Test a=g

[pmorel3]

Hello,

I would like to test Ho: a = g between 2 models with proc model.

I have no error messages but my OLS results do not show me anything from a to f. Did I make misspecification ? If yes, where ?

Can someone help me please ?

2559  options notes;
2560  options mprint;
2561  /*options nonotes nomprint;*/
2562  data work.model;
2563     set test;
2564     array f f1-f5;
2565     do i=1 to dim(f);
2566        label=vname(f[i]);
2567        value=f[i];
2568        output;
2569     end;
2570     keep rm zdy ztbl ztms zdfy rmrsq rm_zdy rm_ztbl rm_ztms rm_zdfy pred_mean rm_pred_mean label value;
2571  run;

NOTE: There were 360 observations read from the data set WORK.TEST.
NOTE: The data set WORK.MODEL has 1800 observations and 14 variables.
NOTE: DATA statement a utilisé (Durée totale du traitement) :
      real time           0.05 seconds
      cpu time            0.01 seconds


2572
2573  proc model data=model outparms=dada;
2574   parm a b c d e f g h i;
2575   test a=g;
2576
2577  value= a + b*rm + c*rm_zdy + d*rm_ztbl + e*rm_ztms + f*rm_zdfy;
2578  value=g + h*rm + i*rm_pred_mean;
2579

2580  fit value / data=model;

NOTE: A l'itération OLS 1 CONVERGE=0.001 Criteria Met.
2581  solve value / data=model;
2582  run;


2582!     quit;

NOTE: The data set WORK.DADA has 1 observations and 9 variables.
NOTE: PROCEDURE MODEL a utilisé (Durée totale du traitement) :
      real time           1.48 seconds
      cpu time            0.67 seconds

Valeurs estimées OLS Paramètre non linéaires
Paramètre	Estimation	Err type approx.	Valeur du test t	Approx Pr > |t|
a	0.0001	0	.	.
b	0.0001	0	.	.
c	0.0001	0	.	.
d	0.0001	0	.	.
e	0.0001	0	.	.
f	0.0001	0	.	.
g	0.501927	0.00703	71.45	<.0001
h	-0.32195	0.7548	-0.43	0.6698
i	55.09007	154.5	0.36	0.7214

Résultats du test
Test	Type	Statistique	Pr > khi-2	Libellé
Test0	Wald	5102.9	<.0001	a=g

[PaigeMiller]

Since we don't have your data, we can't really say what is happening.

Can you please provide (a portion of) your data by following these instructions: How to create a data step version of your data AKA generate sample data for forums

[pmorel3]

Here you are !

I hope it will be helpful !

2378  *data set you want to create demo data for;
2379  %let dataSetName = model;
2380  *number of observations you want to keep;
2381  %let obsKeep = 360;
2382
2383
2384  ******************************************************
2385  DO NOT CHANGE ANYTHING BELOW THIS LINE
2386  ******************************************************;
2387
2388  %let source_path =
2388! https://gist.githubusercontent.com/statgeek/bcc55940dd825a13b9c8ca40a904cba9/raw/865d2cf18f5150b8e887218dde0fc3951d0ff1
2388! 5b/data2datastep.sas;
2389
2390  filename reprex url "&source_path";
2391  %include reprex;
2552    filename reprex;
NOTE: Fileref REPREX has been deassigned.
2553
2554    option linesize=max;
2555    %data2datastep(dsn=&dataSetName, obs=&obsKeep);
MPRINT(DATA2DATASTEP):   option ls=max;
MPRINT(DATA2DATASTEP):   proc sql noprint;
MPRINT(DATA2DATASTEP):   select Name into :varlist separated by ' ' from dictionary.columns where libname="WORK" and memname="MODEL" ;
MPRINT(DATA2DATASTEP):   select case type when 'num' then case when missing(format) then cats(Name,':32.') else cats(Name,':',format) end else cats(Name,':$',length,'.') end into :inputlist separated by ' ' from dictionary.columns where libname="WORK" and
memname="MODEL" ;
MPRINT(DATA2DATASTEP):   select strip(catx('=',Name,put(label,$quote.))) into : lbllist separated by ' ' from dictionary.columns where libname="WORK" and memname="MODEL" and label is not null ;
NOTE: No rows were selected.
MPRINT(DATA2DATASTEP):   select memlabel into :memlabel trimmed from dictionary.tables where libname="WORK" and memname="MODEL" ;
MPRINT(DATA2DATASTEP):   select strip(catx(' ',Name,format)) into :fmtlist separated by ' ' from dictionary.columns where libname="WORK" and memname="MODEL" and format is not null and format not like '$%' ;
NOTE: No rows were selected.
MPRINT(DATA2DATASTEP):   quit;
NOTE: PROCEDURE SQL a utilisé (Durée totale du traitement) :
      real time           0.05 seconds
      cpu time            0.00 seconds


