Thank you so much @SteveDenham!    You are right, adding or removing that coding did not change the results much!   By the way, when I run the code with " dist=beta", it turned out an error: "ERROR: Invalid or missing data." I don't know how to fix this. My response is digestibility which is listed as 1-100% instead of 0-1, does it matter?   proc glimmix data=DF_digestibility; class treatment block; model fIDFdigestibility = treatment/dist=beta; random block; random _residual_/group=treatment; lsmeans treatment/diff lines ilink; covtest homogeneity; output out=second predicted=pred residual=resid residual(noblup)=mresid student=sresid student(noblup)=smresid; title "1-Fecal Insoluble Dietary Fiber Digestibility (%) ANOVA Results"; run;     ... View more

Thank you very much @SteveDenham! very clear explanation!   Can I consult you with one more question? If the result is showing equal variance, in this case, can I use the following code without " random _residual_ / group =treatment" and " covtest homogeneity ".   proc glimmix data=DF_digestibility; class treatment block; model fIDFdigestibility = treatment /dist=beta; ; random block; lsmeans treatment/diff lines ilink; output out=second predicted=pred residual=resid residual(noblup)=mresid student=sresid student(noblup)=smresid; title "1-Fecal Insoluble Dietary Fiber Digestibility (%) ANOVA Results"; run;    ... View more

@SteveDenham Thank you very much for your answer, Steve! I was told in my statistical class that you should always check your residuals (random, independent of treatment and design effects, homogenous) before we look at the results. And if you can see some patterns in your residuals, you should transform your data to a better model before you can compare the treatment effect. I did see some patterns in the residuals in this specific data set. What are your suggestions on this? Thanks! ... View more

@jiltao Thank you for your answer. I am still confusing. If I find out the variances between the two treatment groups are unequal, am I supposed to transform them before I compare them by F test? The unequal covariances are also indicated in the below plots as data are showing some patterns or skewed to one side.       ... View more

@PaigeMiller  thank you for your reply. You are right! It is the RESIDUALS that have to be normally distributed. I think the normality result from SAS I attached is for the RESIDUALS.     Let me explain my question in another way. I want to transform my data here because the RESIDUALS are showing unequal variance between two treatment groups when I run the homogeneity test (code below).    proc glimmix data=DF_digestibility; class treatment block; model fIDFdigestibility = treatment; random block;  random _residual_/group=treatment;  lsmeans treatment/tdiff lines; covtest homogeneity;  output out=second predicted=pred residual=resid residual(noblup)=mresid student=sresid student(noblup)=smresid; title "1-Fecal Insoluble Dietary Fiber Digestibility (%) ANOVA Results"; run;     ... View more

the ods statement as part of the glimmix procedure also works. ... View more

Thank you all for helping me out!    ... View more

Hi Reeza, it works now, thank you so much! The code is ods output LSMeans = myparmdataset; proc glimmix data=data1; class treatment block; model weight = treatment; random block; lsmeans treatment/tdiff lines; run; proc print data=myparmdataset; format estimate D8.6 stderr D8.6; run; ... View more

Also got this warning WARNING: Output 'parameterEstimates' was not created. Make sure that the output object name, label, or path is spelled correctly. Also, verify that the appropriate procedure options are used to produce the requested output object. For example, verify that the NOPRINT option is not used. ... View more

