Running examples in ‘AER-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: CameronTrivedi1998
> ### Title: Data and Examples from Cameron and Trivedi (1998)
> ### Aliases: CameronTrivedi1998
> ### Keywords: datasets
> 
> ### ** Examples
> 
> library("MASS")
> library("pscl")
Classes and Methods for R developed in the
Political Science Computational Laboratory
Department of Political Science
Stanford University
Simon Jackman
hurdle and zeroinfl functions by Achim Zeileis
> 
> ###########################################
> ## Australian health service utilization ##
> ###########################################
> 
> ## data
> data("DoctorVisits", package = "AER")
> 
> ## Poisson regression
> dv_pois <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = poisson)
> dv_qpois <- glm(visits ~ . + I(age^2), data = DoctorVisits, family = quasipoisson)
> 
> ## Table 3.3 
> round(cbind(
+   Coef = coef(dv_pois),
+   MLH = sqrt(diag(vcov(dv_pois))),
+   MLOP = sqrt(diag(vcovOPG(dv_pois))),
+   NB1 = sqrt(diag(vcov(dv_qpois))),
+   RS = sqrt(diag(sandwich(dv_pois)))
+ ), digits = 3)
               Coef   MLH  MLOP   NB1    RS
(Intercept)  -2.224 0.190 0.144 0.219 0.254
genderfemale  0.157 0.056 0.041 0.065 0.079
age           1.056 1.001 0.750 1.153 1.364
income       -0.205 0.088 0.062 0.102 0.129
illness       0.187 0.018 0.014 0.021 0.024
reduced       0.127 0.005 0.004 0.006 0.008
health        0.030 0.010 0.007 0.012 0.014
privateyes    0.123 0.072 0.056 0.083 0.095
freepooryes  -0.440 0.180 0.116 0.207 0.290
freerepatyes  0.080 0.092 0.070 0.106 0.126
nchronicyes   0.114 0.067 0.051 0.077 0.091
lchronicyes   0.141 0.083 0.059 0.096 0.123
I(age^2)     -0.849 1.078 0.809 1.242 1.460
> 
> ## Table 3.4
> ## NM2-ML
> dv_nb <- glm.nb(visits ~ . + I(age^2), data = DoctorVisits)
> summary(dv_nb)

Call:
glm.nb(formula = visits ~ . + I(age^2), data = DoctorVisits, 
    init.theta = 0.9284725333, link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-1.9711  -0.6354  -0.5277  -0.4408   4.0071  

Coefficients:
              Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -2.190007   0.233592  -9.375  < 2e-16 ***
genderfemale  0.216644   0.069697   3.108  0.00188 ** 
age          -0.216159   1.266701  -0.171  0.86450    
income       -0.142202   0.108417  -1.312  0.18965    
illness       0.214341   0.023579   9.090  < 2e-16 ***
reduced       0.143754   0.007311  19.662  < 2e-16 ***
health        0.038060   0.013654   2.788  0.00531 ** 
privateyes    0.118064   0.085806   1.376  0.16884    
freepooryes  -0.496611   0.210803  -2.356  0.01848 *  
freerepatyes  0.144982   0.115970   1.250  0.21124    
nchronicyes   0.099355   0.079303   1.253  0.21026    
lchronicyes   0.190327   0.104357   1.824  0.06818 .  
I(age^2)      0.609158   1.383245   0.440  0.65966    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for Negative Binomial(0.9285) family taken to be 1)

    Null deviance: 3928.7  on 5189  degrees of freedom
Residual deviance: 3028.3  on 5177  degrees of freedom
AIC: 6425.5

Number of Fisher Scoring iterations: 1


              Theta:  0.9285 
          Std. Err.:  0.0864 

 2 x log-likelihood:  -6397.4880 
> ## NB1-GLM = quasipoisson
> summary(dv_qpois)

Call:
glm(formula = visits ~ . + I(age^2), family = quasipoisson, data = DoctorVisits)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.9170  -0.6862  -0.5743  -0.4839   5.7005  

