* . * THIS PROGRAM PERFORMS A PARTIAL PROPORTIONAL ODDS REGRESSION ANALYSIS . * FOR ORDINAL RESPONSE VARIABLES AS DISCUSSED BY . * PATERSON & HARRELL (1990) APPLIED STATISTICS 2:205-217 . * THE PROBIT FORMULATION IS ALSO ALLOWED GIVING MODELS AS DESCRIBED BY . * TERZA (1985) COMMUNICATIONS IN STATISTICAL THEORY AND METHODS 14:1-11 . * . * NOTE: THE TWO FILES THRESHM1 AND THRESHM2 ARE DIFFERENT IN THE FOLLOWING WAY. * THRESHM1 assumes that there are two sets of explanatory variables . * setA: variables following the proportional odds assumption . * setB: variables relaxing the proportional odds assumption . * (this set includes the intercept) . * THRESHM2 only allows setB variables . * Thus, use THRESHM1 to fit proportional or partial proportional odds . * models, and THRESHM2 to fit a strictly non-proportional odds model . * . * . * FOR THE ANALYSIS OF STAGES OF CHANGE DATA . * THIS MODEL HAS BEEN DUBBED THE THRESHOLDS OF CHANGE MODEL: . * HEDEKER, MERMELSTEIN, & WEEKS (1999) . * THE THRESHOLDS OF CHANGE MODEL: . * AN APPROACH TO ANALYZING STAGES OF CHANGE DATA . * ANNALS OF BEHAVIORAL MEDICINE, 21(1):61-70 . * (we're hoping this name will really catch on!!) . * . * ANY COMMENTS OR QUESTIONS CAN BE DIRECTED TO DON HEDEKER hedeker@uic.edu . * PLEASE FEEL FREE TO REDISTRIBUTE THIS MACRO . * . * NOTE THAT IN THE PROGRAM BELOW - THE DATA AND SPSS COMMANDS ARE . * INCLUDED AS 1 FILE - THIS IS ONLY FOR CONVENIENCE IN DISTRIBUTION . * THE DATA CAN BE EXTRACTED INTO A SEPARATE FILE AND THEN READ IN . * BY THE PROGRAM FILE - SEE THE SPSS MANUAL FOR INSTRUCTIONS ON THIS . * . * THE DATA USED BELOW ARE LONGITUDINAL ORDINAL RESPONSES . * BUT FOR THIS EXAMPLE ARE TREATED AS INDEPENDENT RESPONSES . * FOR A MIXED-EFFECTS PARTIAL PROPORTIONAL ODDS MODEL SEE THE MIXOR . * PROGRAM (also available at this website) AND THE PAPER . * HEDEKER & MERMELSTEIN (1998) . * A MULTILEVEL THRESHOLDS OF CHANGE MODEL . * FOR ANALYSIS OF STAGES OF CHANGE DATA . * MULTIVARIATE BEHAVIORAL RESEARCH, 33(4):427-455 . * . * THIS SPSS MACRO PROGRAM IS SLOW IN EXECUTING . * FOR A FASTER PROGRAM TRY MIXOR . * A STAND-ALONE FREEWARE PROGRAM (also available at this website) . * MIXOR DOES THE SAME KIND OF MODELS, BUT CAN ADDITIONALLY INCLUDE RANDOM . * EFFECTS IN THE MODEL AS WELL AS SOME OTHER ADVANCED FEATURES . * . * . * . TITLE THRESHOLDS OF CHANGE - ORDINAL REGRESSION MODEL . SUBTITLE ANALYSIS OF AGRESTI & LANG DATA (Biometrika, 1993). * . DATA LIST FREE / ID RESPONSE ONE ITEM2 ITEM3. * . * . * . VARIABLE LABELS RESPONSE 'Opinions about Sex' / Item2 'Premarital vs Teen' / Item3 'Extramarital vs Teen'. * . VALUE LABELS RESPONSE 1 'Always Wrong' 2 'Almost Always Wrong' 3 'Wrong Sometimes' 4 'Not Wrong'. * . 