I have the following code to calculate percentiles.
proc univariate data=sashelp.cars;
var Horsepower
output pctlpre=P_ pctlpts= 50, 75 to 100 by 5;
run;
I would like to assign these percentiles to a macro variable (so 1 macro variable per percentile) on the fly. Is there any smart way to do this?
What does "smart way to do this" mean? What will you do with the macros as you call them?
proc univariate data=sashelp.cars noprint;
var Horsepower;
output pctlpre=P_ pctlpts= 50, 75 to 100 by 5;
run;
proc print;
run;
data _null_;
set;
array P_ P_:;
do over p_;
call symputx(vname(p_),p_);
end;
stop;
run;
%put _user_;
Based on input from OP about what they are really doing there is no need to put data into macro variables. My opinion it is never "smart" to put data into macro variables when those variable will be used in calculations.
This creates new variables CAP_: capped at &p.
%let p=90;
ods select none;
ods output summary=ptile;
proc means data=sashelp.cars stackods p&P.;
run;
ods output close;
ods select all;
proc print;
run;
/*Flip the rows into 1 obs. Vars with prefix P&P._*/
proc transpose data=ptile out=ptile2(drop=_:) prefix=P&P._;
var P&P.;
id variable;
run;
/*Flip the rows to create new CAP_ variables. Just for the names*/
proc transpose data=ptile out=cap(drop=_:) prefix=Cap_;
var P&P.;
id variable;
run;
/*create capped variables*/
data capped;
set sashelp.cars;
array _v _numeric_;
/*Create array of new variables CAP_*/
if 0 then set cap;
array _cap cap_:;
call missing(of _cap[*]);
/*Create array of Ptile variables*/
if _n_ eq 1 then set ptile2;
array _tile P&P._:;
*drop P&P._:;
do over _v;
_cap = min(_tile,_v);
end;
run;
proc print;
run;
May be not so smart, but will work, i think.
proc univariate data=sashelp.cars;
var Horsepower;
output out=table pctlpre=P_ pctlpts= 50, 75 to 100 by 5;
run;
proc sql noprint;
select 'call symput('|| "'" ||strip(name)||"'," || strip(name) || ");"
into:name_list separated by ' '
from dictionary.columns
where libname ="WORK" and memname="TABLE";
quit;
data _null_;
set table;
&name_list;
run;
%put _ALL_;
Proc sql generates code on fly: call symput('P_50',P_50); call symput('P_75',P_75); call symput('P_80',P_80); call symput('P_85',P_85); call symput('P_90',P_90); call symput('P_95',P_95); call symput('P_100',P_100);
OUTPUT:
GLOBAL P_100 500
GLOBAL P_50 210
GLOBAL P_75 255
GLOBAL P_80 275
GLOBAL P_85 295
GLOBAL P_90 302
GLOBAL P_95 340
Here's a way to do this using only a single proc sql:
proc univariate noprint data=sashelp.class;
var height;
output out=percentiles pctlpre=P_ pctlpts= 50, 75 to 100 by 5;
run;
proc sql noprint;
select
name,
cats(':',name)
into
:COL_NAMES separated by ',',
:MVAR_NAMES separated by ','
from sashelp.vcolumn
where
libname = "WORK"
and memname = "PERCENTILES"
;
select &COL_NAMES into &MVAR_NAMES
from percentiles;
quit;
Result: 1 macro variable per percentile, with the same names as the univariate output.
