Using a new stand-alone computer that has 32 GB RAM, 6 core processors. Trying to tell SAS to go ahead and use as much of the resources as possible. Trouble is, with the settings below, a job is SLOWER than with a previous average computer.   Please advise on which of these 'options' likely needs to be changed:   -JREOPTIONS=( -Xms1g -Xmx1g )   -BUFNO MAX -BUFSIZE MAX -CPUCOUNT 6 -DMSLOGSIZE = 999999 -IBUFNO MAX -IBUFSIZE MAX -LRECL = MAX -MEMSIZE = MAX -REALMEMSIZE = MAX -SET SAS_NO_RANDOM_ACCESS = "0" -SORTSIZE = MAX -SUMSIZE = MAX -THREADS -UBUFNO MAX Thanks very much! Nicholas Kormanik     ... View more

I've conducted many Proc Univariate runs on data, each run creating a seperate small dataset.   Dependent Variables (totalling around 200) are discrete, a rank of: 0, 1, 2, 3, 4, 5, 6   Independent Variable(s): continuous   Example of SAS code for a single run:   proc univariate data=SAS_1.combined; var i_50503; class rg5_21705 rg5_21502 ; ods output moments=SAS_1.i_50503__rg5_21705__rg5_21502 ; run;   In plain English, my thinking is:   50503 is a function of 21705-level and 21502-level   Each small resulting dataset contains the Mean for the particular run. As well as lots of other 'moments' numbers.   I'd like to compare the Means in the different dataset files.   Is there a way for SAS to do this?   Or must I extract the individual Means from each dataset, and put into a new Just-Means dataset?   Might there be an alternative to Proc Univariate that can look at all possible combinations of the dependent variables levels, along with Mean score for the independent variable?   If further information is wanted I'll be happy to provide it.   Thanks, Nicholas Kormanik     ... View more

My dataset has 10 variables and 2000 cases. All variables are continuous. I would like to "standardize" each variable column. Then average those 10 columns, and compare the summary averages for each case, say, sorting from high to low.   I know that several variables data is bi-modal, as opposed to centered, with more data occurring at the extremes.   I'm wondering what the best standardization method might be. SAS offers several. STD, MAD, IQR, ABW, and others.   STD is common -- converting to Z-score: (X1 - mean of X1)/standard deviation of X1. Some of the others are apparently more 'robust,' however, with respect to outliers, and, I suppose, certain other data anomilies.   I'm tentatively thinking of using one of the more esoteric 'robust' ones, such as IQR, based on an example given in SAS documentation.   http://support.sas.com/documentation/cdl/en/statug/68162/HTML/default/viewer.htm#statug_stdize_gettingstarted.htm   I'd greatly appreciate hearing your thoughts or suggestions on how best to proceed.   Nicholas Kormanik     ... View more