Hello all,
I am trying to apply the code from @Rick_SAS:"Detecting outliers in SAS: Part 3: Multivariate location and scatter".
article,
My data has 7 numerical independent variables. For mydata, The output result from " print outIdx; " is a table with1 row and 21 columns in which each value is the observation's number that is outlier ( as I understood, please guide me if I am wrong!).
I do not understand the number 3 inside bracket at this part of the code :
outIdx = loc(dist[3,]=0); /* RD > cutoff */
print outIdx;
and I do not understand the number 8 inside "optn = j(8,1,.); /* default options for MCD */".
Appreciate you all to help me understand these concepts.
proc iml;
use mydata;
read all var{ T1 T2 T3 T4 T5 T6 T7 } into x ;
/* classical estimates */
labl = {"T1" "T2" "T3" "T4" "T5" "T6" "T7" };
mean = mean(x);
cov = cov(x);
print mean[c=labl format=5.2], cov[r=labl c=labl format=5.2];
N = nrow(x); /* 60 observations */
p = ncol(x); /* 7 variables */
optn = j(8,1,.); /* default options for MCD */
optn[1] = 0; /* =1 if you want printed output */
optn[4]= floor(0.75*N); /* h = 75% of obs */
call MCD(sc, est, dist, optn, x);
RobustLoc = est[1, ]; /* robust location */
RobustCov = est[3:2+p, ]; /* robust scatter matrix */
print RobustLoc[c=labl format=5.2], RobustCov[r=labl c=labl format=5.2];
outIdx = loc(dist[3,]=0); /* RD > cutoff */
print outIdx;
You might find it easier to use PROC ROBUSTREG as suggested in the Outliers item in the list of Frequently Asked-for Statistics (FASTats) in the Important Links section of the Statistical Procedures Community page. Just add a random response variable. For example:
data mydata;
set mydata;
y=ranuni(3);
run;
proc robustreg data=a method=lts;
model y = t1-t7 / diagnostics leverage;
run;
You might find it easier to use PROC ROBUSTREG as suggested in the Outliers item in the list of Frequently Asked-for Statistics (FASTats) in the Important Links section of the Statistical Procedures Community page. Just add a random response variable. For example:
data mydata;
set mydata;
y=ranuni(3);
run;
proc robustreg data=a method=lts;
model y = t1-t7 / diagnostics leverage;
run;
Registration is open! SAS is returning to Vegas for an AI and analytics experience like no other! Whether you're an executive, manager, end user or SAS partner, SAS Innovate is designed for everyone on your team. Register for just $495 by 12/31/2023.
If you are interested in speaking, there is still time to submit a session idea. More details are posted on the website.
Learn how to run multiple linear regression models with and without interactions, presented by SAS user Alex Chaplin.
Find more tutorials on the SAS Users YouTube channel.