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0 1 320007 4 1 0 0 320007 4 1 1 0 320007 1 1 0 1 330001 4 1 0 0 330001 4 1 1 0 330001 2 1 0 1 330002 4 1 0 0 330002 4 1 1 0 330002 2 1 0 1 340001 4 1 0 0 340001 4 1 1 0 340001 3 1 0 1 340002 4 1 0 0 340002 4 1 1 0 340002 3 1 0 1 350001 4 1 0 0 350001 4 1 1 0 350001 4 1 0 1 350002 4 1 0 0 350002 4 1 1 0 350002 4 1 0 1 350003 4 1 0 0 350003 4 1 1 0 350003 4 1 0 1 350004 4 1 0 0 350004 4 1 1 0 350004 4 1 0 1 END DATA . DESCRIPTIVES ALL . * . * for the program . * subjects with missing data on . * the dependent variable . * or any of the independent variables . * need to be excluded . * . * . COMPUTE MISSVAR = 0 . IF (MISSING(RESPONSE) OR MISSING(ITEM2) OR MISSING(ITEM3)) MISSVAR = 1. SELECT IF (MISSVAR = 0) . * . DESCRIPTIVES ALL. * . * . * THESHOLDS OF CHANGE . * ORDINAL LOGISTIC OR PROBIT REGRESSION MODEL . * . * N = NUMBER OF OBSERVATIONS . * P = NUMBER OF INDEPENDENT VARIABLES WITH VARYING EFFECTS ON . * THRESHOLDS (including the intercept) . * ==> these are the variables for which proportional odds . * is NOT assumed . * . * modify ONLY the following statements below for a given analysis . * . * GET X / VARIABLES = ONE ITEM2 ITEM3 . * (list the P independent variables . * with varying effects on thresholds, including the intercept) . * GET Y / VARIABLE = RESPONSE . * (list the dependent variable for the analysis) . * COMPUTE VARXLAB = {'Intercpt','Item2vs1', 'Item3vs1'} . * (list 8-character variable names for each independent variable . * with varying effects on thresholds, including the intercept) . * COMPUTE DEPNAME = {'SexOpin'} . * (list an 8-character variable name for the dependent variable) . * COMPUTE MAXJ = 4 . * (list the number of ordered categories for the dependent variable). * COMPUTE CODE = {1, 2, 3, 4} . * (list the values of the ordered dependent variable) . * COMPUTE RESPFN = 1 . * (desired response function: 0=probit, 1=logistic) . * COMPUTE CONVERGE = 0.0001 . * (give the convergence criterion) . * . * . * . SET MXLOOP = 9999999 . MATRIX . * . GET X / VARIABLES = ONE ITEM2 ITEM3 . GET Y / VARIABLE = RESPONSE . COMPUTE VARXLAB = {'Intercpt','Item2vs1', 'Item3vs1'} . COMPUTE DEPNAME = {'SexOpin'} . COMPUTE MAXJ = 4 . COMPUTE CODE = {1, 2, 3, 4} . COMPUTE CONVERGE = 0.0001 . COMPUTE RESPFN = 1 . * . * . * . COMPUTE P = NCOL(X). COMPUTE NPAR = P*(MAXJ-1). DO IF (RESPFN EQ 0) . COMPUTE