Coefficients:
              Estimate Std. Error t value Pr(>|t|)    
(Intercept)  -2.223848   0.218725 -10.167  < 2e-16 ***
genderfemale  0.156882   0.064686   2.425  0.01533 *  
age           1.056299   1.153198   0.916  0.35972    
income       -0.205321   0.101839  -2.016  0.04384 *  
illness       0.186948   0.021065   8.875  < 2e-16 ***
reduced       0.126846   0.005801  21.868  < 2e-16 ***
health        0.030081   0.011637   2.585  0.00977 ** 
privateyes    0.123185   0.082551   1.492  0.13570    
freepooryes  -0.440061   0.207197  -2.124  0.03373 *  
freerepatyes  0.079798   0.106081   0.752  0.45194    
nchronicyes   0.114085   0.076789   1.486  0.13742    
lchronicyes   0.141158   0.095808   1.473  0.14072    
I(age^2)     -0.848704   1.241930  -0.683  0.49440    
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

(Dispersion parameter for quasipoisson family taken to be 1.327793)

    Null deviance: 5634.8  on 5189  degrees of freedom
Residual deviance: 4379.5  on 5177  degrees of freedom
AIC: NA

Number of Fisher Scoring iterations: 6

> 
> ## overdispersion tests (page 79)
> lrtest(dv_pois, dv_nb) ## p-value would need to be halved
Likelihood ratio test

Model 1: visits ~ gender + age + income + illness + reduced + health + 
    private + freepoor + freerepat + nchronic + lchronic + I(age^2)
Model 2: visits ~ gender + age + income + illness + reduced + health + 
    private + freepoor + freerepat + nchronic + lchronic + I(age^2)
  #Df  LogLik Df Chisq Pr(>Chisq)    
1  13 -3355.5                        
2  14 -3198.7  1 313.6  < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> dispersiontest(dv_pois, trafo = 1)

	Overdispersion test

data:  dv_pois
z = 6.5428, p-value = 3.019e-11
alternative hypothesis: true alpha is greater than 0
sample estimates:
    alpha 
0.4144272 

> dispersiontest(dv_pois, trafo = 2)

	Overdispersion test

data:  dv_pois
z = 7.5046, p-value = 3.08e-14
alternative hypothesis: true alpha is greater than 0
sample estimates:
    alpha 
0.9574298 

> 
> 
> ##########################################
> ## Demand for medical care in NMES 1988 ##
> ##########################################
> 
> ## select variables for analysis
> data("NMES1988", package = "AER")
> nmes <- NMES1988[,-(2:6)]
> 
> ## dependent variable
> ## Table 6.1
> table(cut(nmes$visits, c(0:13, 100)-0.5, labels = 0:13))

  0   1   2   3   4   5   6   7   8   9  10  11  12  13 
683 481 428 420 383 338 268 217 188 171 128 115  86 500 
> 
> ## NegBin regression
> nmes_nb <- glm.nb(visits ~ ., data = nmes)
> 
> ## NegBin hurdle
> nmes_h <- hurdle(visits ~ ., data = nmes, dist = "negbin")
> 
> ## from Table 6.3
> lrtest(nmes_nb, nmes_h)
Warning in modelUpdate(objects[[i - 1]], objects[[i]]) :
  original model was of class "negbin", updated model is of class "hurdle"
Likelihood ratio test

Model 1: visits ~ health + chronic + adl + region + age + afam + gender + 
    married + school + income + employed + insurance + medicaid
Model 2: visits ~ .
  #Df LogLik Df  Chisq Pr(>Chisq)    
1  18 -12202                         
2  35 -12110 17 183.35  < 2.2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> 
> ## from Table 6.4
> AIC(nmes_nb)
[1] 24440.34
> AIC(nmes_nb, k = log(nrow(nmes)))
[1] 24555.37
> AIC(nmes_h)
[1] 24290.98
> AIC(nmes_h, k = log(nrow(nmes)))
[1] 24514.66
> 
> ## Table 6.8
> coeftest(nmes_h, vcov = sandwich)

t test of coefficients:

