Dear All: For the purposes of regression, missing is indeed equal to 0. Randy On Mon, 16 Apr 2012 18:45:41 +0000, toby dunn <tobydunn@HOTMAIL.COM> wrote:

>Lets See here: > > >> data _null_; >> set foo; >> array v _numeric_; >> do over v; >> if missing(v) then v=0; >> end; >> run; >> >> NOTE: There were 500000 observations read from the data set WORK.FOO. >> NOTE: DATA statement used (Total process time): >> real time 0.63 seconds >> user cpu time 0.56 seconds >> system cpu time 0.07 seconds > > > > >VS > > >> data _null_; >> set foo; >> array v &var_names; >> do over v; >> if missing(v) then v=0; >> end; >> run; >> >> NOTE: There were 500000 observations read from the data set WORK.FOO. >> NOTE: DATA statement used (Total process time): >> real time 0.63 seconds >> user cpu time 0.54 seconds >> system cpu time 0.09 seconds > > > >So Real time is the same, #2 wins by .o2 on the CPU time While #1 wins by .02 on system CPU Time.. > >Two things comes to mind... I dont see a clear winner and number two who gives a flying rats ass which one is faster with numbers this >freakin close.... > >In short too many people after all these years are still hung up on speed... in reality they should be worried about readability and maintainability of the code. >Why? because people coming behind you will spend more time reading and trying to understand and mainatin your code than you did writing and testing the damn thing.... >Unless there is a significant difference in time why do we waste or efforts on eeking .02 in CPU time. > >In which I am still in favor of #1 over #2 because it is easier to read and maintain. > > >This: > >> proc stdize data=foo out=_null_ reponly missing=0; run; >> >> NOTE: No VAR statement is given. All numerical variables not named >> elsewhere make up the first set of variables. >> NOTE: There were 500000 observations read from the data set WORK.FOO. >> NOTE: PROCEDURE STDIZE used (Total process time): >> real time 0.74 seconds >> user cpu time 0.66 seconds >> system cpu time 0.09 seconds > > >Is the best so far even if it takes a hair longer to run. > > > >Toby Dunn > > >If you get thrown from a horse, you have to get up and get back on, unless you landed on a cactus; then you have to roll around and scream in pain. > >�Any idiot can face a crisis�it�s day to day living that wears you out� >~ Anton Chekhov > > > >> Date: Mon, 16 Apr 2012 12:11:48 -0600 >> From: friedegg2012@GMAIL.COM >> Subject: Re: How to change many missing variables to 0 in a single data step >> To: SAS-L@LISTSERV.UGA.EDU >> >> The generate if statements does appear to be the quickest implementation >> with the given problem ( ~50 columns x ~500k rows). Here is some code to >> generate and compare the given solutions. I also expanded the miss2zero >> macro a little work with non-standardized variable names through >> collection. It would fit nicely into a macro function sandwich (a la Mike >> Rhoads) to avoid the compile and generate steps into a single call. >> >> /* simulate non standardizes variable names */ >> proc sql noprint; >> select distinct compress(Subsidiary,,'ka') >> into :bar_arr separated by ' ' >> from sashelp.shoes; >> %let bar_dim=&sqlobs; >> quit; >> NOTE: PROCEDURE SQL used (Total process time): >> real time 0.01 seconds >> user cpu time 0.00 seconds >> system cpu time 0.00 seconds >> >> >> >> /* generate 53x500,000 sample data with 40% random missing */ >> data foo; >> call streaminit(12345); >> array bar[&bar_dim] &bar_arr; >> do id=1 to 500000; >> do _n_=1 to &bar_dim; >> bar[_n_]=rand('uniform'); >> if rand('table',.6,.4) > 1 then call missing(bar[_n_]); >> end; >> output; >> end; >> run; >> >> NOTE: The data set WORK.FOO has 500000 observations and 54 variables. >> NOTE: DATA statement used (Total process time): >> 2 The SAS System >> 10:43 Monday, April 16, 2012 >> >> real time 3.16 seconds >> user cpu time 2.53 seconds >> system cpu time 0.60 seconds >> >> >> >> /* test variable imputation methods >> will use missing() instead of =. to account for all missing >> values i.e. =.Z */ >> >> *array method using _numeric_ variable list; >> data _null_; >> set foo; >> array v _numeric_; >> do over v; >> if missing(v) then v=0; >> end; >> run; >> >> NOTE: There were 500000 observations read from the data set WORK.FOO. >> NOTE: DATA statement used (Total process time): >> real time 0.63 seconds >> user cpu time 0.56 seconds >> system cpu time 0.07 seconds >> >> >> >> *array method using collected variable list; >> proc sql noprint; >> select name >> into :var_names separated by ' ' >> from sashelp.vcolumn >> where libname='WORK' and memname='FOO'; >> quit; >> NOTE: PROCEDURE SQL used (Total process time): >> real time 0.00 seconds >> user cpu time 0.00 seconds >> system cpu time 0.00 seconds >> >> >> >> data _null_; >> set foo; >> array v &var_names; >> do over v; >> if missing(v) then v=0; >> end; >> run; >> >> NOTE: There were 500000 observations read from the data set WORK.FOO. >> NOTE: DATA statement used (Total process time): >> real time 0.63 seconds >> user cpu time 0.54 seconds >> system cpu time 0.09 seconds >> >> >> >> *proc stdize with reponly option, my personal favorite response to >> this topic; >> proc stdize data=foo out=_null_ reponly missing=0; run; >> >> NOTE: No VAR statement is given. All numerical variables not named >> elsewhere make up the first set of variables. >> NOTE: There were 500000 observations read from the data set WORK.FOO. >> NOTE: PROCEDURE STDIZE used (Total process time): >> real time 0.74 seconds >> user cpu time 0.66 seconds >> system cpu time 0.09 seconds >> >> >> >> *macro with if statements for non-standardized varaible names; >> %macro impute_missing(action= ,libname= ,memname= ,type=num >> ,prefix=n ,impute_value=0); >> %if &action = compile %then %do; >> data _null_; >> do i=1 by 1 until(done); >> set sashelp.vcolumn end=done; >> where libname="%upcase(&libname)" and >> memname="%upcase(&memname)" and type="%lowcase(&type)"; >> call symputx(cats("g_m2z_&prefix",i),name,'g'); >> end; >> call symputx("g_m2z_&prefix.0",i,'g'); >> run; >> %end; >> %if &action = generate %then %do; >> %do i=1 %to &&g_m2z_&prefix.0; >> if missing(&&&g_m2z_&prefix.&i) then &&&g_m2z_&prefix.&i=0; >> %end; >> %end; >> %mend; >> >> %impute_missing(action=compile ,libname=WORK ,memname=FOO >> ,type=num ,prefix=n); >> >> NOTE: The query as specified involves ordering by an item that doesn't >> appear in its SELECT clause. >> NOTE: There were 54 observations read from the data set SASHELP.VCOLUMN. >> WHERE (libname='WORK') and (memname='FOO') and (type='num'); >> NOTE: DATA statement used (Total process time): >> real time 0.08 seconds >> user cpu time 0.06 seconds >> system cpu time 0.02 seconds >> >> >> data _null_; >> set foo; >> %impute_missing(action=generate ,prefix=n ,impute_value=0); >> run; >> >> NOTE: There were 500000 observations read from the data set WORK.FOO. >> NOTE: DATA statement used (Total process time): >> real time 0.28 seconds >> user cpu time 0.18 seconds >> system cpu time 0.09 seconds >