Scott-Knott Multiple Comparison ... Is there any procedure in SAS??

[jonatan_velarde]

Good day my SAS friends:

Always is a pleasure to participate here, so here we go one more time.

I'm working in some research and many authors located in a especial region of Brasil, use to be comfortable using  SCOTT-KNOTT Multiple Comparison, so

I always use the other and classic procedures (Tukey, Duncan, ETC).

Could anyone tell me please!!! how to get SCOTT-KNOTT multiple comparison in SAS.

Thanks.

King regard from Brasil

[Doc_Duke]

Jonatan,

I can't, though I found lots of reference to R packages.

I found it fascinating that the only people publishing papers that use it are from Brazil.  I imagine that there is one 'enthusiast' who has converted the rest!

Doc Muhlbaier

Duke

[lvm]

I am not aware of a sas macro. I wrote a FORTRAN program to do this more than 30 years ago, and used it for a few years. I know it has the intuitive advantage of 'creating' non-overlapping groups of LSMEANS, based on the means and standard errors. Basically, it is a type of cluster analysis (of means). However, early assessments showed that the experiment-wise Type I error rate can be high with this method. This would likely be the reason why the method is very uncommon, and not available within SAS procedures. I have not read anything about it in decades, so there may be some counter-arguments to the criticisms. Someone who has the time and knows PROC IML could probably take one of the multiple-comparison macros that have been written by non-SAS programmers, and add a routine for this method. I would start with the macro written by H.P. Piepho.

http://agrobiol.sggw.waw.pl/~cbcs/articles/CBCS_7_1_2.pdf

This would take some expertise with IML, and time.

Or, you could call a relevant R function for SK from within SAS IML to do this. You would have to learn about the SAS-R linkages first.

[invisibleman]

So, did you get some routine?

[lvm]

I haven't looked since I don't need it.

[jonatan_velarde]

hi there :

Unfortunately still don't have it 😕 , such a bad luck hum?

i think the person deveplops this method for SAS will be very famous

[SteveDenham]

Why? As @lvm said, the experiment wise type I error with this method is high, and there are much better methods for controlling for this sort of error already available using the ADJUST= option in the linear models procedure, and in PROC MULTTEST.

Steve Denham

[jonatan_velarde]

Hi SteveDenham:

For sure, this is the objective to be tested, could you send us an example using these methodologies.

thanks

[SteveDenham]

Start here in the documentation:

https://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_mixed_synta...

See also the Shared Concepts for LSMEANS and the documentation for PROC MULTTEST.

Steve Denham

[LuisCarlos]

Fiz uma busca ampla sobre a existência de tal rotina no SAS, no entanto, nada encontrei. Não sei qual as limitações de uso, mas sei que essa rotina está disponível no R e em um software livre chamado ASSISTAT. No entanto leve em consideração que esse teste não é propriamente para comparações de médias e sim um teste de agrupamentos a partir uma única variável, apresentando limitações teóricas como já descritas acima.

[jonatan_velarde]

Ola Luis Carlos:

obrigado pela tua resposta, nesse sentido voce tem como me informar onde tem aquela rotina de R por favor??

desde ja agradeço tua resposta

abç

[invisibleman]

/*Scott-Knott in R and SAS (integration*/

*;

/*Tutorial by: (Alysson Jalles) Consultoria: www.alyssonjallessite.wordpress.com*/

*;

*I have the solution;

*I've wrote a R/SAS routine for this purpose;

*You can run R inside SAS to do it!;

*To set SAS to run R codes follow my tutorial in: https://communities.sas.com/message/296841#296841;

*Copy all of this message inside SAS to perform the analysis;

*;

*I'll provide an example with a SAS simulated dataset of a Randomized Complete Block Design (RCBD);

*;

*;

*=======================================================================;

*Simulating a RCBD experiment ===========================================BEGIN;

*=======================================================================;

*10 TREATMENTS;