Related
So I'm having trouble getting what appears to be a fairly common model validation macro to run with my code. Here's the code:
%macro hoslem (data=, pred=, y=, ngro=10,print=T,out=hl);
/*---
Macro computes the Hosmer-Lemeshow Chi Square Statistic
for Calibration
Parameters (* = required)
-------------------------
data* input dataset
pred* Predicted Probabilities
y* Outcome 0/1 variable
ngro # of groups for the calibration test (default 10)
print Prints output (set to F to Suppress)
out output dataset (default HL)
Author: Kevin Kennedy
---*/
%let print = %upcase(&print);
proc format;
value pval 0-.0001='<.0001';
run;
data first;set &data;where &y^=. and &pred^=.;run;
proc rank groups=&ngro out=ranks data=first;ranks phat_grp;var &pred;run;
proc sort data=ranks;by phat_grp;run;
proc sql;
create table ranks2 as select *, count(*) as num_dec label='Sample Size', sum(&pred) as sum_pred
label='Sum Probabilities', sum(&y) as sum_y label='Number of Events'
from ranks
group by phat_grp;
create table ranks3 as select distinct(phat_grp),num_dec,sum_pred,sum_y,
((sum_y-sum_pred)**2/(sum_pred*(1-sum_pred/num_dec))) as chi_part label 'Chi-Square Term'
from ranks2 ;
select sum(chi_part) into :chi_sq
from ranks3;
quit;
data &out;
chi_sq=&chi_sq; label chi_sq='Hosmer Lemeshow Chi Square';
df=&ngro-2; label df ='Degree of Freedom';
p_value=1-cdf('chisquared',chi_sq,df);label p_value= 'P-Value';
format p_value pval.;
run;
%if &print=T %then %do;
title 'Hosmer Lemeshow Details';
proc print data=ranks3 noobs label;run;
Options formdlim='-';
title 'Hosmer Lemeshow Calibration Test';
proc print data=&out noobs label;run;
%end;
Options formdlim='';
%mend;
%macro add_predictive(data=, y=, p_old=, p_new= , nripoints=%str(),hoslemgrp=10) ;
/*---
this macro attempts to quantify the added predictive ability of a covariate(s)
in logistic regression based off of the statistics in: M. J. Pencina ET AL. Statistics
in Medicine (2008 27(2):157-72). Statistics returned with be: C-statistics (for 9.2 users),
IDI (INTEGRATED DISCRIMINATION IMPROVEMENT), NRI (net reclassification index)
for both Category-Free and User defined groups, and Hosmer Lemeshow GOF test
with associated pvalues and z scores.
Parameters (* = required)
-------------------------
data* Specifies the SAS dataset
y* Response variable (Outcome must be 0/1)
p_old* Predicted Probability of an Event using Initial Model
p_new* Predicted Probability of an Event using New Model
nripoints Groups for User defined classification (Optional),
Example 3 groups: (<.06, .06-.2, >.2) then nripoints=.06 .2
hoslemgrp # of groups for the Hosmer Lemeshow test (default 10)
Author: Kevin Kennedy and Michael Pencina
Date: May 26, 2010
---*/
options nonotes nodate nonumber;
ods select none;
%local start end ;
%let start=%sysfunc(datetime());
proc format;
value pval 0-.0001='<.0001';
run;
/******Step 1: C-Statistics******/
/********************************/
%if %sysevalf(&sysver >= 9.2) %then %do;
%put ********Running AUC Analysis************;
proc logistic data=&data descending;
model &y=&p_old &p_new;
roc 'first' &p_old;
roc 'second' &p_new;
roccontrast reference('first')/estimate e;
ods output ROCAssociation=rocass ROCContrastEstimate=rocdiff;
run;
proc sql noprint;
select estimate, StdErr, lowercl, uppercl, (ProbChiSq*100) as pval
into :rocdiff, :rocdiff_stderr, :rocdiff_low, :rocdiff_up, :rocp
from rocdiff
where find(contrast,'second');
quit;
data _null_;
set rocass;
if ROCModel='first' then do;
call symputx('c_old',Area);
end;
if ROCModel='second' then do;
call symputx('c_new',Area);
end;
run;
data cstat;
cstat_old=&c_old; label cstat_old='Model1 AUC';
cstat_new=&c_new; label cstat_new='Model2 AUC';
cstat_diff=&rocdiff; label cstat_diff='Difference in AUC';
cstat_stderr=&rocdiff_stderr; label cstat_stderr='Standard Error of Difference in AUC';
cstat_low=&rocdiff_low; label cstat_low='Difference in AUC Lower 95% CI';
cstat_up=&rocdiff_up; label cstat_up='Difference in ACU Upper 95% CI';
cstat_ci='('!!trim(left(cstat_low))!!','!!trim(left(cstat_up))!!')';
label cstat_ci='95% CI for Difference in AUC';
cstat_pval=&rocp/100; label cstat_pval='P-value for AUC Difference';
format cstat_pval pval.;
run;
%end;
%if %sysevalf(&sysver < 9.2) %then %do;
options notes ;
%put *********************;
%put NOTE: You are running a Pre 9.2 version of SAS;
%put NOTE: Go to SAS website to get example of ROC Macro for AUC Comps;
%put NOTE: http://support.sas.com/kb/25/017.html;
%put *********************;
%put;
options nonotes ;
%end;
/******************************/
/*****End step 1***************/
/******************************/
/******Step 2: IDI***************/
%put ********Running IDI Analysis************;
proc sql noprint;
create table idinri as select &y,&p_old, &p_new, (&p_new-&p_old) as pdiff
from &data
where &p_old^=. and &p_new^=.