                        Estimate Std. Error t value  Pr(>|t|)    
count_(Intercept)      1.6309834  0.2710457  6.0174 1.918e-09 ***
count_healthpoor       0.3325087  0.0567090  5.8634 4.869e-09 ***
count_healthexcellent -0.3775071  0.0875557 -4.3116 1.656e-05 ***
count_chronic          0.1429373  0.0135873 10.5199 < 2.2e-16 ***
count_adllimited       0.1290354  0.0515406  2.5036  0.012331 *  
count_regionnortheast  0.1040669  0.0527117  1.9743  0.048414 *  
count_regionmidwest   -0.0163183  0.0475000 -0.3435  0.731207    
count_regionwest       0.1232470  0.0504222  2.4443  0.014553 *  
count_age             -0.0753010  0.0322000 -2.3385  0.019404 *  
count_afamyes          0.0016161  0.0700041  0.0231  0.981583    
count_gendermale       0.0041273  0.0421279  0.0980  0.921960    
count_marriedyes      -0.0920323  0.0436135 -2.1102  0.034899 *  
count_school           0.0216106  0.0056511  3.8242  0.000133 ***
count_income          -0.0022357  0.0058893 -0.3796  0.704241    
count_employedyes      0.0296559  0.0739627  0.4010  0.688471    
count_insuranceyes     0.2271511  0.0566849  4.0073 6.245e-05 ***
count_medicaidyes      0.1847927  0.0665406  2.7771  0.005507 ** 
zero_(Intercept)      -1.4753118  0.6463277 -2.2826  0.022501 *  
zero_healthpoor        0.0708379  0.1687129  0.4199  0.674599    
zero_healthexcellent  -0.3285110  0.1422327 -2.3097  0.020953 *  
zero_chronic           0.5565120  0.0527626 10.5475 < 2.2e-16 ***
zero_adllimited       -0.1881658  0.1299284 -1.4482  0.147625    
zero_regionnortheast   0.1292212  0.1250363  1.0335  0.301441    
zero_regionmidwest     0.1008883  0.1146224  0.8802  0.378810    
zero_regionwest        0.2016633  0.1336291  1.5091  0.131339    
zero_age               0.1904976  0.0811377  2.3478  0.018927 *  
zero_afamyes          -0.3269720  0.1334512 -2.4501  0.014320 *  
zero_gendermale       -0.4644473  0.0985088 -4.7148 2.495e-06 ***
zero_marriedyes        0.2472641  0.1039403  2.3789  0.017407 *  
zero_school            0.0542073  0.0131932  4.1087 4.051e-05 ***
zero_income            0.0067446  0.0184949  0.3647  0.715373    
zero_employedyes      -0.0123197  0.1450825 -0.0849  0.932333    
zero_insuranceyes      0.7624604  0.1172920  6.5005 8.901e-11 ***
zero_medicaidyes       0.5535139  0.1812055  3.0546  0.002267 ** 
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> logLik(nmes_h)
'log Lik.' -12110.49 (df=35)
> 1/nmes_h$theta
    count 
0.7437966 
> 
> 
> ###################################################
> ## Recreational boating trips to Lake Somerville ##
> ###################################################
> 
> ## data
> data("RecreationDemand", package = "AER")
> 
> ## Poisson model:
> ## Cameron and Trivedi (1998), Table 6.11
> ## Ozuna and Gomez (1995), Table 2, col. 3
> fm_pois <- glm(trips ~ ., data = RecreationDemand, family = poisson)
> summary(fm_pois)

Call:
glm(formula = trips ~ ., family = poisson, data = RecreationDemand)