*3 REPLICATION;

proc plan seed=123456;

factors GEN=10 ordered REP=3 ordered/noprint;

output out=RCBD_TEST

/*GEN cvals=('GEN1' 'GEN2' 'GEN3' 'GEN4' 'GEN5' 'GEN6' 'GEN7' 'GEN8' 'GEN9' 'GEN10')*/

GEN nvals=(1 2 3 4 5 6 7 8 9 10)

REP nvals=(1 2 3);

run;

*;

*Simulating VARIABLES - BEGIN;

data RCBD_TEST; set RCBD_TEST;

  call streaminit(8789); /*SEED= 8789*/

VAR1=rand('normal',110, 15); /*MEAN 110: STD: 15*/

run;

data RCBD_TEST; set RCBD_TEST;

  call streaminit(907854); /*SEED= 907854*/

VAR2=rand('normal',3000, 751);run; /*MEAN 3000: STD: 751*/

*Simulating VARIABLES - END;

*=======================================================================;

*Simulating and RCBD experiment =====================================END;

*=======================================================================;

*;

*Printing dataset, first 6 observations of simulated dataset:;

proc print data=RCBD_TEST (obs=6); run;

*;

*;

/*Scott-Knott statistic of VAR1 and VAR2 in R language*/

*Installing "ScottKnott" package in R;

*ScottKnott in an R package to perform Scott-Knott analysis;

*It will appear a windows, just click on the nearest mirror of your current place;

* You just need to do it ONCE!;

proc iml;

title "Installing ScottKnott package in R (integration R with SAS)";

submit / R;

#_______Begin of R code_________________

install.packages("ScottKnott")

#_______End of R code_________________

endsubmit;

*quit;

*;

*;

*Installing "xlsx" package in R;

*xlsx is a package to export Scott-Knott results in excel to your hard drive;

*to use this package you have to install the ultimate version of java in your computer:

https://www.java.com/pt_BR/download/

*It will appear a windows, just click on the nearest mirror of your current place;

* You just need to do it ONCE!;

proc iml;

title "Installing xlsx package in R (integration R with SAS)";

submit/ R;

#_______Begin of R code_________________

  install.packages("xlsx")

#_______End of R code_________________

endsubmit;

*quit;

*;

*;

*****SCOTT-KNOTT ANALYSIS BEGINS HERE..*******************************************************;

*Changing treatments - GEN from numeric to factor type;

*This is necessary;

proc iml;

title "Preparing variables (integration R with SAS)";

*Exporting SAS dataset to R environment;

run ExportDataSetToR("WORK.RCBD_TEST", "RCBD_IN_R");

submit / R;

#_______Begin of R code_________________

#Class of GEN and REP

class(RCBD_IN_R$GEN)

class(RCBD_IN_R$REP)

#CONVERTING "GEN" and "REP" TO CHARACTER

RCBD_IN_R$GEN<-as.character(RCBD_IN_R$GEN)

RCBD_IN_R$REP<-as.character(RCBD_IN_R$REP)

#Class of GEN and REP

class(RCBD_IN_R$GEN)

class(RCBD_IN_R$REP)

#_______End of R code_________________

endsubmit;

*quit;

*;

*;

*;

*Performing the Scott-Knott analysis;

*We'll perform the analysis with 2 VARIABLES simultaneously, but you can extend this routine to N variables;

title "Performing ScottKnott clustering in R (integration R with SAS) - by: Alysson Jalles";

submit / R;

#_______Begin of R code_________________

require(ScottKnott) #Package to perform the SK analysis

require(xlsx)          #Package to perform the output in XLSX of results

#For other kind of design as CRD, factorial, etc, consult the manual of R ScottKnott package

#in: https://cran.r-project.org/web/packages/ScottKnott/ScottKnott.pdf

#Applying univariate Scott-Knott clustering

VAR1a <- SK(x=RCBD_IN_R, y=RCBD_IN_R$VAR1, model="y~GEN+REP", which="GEN", sig.level=0.05)

#summary(VAR1a)

VAR2a <- SK(x=RCBD_IN_R, y=RCBD_IN_R$VAR2, model="y~GEN+REP", which="GEN", sig.level=0.05)