order by &y;
quit;
proc sql noprint; /*define mean probabilities for old and new model and event and nonevent*/
select count(*),avg(&p_old), avg(&p_new),stderr(pdiff) into
:num_event, :p_event_old, :p_event_new,:eventstderr
from idinri
where &y=1 ;
select count(*),avg(&p_old), avg(&p_new),stderr(pdiff) into
:num_nonevent, :p_nonevent_old, :p_nonevent_new ,:noneventstderr
from idinri
where &y=0;
quit;
data fin(drop=slope_noadd slope_add);
pen=&p_event_new; label pen='Mean Probability for Events: Model2';
peo=&p_event_old; label peo='Mean Probability for Events: Model1';
pnen=&p_nonevent_new; label pnen='Mean Probability for NonEvents: Model2';
pneo=&p_nonevent_old; label pneo='Mean Probability for NonEvents: Model1';
idi=(&p_event_new-&p_nonevent_new)-(&p_event_old-&p_nonevent_old);
label idi='Integrated Discrimination Improvement';
idi_stderr=sqrt((&eventstderr**2)+(&noneventstderr**2));
label idi_stderr='IDI Standard Error';
idi_lowci=round(idi-1.96*idi_stderr,.0001);
idi_upci=round(idi+1.96*idi_stderr,.0001);
idi_ci='('!!trim(left(idi_lowci))!!','!!trim(left(idi_upci))!!')';
label idi_ci='IDI 95% CI';
z_idi=abs(idi/(sqrt((&eventstderr**2)+(&noneventstderr**2))));
label z_idi='Z-value for IDI';
pvalue_idi=2*(1-PROBNORM(abs(z_idi))); label pvalue_idi='P-value for IDI';
change_event=&p_event_new-&p_event_old;
label change_event='Probability change for Events';
change_nonevent=&p_nonevent_new-&p_nonevent_old;
label change_nonevent='Probability change for Nonevents';
slope_noadd=&p_event_old-&p_nonevent_old;
slope_add=&p_event_new-&p_nonevent_new;
relative_idi=slope_add/slope_noadd-1; label relative_idi='Relative IDI';
format pvalue_idi pval.;
run;
/************step 3 NRI analysis*******/
%put ********Running NRI Analysis************;
data nri_inf;
set idinri;
if &y=1 then do;
down_event=(pdiff<0);up_event=(pdiff>0);down_nonevent=0;up_nonevent=0;
end;
if &y=0 then do;
down_nonevent=(pdiff<0);up_nonevent=(pdiff>0);down_event=0;up_event=0;
end;
run;
proc sql;
select sum(up_nonevent), sum(down_nonevent), sum(up_event),sum(down_event)
into :num_nonevent_up_user, :num_nonevent_down_user, :num_event_up_user, :num_event_down_user
from nri_inf
quit;
/* Category-Free Groups */
data nri1;
group="Category-Free NRI";
p_up_event=&num_event_up_user/&num_event;
p_down_event=&num_event_down_user/&num_event;
p_up_nonevent=&num_nonevent_up_user/&num_nonevent;
p_down_nonevent=&num_nonevent_down_user/&num_nonevent;
nri=(p_up_event-p_down_event)-(p_up_nonevent-p_down_nonevent);
nri_stderr=sqrt(((&num_event_up_user+&num_event_down_user)/&num_event**2-(&num_event_up_user-
&num_event_down_user)**2/&num_event**3)+
((&num_nonevent_down_user+&num_nonevent_up_user)/&num_nonevent**2-
(&num_nonevent_down_user-&num_nonevent_up_user)**2/&num_nonevent**3));
low_nrici=round(nri-1.96*nri_stderr,.0001);
up_nrici=round(nri+1.96*nri_stderr,.0001);
nri_ci='('!!trim(left(low_nrici))!!','!!trim(left(up_nrici))!!')';
z_nri=nri/sqrt(((p_up_event+p_down_event)/&num_event)
+((p_up_nonevent+p_down_nonevent)/&num_nonevent)) ;
pvalue_nri=2*(1-PROBNORM(abs(z_nri)));
event_correct_reclass=p_up_event-p_down_event;
nonevent_correct_reclass=p_down_nonevent-p_up_nonevent;
z_event=event_correct_reclass/sqrt((p_up_event+p_down_event)/&num_event);
pvalue_event=2*(1-probnorm(abs(z_event)));
