Hello; I just installed SAS 9.4, but it seems that I can not run timeseries procedure on it. The title of the post is exactly the error message I get, below is the exact code I am running. Did I mess something during the installation process? Thanks in advance data one;        input invest income consum @@;     datalines;        180 451  415  179 465  421   185 485  434   192 493  448        211 509  459  202 520  458   207 521  479   214 540  487        231 548  497  229 558  510   234 574  516   237 583  525        206 591  529  250 599  538   259 610  546   263 627  555        264 642  574  280 653  574   282 660  586   292 694  602        286 709  617  302 734  639   304 751  653   307 763  668        317 766  679  314 779  686   306 808  697   304 785  688        292 794  704  275 799  699   273 799  709   301 812  715        280 837  724  289 853  746   303 876  758   322 897  779        315 922  798  339 949  816   364 979  837   371 988  858        375 1025 881  432 1063 905   453 1104 934   460 1131 968        475 1137 983  496 1178 1013  494 1211 1034  498 1256 1064        526 1290 1101 519 1314 1102  516 1346 1145  531 1385 1173        573 1416 1216 551 1436 1229  538 1462 1242  532 1493 1267        558 1516 1295 524 1557 1317  525 1613 1355  519 1642 1371        526 1690 1402 510 1759 1452  519 1756 1485  538 1780 1516        549 1807 1549 570 1831 1567  559 1873 1588  584 1897 1631        611 1910 1650 597 1943 1685  603 1976 1722  619 2018 1752        635 2040 1774 658 2070 1807  675 2121 1831  700 2132 1842        692 2199 1890 759 2253 1958  782 2276 1948  816 2318 1994        844 2369 2061 830 2423 2056  853 2457 2102  852 2470 2121        833 2521 2145 860 2545 2164  870 2580 2206  830 2620 2225        801 2639 2235 824 2618 2237  831 2628 2250  830 2651 2271        ;   run;     data simul1;       set one;       date = intnx( 'year', '01jan1900'd, _n_-1 );       format date year4.;    run;    proc timeseries data=simul1 vectorplot=series;       id date interval=year;       var invest consum;    run; ... View more

I have a small data set with only 20 observations. N= number of trials Y= number of successes Here is the data-set: data temp; input Y N; cards; 97870      270000 12890      55000 120071    313000 43446      150000 1405102   1903000 125402     254000 79192       109000 14087       29000 10714       9000 983775     1587000 316543     654000 8592        29000 76061      130000 217492     501000 132423     354000 29163      127000 57013      161000 82747      192000 101778     344000 44258      77000 ; run; If I create a bunch of totally irrelevant random variable such that: proc temp; set temp; x1=rand('BETA',3,0.1); x2=rand('CAUCHY'); x3=rand('CHISQ',22); x4=rand('ERLANG', 7); x5=rand('EXPO'); x6=rand('F',12,322); run; And then run a LOGISTIC regression such as: Proc Logistic data=temp; model y/n=x1 x2 x3 x4 x5 x6; run; all of the independent variables are statistically significant at p<0.0001 level! despite the fact that they are all random and none of them should logically be significant! I tried it with so many other variables, but it is almost impossible to get an INSIGNIFICANT results from the proc logistic with events/trials option! Do you know why this is happening? ... View more

Hello; I am trying to regress a Ratio variable,Y,  on an independent variable,X; Variable X is endogenous, so I have to use an Instrumental Variable, Z; both X and Z are continues variables. How can I run this model in SAS. I know SYSLIN easily does iIMt if the depandant variable is continues, but what if the dependent variable is a ratio or dichotomous? PROC QLIM only allows the left hand variable to be a binary outcome and endogenous! again, when the right hand side variable is endogenous, QLIM is not useful. If there is no built in procedure, is there any alternative way to run this model in SAS? Thanks for your help in advance. ... View more

Hello All; I am trying to create a Bayesian Logistic Hierarchical using proc MCMC. I am using the exact dataset and codes provided by SAS in the example in the following link, SAS Official Manual Example the only difference between my model and the example model is that the response variable in the example is a continues variable and has a normal distribution while in my model the response is a dichotomous variable and thus has a binary distribution. I create a new variable in the data-set, called overweight which is equal to 1 if the Rat's weight is above average and zero otherwise and then try to model the probability of being over weight as a logistic function of age. data rats; array days[5] (8 15 22 29 36); input weight @@; subject = ceil(_n_/5); index = mod(_n_-1, 5) + 1; age = days[index]; drop index days:; datalines; 151 199 246 283 320 145 199 249 293 354 147 214 263 312 328 155 200 237 272 297 135 188 230 280 323 159 210 252 298 331 141 189 231 275 305 159 201 248 297 338 177 236 285 350 376 134 182 220 260 296 160 208 261 313 352 143 188 220 273 314 154 200 244 289 325 171 221 270 326 358 163 216 242 281 312 160 207 248 288 324 142 187 234 280 316 156 203 243 283 317 157 212 259 307 336 152 203 246 286 321 154 205 253 298 334 139 190 225 267 302 146 191 229 272 302 157 211 250 285 323 132 185 237 286 331 160 207 257 303 345 169 216 261 295 333 157 205 248 289 316 137 180 219 258 291 153 200 244 286 324 ; run; data rats; set rats; if weight>242 then overweight=1; else overweight=0; run; proc mcmc data=rats nmc=10000  outpost=postout seed=17 init=random; ods select Parameters REParameters PostSummaries; array theta[2] alpha beta; array theta_c[2]; array Sig_c[2,2]; array mu0[2]; array Sig0[2,2]; array S[2,2] (0.02 0 0 20); begincnst; call zeromatrix(mu0); call identity(Sig0); call mult(Sig0, 1000, Sig0); endcnst; parms theta_c Sig_c {121 0 0 0.26} var_y; prior theta_c ~ mvn(mu0, Sig0); prior Sig_c ~ iwish(2, S); prior var_y ~ igamma(0.01, scale=0.01); random theta ~ mvn(theta_c, Sig_c) subject=subject; mu = logistic(alpha + beta * age+var_y ); model overweight~ binary(mu); run; When I run the model, I get this error message, Observation 2 of the response variable has a value of 0, and it does not produce a positive log-likelihood value. Possible causes are: the response variable is outside of the support set of the likelihood function or some distribution parameters Would you please help me with this issue?        are missing. ... View more

I have a data set of 434 observation (named as selection) and another data-set with about 17000 observations (named as nodup). The first data-set is my main analysis group (study group) and I want to select one control match from the second data-set for each of the observations in the study group. The matching variables are SIC, FYEAR, SALE. the first two should be exact matches, while SALE should be between a range, defined by lowsale and highsale variables. If I run this PROC SQL, I will get multiple matches for some of the observation in my study group, while for some others, there is only one match. proc sql; create table controls_id as select * from selection, nodup where ((nodup.xSALE between selection.lowsale and selection.highsale) and and selection.SIC=nodup.xsic and selection.fyear=nodup.xfyear ); quit; When I have multiple matches, I want to select the closest match for my observation. the closeness is defined as ((sale-xsale)^2). In other words, I want to select the match which has the smallest difference in the sale variable. Would you please let me know how I can perform this step? Thanks for reading this post ... View more