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 ** 
quality      0.471726   0.017091  27.602  < 2e-16 ***
skiyes       0.418214   0.057190   7.313 2.62e-13 ***
income      -0.111323   0.019588  -5.683 1.32e-08 ***
userfeeyes   0.898165   0.078985  11.371  < 2e-16 ***
costC       -0.003430   0.003118  -1.100  0.27131    
costS       -0.042536   0.001670 -25.467  < 2e-16 ***
costH        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

> logLik(fm_pois)
'log Lik.' -1529.431 (df=8)
> coeftest(fm_pois, vcov = sandwich)

z test of coefficients:

              Estimate Std. Error z value  Pr(>|z|)    
(Intercept)  0.2649934  0.4324810  0.6127 0.5400559    
quality      0.4717259  0.0488508  9.6565 < 2.2e-16 ***
skiyes       0.4182137  0.1938713  2.1572 0.0309922 *  
income      -0.1113232  0.0503083 -2.2128 0.0269101 *  
userfeeyes   0.8981653  0.2469086  3.6376 0.0002751 ***
costC       -0.0034297  0.0146973 -0.2334 0.8154852    
costS       -0.0425364  0.0117348 -3.6248 0.0002892 ***
costH        0.0361336  0.0093860  3.8497 0.0001183 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> 
> ## Negbin model:
> ## Cameron and Trivedi (1998), Table 6.11
> ## Ozuna and Gomez (1995), Table 2, col. 5
> library("MASS")
> fm_nb <- glm.nb(trips ~ ., data = RecreationDemand)
> coeftest(fm_nb, vcov = vcovOPG)

z test of coefficients:

              Estimate Std. Error  z value  Pr(>|z|)    
(Intercept) -1.1219363  0.1909098  -5.8768 4.183e-09 ***
quality      0.7219990  0.0399627  18.0668 < 2.2e-16 ***
skiyes       0.6121388  0.1395255   4.3873 1.148e-05 ***
income      -0.0260588  0.0401183  -0.6495     0.516    
userfeeyes   0.6691676  0.4488554   1.4908     0.136    
costC        0.0480087  0.0103573   4.6353 3.565e-06 ***
costS       -0.0926910  0.0060193 -15.3990 < 2.2e-16 ***
costH        0.0388357  0.0087604   4.4331 9.288e-06 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

> logLik(fm_nb)
'log Lik.' -825.5576 (df=9)
> 
> ## ZIP model:
> ## Cameron and Trivedi (1998), Table 6.11
> fm_zip <- zeroinfl(trips ~  . | quality + income, data = RecreationDemand)
> summary(fm_zip)

Call:
zeroinfl(formula = trips ~ . | quality + income, data = RecreationDemand)

Pearson residuals:
    Min      1Q  Median      3Q     Max 
-6.3255 -0.2714 -0.1809 -0.1646 13.3126 

Count model coefficients (poisson with log link):
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)  2.099163   0.111397  18.844  < 2e-16 ***
quality      0.033833   0.023914   1.415    0.157    
skiyes       0.471691   0.058187   8.106 5.21e-16 ***
income      -0.099780   0.020779  -4.802 1.57e-06 ***
userfeeyes   0.610488   0.079435   7.685 1.53e-14 ***
costC        0.002369   0.003818   0.620    0.535    
costS       -0.037600   0.002038 -18.454  < 2e-16 ***
costH        0.025234   0.003355   7.522 5.40e-14 ***

