thanks for the video. Is it possible to add letters (A, B, C, etc) to indicate significance of the least square means or an additional table showing the treatment means and the letters of significance and what would be the syntax to show this? Thanks!
al parecer eso solo se puede usando unos MACROS que no he podido conseguir. Utilicé proc glimmix y con ese si pude obtener las letras en las medias. proc glimmix data=PARDCD; class REP GRAN PEQ; model REND= GRAN PEQ GRAN*PEQ; random REP GRAN*REP; lsmeans GRAN PEQ GRAN*PEQ / pdiff=all adjust=tukey lines plot=mean(sliceby=PEQ join);
Whenever you put a classification variable in a RANDOM statement you are creating random intercepts for each category. This is essentially what is done with the subsampling model, where random intercepts are included for each experimental unit.
I'm trying to make sure I am including the random var appropriately. I know it appears in the class statement because its categorical, however should it also be in the model statement? ex. variance across sites and across doctors given patient response of satisfaction.
The MODEL statement only includes fixed effects. If you put a categorical effect in the MODEL statement and RANDOM statement then you will be introducing identifiability issues. A random intercept variable is introduced when you have a random categorical variable in the random statement. Each category is given a random effect in the model, hence a random intercept is assigned to all observations having that corresponding category.
Extremely helpful. Thanks!
thanks for the video. Is it possible to add letters (A, B, C, etc) to indicate significance of the least square means or an additional table showing the treatment means and the letters of significance and what would be the syntax to show this? Thanks!
al parecer eso solo se puede usando unos MACROS que no he podido conseguir. Utilicé proc glimmix y con ese si pude obtener las letras en las medias.
proc glimmix data=PARDCD;
class REP GRAN PEQ;
model REND= GRAN PEQ GRAN*PEQ;
random REP GRAN*REP;
lsmeans GRAN PEQ GRAN*PEQ /
pdiff=all adjust=tukey lines
plot=mean(sliceby=PEQ join);
what about setting up random intercepts
Whenever you put a classification variable in a RANDOM statement you are creating random intercepts for each category. This is essentially what is done with the subsampling model, where random intercepts are included for each experimental unit.
I'm trying to make sure I am including the random var appropriately. I know it appears in the class statement because its categorical, however should it also be in the model statement? ex. variance across sites and across doctors given patient response of satisfaction.
What is the difference between stating random variable versus random intercept variable
The MODEL statement only includes fixed effects. If you put a categorical effect in the MODEL statement and RANDOM statement then you will be introducing identifiability issues. A random intercept variable is introduced when you have a random categorical variable in the random statement. Each category is given a random effect in the model, hence a random intercept is assigned to all observations having that corresponding category.