TRIPS SO SKI I FC3 C1 C3 C4 1 0 0 1 4 0 67.59 68.620 76.800 2 0 0 0 9 0 68.86 70.936 84.780 3 0 0 1 5 0 58.12 59.465 72.110 4 0 0 0 2 0 15.79 13.750 23.680 5 0 0 1 3 0 24.02 34.033 34.547 6 0 0 1 5 0 129.46 137.377 137.850 TRIPS 0 1 2 3 4 5 6 7 8 9 10 11 12 15 16 20 25 26 30 40 417 68 38 34 17 13 11 2 8 1 13 2 5 14 1 3 3 1 3 3 50 88 1 1 TRIPS 0 1 2 3 4 5 0.632776935 0.103186646 0.057663126 0.051593323 0.025796662 0.019726859 6 7 8 9 10 11 0.016691958 0.003034901 0.012139605 0.001517451 0.019726859 0.003034901 12 15 16 20 25 26 0.007587253 0.021244310 0.001517451 0.004552352 0.004552352 0.001517451 30 40 50 88 0.004552352 0.004552352 0.001517451 0.001517451 [1] "TRIPS" "SO" "SKI" "I" "FC3" "C1" "C3" "C4" TRIPS SO SKI I Min. : 0.000 Min. :0.000 Min. :0.0000 Min. :1.000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:3.000 Median : 0.000 Median :0.000 Median :0.0000 Median :3.000 Mean : 2.244 Mean :1.419 Mean :0.3672 Mean :3.853 3rd Qu.: 2.000 3rd Qu.:3.000 3rd Qu.:1.0000 3rd Qu.:5.000 Max. :88.000 Max. :5.000 Max. :1.0000 Max. :9.000 FC3 C1 C3 C4 Min. :0.00000 Min. : 4.34 Min. : 4.767 Min. : 5.70 1st Qu.:0.00000 1st Qu.: 28.24 1st Qu.: 33.312 1st Qu.: 28.96 Median :0.00000 Median : 41.19 Median : 47.000 Median : 42.38 Mean :0.01973 Mean : 55.42 Mean : 59.928 Mean : 55.99 3rd Qu.:0.00000 3rd Qu.: 69.67 3rd Qu.: 72.573 3rd Qu.: 68.56 Max. :1.00000 Max. :493.77 Max. :491.547 Max. :491.05 TRIPS SO SKI I FC3 C1 2.24430956 1.41881639 0.36722307 3.85280728 0.01972686 55.42370391 C3 C4 59.92805318 55.99030350 TRIPS SO SKI I FC3 C1 C3 6.2924747 1.8119859 0.4824142 1.8519366 0.1391657 46.6826473 46.3766760 C4 46.1332113 Call: glm(formula = formula.ch06p2model, family = poisson(), data = data.ch06p2) Deviance Residuals: Min 1Q Median 3Q Max -11.8465 -1.1411 -0.8896 -0.4780 18.6071 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.264993 0.093722 2.827 0.00469 ** SO 0.471726 0.017091 27.602 < 2e-16 *** SKI 0.418214 0.057190 7.313 2.62e-13 *** I -0.111323 0.019588 -5.683 1.32e-08 *** FC3 0.898165 0.078985 11.371 < 2e-16 *** C1 -0.003430 0.003118 -1.100 0.27131 C3 -0.042536 0.001670 -25.467 < 2e-16 *** C4 0.036134 0.002710 13.335 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 4849.7 on 658 degrees of freedom Residual deviance: 2305.8 on 651 degrees of freedom AIC: 3074.9 Number of Fisher Scoring iterations: 7 Call: glm(formula = formula.ch06p2model, family = poisson(), data = data.ch06p2) Deviance Residuals: Min 1Q Median 3Q Max -11.8465 -1.1411 -0.8896 -0.4780 18.6071 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) 0.264993 0.093722 2.827 0.00469 ** SO 0.471726 0.017091 27.602 < 2e-16 *** SKI 0.418214 0.057190 7.313 2.62e-13 *** I -0.111323 0.019588 -5.683 1.32e-08 *** FC3 0.898165 0.078985 11.371 < 2e-16 *** C1 -0.003430 0.003118 -1.100 0.27131 C3 -0.042536 0.001670 -25.467 < 2e-16 *** C4 0.036134 0.002710 13.335 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for poisson family taken to be 1) Null deviance: 4849.7 on 658 degrees of freedom Residual deviance: 2305.8 on 651 degrees of freedom AIC: 3074.9 Number of Fisher Scoring iterations: 7 Call: glm.nb(formula = formula.ch06p2model, data = data.ch06p2, init.theta = 0.7292568311, link = log) Deviance Residuals: Min 1Q