals
W = 0.94087, p-value = 0.05406


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

$Yield[[1]][[6]]
[1] "All the main plot 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] "The means of one or more levels of sub plot factor are not same, so go for multiple comparison test"

$Yield[[1]][[9]]
$Yield[[1]][[9]][[1]]
   MSerror Df     Mean       CV  t.value      LSD
  34115.36 20 5023.611 3.676707 2.085963 222.4442

$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
  34115.36 20 5023.611 3.676707 2.085963 314.5836

$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]]
Analysis of Variance Table

Response: dependent.var
                   Df Sum Sq Mean Sq F value  Pr(>F)  
block               2  61557   30779  0.1876 0.84205  
main.plot           1    187     187  0.0011 0.97615  
Ea                  2 328176  164088                  
sub.plot            5 510277  102055  2.9915 0.03556 *
main.plot:sub.plot  5 125447   25089  0.7354 0.60557  
Eb                 20 682307   34115                  
---
Signif. codes:  0***0.001**0.01*0.05.’ 0.1 ‘ ’ 1

$Yield[[1]][[2]]
[1] "CV(a): 8.063 , CV(b) : 3.677"

$Yield[[1]][[3]]
[1] "R Square 0.601"

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

	Shapiro-Wilk normality test

data:  model$residuals
W = 0.94087, p-value = 0.05406


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

$Yield[[1]][[6]]
[1] "All the main plot 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] "The means of one or more levels of sub plot factor are not same, so go for multiple comparison test"

$Yield[[1]][[9]]
$Yield[[1]][[9]][[1]]
   MSerror Df     Mean       CV  t.value      LSD
  34115.36 20 5023.611 3.676707 2.085963 222.4442

$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
  34115.36 20 5023.611 3.676707 2.085963 314.5836

$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]]
Analysis of Variance Table

Response: dependent.var
                   Df  Sum Sq Mean Sq F value Pr(>F)
block               2 3022.06 1511.03  6.7149 0.1296
main.plot           1    0.03    0.03  0.0001 0.9921
Ea                  2  450.06  225.03               
sub.plot            5 1167.14  233.43  1.7106 0.1782
main.plot:sub.plot  5  292.47   58.49  0.4287 0.8232
Eb                 20 2729.22  136.46               

$Plant_Height[[1]][[2]]
[1] "CV(a): 13.104 , CV(b) : 10.205"

$Plant_Height[[1]][[3]]
[1] "R Square 0.644"

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

	Shapiro-Wilk normality test

data:  model$residuals
W = 0.96789, p-value = 0.3707


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

$Plant_Height[[1]][[6]]
[1] "All the main plot 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 sub plot 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
  136.4611 20 114.4722 10.2048 2.085963 14.06859

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


$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
  136.4611 20 114.4722 10.2048 2.085963 19.89599

$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

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