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Normalize by ID

Hi all,

I would like to perform a Normalization into an example set.
My example set is the folowing:
ID att weight
1 att1 12
1 att2 9
1 att3 10
2 att4 5
2 att5 9
2 att6 2

Which means that, for each ID I would like to perfom a Normalization to the weight column.
I've tried to use the "loop attributes" and selecting the single ID attribute operator with a normalize operator in its subprocess. My idea is to create a for cylce using the ID as the iterator.

Any hints on how to achieve this?
Any help is more than welcome.

Thanks a lot,

Ruca

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MariusHelf

Hi Ruca,

to iterate nominal values you can use the Loop Values operator. Inside that you have to use Filter Examples with the attribute_value_condition to filter on the current id.

If your Id is not nominal, you can apply Numerical to Polynominal on it.

Happy Looping!
~Marius

bert0077

I am looking to do a normalization also within an attribute and wonder if I need the same operators as Ruca.

I have a list of product groups and prices. Since prices can vary between a few $/item and up to $1M/item, I need to normalize. Rather than normalizing by ID, I would normalize by product group such that all products of group 1 are normalized to each other, group 2 to each other, etc. so I can make meaningful comparisons of product groups with price. My data are like:

ID group price
1 group1 1
2 group2 10
3 group3 1000
4 group1 2
5 group2 20
6 group3 2000

Group is a polynominal attribute; price is numerical. Of course I don't want to normalize all prices against each other so if I can use the looping operator to normalize each group at a time that would be awesome.

Thanks!

Scott

Roger DeAngelis_21630

I'm not sure if I understand what you want the output to look like, but here is an example of statistical standardization using the Altair Personal SLC.

/* Step 1: Create example dataset */
data products;
input ID group $ price;
datalines;
1 group1 1
2 group2 10
3 group3 1000
4 group1 2
5 group2 20
6 group3 2000
;
run;

/* Step 2: Sort data by group for BY processing */
proc sort data=products;
by group;
run;

/* Step 3: Use PROC STDIZE to normalize prices by group */
proc stdize data=products out=products_norm method=std oprefix=orig_ sprefix=std_;;
by group;
var price;
run;

/* Step 4: View normalized prices */
proc print data=products_norm noobs;
run;

ORIG_ID ORIG_GROUP ORIG_PRICE STD_PRICE

1 group1 1 -0.707106781
4 group1 2 0.7071067812
2 group2 10 -0.707106781
5 group2 20 0.7071067812
3 group3 1000 -0.707106781
6 group3 2000 0.7071067812

LOG

6733
6734 /* Step 1: Create example dataset */
6735 data products;
6736 input ID group $ price;
6737 datalines;

NOTE: Data set "WORK.products" has 6 observation(s) and 3 variable(s)
NOTE: The data step took :
real time : 0.005
cpu time : 0.015

6738 1 group1 1
6739 2 group2 10
6740 3 group3 1000
6741 4 group1 2
6742 5 group2 20
6743 6 group3 2000
6744 ;
6745 run;
6746
6747 /* Step 2: Sort data by group for BY processing */
6748 proc sort data=products;
6749 by group;
6750 run;
NOTE: Automatically set SORTSIZE to 10240MiB
NOTE: 6 observations were read from "WORK.products"
NOTE: Data set "WORK.products" has 6 observation(s) and 3 variable(s)
NOTE: Procedure sort step took :
real time : 0.025
cpu time : 0.000

6751
6752 /* Step 3: Use PROC STDIZE to normalize prices by group */
6753 proc stdize data=products out=products_norm method=std oprefix=orig_ sprefix=std_;;
6754 by group;
6755 var price;
6756 run;
NOTE: Data set "WORK.products_norm" has 6 observation(s) and 4 variable(s)
NOTE: Procedure stdize step took :
real time : 0.012
cpu time : 0.000

6757
6758 /* Step 4: View normalized prices */
6759 proc print data=products_norm noobs;
6760 run;
NOTE: 6 observations were read from "WORK.products_norm"
NOTE: Procedure print step took :
real time : 0.020
cpu time : 0.015