z_nonevent=nonevent_correct_reclass/sqrt((p_up_nonevent+p_down_nonevent)/&num_nonevent);
pvalue_nonevent=2*(1-probnorm(abs(z_nonevent)));
format pvalue_nri pvalue_event pvalue_nonevent pval. event_correct_reclass
nonevent_correct_reclass percent.;
label nri='Net Reclassification Improvement'
nri_stderr='NRI Standard Error'
low_nrici='NRI lower 95% CI'
up_nrici='NRI upper 95% CI'
nri_ci='NRI 95% CI'
z_nri='Z-Value for NRI'
pvalue_nri='NRI P-Value'
pvalue_event='Event P-Value'
pvalue_nonevent='Non-Event P-Value'
event_correct_reclass='% of Events correctly reclassified'
nonevent_correct_reclass='% of Nonevents correctly reclassified';
run;
/*User Defined NRI*/
%if &nripoints^=%str() %then %do;
/*words macro*/
%macro words(list,delim=%str( ));
%local count;
%let count=0;
%do %while(%qscan(%bquote(&list),&count+1,%str(&delim)) ne %str());
%let count=%eval(&count+1);
%end;
&count
%mend words;
%let numgroups=%eval(%words(&nripoints)+1); /*figure out how many ordinal groups*/
proc format ;
value group
1 = "0 to %scan(&nripoints,1,%str( ))"
%do i=2 %to %eval(&numgroups-1);
%let j=%eval(&i-1);
&i="%scan(&nripoints,&j,%str( )) to %scan(&nripoints,&i,%str( ))"
%end;
%let j=%eval(&numgroups-1);
&numgroups="%scan(&nripoints,&j,%str( )) to 1";
run;
data idinri;
set idinri;
/*define first ordinal group for pre and post*/
if 0<=&p_old<=%scan(&nripoints,1,%str( )) then group_pre=1;
if 0<=&p_new<=%scan(&nripoints,1,%str( )) then group_post=1;
%let i=1;
%do %until(&i>%eval(&numgroups-1));
if %scan(&nripoints,&i,%str( ))<&p_old then do;
group_pre=&i+1;
end;
if %scan(&nripoints,&i,%str( ))<&p_new then do;
group_post=&i+1;
end;
%let i=%eval(&i+1);
%end;
if &y=0 then do;
up_nonevent=(group_post>group_pre);
down_nonevent=(group_post<group_pre);
down_event=0; up_event=0;
end;
if &y=1 then do;
up_event=(group_post>group_pre);
down_event=(group_post<group_pre);
down_nonevent=0; up_nonevent=0;
end;
format group_pre group_post group.;
run;
proc sql;
select sum(up_nonevent), sum(down_nonevent), sum(up_event),sum(down_event),avg(&y)
into :num_nonevent_up_user, :num_nonevent_down_user, :num_event_up_user,
:num_event_down_user, :eventrate
from idinri
quit;
data nri2;
group='User Category NRI';
p_up_event=&num_event_up_user/&num_event;
p_down_event=&num_event_down_user/&num_event;
p_up_nonevent=&num_nonevent_up_user/&num_nonevent;
p_down_nonevent=&num_nonevent_down_user/&num_nonevent;
nri=(p_up_event-p_down_event)-(p_up_nonevent-p_down_nonevent);
nri_stderr=sqrt(((&num_event_up_user+&num_event_down_user)/&num_event**2-
(&num_event_up_user-&num_event_down_user)**2/&num_event**3)+
((&num_nonevent_down_user+&num_nonevent_up_user)/&num_nonevent**2-
(&num_nonevent_down_user-&num_nonevent_up_user)**2/&num_nonevent**3));
low_nrici=round(nri-1.96*nri_stderr,.0001);
up_nrici=round(nri+1.96*nri_stderr,.0001);
nri_ci='('!!trim(left(low_nrici))!!','!!trim(left(up_nrici))!!')';
z_nri=nri/sqrt(((p_up_event+p_down_event)/&num_event)
+((p_up_nonevent+p_down_nonevent)/&num_nonevent)) ;
pvalue_nri=2*(1-PROBNORM(abs(z_nri)));
event_correct_reclass=p_up_event-p_down_event;
nonevent_correct_reclass=p_down_nonevent-p_up_nonevent;
z_event=event_correct_reclass/sqrt((p_up_event+p_down_event)/&num_event);
pvalue_event=2*(1-probnorm(abs(z_event)));
z_nonevent=nonevent_correct_reclass/sqrt((p_up_nonevent+p_down_nonevent)/&num_nonevent);