DATA2DATASTEP DSN MODEL
DATA2DATASTEP FILE create_WORK_MODEL_data.sas
DATA2DATASTEP FMT YES
DATA2DATASTEP FMTLIST
DATA2DATASTEP INPUTLIST zdy:32. ztbl:32. ztms:32. zdfy:32. pred_mean:32. rm:32. rmrsq:32. rm_zdy:32. rm_ztbl:32. rm_ztms:32. rm_zdfy:32. rm_pred_mean:32. label:$200. value:32.
DATA2DATASTEP LBL YES
DATA2DATASTEP LIB WORK
DATA2DATASTEP LS 256
DATA2DATASTEP MEMLABEL
DATA2DATASTEP MSGTYPE NOTE
DATA2DATASTEP OBS 360
DATA2DATASTEP OUTLIB WORK
DATA2DATASTEP VARLIST zdy ztbl ztms zdfy pred_mean rm rmrsq rm_zdy rm_ztbl rm_ztms rm_zdfy rm_pred_mean label value
MPRINT(DATA2DATASTEP):   data _null_;
MPRINT(DATA2DATASTEP):   *file "&file" dsd;
MPRINT(DATA2DATASTEP):   if _n_ =1 then do;
MPRINT(DATA2DATASTEP):   put "data WORK.MODEL;";
MPRINT(DATA2DATASTEP):   put @3 "infile datalines dsd truncover;";
MPRINT(DATA2DATASTEP):   put @3 "input zdy:32. ztbl:32. ztms:32. zdfy:32. pred_mean:32. rm:32. rmrsq:32. rm_zdy:32. rm_ztbl:32. rm_ztms:32. rm_zdfy:32. rm_pred_mean:32. label:$200. value:32.;";
WARNING: Apparent symbolic reference LBLLIST not resolved.
MPRINT(DATA2DATASTEP):   put "datalines;";
MPRINT(DATA2DATASTEP):   end;
MPRINT(DATA2DATASTEP):   set WORK.MODEL(obs=360) end=last;
MPRINT(DATA2DATASTEP):   put zdy ztbl ztms zdfy pred_mean rm rmrsq rm_zdy rm_ztbl rm_ztms rm_zdfy rm_pred_mean label value @;
MPRINT(DATA2DATASTEP):   if last then do;
MPRINT(DATA2DATASTEP):   put;
MPRINT(DATA2DATASTEP):   put ';;;;';
MPRINT(DATA2DATASTEP):   end;
MPRINT(DATA2DATASTEP):   else put;
MPRINT(DATA2DATASTEP):   run;

