It appears that I need to use proc nlin. I drafted some code, see below. It runs but I'm a bit perplexed by the output. I try to limit z1-z9 from 0.25 to 1, but there are estimates for z over 2? Additionally, I tried to set z1-z9 from 0 to 1 by 0.01 and got an "insufficient memory" error. Is there a way to have sas search for the best fit by 0.01? Perhaps employ some logic that would reduce the possible number of combinations? Also, I got a warning stating that DER.z1 to DER.z9 are missing and sas will compute them automatically. Should I specify DER.z1 to DER.z9?
Thanks for any suggestions.
-Bill
dm 'clear log';
data input;
input y x1 x2 x3 x4 x5 x6 x7 x8 x9;
datalines;
0.433009378 0.260269441 1.092278706 0.425360662 0.164029628 0.429880491 0.950489941 0.047595925 0.531987245 0.336139451
0.386133454 0.184529366 0.033661616 0.245561093 0.105340609 0.882803079 0.981167833 1.07619815 0.781841268 0.233372548
0.258993942 1.119821022 0.109476847 0.000812581 0.419804897 0.391490911 0.769809934 0.659436672 0.144127286 0.746489656
0.359816223 1.046465239 0.268638572 0.137649556 1.059282484 0.142724361 0.902195762 0.073187585 1.044987784 0.080340265
0.41451314 0.07467261 1.142594057 0.097833794 0.6281817 0.804104506 0.953123478 0.877716887 0.986232903 0.413012511
0.499895162 0.388482105 0.769424554 0.449226135 0.864276043 0.093162782 0.735842414 0.729262551 0.021170317 0.946582289
0.554731409 0.059169663 0.671669625 0.573134236 0.059555794 0.688838956 1.112734086 0.082975813 0.985157911 0.954789222
0.933065823 1.09077688 0.937373275 0.961720206 1.137356787 0.912617055 0.414739423 1.179989148 0.793556015 0.527731975
0.449350652 0.889961987 0.448923153 0.16477621 1.039273537 1.101642002 0.417949375 0.892596492 0.107396261 0.300592589
0.436482177 0.218474064 0.817187061 0.444305392 0.010617679 0.316182089 0.871165126 0.396994326 0.274244319 1.054925185
0.844492128 1.199795926 0.562302873 1.087265891 0.857350825 0.717381217 0.361552016 0.188030616 0.078408845 0.477629287
0.986259829 1.11546778 0.67784779 1.076318082 0.711326509 1.147214979 0.949628603 1.159315584 0.683949937 0.658723083
0.536026142 0.03874538 0.603083965 0.615275769 1.199564925 0.097081463 1.116060342 0.025003849 0.125264809 0.066314023
0.478861694 0.623029236 1.118083449 0.343243377 0.445421704 0.990867977 0.074432208 0.093372058 0.301435613 1.061868184
0.755038738 0.917477552 0.568455848 0.653801794 1.11512354 1.167247795 0.78977679 0.620451789 0.21932299 0.882529179
0.870345527 0.278402165 0.645632762 1.04345974 1.192690633 0.317710531 0.946307396 0.799783639 0.66667504 0.614709813
0.653262402 1.077775155 0.181791546 0.669990623 0.776631367 0.513769454 0.39812198 0.078470159 0.870051912 1.178329425
0.577799576 0.545255692 0.898698641 0.635702755 0.768766323 0.60748745 0.478514258 0.208562345 0.057506345 0.257919144
0.640086114 0.347670625 0.760234063 0.733585918 0.638387923 0.510148474 0.07436439 0.352510307 0.593989168 1.040021751
0.823371633 0.638037368 1.01259897 1.097597318 0.644682512 0.266643423 0.344110563 0.450192716 0.383177214 0.840690779
0.62449019 0.282835785 1.007170731 0.610136182 1.029590947 0.074149805 1.116463349 0.503368539 1.007803905 0.263318173
0.444804746 0.017693426 0.526388959 0.187740591 1.129412956 0.915406787 1.073308894 0.619777948 0.146676128 0.54520417
0.484480172 0.497735344 0.104527198 0.500746335 0.985787824 0.199781266 0.065829102 0.202553479 0.626455295 0.813901488
0.317012256 1.192924294 0.251183508 0.093961339 0.174368642 0.477715786 0.657812327 1.031320803 0.245768093 0.717453859
0.588034285 0.489578192 0.207186181 0.699312378 0.4967007 0.891018763 0.097971171 0.532467973 0.37236279 0.292556691