pvalue_nonevent=2*(1-probnorm(abs(z_nonevent)));
format pvalue_nri pval.;
run;
data nri1;
set nri1 nri2;
run;
%end;
/**************/
/*step 4 gof */
/**************/
%hoslem(data=idinri,pred=&p_old,y=&y,ngro=&hoslemgrp,out=m1,print=F);
%hoslem(data=idinri,pred=&p_new,y=&y,ngro=&hoslemgrp,out=m2,print=F);
data hoslem(drop=cnt);
retain model;
set m1 m2;
cnt+1;
if cnt=1 then model='Model1';
else model='Model2';
run;
ods select all;
/*output for cstat*/
%if %sysevalf(&sysver >= 9.2) %then %do;
proc print data=cstat label noobs;
title1 "Evaluating added predictive ability of model2";
title2 'AUC Analysis';run;
%END;
/*output for IDI*/
proc print data=fin label noobs;
title1 "Evaluating added predictive ability of model2";
title2 'IDI Analysis';
var idi idi_stderr z_idi pvalue_idi idi_ci
pen peo pnen pneo change_event change_nonevent relative_idi;
run;
/*output for NRI*/
proc print data=nri1 label noobs;
title1 "Evaluating added predictive ability of model2";
title2 'NRI Analysis';
var group nri nri_stderr z_nri pvalue_nri nri_ci event_correct_reclass pvalue_event
nonevent_correct_reclass pvalue_nonevent;
run;
%if &nripoints^=%str() %then %do;
proc freq data=idinri;
where &y=0;
title 'NRI Table for Non-Events';
tables group_pre*group_post/nopercent nocol;
run;
proc freq data=idinri;
where &y=1;
title 'NRI Table for Events';
tables group_pre*group_post/nopercent nocol;
run;
%end;
/*print HL gof*/
proc print data=hoslem noobs label;
title "Hosmer Lemeshow Test with %sysevalf(&hoslemgrp-2) df";
run;
proc datasets library=work nolist;
delete fin idinri nri1 nri2 nri_inf stderr;
quit;
options notes;
%put NOTE: Macro %nrstr(%%)add_predictive completed.;
%let end=%sysfunc(datetime());
%let runtime=%sysfunc(round(%sysevalf(&end-&start)));
%put NOTE: Macro Real Run Time=&runtime seconds;
title;
%mend;
/*initial model*/
proc logistic data=DATA;
model value(event='1')=X1-X20;
output out=m1 pred=p1;
run;
/*new model*/
proc logistic data=data;
model value(event='1')=X1-X20;
output out=m2 pred=p2;
run;
proc sql;
create table TABLENAME as select *
from m1 as a left join m2 as b on a.cnt=b.cnt;
quit;
%add_predictive(data=DATA,y=value,p_old=p1,p_new=p2, nripoints=.1 .3);
The error I get after running is:
ERROR: Column cnt could not be found in the table/view identified with the correlation name A.
ERROR: Column cnt could not be found in the table/view identified with the correlation name A.
ERROR: Column cnt could not be found in the table/view identified with the correlation name B.
ERROR: Column cnt could not be found in the table/view identified with the correlation name B.
However when trouble shooting it seems to take me down a never ending path of unrecognized variables. Am I calling the macro incorrectly?
In the following code, how could I keep only the observations superior to the 95th quantile?
data test;
input business_ID $ count;
datalines;
'busi1' 2
'busi1' 10
'busi1' 4
'busi2' 1
'busi3' 2
'busi3' 1
;
run;
proc sort data = test;
by descending count;
run;
I don't know how to cleanly stock the quartile and then re-use it with an if condition.
Thanks
Edit : I can determine the quantile with this code :
proc means data=test noprint;
var count;
output out=quantile P75= / autoname;
run;
But how can I relate to it in the Test dataset so that I can select every observations above that quantile?