data WORK.MODEL;
  infile datalines dsd truncover;
  input zdy:32. ztbl:32. ztms:32. zdfy:32. pred_mean:32. rm:32. rmrsq:32. rm_zdy:32. rm_ztbl:32. rm_ztms:32. rm_zdfy:32. rm_pred_mean:32. label:$200. value:32.;
datalines;
-1.478553 0.0553 0.0236 0.0148 0.0056389376 0.130658 0.017072 -0.193184778 0.0072253874 0.0030835288 0.0019337384 0.0007367723 f1 0.4514198352
-1.478553 0.0553 0.0236 0.0148 0.0056389376 0.130658 0.017072 -0.193184778 0.0072253874 0.0030835288 0.0019337384 0.0007367723 f2 0.6499107157
-1.478553 0.0553 0.0236 0.0148 0.0056389376 0.130658 0.017072 -0.193184778 0.0072253874 0.0030835288 0.0019337384 0.0007367723 f3 0.0149831814
-1.478553 0.0553 0.0236 0.0148 0.0056389376 0.130658 0.017072 -0.193184778 0.0072253874 0.0030835288 0.0019337384 0.0007367723 f4 0.9864671244
-1.478553 0.0553 0.0236 0.0148 0.0056389376 0.130658 0.017072 -0.193184778 0.0072253874 0.0030835288 0.0019337384 0.0007367723 f5 0.4169442842
-1.465042 0.0543 0.0235 0.0136 0.0054392209 0.03712 0.001378 -0.054382359 0.002015616 0.00087232 0.000504832 0.0002019039 f1 0.6486608352
-1.465042 0.0543 0.0235 0.0136 0.0054392209 0.03712 0.001378 -0.054382359 0.002015616 0.00087232 0.000504832 0.0002019039 f2 0.3512488917
-1.465042 0.0543 0.0235 0.0136 0.0054392209 0.03712 0.001378 -0.054382359 0.002015616 0.00087232 0.000504832 0.0002019039 f3 0.804965401
-1.465042 0.0543 0.0235 0.0136 0.0054392209 0.03712 0.001378 -0.054382359 0.002015616 0.00087232 0.000504832 0.0002019039 f4 0.7966184741
-1.465042 0.0543 0.0235 0.0136 0.0054392209 0.03712 0.001378 -0.054382359 0.002015616 0.00087232 0.000504832 0.0002019039 f5 0.2634030447
-1.517754 0.0559 0.0204 0.0127 0.0052195006 0.021678 0.00047 -0.032901871 0.0012118002 0.0004422312 0.0002753106 0.0001131483 f1 0.7152042658
-1.517754 0.0559 0.0204 0.0127 0.0052195006 0.021678 0.00047 -0.032901871 0.0012118002 0.0004422312 0.0002753106 0.0001131483 f2 0.4090506567
-1.517754 0.0559 0.0204 0.0127 0.0052195006 0.021678 0.00047 -0.032901871 0.0012118002 0.0004422312 0.0002753106 0.0001131483 f3 0.4839264133
-1.517754 0.0559 0.0204 0.0127 0.0052195006 0.021678 0.00047 -0.032901871 0.0012118002 0.0004422312 0.0002753106 0.0001131483 f4 0.5735668366
-1.517754 0.0559 0.0204 0.0127 0.0052195006 0.021678 0.00047 -0.032901871 0.0012118002 0.0004422312 0.0002753106 0.0001131483 f5 0.6690473872
-1.532458 0.0559 0.0236 0.0125 0.005002563 -0.013745 0.000189 0.0210636352 -0.000768346 -0.000324382 -0.000171813 -0.00006876 f1 0.2610319272
-1.532458 0.0559 0.0236 0.0125 0.005002563 -0.013745 0.000189 0.0210636352 -0.000768346 -0.000324382 -0.000171813 -0.00006876 f2 0.1472747327
-1.532458 0.0559 0.0236 0.0125 0.005002563 -0.013745 0.000189 0.0210636352 -0.000768346 -0.000324382 -0.000171813 -0.00006876 f3 0.7546329865
-1.532458 0.0559 0.0236 0.0125 0.005002563 -0.013745 0.000189 0.0210636352 -0.000768346 -0.000324382 -0.000171813 -0.00006876 f4 0.7060750368
-1.532458 0.0559 0.0236 0.0125 0.005002563 -0.013745 0.000189 0.0210636352 -0.000768346 -0.000324382 -0.000171813 -0.00006876 f5 0.2728229693
-1.540657 0.0564 0.0295 0.0119 0.0044233349 0.004411 0.000019 -0.006795838 0.0002487804 0.0001301245 0.0000524909 0.0000195113 f1 0.3251193605
-1.540657 0.0564 0.0295 0.0119 0.0044233349 0.004411 0.000019 -0.006795838 0.0002487804 0.0001301245 0.0000524909 0.0000195113 f2 0.0298449365
-1.540657 0.0564 0.0295 0.0119 0.0044233349 0.004411 0.000019 -0.006795838 0.0002487804 0.0001301245 0.0000524909 0.0000195113 f3 0.948264875
