I'm no expert on hurdle models, but it seems that you are unaware that the negative binomial and the truncated negative binomial are quite different things.
-pd On 29 Aug 2014, at 05:57 , Nick Livingston <nlivings...@ymail.com> wrote: > 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. -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.com ______________________________________________ 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.