0.598541777 0.331222229 1.194910587 0.566202209 1.181182139 0.344148387 1.135667393 0.369079141 0.224891936 0.002381108
0.789085218 0.561051456 0.430842595 1.05544095 0.176816812 0.475019069 0.669435603 0.443071061 0.681417633 1.137804746
0.487889626 0.634337348 1.155309283 0.341090334 0.523437654 1.041496843 0.658849627 0.407481423 0.212381753 0.148660757
0.500437513 1.003388064 0.028107549 0.408941718 1.140653445 0.546878092 0.312132981 0.164647159 0.896599531 0.139394726
0.648913057 0.23470331 0.708473294 0.715658801 0.503438628 0.778258155 0.824497121 1.003766639 0.281078622 0.205760578
0.47675595 0.226090918 0.267337491 0.207966394 0.959121283 1.097665676 0.222061633 0.836446043 0.960294339 0.82965073
0.709378295 0.009130251 0.192221036 0.993585371 0.209080202 0.157499618 1.190016415 0.748792229 0.836817845 0.541574774
0.696826726 0.879948846 1.125725147 0.744493446 0.740876159 0.219321144 1.089204287 0.920192756 0.351082149 0.205052275
0.260997361 0.00973165 0.176036305 0.059693106 0.111782576 1.025158879 0.136361192 0.918763623 0.595752643 0.512487841
0.570310745 0.632237184 0.304292779 0.526779408 0.822717014 1.004175775 0.548448316 0.063847267 0.41276116 0.523048535
0.736222587 0.24588289 0.14861863 0.844209142 0.629919526 0.941650487 0.532280328 0.696269823 0.870934505 0.645234111
0.434171719 0.948062641 0.696583303 0.254165508 0.674046478 0.242673335 1.188268109 0.934818027 0.506338586 0.034269001
0.705494851 0.68325986 0.106653146 0.811958384 0.269531941 0.696427753 1.081084338 1.057956238 0.729979889 0.399460328
0.280503587 0.416168159 0.409217298 0.05717669 0.130358799 0.904330781 0.486648621 0.915596266 0.580948113 0.160347238
0.458996388 1.029620307 0.152837123 0.460200554 0.306766207 0.638229727 0.323942705 0.470856463 0.595152795 0.115520964
0.740670108 0.829959193 0.801371222 0.700047998 1.02184578 1.130850744 0.500220331 0.29845238 1.054722256 0.022803759
0.628111577 0.775435333 0.899677969 0.809379813 0.630802294 0.346100793 0.296477882 0.378814672 0.117778822 0.04644256
0.616620558 1.195580632 0.66884203 0.629953026 0.580271276 0.452977027 0.748081499 0.103261188 0.903194269 0.347424679
0.485100919 0.640793811 0.984983366 0.174869342 0.568463983 1.075565391 0.768921803 1.017558465 0.700763394 0.552245365
0.380522255 1.026669851 0.023575147 0.181301495 0.678121679 0.126734702 0.498002422 1.116297938 0.781122541 0.742049492
0.386310145 0.489322502 0.928953839 0.108098712 0.887426928 0.61118902 0.460495358 0.892752621 0.013579578 0.862879983
0.644081655 0.791733164 0.807384987 0.832247035 0.041075155 0.063473692 0.424127906 0.853328728 0.293989069 1.179501191
0.664011771 0.235924253 0.284018069 0.772072735 0.303979747 0.781867879 0.345935375 0.990578377 0.83253673 0.698820026
0.695065576 0.263981518 0.911212968 0.780187919 0.956728666 0.033297408 0.486447384 1.160349181 0.697104382 0.600284743
0.742614249 0.389088378 0.856478819 0.855145861 0.507586603 0.737463437 0.021562337 1.154630872 0.806242913 0.582722966
;
run;
*Set weights;
%let w1=0.23; %let w2=0.07; %let w3=0.21; %let w4=0.09; %let w5=0.05; %let w6=0.15; %let w7=0.10; %let w8=0.04; %let w9=0.06;
%let start=0.25;
%let end=1.0;
%let inc=0.25;
PROC NLIN DATA=input METHOD=GAUSS NOITPRINT;
PARMS z1 = &start. to &end. by &inc. z2 =&start. to &end. by &inc. z3 = &start. to &end. by &inc. z4 = &start. to &end. by &inc.
z5 = &start. to &end. by &inc. z6 = &start. to &end. by &inc. z7 = &start. to &end. by &inc. z8 = &start. to &end. by &inc.
z9 = &start. to &end. by &inc.;
MODEL Y = &w1.*((x1-1)*z1+1) + &w2.*((x2-1)*z2+1) + &w3.*((x3-1)*z3+1) + &w4.*((x4-1)*z4+1) + &w5.*((x5-1)*z5+1) + &w6.*((x6-1)*z6+1) + &w7.*((x7-1)*z7+1)
+&w8.*((x8-1)*z8+1) + &w9.*((x9-1)*z9+1) ;
OUTPUT OUT=PRED P=PR ;
RUN;
output from proc nlin