-1.540657 0.0564 0.0295 0.0119 0.0044233349 0.004411 0.000019 -0.006795838 0.0002487804 0.0001301245 0.0000524909 0.0000195113 f4 0.5303066683
-1.540657 0.0564 0.0295 0.0119 0.0044233349 0.004411 0.000019 -0.006795838 0.0002487804 0.0001301245 0.0000524909 0.0000195113 f5 0.7114415056
-1.532565 0.0566 0.0314 0.0118 0.0051189713 0.045415 0.002063 -0.069601439 0.002570489 0.001426031 0.000535897 0.0002324781 f1 0.6523307602
-1.532565 0.0566 0.0314 0.0118 0.0051189713 0.045415 0.002063 -0.069601439 0.002570489 0.001426031 0.000535897 0.0002324781 f2 0.6001239198
-1.532565 0.0566 0.0314 0.0118 0.0051189713 0.045415 0.002063 -0.069601439 0.002570489 0.001426031 0.000535897 0.0002324781 f3 0.8622382571
-1.532565 0.0566 0.0314 0.0118 0.0051189713 0.045415 0.002063 -0.069601439 0.002570489 0.001426031 0.000535897 0.0002324781 f4 0.7346364687
-1.532565 0.0566 0.0314 0.0118 0.0051189713 0.045415 0.002063 -0.069601439 0.002570489 0.001426031 0.000535897 0.0002324781 f5 0.965074412
-1.532108 0.0567 0.031 0.012 0.0055411911 0.045101 0.002034 -0.069099603 0.0025572267 0.001398131 0.000541212 0.0002499133 f1 0.4780332853
-1.532108 0.0567 0.031 0.012 0.0055411911 0.045101 0.002034 -0.069099603 0.0025572267 0.001398131 0.000541212 0.0002499133 f2 0.0072908383
-1.532108 0.0567 0.031 0.012 0.0055411911 0.045101 0.002034 -0.069099603 0.0025572267 0.001398131 0.000541212 0.0002499133 f3 0.8173511433
-1.532108 0.0567 0.031 0.012 0.0055411911 0.045101 0.002034 -0.069099603 0.0025572267 0.001398131 0.000541212 0.0002499133 f4 0.3370460013
-1.532108 0.0567 0.031 0.012 0.0055411911 0.045101 0.002034 -0.069099603 0.0025572267 0.001398131 0.000541212 0.0002499133 f5 0.568171473
-1.550062 0.0569 0.0338 0.0119 0.0055286697 0.034075 0.001161 -0.052818363 0.0019388675 0.001151735 0.0004054925 0.0001883894 f1 0.1771195667
-1.550062 0.0569 0.0338 0.0119 0.0055286697 0.034075 0.001161 -0.052818363 0.0019388675 0.001151735 0.0004054925 0.0001883894 f2 0.0111325006
-1.550062 0.0569 0.0338 0.0119 0.0055286697 0.034075 0.001161 -0.052818363 0.0019388675 0.001151735 0.0004054925 0.0001883894 f3 0.8168642618
-1.550062 0.0569 0.0338 0.0119 0.0055286697 0.034075 0.001161 -0.052818363 0.0019388675 0.001151735 0.0004054925 0.0001883894 f4 0.0229171352
-1.550062 0.0569 0.0338 0.0119 0.0055286697 0.034075 0.001161 -0.052818363 0.0019388675 0.001151735 0.0004054925 0.0001883894 f5 0.9097481379
-1.568156 0.0604 0.0332 0.0113 0.0052581205 -0.027273 0.000744 0.0427683186 -0.001647289 -0.000905464 -0.000308185 -0.000143405 f1 0.8774017258
-1.568156 0.0604 0.0332 0.0113 0.0052581205 -0.027273 0.000744 0.0427683186 -0.001647289 -0.000905464 -0.000308185 -0.000143405 f2 0.5694861866
-1.568156 0.0604 0.0332 0.0113 0.0052581205 -0.027273 0.000744 0.0427683186 -0.001647289 -0.000905464 -0.000308185 -0.000143405 f3 0.8095250441
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-1.568156 0.0604 0.0332 0.0113 0.0052581205 -0.027273 0.000744 0.0427683186 -0.001647289 -0.000905464 -0.000308185 -0.000143405 f5 0.1756782798
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-1.567608 0.0613 0.0313 0.011 0.0051239901 -0.086616 0.007502 0.1357799345 -0.005309561 -0.002711081 -0.000952776 -0.00044382 f1 0.4731746653
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-1.458534 0.0569 0.0362 0.0122 0.0046474209 0.069114 0.004777 -0.100805119 0.0039325866 0.0025019268 0.0008431908 0.0003212018 f1 0.7489646071
