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Posted 09-23-2009 12:27 PM
(3677 views)
Hi all,
I want to create a normal probability plot or qq plot using SG procedures.
I know I can do it using proc univariate, but is it possible using SG procedures.
Thank for all your help.
Regards,
Amit
I want to create a normal probability plot or qq plot using SG procedures.
I know I can do it using proc univariate, but is it possible using SG procedures.
Thank for all your help.
Regards,
Amit
5 REPLIES 5
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Hey Amit,
ODS GRAPHICS is also supported in PROC UNIVARIATE. Is there a particular reason that you need to use the SGPLOT?
Thanks!
ODS GRAPHICS is also supported in PROC UNIVARIATE. Is there a particular reason that you need to use the SGPLOT?
Thanks!
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Hi Dan,
I am trying to develop a report for presentation. The Histogram in SGplot looks more clean than in proc univariate.
Thats the reason I wanted to see if I replecate my q-q plot using proc sgplot.
I am trying to develop a report for presentation. The Histogram in SGplot looks more clean than in proc univariate.
Thats the reason I wanted to see if I replecate my q-q plot using proc sgplot.
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Right now, we do not have a qqplot statement in SGPLOT. However, if you turn on ODS GRAPHICS for UNIVARIATE, you should get a comparable histogram to the one from SGPLOT. Give it a try and see what you think.
Thanks!
Dan
ods graphics on;
proc univariate data=sashelp.class;
histogram weight;
run;
ods graphics off;
proc sgplot data=sashelp.class;
histogram weight;
run;
Thanks!
Dan
ods graphics on;
proc univariate data=sashelp.class;
histogram weight;
run;
ods graphics off;
proc sgplot data=sashelp.class;
histogram weight;
run;
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I just ran the two histograms. The ODS graphics statement look a little better (better shading), but they were very similar.
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Yes, you can do it. You need to compute the quantiles first. If you want a normal Q-Q plot, use PROC RANK:
/* get sample mean and stddev */
proc means data=sashelp.class mean std;
var weight;
run;
proc rank data=sashelp.class normal=blom out=QQ;
var weight;
ranks Quantile;
run;
proc sgplot data=QQ noautolegend;
scatter x=Quantile y=weight;
lineparm x=0 y=100 slope=22.77; /* y-int=mean slope=stddev */
xaxis label="Normal Quantiles";
run;
For more discussion, see
http://blogs.sas.com/content/iml/2016/11/23/sampling-variation-small-samples.html
and for the general case of Q-Q plots for arbitrary distributions, see
http://blogs.sas.com/content/iml/2011/10/28/modeling-the-distribution-of-data-create-a-qq-plot.html