Objective: You are running ANOVAs or regressions in SAS, and wish there was a way to avoid writing the dozens of commands needed to conduct the analysis and generate recommended diagnostics and summary of results, not to mention the hundreds of possible options that might be needed to access recommended methods. A possible solution is to download a copy of danda.sas below, and use this macro collection to run the dozens of commands with one statement. We will also have future posts covering various uses of danda.sas, giving examples as always.
danda.sas is under continued development, check this page for updates.
danda.sas is under continued development, check this page for updates.
Example: You have an RBD split-plot design, so typical SAS code for mixed model ANOVA is
proc mixed data=one;
class block treat week;
model height = treat | week / outp=rrr;
random block block*treat;
lsmeans treat | week /pdiff;
run;
proc univariate plot normal data=rrr;
var resid;
run;
proc means data=rrr;
class treat week;
var resid;
run;
and on and on to get other results. Note that the above code has errors, illustrating how easily researchers may overlook recommended options, such as ddfm=kr to request the popular Kenward-Roger degrees of freedom adjustment, needed for models with multiple error terms such as this example. Least squares means differences will be printed as a huge table, very difficult to condense into the typical ABC letter grouping indicating which means differ. And if you have multiple y-variables, you have to repeat all this code for each y-variable, as Proc Mixed only allows one dependent variable. If you needed to switch to Proc Glimmix, then option names change as well as some of the commands, resulting in more potential coding mistakes. To be fair, SAS has made improvements recently with ODS graphics incorporated into all procs, potentially making the procs following Proc Mixed unnecessary.
In comparison, the above analysis could be run by
%include 'd:\myfolder\danda.sas';
%mixedaov(one, yvar1 yvar2 aaa--zzz, class=block treat week, fixed=treat|week, random=block block*treat);
That is it! The user only needs to specify the elements of the model, and list all dependent variables. The macro implements the standard recommended options and diagnostic checks (eg. normality) automatically. The %include makes the macros available, with the user specifying where on the computer the danda.sas file is saved.
proc mixed data=one;
class block treat week;
model height = treat | week / outp=rrr;
random block block*treat;
lsmeans treat | week /pdiff;
run;
proc univariate plot normal data=rrr;
var resid;
run;
proc means data=rrr;
class treat week;
var resid;
run;
and on and on to get other results. Note that the above code has errors, illustrating how easily researchers may overlook recommended options, such as ddfm=kr to request the popular Kenward-Roger degrees of freedom adjustment, needed for models with multiple error terms such as this example. Least squares means differences will be printed as a huge table, very difficult to condense into the typical ABC letter grouping indicating which means differ. And if you have multiple y-variables, you have to repeat all this code for each y-variable, as Proc Mixed only allows one dependent variable. If you needed to switch to Proc Glimmix, then option names change as well as some of the commands, resulting in more potential coding mistakes. To be fair, SAS has made improvements recently with ODS graphics incorporated into all procs, potentially making the procs following Proc Mixed unnecessary.
In comparison, the above analysis could be run by
%include 'd:\myfolder\danda.sas';
%mixedaov(one, yvar1 yvar2 aaa--zzz, class=block treat week, fixed=treat|week, random=block block*treat);
That is it! The user only needs to specify the elements of the model, and list all dependent variables. The macro implements the standard recommended options and diagnostic checks (eg. normality) automatically. The %include makes the macros available, with the user specifying where on the computer the danda.sas file is saved.
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