About ANKH1

ANKH1 · ‎07-06-2021

Hello, thank you for your answer. The design is a two by two. Each unit receives a different treatment. This outcome is not repeated. Only measured at the end of the experiment. Is it correct just to add the random statement like we did below? proc mixed DATA=df; class ID Diet Drug ; model outcome = Diet|Drug; random /type=ar(1) sub=ID; lsmeans Diet|Drug/adjust=tukey; run; Thank you!

ANKH1 · ‎07-04-2021

Hi, We are testing the effect of two main factors (factor Diet: A or B) (factor: Drug C or D) and their interaction and have some missing data. There are 4 different groups: AC, AD, BC, BD. In order to be able to select the best covariance structure we are including the repeated statement with the option TYPE= un (or cs, ar(1)) to look at the lowest AIC. But we don't know if we should include a SUB = ID since we are not using repeated measures on the same subjects. Our code is like this: proc mixed DATA=df; class ID Diet Drug ; model outcome = Diet|Drug; repeated /type=ar(1) sub=ID; lsmeans Diet|Drug/adjust=tukey; run; Is the "sub" option necessary? Thank you!

ANKH1 · ‎08-24-2020

Thank you so much! Your code worked perfectly. I added on the keep statement: data want_long; set sample1; value=var1; varlevel='var1';output; value=var2; varlevel='var2';output; value=var3; varlevel='var3';output; keep id DHHDDRI WTS_P BSW1-BSW500 varlevel value; run; I used the below option you gave since we want to know the overall mean of values by ignoring specific varlevel proc surveymeans data=want_long2 mean sum varmethod=BRR; domain DHHDDRI; var value; weight WTS_P; repweights BSW1-BSW500; run; Yes, the domain, weight and repweights are fixed for ID. The conversion worked perfectly. This is a snapshot of our dataframe. I import data from excel. I think your code is what we needed. What do you think? Thanks again for your help!

ANKH1 · ‎08-24-2020

Thanks! How do I set to missing values? "What is the actual relationship between ID1, ID2 and ID3 that these should be treated together in the manner you suggest?They are all types of fruits (g of fruits), but I want to know the mean for all fruits, not individual types of fruits. "This may not be a simple, i.e. one step, solution because of the way that the Survey procs treat the weights." I agree, that is why I wanted to get experts' input.

ANKH1 · ‎08-24-2020

Hello, I would like to calculate the mean of multiple variables (VAR1, VAR2, VAR3), not by column or row, but all the values. For example, I would like to get the mean of 34, 45, 31, 35, 41 = 186/5 = 37.2 This is an example of the data ID VAR1 VAR2 VAR3 ID1 34 45 0 ID2 31 0 35 ID3 0 0 41 I only have the code for a single variable. PROC SURVEYMEANS DATA=sample1 MEAN SUM VARMETHOD=BRR; DOMAIN DHHDDRI; WHERE VAR1 > 0; VAR VAR1; WEIGHT WTS_P; REPWEIGHTS BSW1-BSW500; RUN; Thanks in advance!

ANKH1 · ‎07-30-2020

Thank you!

ANKH1 · ‎07-30-2020

Hi, I would like to calculate a survey mean from a variable (VAR1) only with values above zero. Is this the right syntax? PROC SURVEYMEANS DATA=sample1 MEAN SUM VARMETHOD=BRR; DOMAIN DHHDDRI; WHERE VAR1 ne 0; VAR VAR1; WEIGHT WTS_P; REPWEIGHTS BSW1-BSW500; RUN;

ANKH1 · ‎04-27-2020

Thank you for all your help!

ANKH1 · ‎04-23-2020

This is great! I will read this paper. Another last question, with LSESTIMATE I can decide a priori what 2 way comparisons and main effects I am interested in, correct? Thanks again for your help, it is very much appreciated.

ANKH1 · ‎04-22-2020

Your posts have been really helpful and I've learned a lot. When using LSMESTIMATE, can I have one contrast for a two way interaction and one contrast per main effect? Also, do you recommend ADJUST= BON for pairwise comparisons using LSMESTIMATE? Apparently Tukey is not an option. Thanks again!

ANKH1 · ‎04-21-2020

By choosing to look at Adj T K values adjusted for all 105 comparisons am I increasing my type 1 error?

ANKH1 · ‎04-21-2020

Thank you! Sorry about the two missing lines for tukey's. What if I want to look at all pairwise comparisons between lsmeans calculated from the output? Is it valid to look at all 6 pairwise comparisons coming from tukey-kramer from my previous output? Thanks again!

ANKH1 · ‎04-21-2020

Hi, In a 3-Way ANOVA analyses, is it possible to decide a priori what comparisons between different factors we want to look at? We would like to look at tukey's where there is a two factor interaction. Is this valid since tukey's uses adjusted p values per tested comparison? This is the code for the overall 3 Way ANOVA (2*2*2): PROC GLIMMIX DATA= data; CLASS id dose form gen; MODEL var= dose|form|gen; RANDOM residual/ subject = ID type=CS; LSMEANS dose|form|gen /ADJUST=TUKEY; RUN;QUIT; These are the results for type 3 effects This is the output where a two factor interaction was found:

ANKH1 · ‎03-17-2020

Hi, I figure what was wrong. I left a space between the quotation and the category B data sample2; set sample1; if ANIMALCAT in ('A', ' B') then ANIMALCAT2=ANIMALCAT; else ANIMALCAT2='.'; RUN; Now without the space I get the right output: data sample2; set sample1; if ANIMALCAT in ('A', 'B') then ANIMALCAT2=ANIMALCAT; else ANIMALCAT2='.'; RUN; So this is comparing A and B taking into account the other two groups for standard errors, right? Please confirm I am interpreting this correctly. Thank you again

ANKH1 · ‎03-17-2020

Hello, Sorry, I didn't copy the right output. How can I check I'm sub-setting the data correctly? It will be more efficient if I did not have to copy the datalines since this is a huge data set and I have to run tests multiple times with different dependent variables. You mentioned before that it is important to include the other groups that are not being compared as missing values. Why is this missing on the last code? This is the code that I used: data sample2; set sample1; if ANIMALCAT in ('A', 'B') then ANIMALCAT2=ANIMALCAT; RUN; ods graphics off; proc surveyfreq data=sample1; tables ANIMALCAT*EAR_F_PROTEIN/col row nostd nowt wchisq chisq; weight WTS_P; run; ods graphics off; proc surveyfreq data=sample2 nomcar; tables ANIMALCAT2*EAR_F_PROTEIN/col row nostd nowt wchisq chisq; weight WTS_P; run; This is the output for the last proc. How can I compare A and B, A and C and A and D? Thank you in advance

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