Last data update: 2014.03.03

 R: The ScottKnott Clustering Algoritm for Single Experiments
SKR Documentation

The ScottKnott Clustering Algoritm for Single Experiments

Description

These are methods for objects of class vector, matrix or data.frame joined as default, aov and aovlist for single experiments.

Usage

  ## Default S3 method:
SK(x,
   y=NULL,
   model,
   which,
   id.trim=3,
   error,
   sig.level=.05,
   dispersion=c('mm', 's', 'se'), ...)
  ## S3 method for class 'aov'
SK(x,
   which=NULL, 
   id.trim=3,
   sig.level=.05,
   dispersion=c('mm', 's', 'se'), ...)
  ## S3 method for class 'aovlist'
SK(x,
   which, 
   id.trim=3,
   error,
   sig.level=.05,
   dispersion=c('mm', 's', 'se'), ...)

Arguments

x

A design matrix, data.frame or an aov object.

y

A vector of response variable. It is necessary to inform this parameter only if x represent the design matrix.

which

The name of the treatment to be used in the comparison. The name must be inside quoting marks.

model

If x is a data.frame object, the model to be used in the aov must be specified.

id.trim

The number of character to trim the id label.

error

The error to be considered.

sig.level

Level of Significance used in the SK algorithm to create the groups of means. The default value is 0.05.

dispersion

The dispersion to be considered to the means.
The possible vaues are: 'mm' = minimum and maximum, 's' = standart deviation, 'se' = standart deviation of the mean.

...

Potential further arguments (require by generic).

Details

The function SK returns an object of class SK respectivally containing the groups of means plus other necessary variables for summary and plot.

The generic functions summary and plot are used to obtain and print a summary and a plot of the results.

Value

The function SK returns a list of the class SK with the slots:

av

A list storing the result of aov.

groups

A vector of length equal the number of factor levels marking the groups generated.

nms

A vector of the labels of the factor levels.

ord

A vector which keeps the position of the means of the factor levels in decreasing order.

m.inf

A matrix which keeps the means and the dispersion of the factor levels in decreasing order.

sig.level

A vector of length 1 giving the level of significance of the test.

Author(s)

Enio Jelihovschi (eniojelihovs@gmail.com)
Jos<c3><a9> Cl<c3><a1>udio Faria (joseclaudio.faria@gmail.com)
Ivan Bezerra Allaman (ivanalaman@gmail.com)

References

Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimenta<c3><a7><c3><a3>o em Gen<c3><a9>tica e Melhoramento de Plantas. Editora UFLA.

Scott R.J., Knott M. 1974. A cluster analysis method for grouping mans in the analysis of variance. Biometrics, 30, 507-512.

Examples

  ##
  ## Examples: Completely Randomized Design (CRD)
  ## More details: demo(package='ScottKnott')
  ##
  
  ## The parameters can be: vectors, design matrix and the response variable,
  ## data.frame or aov
  data(CRD2)
  
  ## From: design matrix (dm) and response variable (y)
  sk1 <- with(CRD2,
              SK(x=dm,
                 y=y,
                 model='y ~ x',
                 which='x'))
  summary(sk1)
  plot(sk1,
       col=rainbow(max(sk1$groups)),
       mm.lty=3,
       id.las=2,
       rl=FALSE,
       title='factor levels')
  
  ## From: data.frame (dfm)
  sk2 <- with(CRD2,
              SK(x=dfm,
                 model='y ~ x',
                 which='x',
                 dispersion='s'))
  summary(sk2)
  plot(sk2,
       col=rainbow(max(sk2$groups)),
       id.las=2,
       rl=FALSE)
  
  ## From: aov
  av <- with(CRD2,
             aov(y ~ x,
                 data=dfm))
  summary(av)
  
  sk3 <- with(CRD2,
              SK(x=av,
                 which='x',
                 dispersion='se'))
  summary(sk3)
  plot(sk3,
       col=rainbow(max(sk3$groups)),
       rl=FALSE,
       id.las=2,
       title=NULL)

  ##
  ## Example: Randomized Complete Block Design (RCBD)
  ## More details: demo(package='ScottKnott')
  ##
  
  ## The parameters can be: design matrix and the response variable,
  ## data.frame or aov
  
  data(RCBD)
  