Zero-inflation model coefficients (binomial with logit link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  3.29191    0.51608   6.379 1.79e-10 ***
quality     -1.91407    0.20619  -9.283  < 2e-16 ***
income      -0.04502    0.10797  -0.417    0.677    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Number of iterations in BFGS optimization: 23 
Log-likelihood: -1181 on 11 Df
> logLik(fm_zip)
'log Lik.' -1180.795 (df=11)
> 
> ## Hurdle models
> ## Cameron and Trivedi (1998), Table 6.13
> ## poisson-poisson
> sval <- list(count = c(2.15, 0.044, .467, -.097, .601, .002, -.036, .024), 
+              zero = c(-1.88, 0.815, .403, .01, 2.95, 0.006, -.052, .046))
> fm_hp0 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson",
+   zero = "poisson", start = sval, maxit = 0)
> fm_hp1 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson",
+   zero = "poisson", start = sval)
> fm_hp2 <- hurdle(trips ~ ., data = RecreationDemand, dist = "poisson",
+   zero = "poisson")
> sapply(list(fm_hp0, fm_hp1, fm_hp2), logLik)
[1] -1209.582 -1181.612 -1181.612
> 
> ## negbin-negbin
> fm_hnb <- hurdle(trips ~ ., data = RecreationDemand, dist = "negbin", zero = "negbin")
> summary(fm_hnb)

Call:
hurdle(formula = trips ~ ., data = RecreationDemand, dist = "negbin", 
    zero.dist = "negbin")

Pearson residuals:
       Min         1Q     Median         3Q        Max 
-1.663e+00 -2.497e-01 -4.411e-04 -3.803e-06  1.038e+01 

Count model coefficients (truncated negbin with log link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  0.84193    0.38278   2.200  0.02784 *  
quality      0.17170    0.07234   2.374  0.01762 *  
skiyes       0.62236    0.19013   3.273  0.00106 ** 
income      -0.05709    0.06452  -0.885  0.37629    
userfeeyes   0.57634    0.38508   1.497  0.13448    
costC        0.05707    0.02169   2.632  0.00850 ** 
costS       -0.07752    0.01155  -6.713  1.9e-11 ***
costH        0.01237    0.01490   0.830  0.40640    
Log(theta)  -0.53031    0.26114  -2.031  0.04228 *  
Zero hurdle model coefficients (censored negbin with log link):
            Estimate Std. Error z value Pr(>|z|)    
(Intercept)  -7.8323     9.3454  -0.838 0.401982    
quality      54.8892    65.9351   0.832 0.405142    
skiyes       11.7555    15.7057   0.748 0.454168    
income       -0.2296     1.3009  -0.176 0.859930    
userfeeyes  276.1970   366.8623   0.753 0.451532    
costC         0.6131     0.8915   0.688 0.491622    
costS        -1.1953     1.4628  -0.817 0.413848    
costH         0.5564     0.8195   0.679 0.497195    
Log(theta)   -4.4881     1.2033  -3.730 0.000192 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 

Theta: count = 0.5884, zero = 0.0112
Number of iterations in BFGS optimization: 550 
Log-likelihood: -718.3 on 18 Df
> logLik(fm_hnb)
'log Lik.' -718.3406 (df=18)
> 
> sval <- list(count = c(0.841, 0.172, .622, -.057, .576, .057, -.078, .012), 
+              zero = c(-3.046, 4.638, -.025, .026, 16.203, 0.030, -.156, .117),
+              theta = c(count = 1/1.7, zero = 1/5.609))
> fm_hnb2 <- hurdle(trips ~ ., data = RecreationDemand,
+   dist = "negbin", zero = "negbin", start = sval)
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, mu = mu, size = theta, lower.tail = FALSE, log.p = TRUE) :
  NaNs produced
Warning in pnbinom(0, size = theta["zero"], mu = phi, lower.tail = FALSE,  :
  NaNs produced
> summary(fm_hnb2)
Warning in pnbinom(0, size = object$theta["zero"], mu = phi, lower.tail = FALSE,  :
  NaNs produced
Warning in pnbinom(0, size = object$theta["zero"], mu = phi, lower.tail = FALSE,  :
  NaNs produced

Call:
hurdle(formula = trips ~ ., data = RecreationDemand, dist = "negbin", 
    zero.dist = "negbin", start = sval)

Pearson residuals:
Error in quantile.default(x$residuals) : 
  missing values and NaN's not allowed if 'na.rm' is FALSE
Calls: <Anonymous> ... print.summary.hurdle -> print -> structure -> quantile -> quantile.default
Execution halted