#summary(VAR2a)

#Summary

VAR1b<-(summary(VAR1a))

VAR2b<-(summary(VAR2a))

#_____________________________

#RENAMING MEANS TO THE NAME OF VARIABLE EVALUATED

names(VAR1b)[names(VAR1b)=="Means"] <- "VAR1"

names(VAR2b)[names(VAR2b)=="Means"] <- "VAR2"

#RENAMING SK(5%)TO SK PLUS NAME OF VARIABLE EVALUATED

names(VAR1b)[names(VAR1b)=="SK(5%)"] <- "SK_VAR1"

names(VAR2b)[names(VAR2b)=="SK(5%)"] <- "SK_VAR2"

#MERGING SCOTT-KNOTT RESULTS IN A SINGLE DATASET

SK <-Reduce(function(x, y) merge(x, y, all=TRUE),

            list(

  VAR1b,  #VAR1

  VAR2b  #VAR2

  ))

#RENAMING NAME OF VAR "Levels" TO GEN [TREATMENTS]

names(SK)[names(SK)=="Levels"] <- "GEN"

#_______End of R code_________________

endsubmit;

*quit;

*;

*;

*Exporting R results to your hard drive and SAS Environment;

title "Exporting results to Windows hard drive and SAS Environment (integration R with SAS)";

title2 "Your file: 'Scott_Knott_output.xlsx' is in C:\ of your HD";

*Exporting results from R to SAS;

run importdatasetfromR("WORK.SCOTT_KNOTT_SAS","SK");

submit / R;

#_______Begin of R code_________________

write.xlsx(SK, "Scott_Knott_output.xlsx")

#_______End of R code_________________

endsubmit;

*quit;

*;

*;

*Sorting treatments - GEN;

proc sort; by GEN; run;

*;

*;

*Printing results of Scott-Knott analysis in SAS (sig.level=0.05);

Title "Scott-Knott Analysis results to RCBD design in SAS, by: Alysson Jalles";

proc print data=SCOTT_KNOTT_SAS noobs; run;

*****SCOTT-KNOTT ANALYSIS ENDS HERE..*******************************************************;

Thanks for read my material :smileygrin:...

[jonatan_velarde]

Thank you for your contribution.

we'll test and compare this protocol and them we'll leave you any comment. Untill now we appreciate this.

Thank you

[jonatan_velarde]

Good day my friend:

As you know, ths SAS university edition is improveing each day, so i tried to use iyour code in SAS university edition and i could not finish the statement.

Anybody caould help with this???

Thank you

[invisibleman · Accepted solution]

Hi jonatan_velarde..the bad news is that SAS university edition does not run any R code. 

If you want to run this routine you should have the Commercial SAS version.

Or by other hand, you can run the second part of the routine totally in R or RStudio.

#####################################################################################

#(English title:)Scott-Knott univariate clustering in R to RCBD.
#(Portuguese-Br title): Agrupamento univariado de Scott-Knott para DBC)
#routine By Alysson Jalles.
#www.alyssonjallessite.wordpress.com

#Here we have 2 variables to evaluate.
#Creating a dataset with RCBD data...
RCBD_IN_R<-read.table(header=TRUE, text=("
GEN REP VAR1 VAR2
1 1 109.797 4296.39
1 2 109.325 3159.55
1 3 129.079 2550.13
2 1 124.041 3206.70
2 2 102.700 3498.19
2 3 90.762 2676.06
3 1 127.014 3287.07
3 2 127.461 2345.28
3 3 122.337 3649.71
4 1 95.214 2339.48
4 2 106.913 2118.81
4 3 107.182 2426.04
5 1 119.641 3811.88
5 2 94.830 4166.51
5 3 124.218 2457.73
6 1 110.261 2276.99
6 2 106.819 1494.28
6 3 103.399 1452.75
7 1 123.564 740.09
7 2 109.369 1561.06
7 3 122.680 1951.73
8 1 103.247 3259.35
8 2 103.542 3363.61
8 3 109.542 3149.99
9 1 113.026 1924.95
9 2 76.786 3041.53
9 3 91.773 1807.83
10 1 81.236 3458.72
10 2 95.125 3185.58
10 3 95.898 3039.91
                    "))