  ## Design matrix (dm) and response variable (y)
  sk1 <- with(RCBD,
              SK(x=dm,
                 y=y,
                 model='y ~ blk + tra',
                 which='tra'))
  summary(sk1)
  plot(sk1)
  
  ## From: data.frame (dfm), which='tra'
  sk2 <- with(RCBD,
              SK(x=dfm,
                 model='y ~ blk + tra',
                 which='tra'))
  summary(sk2)
  plot(sk2,
       mm.lty=3,
       title='Factor levels')
  
  ##
  ## Example: Latin Squares Design (LSD)
  ## More details: demo(package='ScottKnott')
  ##
  
  ## The parameters can be: design matrix and the response variable,
  ## data.frame or aov
  
  data(LSD)
  
  ## From: design matrix (dm) and response variable (y)
  sk1 <- with(LSD,
              SK(x=dm,
                 y=y,
                 model='y ~ rows + cols + tra',
                 which='tra'))
  summary(sk1)
  plot(sk1)
  
  ## From: data.frame
  sk2 <- with(LSD,
              SK(x=dfm,
                 model='y ~ rows + cols + tra',
                 which='tra'))
  summary(sk2)
  plot(sk2,
       title='Factor levels')
  
  ## From: aov
  av <- with(LSD,
             aov(y ~ rows + cols + tra,
                 data=dfm))
  summary(av)
  
  sk3 <- SK(av,
            which='tra')
  summary(sk3)
  plot(sk3,
       title='Factor levels')

  ##
  ## Example: Factorial Experiment (FE)
  ## More details: demo(package='ScottKnott')
  ##
  
  ## The parameters can be: design matrix and the response variable,
  ## data.frame or aov
  
  ## Note: The factors are in uppercase and its levels in lowercase!

  data(FE)
  ## From: design matrix (dm) and response variable (y)
  ## Main factor: N
  sk1 <- with(FE,
              SK(x=dm,
                 y=y,
                 model='y ~ blk + N*P*K', 
                 which='N'))
  summary(sk1)
  plot(sk1,
       title='Main effect: N')

  ## Nested: p1/N
  nsk1 <- with(FE,
               SK.nest(x=dm,
                       y=y,
                       model='y ~ blk + N*P*K',
                       which='P:N',
                       fl1=1))
  summary(nsk1)
  plot(nsk1,
       title='Effect: p1/N')

