原文链接：http://tecdat.cn/?p=6145 泊松模型 proc fmm data = tmp1 tech = trureg;model majordrg = age acadmos minordrg logspend / dist = truncpoisson; probmodel age acadmos minordrg logspend; /*Fit Statistics-2 Log Likelihood 8201.0A
原文链接：http://tecdat.cn/?p=6145
泊松模型
proc fmm data = tmp1 tech = trureg; model majordrg = age acadmos minordrg logspend / dist = truncpoisson; probmodel age acadmos minordrg logspend; /* Fit Statistics -2 Log Likelihood 8201.0 AIC (smaller is better) 8221.0 AICC (smaller is better) 8221.0 BIC (smaller is better) 8293.5 Parameter Estimates for ‘Truncated Poisson‘ Model Standard Component Effect Estimate Error z Value Pr > |z| 1 Intercept -2.0706 0.3081 -6.72 <.0001 1 AGE 0.01796 0.005482 3.28 0.0011 1 ACADMOS 0.000852 0.000700 1.22 0.2240 1 MINORDRG 0.1739 0.03441 5.05 <.0001 1 LOGSPEND 0.1229 0.04219 2.91 0.0036 Parameter Estimates for Mixing Probabilities Standard Effect Estimate Error z Value Pr > |z| Intercept -4.2309 0.1808 -23.40 <.0001 AGE 0.01694 0.003323 5.10 <.0001 ACADMOS 0.002240 0.000492 4.55 <.0001 MINORDRG 0.7653 0.03842 19.92 <.0001 LOGSPEND 0.2301 0.02683 8.58 <.0001 */ *** HURDLE POISSON MODEL WITH NLMIXED PROCEDURE ***; proc nlmixed data = tmp1 tech = trureg maxit = 500; parms B1_intercept = -4 B1_age = 0 B1_acadmos = 0 B1_minordrg = 0 B1_logspend = 0 B2_intercept = -2 B2_age = 0 B2_acadmos = 0 B2_minordrg = 0 B2_logspend = 0; eta1 = B1_intercept + B1_age * age + B1_acadmos * acadmos + B1_minordrg * minordrg + B1_logspend * logspend; exp_eta1 = exp(eta1); p0 = 1 / (1 + exp_eta1); eta2 = B2_intercept + B2_age * age + B2_acadmos * acadmos + B2_minordrg * minordrg + B2_logspend * logspend; exp_eta2 = exp(eta2); if majordrg = 0 then _prob_ = p0; else _prob_ = (1 - p0) * exp(-exp_eta2) * (exp_eta2 ** majordrg) / ((1 - exp(-exp_eta2)) * fact(majordrg)); ll = log(_prob_); model majordrg ~ general(ll); run; /* Fit Statistics -2 Log Likelihood 8201.0 AIC (smaller is better) 8221.0 AICC (smaller is better) 8221.0 BIC (smaller is better) 8293.5 Parameter Estimates Standard Parameter Estimate Error DF t Value Pr > |t| B1_intercept -4.2309 0.1808 1E4 -23.40 <.0001 B1_age 0.01694 0.003323 1E4 5.10 <.0001 B1_acadmos 0.002240 0.000492 1E4 4.55 <.0001 B1_minordrg 0.7653 0.03842 1E4 19.92 <.0001 B1_logspend 0.2301 0.02683 1E4 8.58 <.0001 ============ B2_intercept -2.0706 0.3081 1E4 -6.72 <.0001 B2_age 0.01796 0.005482 1E4 3.28 0.0011 B2_acadmos 0.000852 0.000700 1E4 1.22 0.2240 B2_minordrg 0.1739 0.03441 1E4 5.05 <.0001 B2_logspend 0.1229 0.04219 1E4 2.91 0.0036 */
零膨胀泊松模型