Median 3Q Max -2.9727 -0.6256 -0.4619 -0.2897 5.0494 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -1.121936 0.214303 -5.235 1.65e-07 *** SO 0.721999 0.040117 17.998 < 2e-16 *** SKI 0.612139 0.150303 4.073 4.65e-05 *** I -0.026059 0.042453 -0.614 0.539 FC3 0.669168 0.353021 1.896 0.058 . C1 0.048009 0.009185 5.227 1.72e-07 *** C3 -0.092691 0.006653 -13.931 < 2e-16 *** C4 0.038836 0.007751 5.011 5.42e-07 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for Negative Binomial(0.7293) family taken to be 1) Null deviance: 1244.61 on 658 degrees of freedom Residual deviance: 425.42 on 651 degrees of freedom AIC: 1669.1 Number of Fisher Scoring iterations: 1 Theta: 0.7293 Std. Err.: 0.0747 2 x log-likelihood: -1651.1150 GAMLSS-RS iteration 1: Global Deviance = 1667.438 GAMLSS-CG iteration 1: Global Deviance = 1655.475 GAMLSS-CG iteration 2: Global Deviance = 1652.312 GAMLSS-CG iteration 3: Global Deviance = 1651.584 GAMLSS-CG iteration 4: Global Deviance = 1651.313 GAMLSS-CG iteration 5: Global Deviance = 1651.203 GAMLSS-CG iteration 6: Global Deviance = 1651.155 GAMLSS-CG iteration 7: Global Deviance = 1651.133 GAMLSS-CG iteration 8: Global Deviance = 1651.123 GAMLSS-CG iteration 9: Global Deviance = 1651.119 GAMLSS-CG iteration 10: Global Deviance = 1651.117 GAMLSS-CG iteration 11: Global Deviance = 1651.116 ******************************************************************* Family: c("NBI", "Negative Binomial type I") Call: gamlss(formula = formula.ch06p2model, family = NBI, data = data.ch06p2, method = mixed(1, 20)) Fitting method: mixed(1, 20) ------------------------------------------------------------------- Mu link function: log Mu Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.12157 0.214435 -5.230 2.283e-07 SO 0.72202 0.040139 17.988 4.949e-59 SKI 0.61247 0.150387 4.073 5.222e-05 I -0.02608 0.042472 -0.614 5.394e-01 FC3 0.66819 0.353252 1.892 5.900e-02 C1 0.04845 0.009186 5.274 1.818e-07 C3 -0.09285 0.006657 -13.949 7.024e-39 C4 0.03856 0.007753 4.974 8.420e-07 ------------------------------------------------------------------- Sigma link function: log Sigma Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.3167 0.07496 4.225 2.728e-05 ------------------------------------------------------------------- No. of observations in the fit: 659 Degrees of Freedom for the fit: 9 Residual Deg. of Freedom: 650 at cycle: 11 Global Deviance: 1651.116 AIC: 1669.116 SBC: 1709.532 ******************************************************************* GAMLSS-RS iteration 1: Global Deviance = 2184.173 GAMLSS-CG iteration 1: Global Deviance = 2168.128 GAMLSS-CG iteration 2: Global Deviance = 2154.222 GAMLSS-CG iteration 3: Global Deviance = 2141.378 GAMLSS-CG iteration 4: Global Deviance = 2129.302 GAMLSS-CG iteration 5: Global Deviance = 2118.116 GAMLSS-CG iteration 6: Global Deviance = 2107.906 GAMLSS-CG iteration 7: Global Deviance = 2098.74 GAMLSS-CG iteration 8: Global Deviance = 2090.642 GAMLSS-CG iteration 9: Global Deviance = 2083.589 GAMLSS-CG iteration 10: Global Deviance = 2077.508 GAMLSS-CG iteration 11: Global Deviance = 2072.291 GAMLSS-CG iteration 12: Global Deviance = 2067.811 GAMLSS-CG iteration 13: Global Deviance = 2063.931 GAMLSS-CG iteration 14: Global Deviance = 