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(ScottKnott)
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ScottKnott/SK.Rd_%03d_medium.png", width=480, height=480)
> ### Name: SK
> ### Title: The ScottKnott Clustering Algoritm for Single Experiments
> ### Aliases: SK SK.default SK.aov SK.aovlist
> ### Keywords: package htest univar tree design
> 
> ### ** Examples
> 
>   ##
>   ## Examples: Completely Randomized Design (CRD)
>   ## More details: demo(package='ScottKnott')
>   ##
>   
>   ## The parameters can be: vectors, design matrix and the response variable,
>   ## data.frame or aov
>   data(CRD2)
>   
>   ## From: design matrix (dm) and response variable (y)
>   sk1 <- with(CRD2,
+               SK(x=dm,
+                  y=y,
+                  model='y ~ x',
+                  which='x'))
>   summary(sk1)
 Levels    Means SK(5%)
  tr-35 459.1650      a
  tr-20 458.5300      a
   tr-5 456.3500      a
  tr-19 440.0750      a
   tr-4 438.9850      a
  tr-34 438.4225      a
  tr-22 410.9100      b
   tr-7 403.8800      b
  tr-37 402.5925      b
  tr-28 399.6675      b
  tr-43 397.6000      b
  tr-13 397.5025      b
  tr-33 396.8950      b
  tr-12 395.4875      b
  tr-42 394.7650      b
  tr-18 394.2600      b
  tr-27 393.3200      b
  tr-39 392.8200      b
  tr-25 392.4100      b
   tr-3 392.0725      b
  tr-30 390.7950      b
  tr-10 390.2025      b
  tr-29 388.1975      b
  tr-24 387.1950      b
   tr-9 386.9500      b
  tr-15 386.2050      b
  tr-44 385.8675      b
  tr-45 385.3325      b
  tr-41 384.9825      b
  tr-26 384.4675      b
  tr-14 383.6075      b
  tr-21 383.0625      b
  tr-36 380.7450      b
  tr-11 380.3875      b
   tr-6 379.3175      b
  tr-40 378.5675      b
  tr-38 377.3475      b
   tr-8 377.0425      b
  tr-23 376.8125      b
  tr-32 353.7575      c
  tr-17 353.6525      c
   tr-2 349.3500      c
  tr-31 307.3700      d
  tr-16 296.6125      d
   tr-1 294.6800      d
>   plot(sk1,
+        col=rainbow(max(sk1$groups)),
+        mm.lty=3,
+        id.las=2,
+        rl=FALSE,
+        title='factor levels')
>   
>   ## From: data.frame (dfm)
>   sk2 <- with(CRD2,
+               SK(x=dfm,
+                  model='y ~ x',
+                  which='x',
+                  dispersion='s'))
>   summary(sk2)
 Levels    Means SK(5%)
  tr-35 459.1650      a
  tr-20 458.5300      a
   tr-5 456.3500      a
  tr-19 440.0750      a
   tr-4 438.9850      a
  tr-34 438.4225      a
  tr-22 410.9100      b
   tr-7 403.8800      b
  tr-37 402.5925      b
  tr-28 399.6675      b
  tr-43 397.6000      b
  tr-13 397.5025      b
  tr-33 396.8950      b
  tr-12 395.4875      b
  tr-42 394.7650      b
  tr-18 394.2600      b
  tr-27 393.3200      b
  tr-39 392.8200      b
  tr-25 392.4100      b
   tr-3 392.0725      b
  tr-30 390.7950      b
  tr-10 390.2025      b
  tr-29 388.1975      b
  tr-24 387.1950      b
   tr-9 386.9500      b
  tr-15 386.2050      b
  tr-44 385.8675      b
  tr-45 385.3325      b
  tr-41 384.9825      b
  tr-26 384.4675      b
  tr-14 383.6075      b
  tr-21 383.0625      b
  tr-36 380.7450      b
  tr-11 380.3875      b
   tr-6 379.3175      b
  tr-40 378.5675      b
  tr-38 377.3475      b
   tr-8 377.0425      b
  tr-23 376.8125      b
  tr-32 353.7575      c
  tr-17 353.6525      c
   tr-2 349.3500      c
  tr-31 307.3700      d
  tr-16 296.6125      d
   tr-1 294.6800      d
>   plot(sk2,
+        col=rainbow(max(sk2$groups)),
+        id.las=2,
+        rl=FALSE)
>   
>   ## From: aov
>   av <- with(CRD2,
+              aov(y ~ x,
+                  data=dfm))
>   summary(av)
             Df Sum Sq Mean Sq F value   Pr(>F)    
x            44 209136    4753   3.273 7.69e-08 ***
Residuals   135 196045    1452                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>   
>   sk3 <- with(CRD2,
+               SK(x=av,
+                  which='x',
+                  dispersion='se'))
>   summary(sk3)
 Levels    Means SK(5%)
  tr-35 459.1650      a
  tr-20 458.5300      a
   tr-5 456.3500      a
  tr-19 440.0750      a
   tr-4 438.9850      a
  tr-34 438.4225      a
  tr-22 410.9100      b
   tr-7 403.8800      b
  tr-37 402.5925      b
  tr-28 399.6675      b
  tr-43 397.6000      b
  tr-13 397.5025      b
  tr-33 396.8950      b
  tr-12 395.4875      b
  tr-42 394.7650      b
  tr-18 394.2600      b
  tr-27 393.3200      b
  tr-39 392.8200      b
  tr-25 392.4100      b
   tr-3 392.0725      b
  tr-30 390.7950      b
  tr-10 390.2025      b