*** ZERO-INFLATED POISSON MODEL WITH FMM PROCEDURE ***; proc fmm data = tmp1 tech = trureg; model majordrg = age acadmos minordrg logspend / dist = poisson; probmodel age acadmos minordrg logspend; run; /* Fit Statistics -2 Log Likelihood 8147.9 AIC (smaller is better) 8167.9 AICC (smaller is better) 8167.9 BIC (smaller is better) 8240.5 Parameter Estimates for ‘Poisson‘ Model Standard Component Effect Estimate Error z Value Pr > |z| 1 Intercept -2.2780 0.3002 -7.59 <.0001 1 AGE 0.01956 0.006019 3.25 0.0012 1 ACADMOS 0.000249 0.000668 0.37 0.7093 1 MINORDRG 0.1176 0.02711 4.34 <.0001 1 LOGSPEND 0.1644 0.03531 4.66 <.0001 Parameter Estimates for Mixing Probabilities Standard Effect Estimate Error z Value Pr > |z| Intercept -1.9111 0.4170 -4.58 <.0001 AGE -0.00082 0.008406 -0.10 0.9218 ACADMOS 0.002934 0.001085 2.70 0.0068 MINORDRG 1.4424 0.1361 10.59 <.0001 LOGSPEND 0.09562 0.05080 1.88 0.0598 */ *** ZERO-INFLATED POISSON MODEL WITH NLMIXED PROCEDURE ***; proc nlmixed data = tmp1 tech = trureg maxit = 500; parms B1_intercept = -2 B1_age = 0 B1_acadmos = 0 B1_minordrg = 0 B1_logspend = 0 B2_intercept = -2 B2_age = 0 B2_acadmos = 0 B2_minordrg = 0 B2_logspend = 0; eta1 = B1_intercept + B1_age * age + B1_acadmos * acadmos + B1_minordrg * minordrg + B1_logspend * logspend; exp_eta1 = exp(eta1); p0 = 1 / (1 + exp_eta1); eta2 = B2_intercept + B2_age * age + B2_acadmos * acadmos + B2_minordrg * minordrg + B2_logspend * logspend; exp_eta2 = exp(eta2); if majordrg = 0 then _prob_ = p0 + (1 - p0) * exp(-exp_eta2); else _prob_ = (1 - p0) * exp(-exp_eta2) * (exp_eta2 ** majordrg) / fact(majordrg); ll = log(_prob_); model majordrg ~ general(ll); run; /* Fit Statistics -2 Log Likelihood 8147.9 AIC (smaller is better) 8167.9 AICC (smaller is better) 8167.9 BIC (smaller is better) 8240.5 Parameter Estimates Standard Parameter Estimate Error DF t Value Pr > |t| B1_intercept -1.9111 0.4170 1E4 -4.58 <.0001 B1_age -0.00082 0.008406 1E4 -0.10 0.9219 B1_acadmos 0.002934 0.001085 1E4 2.70 0.0068 B1_minordrg 1.4424 0.1361 1E4 10.59 <.0001 B1_logspend 0.09562 0.05080 1E4 1.88 0.0598 ============ B2_intercept -2.2780 0.3002 1E4 -7.59 <.0001 B2_age 0.01956 0.006019 1E4 3.25 0.0012 B2_acadmos 0.000249 0.000668 1E4 0.37 0.7093 B2_minordrg 0.1176 0.02711 1E4 4.34 <.0001 B2_logspend 0.1644 0.03531 1E4 4.66 <.0001 */
两类有限混合Poisson模型
