Thanks, will do, trying to cover my bases for a paper more so than a production problem so not as urgent. ... View more

Check this website for help with learning SAS: http://www.ats.ucla.edu/stat/sas/ ... View more

Macro variables are always stored as characters. If you want it resolved to numeric you can apply a format where you would resolve the variable, using sysfunc and inputn functions. See http://support.sas.com/documentation/cdl/en/mcrolref/61885/HTML/default/viewer.htm#z3514sysfunc.htm HTH ... View more

Yes 🙂 You do need to make sure your data is the EXACT same in each of the datasets when merging by character variables. I usually upcase everything to avoid cases not matching. You can use the merge and by statement; data want; merge have1 have2 have3; by name; run; Or you could do some joins in SQL. Basically an extension of a two way join. Search either SAS Support or LexJansen.com for papers on joining if you need details on joining. ... View more

If your file is CSV read the file using Proc Import. Then copy the code from the log and modify it to read correctly by changing the formats required. You can use ALT+mouse to select the data without the row numbers. ... View more

Could you use one macro variable for the name of the data set and the distribution? The following is an example (if it shows up) of using one name to generate the sample. %macro generate_random_sample(distribution, sample_size); data &distribution; if upcase("&distribution.")='NORMAL' then do; do i=1 to &sample_size; response=rand('Normal',0,1); output; end; end; else if upcase("&distribution.")='BINOMIAL' then do; do i=1 to &sample_size; response =rand('Binomial', 0.5, 1000); output; end; end; run; %mend; %generate_random_sample(Normal, 50); %generate_random_sample(Binomial, 50); ... View more

It looks to me like you're creating a table then assigning different probabilities based on some demographic statuses. I'd consider separating the process. Ie generate the table with the different combinations of probabilities you'd like and then fill in the probabilities using either a lookup, a format, a merge or a hashtable. It also looks like you're trying to define your own 'function' but SAS does not do that as far as I know. You can see this doc http://www.lexjansen.com/phuse/2006/cs/cs06.pdf if you want to try and go down the route of defining your own function. That's beyond me. ... View more

Couple of things: 1. You cannot assign a macro variable (your %let statements) and then use it again in the same dataset.(not easily) 2. Why assign macro variables for variables in a dataset when you could just use them in the call statement for the forecast. 3. Why call a macro at all and not include that code nested in the do loop. 4. Why do you have i/j/k/l when they don't appear to be used? I don't know enough about what you're trying to achieve beyond this :(. You could put what the data looks like and what you need, if you want more help. Cheers, Reeza ... View more

You have a good example above, but I'll add in a brief example of the do loop since you don't want to change type output manually 96 times. data have; set sashelp.class; run; data want; set have; do i=1 to 96; job=i; sample_weight=i/96; output; end; run; ... View more

You CAN do the following, it will give you two columns with that data value though and that can lead to all sorts of trouble. Ideally you should drop the old one with the long name which will be truncated in SAS. ***SQL code*** select *, long_variable_name as short_variable_name From ... ***SQL code*** ... View more

I don't think you need a macro for this...I think you need a data step, possibly an array (since you say 96 outputs) and control the output with output statements. Could you post what you want your original data looks like and what you'd like as a result? ... View more

You wanted to store a superscript in a dataset? Or does the above answer your question. ... View more

What about truncating the data to the time point of interest and comparing the differences of the curves up to that point? I think that's really what's of interest, ie is the surivival different for the first year, second year, etc... If you truncate at 12 months your estimate is the same as if you hadn't truncated but information afterwards is censoring/truncation in effect. Does that make any sense? ... View more

Lookup the scan function. s_comp=scan (var, 1, "!"); r_comp=scan(var, 2, "!"); This assumes that R and S are always in the order stated. Use it once to separate the S and R components into separate strings, then in each string you can scan or using "S," or "R," as the delimiter and a loop to get the numbers. ... View more