#Changing treatments - GEN from numeric to character type;
#Class of GEN and REP
class(RCBD_IN_R$GEN)
class(RCBD_IN_R$REP)
#CONVERTING "GEN" and "REP" TO CHARACTER
RCBD_IN_R$GEN<-as.character(RCBD_IN_R$GEN)
RCBD_IN_R$REP<-as.character(RCBD_IN_R$REP)
#Class of GEN and REP
class(RCBD_IN_R$GEN)
class(RCBD_IN_R$REP)

#Intalling packages to Analysis.
  #Scott-Knott statistic of VAR1 and VAR2 in R language*/
  #Installing "ScottKnott" package in R;
  #ScottKnott in an R package to perform Scott-Knott analysis;
  #It will appear a windows, just click on the nearest mirror of your current place;
  #You just need to do it ONCE!;
install.packages("ScottKnott") #To Performa SK analysis

#Installing "xlsx" package in R;
  #xlsx is a package to export Scott-Knott results in excel to your hard drive;
  #to use this package you have to install the ultimate version of java in your computer:
  #https://www.java.com/pt_BR/download/
  #When you click in install.packages("xlsx") It will appear a windows,
  #just click on the nearest mirror of your current place;
  #You just need to do it ONCE!;
install.packages("xlsx")       #Package to export your dataset to excel

#Performing the Scott-Knott analysis;
  #We'll perform the analysis with 2 VARIABLES simultaneously,
  #but you can extend this routine to N variables;
  #_______Begin of R code_________________
require(ScottKnott) #Package to perform the SK analysis
require(xlsx)          #Package to perform the output in XLSX of results
  #For other kind of design as CRD, factorial, etc, consult the manual of R ScottKnott package
  #in: https://cran.r-project.org/web/packages/ScottKnott/ScottKnott.pdf
  #Applying univariate Scott-Knott clustering
VAR1a <- SK(x=RCBD_IN_R, y=RCBD_IN_R$VAR1, model="y~GEN+REP", which="GEN", sig.level=0.05)
#summary(VAR1a)
VAR2a <- SK(x=RCBD_IN_R, y=RCBD_IN_R$VAR2, model="y~GEN+REP", which="GEN", sig.level=0.05)
#summary(VAR2a)
#Summary
VAR1b<-(summary(VAR1a))
VAR2b<-(summary(VAR2a))
#_____________________________
#RENAMING MEANS TO THE NAME OF VARIABLE EVALUATED
names(VAR1b)[names(VAR1b)=="Means"] <- "VAR1"
names(VAR2b)[names(VAR2b)=="Means"] <- "VAR2"
#RENAMING SK(5%)TO SK PLUS NAME OF VARIABLE EVALUATED
names(VAR1b)[names(VAR1b)=="SK(5%)"] <- "SK_VAR1"
names(VAR2b)[names(VAR2b)=="SK(5%)"] <- "SK_VAR2"
#MERGING SCOTT-KNOTT RESULTS IN A SINGLE DATASET
SK <-Reduce(function(x, y) merge(x, y, all=TRUE),
            list(
              VAR1b,  #VAR1
              VAR2b  #VAR2
            ))
#RENAMING NAME OF VAR "Levels" TO GEN [TREATMENTS]
names(SK)[names(SK)=="Levels"] <- "GEN"
#_______End of R code_________________


#Exporting R results to your hard drive and SAS Environment;
#"Your file: 'Scott_Knott_output.xlsx' is in C:\ of your HD";
#Exporting results from R to SAS;
#_______Begin of R code_________________
setwd("C:/") #Assigning path to output file
getwd()       #Checking out if your file was exported sucessfully
write.xlsx(SK, "Scott_Knott_output.xlsx")
View(SK) #Once you View your table in RStudio you can copy and paste your results in excel.
#_______End of R code_________________