RESPNAME = {'Probit'} . ELSE . COMPUTE RESPNAME = {'Logistic'} . END IF . * . PRINT DEPNAME / FORMAT = 'A8' / TITLE = 'THRESHOLDS OF CHANGE (ordinal regression) MODEL' / SPACE = NEWPAGE . PRINT RESPNAME / FORMAT = 'A8' / TITLE = 'Response Function' . * . * FIGURE OUT NUMBER OF OBSERVATIONS . * . COMPUTE N = NROW(Y) . * . PRINT N / TITLE = 'NUMBER OF OBSERVATIONS' . * . * . * starting values . * . COMPUTE MEANY = MSUM(Y) / N . COMPUTE MEANX = CSUM(X) / N . COMPUTE STDY = SQRT(MSSQ(Y - MEANY) / (N-1)) . * . COMPUTE XDEV = MAKE(N,P-1,0) . LOOP L = 2 TO P . + COMPUTE XDEV(:,L-1) = X(:,L) - MEANX(L) . END LOOP . COMPUTE B = (INV(SSCP(XDEV)) * T(XDEV) * (Y - MEANY(1))) / STDY(1) . COMPUTE XB = MEANX(2:P) * B . * . COMPUTE FREQY = MAKE(MAXJ,1,0) . COMPUTE BETA = MAKE(P,MAXJ-1,0) . COMPUTE CUMSUM= 0 . LOOP I = 1 TO N . + LOOP J = 1 TO MAXJ . + DO IF (Y(I) EQ CODE(J)) . + COMPUTE FREQY(J) = FREQY(J) + 1 . + BREAK . + END IF . + END LOOP . END LOOP . LOOP J = 1 TO MAXJ-1 . + COMPUTE CUMSUM = CUMSUM + FREQY(J) . + COMPUTE CUMODDS= CUMSUM / (N - CUMSUM) . + COMPUTE LNCUMOD= LN(CUMODDS) . + DO IF (RESPFN EQ 0) . + COMPUTE TEMPPAR= 0.625 * (LNCUMOD + XB) . + ELSE . + COMPUTE TEMPPAR= LNCUMOD + XB . + END IF . + DO IF (J EQ 1) . + COMPUTE BETA(1,1) = 0 - TEMPPAR . + ELSE . + COMPUTE BETA(1,J) = TEMPPAR + BETA(1,1) . + END IF . END LOOP . LOOP J = 1 TO MAXJ-1. + LOOP I = 2 TO P. + COMPUTE BETA(I,J) = B(I-1) . + END LOOP. END LOOP. * . * . PRINT FREQY / TITLE = 'CATEGORY FREQUENCIES' . * . * start iterations . * . * . COMPUTE IT = 0. * . LOOP . + COMPUTE IT = IT + 1. + PRINT . + PRINT / TITLE='*********'. + PRINT IT / FORMAT = 'F5.0'/ TITLE = 'ITERATION'. + PRINT / TITLE='*********'. * . * . * calculate and save the sample log-likelihood value logl. * . + COMPUTE LOGL= 0 . * . * . * calculate the derivatives and information matrix . * . * . + COMPUTE der2 = MAKE(npar,npar,0). + COMPUTE der = MAKE(npar,1,0) . * . + LOOP i=1 TO n. * . + COMPUTE derp=MAKE(npar,1,0). * . + LOOP J=1 TO MAXJ. + DO IF (Y(I) = CODE(J)). + DO IF (J=1). + COMPUTE Z1 = 0. + LOOP L=1 TO P. + COMPUTE Z1 = Z1 + BETA(L,J)*X(I,L). + END LOOP. + DO IF (RESPFN EQ 0). + COMPUTE prob=cdfnorm(z1). + DO IF (Z1 LT 10 AND Z1 GT -10). + COMPUTE PDFZ1 = EXP((0-Z1)*Z1/2) / 2.506628275. + ELSE . + COMPUTE PDFZ1 = 0. + END IF . + ELSE. + COMPUTE prob=1/(1 + EXP(0-z1)). + DO IF (Z1 LT 16 AND Z1 GT -16). + COMPUTE PDFZ1 = EXP(0-Z1) / ((1 + EXP(0-z1))**2). + ELSE . + COMPUTE PDFZ1 = 0. + END IF . + END IF. + COMPUTE probp0=0. + COMPUTE probp1=pdfz1. + else if (j>1 and j=2 and j