  tr-29 388.1975      b
  tr-24 387.1950      b
   tr-9 386.9500      b
  tr-15 386.2050      b
  tr-44 385.8675      b
  tr-45 385.3325      b
  tr-41 384.9825      b
  tr-26 384.4675      b
  tr-14 383.6075      b
  tr-21 383.0625      b
  tr-36 380.7450      b
  tr-11 380.3875      b
   tr-6 379.3175      b
  tr-40 378.5675      b
  tr-38 377.3475      b
   tr-8 377.0425      b
  tr-23 376.8125      b
  tr-32 353.7575      c
  tr-17 353.6525      c
   tr-2 349.3500      c
  tr-31 307.3700      d
  tr-16 296.6125      d
   tr-1 294.6800      d
>   plot(sk3,
+        col=rainbow(max(sk3$groups)),
+        rl=FALSE,
+        id.las=2,
+        title=NULL)
> 
>   ##
>   ## Example: Randomized Complete Block Design (RCBD)
>   ## More details: demo(package='ScottKnott')
>   ##
>   
>   ## The parameters can be: design matrix and the response variable,
>   ## data.frame or aov
>   
>   data(RCBD)
>   
>   ## Design matrix (dm) and response variable (y)
>   sk1 <- with(RCBD,
+               SK(x=dm,
+                  y=y,
+                  model='y ~ blk + tra',
+                  which='tra'))
>   summary(sk1)
 Levels    Means SK(5%)
      E 155.3700      a
      A 142.9325      b
      D 140.3950      b
      B 138.5750      b
      C 138.5650      b
>   plot(sk1)
>   
>   ## From: data.frame (dfm), which='tra'
>   sk2 <- with(RCBD,
+               SK(x=dfm,
+                  model='y ~ blk + tra',
+                  which='tra'))
>   summary(sk2)
 Levels    Means SK(5%)
      E 155.3700      a
      A 142.9325      b
      D 140.3950      b
      B 138.5750      b
      C 138.5650      b
>   plot(sk2,
+        mm.lty=3,
+        title='Factor levels')
>   
>   ##
>   ## Example: Latin Squares Design (LSD)
>   ## More details: demo(package='ScottKnott')
>   ##
>   
>   ## The parameters can be: design matrix and the response variable,
>   ## data.frame or aov
>   
>   data(LSD)
>   
>   ## From: design matrix (dm) and response variable (y)
>   sk1 <- with(LSD,
+               SK(x=dm,
+                  y=y,
+                  model='y ~ rows + cols + tra',
+                  which='tra'))
>   summary(sk1)
 Levels  Means SK(5%)
      C 60.910      a
      A 49.258      b
      B 44.216      b
      D 41.686      b
      E 39.464      b
>   plot(sk1)
>   
>   ## From: data.frame
>   sk2 <- with(LSD,
+               SK(x=dfm,
+                  model='y ~ rows + cols + tra',
+                  which='tra'))
>   summary(sk2)
 Levels  Means SK(5%)
      C 60.910      a
      A 49.258      b
      B 44.216      b
      D 41.686      b
      E 39.464      b
>   plot(sk2,
+        title='Factor levels')
>   
>   ## From: aov
>   av <- with(LSD,
+              aov(y ~ rows + cols + tra,
+                  data=dfm))
>   summary(av)
            Df Sum Sq Mean Sq F value   Pr(>F)    
rows         4  398.8    99.7   4.193 0.023679 *  
cols         4  589.9   147.5   6.201 0.006059 ** 
tra          4 1456.6   364.1  15.313 0.000116 ***
Residuals   12  285.4    23.8                     
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
>   
>   sk3 <- SK(av,
+             which='tra')
>   summary(sk3)
 Levels  Means SK(5%)
      C 60.910      a
      A 49.258      b
      B 44.216      b
      D 41.686      b
      E 39.464      b
>   plot(sk3,
+        title='Factor levels')
> 
>   ##
>   ## Example: Factorial Experiment (FE)
>   ## More details: demo(package='ScottKnott')
>   ##
>   
>   ## The parameters can be: design matrix and the response variable,
>   ## data.frame or aov
>   
>   ## Note: The factors are in uppercase and its levels in lowercase!
> 
>   data(FE)
>   ## From: design matrix (dm) and response variable (y)
>   ## Main factor: N
>   sk1 <- with(FE,
+               SK(x=dm,
+                  y=y,
+                  model='y ~ blk + N*P*K', 
+                  which='N'))
>   summary(sk1)
 Levels    Means SK(5%)
     n1 2.750000      a
     n0 2.306875      b
>   plot(sk1,
+        title='Main effect: N')
> 
>   ## Nested: p1/N
>   nsk1 <- with(FE,
+                SK.nest(x=dm,
+                        y=y,
+                        model='y ~ blk + N*P*K',
+                        which='P:N',
+                        fl1=1))
>   summary(nsk1)
Nested: N/P 
 Levels   Means SK(5%)
  p0/n1 2.60375      a
  p0/n0 2.41125      a
>   plot(nsk1,
+        title='Effect: p1/N')
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>


Created & Maintained by Osamu Ogasawara (osamu.ogasawara@gmail.com) and IMSBIO