You could either read the value of the quantile in a macro variable to use in a subsequent if or where condition:
proc means data=test noprint;
var count;
output out=quantile P75= / autoname;
run;
data _null_;
set quantile;
call symput('quantile',count_p75);
run;
data test;
set test;
where count > &quantile.;
run;
or you could use an SQL subquery
proc means data=test noprint;
var count;
output out=quantile P75= / autoname;
run;
proc sql undo_policy=none;
create table test as
select *
from test
where count > (select count_p75 from quantile)
;
quit;
(Note that your question mentions the 95th quantile whereas your sample code mentions the 75th)
User2877959's solution is solid. Recently I did this with Proc Rank. The solution is a bit 'work around-y', but saves a lot of typing.
proc rank data=Input groups=1000 out=rank_out;
var var_to_rank;
ranks Rank_val;
run;
data seventy_five;
set rank_out;
if rank_val>750;
run;
More on Rank: http://documentation.sas.com/?docsetId=proc&docsetTarget=p0le3p5ngj1zlbn1mh3tistq9t76.htm&docsetVersion=9.4&locale=en
In SAS, you can use PROC PRINT to sum a column and display the sum:
proc print data = dataset.project_out;
sum variable;
run;
How can I get this function to only print the sum line and not the rest of the data?
I don't think you can do it with proc print. The closest you can come is the empty var statement:
proc print data=sashelp.class;
var ;
sum age;
run;
But sum adds the sum variable to the var list.
You can certainly accomplish this a number of other ways.
PROC SQL is the one I'd use:
proc sql;
select sum(Age) from sashelp.class;
quit;
PROC REPORT, often called "pretty PROC PRINT", can do it also:
proc report data=sashelp.class;
columns age;
define age/analysis sum;
run;
PROC TABULATE can do it:
proc tabulate data=sashelp.class;
var age;
tables age*sum;
run;
PROC MEANS:
proc means data=sashelp.class sum;
var age;
run;
Etc., plenty of ways to do the same thing.
I have a table like this:
Lista_ID 1 4 7 10 ...
in total there are 100 numbers.
I want to call each one of these numbers to a macro i created. I was trying to use 'scan' but read that it's just for character variables.
the error when i runned the following code was
there's the code:
proc sql;
select ID INTO: LISTA_ID SEPARATED BY '*' from
WORK.AMOSTRA;
run;
PROC SQL;
SELECT COUNT(*) INTO: NR SEPARATED BY '*' FROM
WORK.AMOSTRA;
RUN;
%MACRO CICLO_teste();
%LET LIM_MSISDN = %EVAL(NR);
%LET I = %EVAL(1);
%DO %WHILE (&I<= &LIM_MSISDN);
%LET REF = %SCAN(LISTA_ID,&I,,'*');
DATA WORK.UP&REF;
SET WORK.BASE&REF;
FORMAT PERC_ACUM 9.3;
IF FIRST.ID_CLIENTE THEN PERC_ACUM=0;
PERC_ACUM+PERC;
RUN;
%LET I = %EVAL(&I+1);
%END;
%MEND;
%CICLO_TESTE;
the error was that:
VARIABLE PERC IS UNITIALIZED and
VARIABLE FIRST.ID_CLIENTE IS UNITIALIZED.
What I want is to run this macro for each one of the Id's in the List I showed before, and that are referenced in work.base&ref and work.up&ref.
How can I do it? What I'm doing wrong?
thanks!
Here's the CALL EXECUTE version.
%MACRO CICLO_teste(REF);
DATA WORK.UP&REF;
SET WORK.BASE&REF;
BY ID_CLIENTE;
FORMAT PERC_ACUM 9.3;
IF FIRST.ID_CLIENTE THEN PERC_ACUM=0;
PERC_ACUM+PERC;
RUN;
%CICLO_TESTE;
DATA _NULL_;
SET amostra;
*CREATE YOUR MACRO CALL;
STR = CATT('%CLIO_TESTE(', ID, ')');
CALL EXECUTE(STR);
RUN;
First you should note that SAS macro variable resolve is intrinsically a "text-based" copy-paste action. That is, all the user-defined macro variables are texts. Therefore, %eval is unnecessary in this case.
Other miscellaneous corrections include:
Check the %scan() function for correct usage. The first argument should be a text string WITHOUT QUOTES.
run is redundant in proc sql since each sql statement is run as soon as they are sent. Use quit; to exit proc sql.