-1.458534 0.0569 0.0362 0.0122 0.0046474209 0.069114 0.004777 -0.100805119 0.0039325866 0.0025019268 0.0008431908 0.0003212018 f2 0.2143482436
-1.458534 0.0569 0.0362 0.0122 0.0046474209 0.069114 0.004777 -0.100805119 0.0039325866 0.0025019268 0.0008431908 0.0003212018 f3 0.9192034416
-1.458534 0.0569 0.0362 0.0122 0.0046474209 0.069114 0.004777 -0.100805119 0.0039325866 0.0025019268 0.0008431908 0.0003212018 f4 0.2035216224
-1.458534 0.0569 0.0362 0.0122 0.0046474209 0.069114 0.004777 -0.100805119 0.0039325866 0.0025019268 0.0008431908 0.0003212018 f5 0.6505407755
-1.417318 0.0577 0.0343 0.0118 0.0049056172 0.03787 0.001434 -0.053673833 0.002185099 0.001298941 0.000446866 0.0001857757 f1 0.3253429501
-1.417318 0.0577 0.0343 0.0118 0.0049056172 0.03787 0.001434 -0.053673833 0.002185099 0.001298941 0.000446866 0.0001857757 f2 0.7325578545
-1.417318 0.0577 0.0343 0.0118 0.0049056172 0.03787 0.001434 -0.053673833 0.002185099 0.001298941 0.000446866 0.0001857757 f3 0.8955283355
-1.417318 0.0577 0.0343 0.0118 0.0049056172 0.03787 0.001434 -0.053673833 0.002185099 0.001298941 0.000446866 0.0001857757 f4 0.1699969997
-1.417318 0.0577 0.0343 0.0118 0.0049056172 0.03787 0.001434 -0.053673833 0.002185099 0.001298941 0.000446866 0.0001857757 f5 0.2680122621
-1.445567 0.0581 0.0271 0.0119 0.0046603034 0.042728 0.001826 -0.061766187 0.0024824968 0.0011579288 0.0005084632 0.0001991254 f1 0.738105947
-1.445567 0.0581 0.0271 0.0119 0.0046603034 0.042728 0.001826 -0.061766187 0.0024824968 0.0011579288 0.0005084632 0.0001991254 f2 0.9659941094
-1.445567 0.0581 0.0271 0.0119 0.0046603034 0.042728 0.001826 -0.061766187 0.0024824968 0.0011579288 0.0005084632 0.0001991254 f3 0.0271218256
-1.445567 0.0581 0.0271 0.0119 0.0046603034 0.042728 0.001826 -0.061766187 0.0024824968 0.0011579288 0.0005084632 0.0001991254 f4 0.173806836
-1.445567 0.0581 0.0271 0.0119 0.0046603034 0.042728 0.001826 -0.061766187 0.0024824968 0.0011579288 0.0005084632 0.0001991254 f5 0.8157283211
-1.460499 0.0566 0.0288 0.0122 0.0048237028 -0.035597 0.001267 0.0519893829 -0.00201479 -0.001025194 -0.000434283 -0.000171709 f1 0.7406171899
-1.460499 0.0566 0.0288 0.0122 0.0048237028 -0.035597 0.001267 0.0519893829 -0.00201479 -0.001025194 -0.000434283 -0.000171709 f2 0.9155467916
-1.460499 0.0566 0.0288 0.0122 0.0048237028 -0.035597 0.001267 0.0519893829 -0.00201479 -0.001025194 -0.000434283 -0.000171709 f3 0.867587082
-1.460499 0.0566 0.0288 0.0122 0.0048237028 -0.035597 0.001267 0.0519893829 -0.00201479 -0.001025194 -0.000434283 -0.000171709 f4 0.8823737013
-1.460499 0.0566 0.0288 0.0122 0.0048237028 -0.035597 0.001267 0.0519893829 -0.00201479 -0.001025194 -0.000434283 -0.000171709 f5 0.5786429507
-1.47602 0.057 0.0331 0.0118 0.0042514594 0.005591 0.000031 -0.008252428 0.000318687 0.0001850621 0.0000659738 0.0000237699 f1 0.9674688806
-1.47602 0.057 0.0331 0.0118 0.0042514594 0.005591 0.000031 -0.008252428 0.000318687 0.0001850621 0.0000659738 0.0000237699 f2 0.1789891162
-1.47602 0.057 0.0331 0.0118 0.0042514594 0.005591 0.000031 -0.008252428 0.000318687 0.0001850621 0.0000659738 0.0000237699 f3 0.7304435492
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;;;;
NOTE: There were 360 observations read from the data set WORK.MODEL.
NOTE: DATA statement a utilisé (Durée totale du traitement) :
      real time           0.02 seconds
      cpu time            0.03 seconds


