I have sought consultation online and in person, to no avail. I hope someone on here might have some insight. Any feedback would be most welcome.
I am attempting to plot predicted values from a two-component hurdle model (logistic [suicide attempt yes/no] and negative binomial count [number of attempts thereafter]). To do so, I estimated each component separately using glm (MASS). While I am able to reproduce hurdle results for the logit portion in glm, estimates for the negative binomial count component are different. Call: hurdle(formula = Suicide. ~ Age + gender + Victimization * FamilySupport | Age + gender + Victimization * FamilySupport, dist = "negbin", link = "logit") Pearson residuals: Min 1Q Median 3Q Max -0.9816 -0.5187 -0.4094 0.2974 5.8820 Count model coefficients (truncated negbin with log link): Estimate Std. Error z value Pr(>|z|) (Intercept) -0.29150 0.33127 -0.880 0.3789 Age 0.17068 0.07556 2.259 0.0239 * gender 0.28273 0.31614 0.894 0.3712 Victimization 1.08405 0.18157 5.971 2.36e-09 *** FamilySupport 0.33629 0.29302 1.148 0.2511 Victimization:FamilySupport -0.96831 0.46841 -2.067 0.0387 * Log(theta) 0.12245 0.54102 0.226 0.8209 Zero hurdle model coefficients (binomial with logit link): Estimate Std. Error z value Pr(>|z|) (Intercept) -0.547051 0.215981 -2.533 0.01131 * Age -0.154493 0.063994 -2.414 0.01577 * gender -0.030942 0.284868 -0.109 0.91350 Victimization 1.073956 0.338015 3.177 0.00149 ** FamilySupport -0.380360 0.247530 -1.537 0.12439 Victimization\:FamilySupport -0.813329 0.399905 -2.034 0.04197 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 Theta: count = 1.1303 Number of iterations in BFGS optimization: 23 Log-likelihood: -374.3 on 25 Df > summary(logistic) Call: glm(formula = SuicideBinary ~ Age + gender = Victimization * FamilySupport, family = "binomial") Deviance Residuals: Min 1Q Median 3Q Max -1.9948 -0.8470 -0.6686 1.1160 2.0805 Coefficients: Estimate Std. Error z value Pr(>|z|) (Intercept) -0.547051 0.215981 -2.533 0.01131 * Age -0.154493 0.063994 -2.414 0.01577 * gender -0.030942 0.284868 -0.109 0.91350 Victimization 1.073956 0.338014 3.177 0.00149 ** FamilySupport -0.380360 0.247530 -1.537 0.12439 Victimization:FamilySupport -0.813329 0.399904 -2.034 0.04197 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for binomial family taken to be 1) Null deviance: 452.54 on 359 degrees of freedom Residual deviance: 408.24 on 348 degrees of freedom (52 observations deleted due to missingness) AIC: 432.24 Number of Fisher Scoring iterations: 4 > summary(Count1) Call: glm(formula = NegBinSuicide ~ Age + gender + Victimization * FamilySupport, family = negative.binomial(theta = 1.1303)) Deviance Residuals: Min 1Q Median 3Q Max -1.6393 -0.4504 -0.1679 0.2350 2.1676 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.60820 0.13779 4.414 2.49e-05 *** Age 0.08836 0.04189 2.109 0.0373 * gender 0.10983 0.17873 0.615 0.5402 Victimization 0.73270 0.10776 6.799 6.82e-10 *** FamilySupport 0.10213 0.15979 0.639 0.5241 Victimization:FamilySupport -0.60146 0.24532 -2.452 0.0159 * --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 (Dispersion parameter for Negative Binomial(1.1303) family taken to be 0.4549082) Null deviance: 76.159 on 115 degrees of freedom Residual deviance: 35.101 on 104 degrees of freedom (296 observations deleted due to missingness) AIC: 480.6 Number of Fisher Scoring iterations: 15 Alternatively, if there is a simpler way to plot hurdle regression output, or if anyone is award of another means of estimating NB models (I haven't had much luck with vglm from VGAM either), I would be happy to hear about that as well. I'm currently using the "visreg" package for plotting. Thanks! ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.