A semicolon is not required for macro call (causes unexpected problems sometimes).
use %do %to for loops
The code below should work.
data work.amostra;
input id;
cards;
1
4
7
10
;
run;
proc sql noprint;
select id into :lista_id separated by ' ' from work.amostra;
select count(*) into :nr separated by ' ' from work.amostra;
quit;
* check;
%put lista_id=&lista_id nr=&nr;
%macro ciclo_teste();
%local ref;
%do i = 1 %to &nr;
%let ref = %scan(&lista_id, &i);
%*check;
%put ref = &ref;
/* your task below */
/* data work.up&ref;*/
/* set work.base&ref;*/
/* format perc_acum 9.3;*/
/* if first.id_cliente then perc_acum=0;*/
/* perc_acum + perc;*/
/* run; */
%end;
%mend;
%ciclo_teste()
tested on SAS 9.4 win7 x64
Edited:
In fact I would recommend doing this to avoid scanning a long string which is inefficient.
%macro tester();
/* get the number of obs (a more efficient way) */
%local NN;
proc sql noprint;
select nobs into :NN
from dictionary.tables
where upcase(libname) = 'WORK'
and upcase(memname) = 'AMOSTRA';
quit;
/* assign &ref by random access */
%do i = 1 %to &NN;
data _null_;
a = &i;
set work.amostra point=a;
call symputx('ref',id,'L');
stop;
run;
%*check;
%put ref = &ref;
/* your task below */
%end;
%mend;
%tester()
Please let me know if you have further questions.
Wow that seems like a lot of work. Why not just do the following:
data work.amostra;
input id;
cards;
1
4
7
10
;
run;
%macro test001;
proc sql noprint;
select count(*) into: cnt
from amostra;
quit;
%let cnt = &cnt;
proc sql noprint;
select id into: x1 - :x&cnt
from amostra;
quit;
%do i = 1 %to &cnt;
%let x&i = &&x&i;
%put &&x&i;
%end;
%mend test001;
%test001;
now in variables &x1 - &&x&cnt you have your values and you can process them however you like.
In general if your list is small enough (macro variables are limited to 64K characters) then you are better off passing the list in a single delimited macro variable instead of multiple macro variables.Remember that PROC SQL will automatically set the count into the macro variable SQLOBS so there is no need to run the query twice. Or you can use %sysfunc(countw()) to count the number of entries in your delimited list.
proc sql noprint ;
select id into :idlist separated by '|' from .... ;
%let nr=&sqlobs;
quit;
...
%do i=1 %to &nr ;
%let id=%scan(&idlist,&i,|);
data up&id ;
...
%end;
If you do generate multiple macro variables there is no need to set the upper bound in advance as SAS will only create the number of macro variables it needs based on the number of observations returned by the query.
select id into :idval1 - from ... ;
%let nr=&sqlobs;
If you are using an older version of SAS the you need set an upper bound on the macro variable range.
select id into :idval1 - :idval99999 from ... ;
I'm a beginner in SAS and I have the following problem.
I need to calculate counts and percents of several variables (A B C) from one dataset and save the results to another dataset.
my code is:
proc freq data=mydata;
tables A B C / out=data_out ; run;
the result of the procedure for each variable appears in the SAS output window, but data_out contains the results only for the last variable. How to save them all in data_out?
Any help is appreciated.
ODS OUTPUT is your answer. You can't output directly using the OUT=, but you can output them like so:
ods output OneWayFreqs=freqs;
proc freq data=sashelp.class;
tables age height weight;
run;
ods output close;
OneWayFreqs is the one-way tables, (n>1)-way tables are CrossTabFreqs:
ods output CrossTabFreqs=freqs;
ods trace on;
proc freq data=sashelp.class;
tables age*height*weight;
run;
ods output close;
You can find out the correct name by running ods trace on; and then running your initial proc whatever (to the screen); it will tell you the names of the output in the log. (ods trace off; when you get tired of seeing it.)
Lots of good basic sas stuff to learn here
1) Run three proc freq statements (one for each variable a b c) with a different output dataset name so the datasets are not over written.