MPRINT(DATA2DATASTEP):   option linesize=256;

[pmorel3]

How can I do automatically this test by label (imagine I have 100 labels instead of 5) ?

[pmorel3]

I try this test by label but I have now error messages. Can someone help me please ?

2459  options notes;
2460  options mprint;
2461  /*options nonotes nomprint;*/
2462  data work.model;
2463     set test;
2464     array f f1-f5;
2465     do i=1 to dim(f);
2466        label=vname(f[i]);
2467        value=f[i];
2468        output;
2469     end;
2470     keep rm zdy ztbl ztms zdfy rmrsq rm_zdy rm_ztbl rm_ztms rm_zdfy pred_mean rm_pred_mean label value;
2471  run;

NOTE: There were 360 observations read from the data set WORK.TEST.
NOTE: The data set WORK.MODEL has 1800 observations and 14 variables.
NOTE: DATA statement a utilisé (Durée totale du traitement) :
      real time           0.00 seconds
      cpu time            0.01 seconds


2472
2473  proc model data=model outparms=dada;
2474   parm a b c d e f g h i;
2475
2476  value= a + b*rm + c*rm_zdy + d*rm_ztbl + e*rm_ztms + f*rm_zdfy;
2477  value= g + h*rm + i*rm_pred_mean;
2478

