Last updated on 2019-05-01 01:52:17 CEST.
| Package | FAIL | ERROR | NOTE | OK |
|---|---|---|---|---|
| AER | 3 | 8 | ||
| betareg | 11 | |||
| colorspace | 11 | |||
| ctv | 11 | |||
| dynlm | 11 | |||
| exams | 5 | 6 | ||
| Formula | 11 | |||
| fortunes | 11 | |||
| fxregime | 1 | 10 | ||
| glmx | 11 | |||
| glogis | 11 | |||
| ineq | 11 | |||
| lagsarlmtree | 6 | 5 | ||
| lmtest | 11 | |||
| psychotools | 11 | |||
| psychotree | 11 | |||
| pwt | 6 | 5 | ||
| pwt8 | 11 | |||
| pwt9 | 11 | |||
| sandwich | 11 | |||
| strucchange | 11 | |||
| zoo | 11 |
Current CRAN status: ERROR: 3, OK: 8
Version: 1.2-6
Check: examples
Result: ERROR
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
-1.665e+00 -2.500e-01 -4.334e-04 -3.701e-06 1.036e+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) -7.9789 9.4817 -0.841 0.400068
quality 54.3970 65.0362 0.836 0.402924
skiyes 11.8442 15.8841 0.746 0.455872
income -0.2242 1.2902 -0.174 0.862047
userfeeyes 268.8310 353.1813 0.761 0.446556
costC 0.6039 0.8773 0.688 0.491266
costS -1.1900 1.4565 -0.817 0.413896
costH 0.5608 0.8254 0.679 0.496852
Log(theta) -4.4784 1.1971 -3.741 0.000183 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Theta: count = 0.5884, zero = 0.0114
Number of iterations in BFGS optimization: 645
Log-likelihood: -718.3 on 18 Df
> logLik(fm_hnb)
'log Lik.' -718.3488 (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
Flavors: r-devel-linux-x86_64-debian-clang, r-release-linux-x86_64
Version: 1.2-6
Check: examples
Result: ERROR
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
Flavor: r-patched-linux-x86_64
Current CRAN status: OK: 11
Current CRAN status: OK: 11
Current CRAN status: OK: 11
Current CRAN status: OK: 11
Current CRAN status: NOTE: 5, OK: 6
Version: 2.3-2
Check: R code for possible problems
Result: NOTE
Found the following assignments to the global environment:
File 'exams/R/xexams.R':
assign(".xweave_svg_grdevice", .xweave_svg_grdevice, envir = .GlobalEnv)
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-patched-linux-x86_64, r-release-linux-x86_64
Current CRAN status: OK: 11
Current CRAN status: OK: 11
Current CRAN status: FAIL: 1, NOTE: 10
Version: 1.0-3
Flags: --no-vignettes
Check: dependencies in R code
Result: NOTE
'library' or 'require' call to 'foreach' in package code.
Please use :: or requireNamespace() instead.
See section 'Suggested packages' in the 'Writing R Extensions' manual.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64
Version: 1.0-3
Flags: --no-vignettes
Check: R code for possible problems
Result: NOTE
fxreturns: no visible binding for global variable 'FXRatesCHF'
fxreturns : <anonymous>: no visible global function definition for
'tail'
gbreakpoints: no visible global function definition for '%dopar%'
gbreakpoints: no visible global function definition for 'foreach'
Undefined global functions or variables:
%dopar% FXRatesCHF foreach tail
Consider adding
importFrom("utils", "tail")
to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-oldrel-windows-ix86+x86_64
Version: 1.0-3
Check: dependencies in R code
Result: NOTE
'library' or 'require' call to ‘foreach’ in package code.
Please use :: or requireNamespace() instead.
See section 'Suggested packages' in the 'Writing R Extensions' manual.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64
Version: 1.0-3
Check: R code for possible problems
Result: NOTE
fxreturns: no visible binding for global variable ‘FXRatesCHF’
fxreturns : <anonymous>: no visible global function definition for
‘tail’
gbreakpoints: no visible global function definition for ‘%dopar%’
gbreakpoints: no visible global function definition for ‘foreach’
Undefined global functions or variables:
%dopar% FXRatesCHF foreach tail
Consider adding
importFrom("utils", "tail")
to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-solaris-x86, r-release-osx-x86_64, r-oldrel-osx-x86_64
Version: 1.0-3
Check: re-building of vignette outputs
Result: FAIL
Flavor: r-release-osx-x86_64
Current CRAN status: OK: 11
Current CRAN status: OK: 11
Current CRAN status: OK: 11
Current CRAN status: NOTE: 6, OK: 5
Version: 1.0-1
Check: installed package size
Result: NOTE
installed size is 8.4Mb
sub-directories of 1Mb or more:
data 8.3Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-patched-solaris-x86, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64
Current CRAN status: OK: 11
Current CRAN status: OK: 11
Current CRAN status: OK: 11
Current CRAN status: NOTE: 6, OK: 5
Version: 7.1-1
Check: installed package size
Result: NOTE
installed size is 7.3Mb
sub-directories of 1Mb or more:
data 7.2Mb
Flavors: r-devel-linux-x86_64-fedora-clang, r-patched-solaris-x86, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64
Current CRAN status: OK: 11
Current CRAN status: OK: 11
Current CRAN status: OK: 11
Current CRAN status: NOTE: 11
Version: 1.5-1
Check: R code for possible problems
Result: NOTE
breakpoints.formula: no visible global function definition for
'%dopar%'
breakpoints.formula: no visible binding for global variable 'i'
efpFunctional: multiple local function definitions for 'plotProcess'
with different formal arguments
mefp.efp: multiple local function definitions for 'computeEmpProc' with
different formal arguments
sctest.default: no visible global function definition for 'tail'
Undefined global functions or variables:
%dopar% i tail
Consider adding
importFrom("utils", "tail")
to your NAMESPACE file.
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-patched-linux-x86_64, r-patched-solaris-x86, r-release-linux-x86_64, r-release-windows-ix86+x86_64, r-release-osx-x86_64, r-oldrel-windows-ix86+x86_64, r-oldrel-osx-x86_64
Current CRAN status: OK: 11