2) use a rename option on the out = statement to change the count and percent variables for when you combine the datasets
3) sort by category and merge all datasets together
(I'm assuming there are values that appear in in multiple variables, if not you could just stack the data sets)
data mydata;
input a $ b $ c$;
datalines;
r r g
g r b
b b r
r r r
g g b
b r r
;
run;
proc freq noprint data = mydata;
tables a / out = data_a
(rename = (a = category count = count_a percent = percent_a));
run;
proc freq noprint data = mydata;
tables b / out = data_b
(rename = (b = category count = count_b percent = percent_b));
run;
proc freq noprint data = mydata;
tables c / out = data_c
(rename = (c = category count = count_c percent = percent_c));
run;
proc sort data = data_a; by category; run;
proc sort data = data_b; by category; run;
proc sort data = data_c; by category; run;
data data_out;
merge data_a data_b data_c;
by category;
run;
As ever, there are lots of different ways of doing this sort of thing in SAS. Here are a couple of other options:
1. Use proc summary rather than proc freq:
proc summary data = sashelp.class;
class age height weight;
ways 1;
output out = freqs;
run;
2. Use multiple table statements in a single proc freq
This is more efficient than running 3 separate proc freq statements, as SAS only has to read the input dataset once rather than 3 times:
proc freq data = sashelp.class noprint;
table age /out = freq_age;
table height /out = freq_height;
table weight /out = freq_weight;
run;
data freqs;
informat age height weight count percent;
set freq_age freq_height freq_weight;
run;
This is a question I've dealt with many times and I WISH SAS had a better way of doing this.
My solution has been a macro that is generalized, provide your input data, your list of variables and the name of your output dataset. I take into consideration the format/type/label of the variable which you would have to do
Hope it helps:
https://gist.github.com/statgeek/c099e294e2a8c8b5580a
/*
Description: Creates a One-Way Freq table of variables including percent/count
Parameters:
dsetin - inputdataset
varlist - list of variables to be analyzed separated by spaces
dsetout - name of dataset to be created
Author: F.Khurshed
Date: November 2011
*/
%macro one_way_summary(dsetin, varlist, dsetout);
proc datasets nodetails nolist;
delete &dsetout;
quit;
*loop through variable list;
%let i=1;
%do %while (%scan(&varlist, &i, " ") ^=%str());
%let var=%scan(&varlist, &i, " ");
%put &i &var;
*Cross tab;
proc freq data=&dsetin noprint;
table &var/ out=temp1;
run;
*Get variable label as name;
data _null_;
set &dsetin (obs=1);
call symput('var_name', vlabel(&var.));
run;
%put &var_name;
*Add in Variable name and store the levels as a text field;
data temp2;
keep variable value count percent;
Variable = "&var_name";
set temp1;
value=input(&var, $50.);
percent=percent/100; * I like to store these as decimals instead of numbers;
format percent percent8.1;
drop &var.;
run;
%put &var_name;
*Append datasets;
proc append data=temp2 base=&dsetout force;
run;
/*drop temp tables so theres no accidents*/
proc datasets nodetails nolist;
delete temp1 temp2;
quit;
*Increment counter;
%let i=%eval(&i+1);
%end;
%mend;
%one_way_summary(sashelp.class, sex age, summary1);
proc report data=summary1 nowd;
column variable value count percent;
define variable/ order 'Variable';
define value / format=$8. 'Value';
define count/'N';
define percent/'Percentage %';
run;
EDIT (2022):
Better way of doing this is to use the ODS Tables:
/*This code is an example of how to generate a table with
Variable Name, Variable Value, Frequency, Percent, Cumulative Freq and Cum Pct
No macro's are required
Use Proc Freq to generate the list, list variables in a table statement if only specific variables are desired
Use ODS Table to capture the output and then format the output into a printable table.
*/
*Run frequency for tables;
ods table onewayfreqs=temp;
proc freq data=sashelp.class;
table sex age;
run;
*Format output;
data want;
length variable $32. variable_value $50.;
set temp;
Variable=scan(table, 2);
Variable_Value=strip(trim(vvaluex(variable)));
keep variable variable_value frequency percent cum:;
label variable='Variable'
variable_value='Variable Value';
run;
*Display;
proc print data=want(obs=20) label;
run;
The option STACKODS(OUTPUT) added to PROC MEANS in 9.3 makes this a much simpler task.
proc means data=have n nmiss stackods;
ods output summary=want;
run;
| Variable | N | NMiss |
| ------ | ----- | ----- |
| a | 4 | 3 |
| b | 7 | 0 |
| c | 6 | 1 |