2479  fit value / data=model;
2480  test a=g;
2481  by label;
2482

ERROR: A l'itération OLS 0 there are 1 nonmissing observations available. At least 9 are needed to compute the next
       iteration.
NOTE: The above message was for the following BY group:
      label=f1
ERROR: A l'itération OLS 0 there are 1 nonmissing observations available. At least 9 are needed to compute the next
       iteration.
NOTE: The above message was for the following BY group:
      label=f2
ERROR: A l'itération OLS 0 there are 1 nonmissing observations available. At least 9 are needed to compute the next
       iteration.
NOTE: The above message was for the following BY group:
      label=f3
ERROR: A l'itération OLS 0 there are 1 nonmissing observations available. At least 9 are needed to compute the next
       iteration.
NOTE: The above message was for the following BY group:
      label=f4
ERROR: Data set WORK.MODEL is not sorted in croissant sequence. The current BY group has label = f5 and the next BY group
       has label = f1.
NOTE: The SAS System stopped processing this step because of errors.
WARNING: The data set WORK.DADA may be incomplete.  When this step was stopped there were 0 observations and 0 variables.
WARNING: Data set WORK.DADA was not replaced because cette étape a été interrompue.
NOTE: PROCEDURE MODEL a utilisé (Durée totale du traitement) :
      real time           0.04 seconds
      cpu time            0.01 seconds

2483  solve value / data=model;
2484  run;quit;

[pmorel3]

I tried to put test statement after the solve statement but always error message ! Please help me !

2533  data work.model;
2534     set test;
2535     array f f1-f5;
2536     do i=1 to dim(f);
2537        label=vname(f[i]);
2538        value=f[i];
2539        output;
2540     end;
2541     keep rm zdy ztbl ztms zdfy rmrsq rm_zdy rm_ztbl rm_ztms rm_zdfy pred_mean rm_pred_mean label value;
2542  run;

NOTE: There were 360 observations read from the data set WORK.TEST.
NOTE: The data set WORK.MODEL has 1800 observations and 14 variables.
NOTE: DATA statement a utilisé (Durée totale du traitement) :
      real time           0.01 seconds
      cpu time            0.01 seconds


2543
2544  proc model data=model outparms=dada;
2545   parm a b c d e f g h i;
2546
2547  value= a + b*rm + c*rm_zdy + d*rm_ztbl + e*rm_ztms + f*rm_zdfy;
2548  value= g + h*rm + i*rm_pred_mean;
2549

2550  fit value / data=model;

NOTE: A l'itération OLS 1 CONVERGE=0.001 Criteria Met.
2551  solve value / data=model;
2552
2553  test a=g;
2554  by label;
2555  run;


WARNING: The following TEST statements will be ignored because they were erroneously applied to a SOLVE statement:
test a=g;
ERROR: Data set WORK.MODEL is not sorted in croissant sequence. The current BY group has label = f5 and the next BY group
       has label = f1.
NOTE: The SAS System stopped processing this step because of errors.
WARNING: The data set WORK.DADA may be incomplete.  When this step was stopped there were 0 observations and 0 variables.
WARNING: Data set WORK.DADA was not replaced because cette étape a été interrompue.
NOTE: PROCEDURE MODEL a utilisé (Durée totale du traitement) :
      real time           1.51 seconds
      cpu time            0.93 seconds

2555!     quit;

[ballardw]

Any procedure that uses BY group processing will require the data to be sorted by the variables as indicated on the BY statement.

[pmorel3]

Is it not the case here ?

[ballardw]

---

@pmorel3 wrote:

Is it not the case here ?

---

Not sure what that question means.

Your data is NOT sorted. That is this error message:

ERROR: Data set WORK.MODEL is not sorted in croissant sequence. The current BY group has label = f5 and the next BY group
       has label = f1.
NOTE: The SAS System stopped processing this step because of errors.

So you would need to run something like

Proc sort data=model;

   by label;

run;

Before the procedure so the data is in the By order you specify.