2060.524 GAMLSS-CG iteration 15: Global Deviance = 2057.477 GAMLSS-CG iteration 16: Global Deviance = 2054.698 GAMLSS-CG iteration 17: Global Deviance = 2052.112 GAMLSS-CG iteration 18: Global Deviance = 2049.665 GAMLSS-CG iteration 19: Global Deviance = 2047.314 GAMLSS-CG iteration 20: Global Deviance = 2045.03 ******************************************************************* Family: c("NBII", "Negative Binomial type II") Call: gamlss(formula = formula.ch06p2model, family = NBII, data = data.ch06p2, method = mixed(1, 20)) Fitting method: mixed(1, 20) ------------------------------------------------------------------- Mu link function: log Mu Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -0.64927 0.186773 -3.4763 5.422e-04 SO 0.59291 0.034708 17.0828 2.464e-54 SKI 0.42786 0.107437 3.9824 7.592e-05 I -0.01840 0.037180 -0.4949 6.208e-01 FC3 0.55163 0.151536 3.6403 2.940e-04 C1 0.03487 0.003226 10.8089 3.687e-25 C3 -0.06825 0.001484 -45.9926 1.082e-206 C4 0.02789 0.003339 8.3528 4.019e-16 ------------------------------------------------------------------- Sigma link function: log Sigma Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.714 0.008366 204.9 0 ------------------------------------------------------------------- No. of observations in the fit: 659 Degrees of Freedom for the fit: 9 Residual Deg. of Freedom: 650 at cycle: 20 Global Deviance: 2045.03 AIC: 2063.03 SBC: 2103.446 ******************************************************************* Call: flexmix(formula = formula.ch06p2model, data = data.ch06p2, k = 2, model = FLXMRglm(family = "poisson")) prior size post>0 ratio Comp.1 0.217 64 599 0.107 Comp.2 0.783 595 642 0.927 'log Lik.' -941.6814 (df=17) AIC: 1917.363 BIC: 1993.705 Call: flexmix(formula = formula.ch06p2model, data = data.ch06p2, k = 3, model = FLXMRglm(family = "poisson")) prior size post>0 ratio Comp.1 0.5453 522 626 0.8339 Comp.2 0.3856 108 617 0.1750 Comp.3 0.0691 29 567 0.0511 'log Lik.' -792.0077 (df=26) AIC: 1636.015 BIC: 1752.774 Call: zeroinfl(formula = formula.ch06p2model, data = data.ch06p2, dist = "poisson") Pearson residuals: Min 1Q Median 3Q Max -6.4184 -0.3615 -0.1524 -0.1142 14.6924 Count model coefficients (poisson with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 2.113706 0.110388 19.148 < 2e-16 *** SO 0.039679 0.023969 1.655 0.0978 . SKI 0.469118 0.058237 8.055 7.92e-16 *** I -0.094353 0.020359 -4.634 3.58e-06 *** FC3 0.605071 0.079311 7.629 2.36e-14 *** C1 0.002354 0.003902 0.603 0.5463 C3 -0.036443 0.002028 -17.968 < 2e-16 *** C4 0.023589 0.003414 6.910 4.86e-12 *** Zero-inflation model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|) (Intercept) 3.558e+00 5.545e-01 6.417 1.39e-10 *** SO -1.652e+00 1.565e-01 -10.555 < 2e-16 *** SKI 5.882e-02 4.162e-01 0.141 0.887624 I -7.191e-02 1.030e-01 -0.698 0.485013 FC3 -1.566e+01 1.980e+03 -0.008 0.993688 C1 -5.811e-03 3.191e-02 -0.182 0.855514 C3 7.232e-02 1.953e-02 3.704 0.000212 *** C4 -7.540e-02 2.584e-02 -2.918 0.003518 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of iterations in BFGS optimization: 23 Log-likelihood: -1163 on 16 Df Call: zeroinfl(formula = formula.ch06p2model, data = data.ch06p2, dist = "poisson") Pearson residuals: Min 1Q Median 3Q Max -6.4184 -0.3615 -0.1524 -0.1142 14.6924 Count model coefficients (poisson with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 2.113706 0.110388 19.148 < 2e-16 *** SO 0.039679 0.023969 1.655 0.0978 . SKI 0.469118 0.058237 8.055 7.92e-16 *** I -0.094353 0.020359 -4.634 3.58e-06 *** FC3 0.605071 0.079311 7.629 2.36e-14 *** C1 0.002354 0.003902 0.603 0.5463 C3 -0.036443 0.002028 -17.968 < 2e-16 *** C4 0.023589 0.003414 6.910 4.86e-12 *** Zero-inflation model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|) (Intercept) 3.558e+00 5.545e-01 6.417 1.39e-10 *** SO -1.652e+00 1.565e-01 -10.555 < 2e-16 *** SKI 5.882e-02 4.162e-01 0.141 0.887624 I -7.191e-02 1.030e-01 -0.698 0.485013 FC3 -1.566e+01 1.980e+03 -0.008 0.993688 C1 -5.811e-03 3.191e-02 -0.182 0.855514 C3 7.232e-02 1.953e-02 3.704 0.000212 *** C4 -7.540e-02 2.584e-02 -2.918 0.003518 ** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Number of iterations in BFGS optimization: 23 Log-likelihood: -1163 on 16 Df Call: hurdle(formula = TRIPS ~ SO + SKI + FC3 + I + C1 + C3 + C4 | SO + SKI + I + C1 + C3 + C4, data = data.ch06p2, dist = "negbin") Pearson residuals: Min 1Q Median 3Q Max -1.5072 -0.2641 -0.1469 -0.1089 10.0228 Count model coefficients (truncated negbin with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 0.84193 0.38278 2.200 0.02784 * SO 0.17170 0.07234 2.374 0.01762 * SKI 0.62236 0.19013 3.273 0.00106 ** FC3 0.57634 0.38508 1.497 0.13448 I -0.05709 0.06452 -0.885 0.37629 C1 0.05707 0.02169 2.632 0.00850 ** C3 -0.07752 0.01155 -6.713 1.9e-11 *** C4 0.01237 0.01490 0.830 0.40640 Log(theta) -0.53031 0.26114 -2.031 0.04228 * Zero hurdle model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|) (Intercept) -2.83959 0.43026 -6.600 4.12e-11 *** SO 1.44508 0.10574 13.666 < 2e-16 *** SKI 0.36758 0.32770 1.122 0.261983 I 0.02768 0.08368 0.331 0.740764 C1 0.01304 0.02486 0.525 0.599847 C3 -0.08656 0.01649 -5.249 1.53e-07 *** C4 0.07215 0.02066 3.493 0.000478 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Theta: count = 0.5884 Number of iterations in BFGS optimization: 18 Log-likelihood: -743.3 on 16 Df Call: zeroinfl(formula = TRIPS ~ SO + SKI + I + FC3 + C1 + C3 + C4 | SO + SKI + I + C1 + C3 + C4, data = data.ch06p2, dist = "negbin") Pearson residuals: Min 1Q Median 3Q Max -1.0848786 -0.2161436 -0.0015220 -0.0006693 22.6949949 Count model coefficients (negbin with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) 1.092899 0.249582 4.379 1.19e-05 *** SO 0.170792 0.051168 3.338 0.000844 *** SKI 0.492161 0.134480 3.660 0.000252 *** I -0.068797 0.043884 -1.568 0.116951 FC3 0.545589 0.283232 1.926 0.054067 . C1 0.039922 0.014577 2.739 0.006168 ** C3 -0.065855 0.007748 -8.499 < 2e-16 *** C4 0.020734 0.010263 2.020 0.043347 * Log(theta) 0.183375 0.110437 1.660 0.096825 . Zero-inflation model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|) (Intercept) 13.58295 32.79525 0.414 0.679 SO -27.11742 414.48332 -0.065 0.948 SKI -8.68364 32.71908 -0.265 0.791 I -0.20281 0.33965 -0.597 0.550 C1 -0.02368 0.03911 -0.605 0.545 C3 0.07769 0.09009 0.862 0.389 C4 -0.06288 0.07915 -0.794 0.427 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Theta = 1.2013 Number of iterations in BFGS optimization: 67 Log-likelihood: -719.4 on 16 Df