*** TWO-CLASS FINITE MIXTURE POISSON MODEL WITH FMM PROCEDURE ***; proc fmm data = tmp1 tech = trureg; model majordrg = age acadmos minordrg logspend / dist = poisson k = 2; run; /* Fit Statistics -2 Log Likelihood 8136.8 AIC (smaller is better) 8166.8 AICC (smaller is better) 8166.9 BIC (smaller is better) 8275.7 Parameter Estimates for ‘Poisson‘ Model Standard Component Effect Estimate Error z Value Pr > |z| 1 Intercept -2.4449 0.3497 -6.99 <.0001 1 AGE 0.02214 0.006628 3.34 0.0008 1 ACADMOS 0.000529 0.000770 0.69 0.4920 1 MINORDRG 0.05054 0.04015 1.26 0.2081 1 LOGSPEND 0.2140 0.04127 5.18 <.0001 2 Intercept -8.0935 1.5915 -5.09 <.0001 2 AGE 0.01150 0.01294 0.89 0.3742 2 ACADMOS 0.004567 0.002055 2.22 0.0263 2 MINORDRG 0.2638 0.6770 0.39 0.6968 2 LOGSPEND 0.6826 0.2203 3.10 0.0019 Parameter Estimates for Mixing Probabilities Standard Effect Estimate Error z Value Pr > |z| Intercept -1.4275 0.5278 -2.70 0.0068 AGE -0.00277 0.01011 -0.27 0.7844 ACADMOS 0.001614 0.001440 1.12 0.2623 MINORDRG 1.5865 0.1791 8.86 <.0001 LOGSPEND -0.06949 0.07436 -0.93 0.3501 */ *** TWO-CLASS FINITE MIXTURE POISSON MODEL WITH NLMIXED PROCEDURE ***; proc nlmixed data = tmp1 tech = trureg maxit = 500; B2_intercept = -8 B2_age = 0 B2_acadmos = 0 B2_minordrg = 0 B2_logspend = 0 eta1 = B1_intercept + B1_age * age + B1_acadmos * acadmos + B1_minordrg * minordrg + B1_logspend * logspend; exp_eta1 = exp(eta1); prob1 = exp(-exp_eta1) * exp_eta1 ** majordrg / fact(majordrg); eta2 = B2_intercept + B2_age * age + B2_acadmos * acadmos + B2_minordrg * minordrg + B2_logspend * logspend; exp_eta2 = exp(eta2); prob2 = exp(-exp_eta2) * exp_eta2 ** majordrg / fact(majordrg); eta3 = B3_intercept + B3_age * age + B3_acadmos * acadmos + B3_minordrg * minordrg + B3_logspend * logspend; exp_eta3 = exp(eta3); p = exp_eta3 / (1 + exp_eta3); _prob_ = p * prob1 + (1 - p) * prob2; ll = log(_prob_); model majordrg ~ general(ll); run; /* Fit Statistics -2 Log Likelihood 8136.8 AIC (smaller is better) 8166.8 AICC (smaller is better) 8166.9 BIC (smaller is better) 8275.7 Parameter Estimates Standard Parameter Estimate Error DF t Value Pr > |t| B1_intercept -2.4449 0.3497 1E4 -6.99 <.0001 B1_age 0.02214 0.006628 1E4 3.34 0.0008 B1_acadmos 0.000529 0.000770 1E4 0.69 0.4920 B1_minordrg 0.05054 0.04015 1E4 1.26 0.2081 B1_logspend 0.2140 0.04127 1E4 5.18 <.0001 ============ B2_intercept -8.0935 1.5916 1E4 -5.09 <.0001 B2_age 0.01150 0.01294 1E4 0.89 0.3742 B2_acadmos 0.004567 0.002055 1E4 2.22 0.0263 B2_minordrg 0.2638 0.6770 1E4 0.39 0.6968 B2_logspend 0.6826 0.2203 1E4 3.10 0.0020 ============ B3_intercept -1.4275 0.5278 1E4 -2.70 0.0068 B3_age -0.00277 0.01011 1E4 -0.27 0.7844 B3_acadmos 0.001614 0.001440 1E4 1.12 0.2623 B3_minordrg 1.5865 0.1791 1E4 8.86 <.0001 B3_logspend -0.06949 0.07436 1E4 -0.93 0.3501 */
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