Ordinal Variables in EM, please urgent help! Thank You

[Kanyange]

Hi All,

A quick question on ordinal variables..I have entered some ordinal variables and use them in Logistic Regression...but EM treats them as a categorical variables.

The variables I entered were continuous originally and I have binned them (using the median) so they can have a normal distribution shape! So when I enter them in EM as ordinal, the outpout of the Logistic Regression shows them as categorical variables!

Could you please let me know your suggestions?

Many Thanks

[Reeza]

From a 'mechanical' point of view ordinal and categorical are treated the same, the interpretation is different.

BInning to to obtain normal distribution isn't something I'be heard of before, do you have a reference for that?

[Kanyange]

H Reeza,

The binning is a bit manual and is based on median , something like this below...

So the final model looks like the file attached..Obviously something is wrong with the model..What do you think is the problem looking at the lift..?

And the final equation looks like that...the variables have been regrouped by Variable Selection Node, that's why they have a G Prefixe.

Your help would be much appreciated..

Thank you

Parameter		DF	Estimate	Standard Error	aldChi-Square	Pr > ChiSq	Exp(Est)
Intercept		1	-7.2641	0.788	84.97	<.0001	0.001
G_bin_aip_mth_03	0	1	0.2125	0.0048	1963.51	<.0001	1.237
G_bin_aip_mth_03	1	1	-0.3749	0.00515	5297.46	<.0001	0.687
G_bin_aip_mth_03	2	1	-0.0866	0.00307	795.05	<.0001	0.917
G_bin_aip_mth_03	3	1	0.0814	0.00281	841.56	<.0001	1.085
G_bin_prodosales_pfe_543	0	1	-0.2261	0.00367	3796.88	<.0001	0.798
G_bin_prodosales_pfe_543	1	1	-0.0544	0.00454	143.35	<.0001	0.947
G_bin_prodosales_pfe_543	2	1	0.0712	0.00357	397.86	<.0001	1.074
G_bin_prodosales_pfe_543	3	1	0.1613	0.00356	2052.07	<.0001	1.175
G_bin_prodosales_pfe_543	4	1	0.1879	0.00328	3272.51	<.0001	1.207
G_bin_prodosales_frt_43	0	1	0.1862	0.00407	2088.31	<.0001	1.205
G_bin_prodosales_frt_43	1	1	0.2896	0.00391	5484.97	<.0001	1.336
G_bin_prodosales_frt_43	2	1	0.2691	0.00415	4201.51	<.0001	1.309
G_bin_prodosales_frt_43	3	1	0.1832	0.00339	2928.51	<.0001	1.201
G_bin_prodosales_frt_43	4	1	-0.0497	0.00429	134.47	<.0001	0.951
G_bin_prodosales_frt_43	5	1	-0.3143	0.00507	3839.66	<.0001	0.73
G_bin_trans_34_pen	0	1	-0.6339	0.00267	56480.32	<.0001	0.531
G_bin_trans_34_pen	1	1	0.1799	0.00309	3382.14	<.0001	1.197
G_bin_wtr_trans_54_3	0	1	-0.2524	0.0037	4643.1	<.0001	0.777
G_bin_wtr_trans_54_3	1	1	0.2173	0.00492	1948.88	<.0001	1.243
G_bin_units_bst_543	0	1	-0.2345	0.00215	11879.58	<.0001	0.791
G_bin_units_bst_543	1	1	0.1112	0.00243	2095.4	<.0001	1.118
G_bin_sales_change	0	1	-7.9832	0.788	102.63	<.0001	0

/*Binning to normalize the variable*/

data &init._base_&model._&vers.;

   set &init._base_&model._&vers.;

   if &in_name. = 0 or missing(&in_name.)= 1 then &out_name. = 0;

   else if &in_name. < 0 then &out_name. = -1;

   else if &in_name. < 0.2 * &med. then &out_name. = 1;

   else if &in_name. < 0.6 * &med. then &out_name. = 2;

   else if &in_name. < 0.9 * &med. then &out_name. = 3;

   else if &in_name. < 1.3 * &med. then &out_name. = 4;

   else if &in_name. < 2.0 * &med. then &out_name. = 5;

   else if &in_name. < 2.9 * &med. then &out_name. = 6;

   else if &in_name. < 3.9 * &med. then &out_name. = 7;

   else if &in_name. < 5.0 * &med. then &out_name. = 8;

   else if &in_name. < 6.2 * &med. then &out_name. = 9;

   else &out_name. = 10;

  run;