code data cecal_kingdom_taxonomy; length treatment $20; Infile "/home/u39233094/sasuser.v94/Thesis/CSV file/1-kingdom cecal taxonomy for SAS.csv" dlm="," firstobs=5; input diet$ treatment$ block pen pig_number DNAsamcode$ unidentifiedR unidentifiedP bacteriaR bacteriaP eukaryotaR eukaryotaP totalR totalP; run; Proc print data=cecal_kingdom_taxonomy; run; proc glimmix data=cecal_kingdom_taxonomy; class treatment block; model unidentifiedP = treatment; random block; lsmeans treatment/tdiff lines; ods output parameterEstimates = want; run; log: 1 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK; NOTE: ODS statements in the SAS Studio environment may disable some output features. 73 74 data cecal_kingdom_taxonomy; 75 length treatment $20; 76 Infile "/home/u39233094/sasuser.v94/Thesis/CSV file/1-kingdom cecal taxonomy for SAS.csv" dlm="," firstobs=5; 77 input diet$ treatment$ block pen pig_number DNAsamcode$ unidentifiedR unidentifiedP bacteriaR bacteriaP eukaryotaR 77 ! eukaryotaP totalR totalP; 78 run; NOTE: The infile "/home/u39233094/sasuser.v94/Thesis/CSV file/1-kingdom cecal taxonomy for SAS.csv" is: Filename=/home/u39233094/sasuser.v94/Thesis/CSV file/1-kingdom cecal taxonomy for SAS.csv, Owner Name=u39233094,Group Name=oda, Access Permission=-rw-r--r--, Last Modified=03Jun2021:11:31:01, File Size (bytes)=3921 NOTE: 35 records were read from the infile "/home/u39233094/sasuser.v94/Thesis/CSV file/1-kingdom cecal taxonomy for SAS.csv". The minimum record length was 99. The maximum record length was 109. NOTE: The data set WORK.CECAL_KINGDOM_TAXONOMY has 35 observations and 14 variables. NOTE: DATA statement used (Total process time): real time 0.00 seconds user cpu time 0.00 seconds system cpu time 0.00 seconds memory 790.53k OS Memory 39336.00k Timestamp 06/08/2021 09:22:05 PM Step Count 132 Switch Count 2 Page Faults 0 Page Reclaims 94 Page Swaps 0 Voluntary Context Switches 19 Involuntary Context Switches 0 Block Input Operations 0 Block Output Operations 264 79 Proc print data=cecal_kingdom_taxonomy; 80 run; NOTE: There were 35 observations read from the data set WORK.CECAL_KINGDOM_TAXONOMY. NOTE: PROCEDURE PRINT used (Total process time): real time 0.16 seconds user cpu time 0.17 seconds system cpu time 0.00 seconds memory 3365.93k OS Memory 39336.00k Timestamp 06/08/2021 09:22:06 PM Step Count 133 Switch Count 0 Page Faults 0 Page Reclaims 63 Page Swaps 0 Voluntary Context Switches 0 Involuntary Context Switches 0 Block Input Operations 0 Block Output Operations 48 81 proc glimmix data=cecal_kingdom_taxonomy; 82 class treatment block; 83 model unidentifiedP = treatment; 84 random block; 85 lsmeans treatment/tdiff lines; 86 ods output parameterEstimates = want; 87 run; NOTE: Convergence criterion (GCONV=1E-8) satisfied. WARNING: Output 'parameterEstimates' was not created. Make sure that the output object name, label, or path is spelled correctly. Also, verify that the appropriate procedure options are used to produce the requested output object. For example, verify that the NOPRINT option is not used. NOTE: PROCEDURE GLIMMIX used (Total process time): real time 0.12 seconds user cpu time 0.12 seconds system cpu time 0.01 seconds memory 4969.46k OS Memory 41476.00k Timestamp 06/08/2021 09:22:06 PM Step Count 134 Switch Count 13 Page Faults 0 Page Reclaims 900 Page Swaps 0 Voluntary Context Switches 76 Involuntary Context Switches 1 Block Input Operations 0 Block Output Operations 1632 88 89 OPTIONS NONOTES NOSTIMER NOSOURCE NOSYNTAXCHECK; 101 ... View more

It turned out this warning: WARNING: Output 'parameterEstimates' was not created. Make sure that the output object name, label, or path is spelled correctly. Also, verify that the appropriate procedure options are used to produce the requested output object. For example, verify that the NOPRINT option is not used. ... View more

Thank you so much @SteveDenham ! Your reply really help! The reason I am testing homogeneity is that my supervisor wants to see this data. And if I removed the group option in my random_residual_statement, the homogeneity data will not show up. You mentioned in your reply that "or add in a method= statement to switch to RMPL (residual marginal pseudo-likelihood)" , I do not have experience in this statement and don't know whether it will help to get the homogeneity data by adding this to my coding.     And by catching this great opportunity, can I ask you another question.  To analyse my data, I first used the code blow. It did not work at first, but when I added parms statement, it worked. And I also try your coding suggestions, it also work. But the result is a bit different. Which should I choose to be more accurate.  My SAS code: data Activity; length treatment $20; input treatment$ block Km Vmax; datalines; Non-Antibiotics 5 46.18 28.06 Non-Antibiotics 3 55.61 24.64 Non-Antibiotics 4 58.71 27.35 Non-Antibiotics 3 47.32 22.15 Non-Antibiotics 4 43.61 12.53 Non-Antibiotics 8 52.00 25.94 Non-Antibiotics 11 54.65 21.34 Non-Antibiotics 5 64.54 25.73 Non-Antibiotics 10 51.27 24.36 Non-Antibiotics 5 57.61 23.04 Non-Antibiotics 5 48.37 32.42 Non-Antibiotics 9 56.04 40.59 Non-Antibiotics 6 53.89 11.33 Antibiotics 8 39.02 27.74 Antibiotics 4 55.56 16.92 Antibiotics 7 56.97 25.41 Antibiotics 9 58.29 31.49 Antibiotics 4 53.18 28.04 Antibiotics 11 41.89 43.48 Antibiotics 5 40.35 38.12 Antibiotics 3 54.84 40.96 Antibiotics 10 47.66 32.51 Antibiotics 6 61.14 35.47 Antibiotics 5 52.42 38.02 ; run; Proc print data=Activity; run; proc glimmix data=Activity nobound; class treatment block; model Km = treatment; random block/subject=treatment*block type=cs residual; parms (-8.6732) (52.0395); lsmeans treatment/tdiff lines; covtest "block=0" 0 .; output out=second predicted=pred residual=resid residual(noblup)=mresid student=sresid student(noblup)=smresid; run; proc print data=second; run; proc sgplot data=second; scatter y=smresid x=pred; refline 0; run; proc sgplot data=second; scatter y=smresid x=treatment; refline 0; run; proc sgplot data=second; vbox smresid/group=treatment datalabel; run; proc sgscatter data=second; plot sresid*(pred treatment block); run; proc univariate data=second normal plot; var sresid; histogram sresid / normal kernel; run; Result: Adapting your code by change a bit to GLMM statement: proc glimmix data=Activity nobound; class treatment block; model Km = treatment; random intercept/subject=block; lsmeans treatment/tdiff lines; covtest "block=0" 0 .; output out=second predicted=pred residual=resid residual(noblup)=mresid student=sresid student(noblup)=smresid; run; Result: Great thanks!!!     ... View more