thr3ads.net - R help - [R] Question regarding the discrepancy between count model parameter estimates between "pscl" and "MASS" [Aug 2014]

If this information is useful, please help other people find it:
Share via:

Nick Livingston

2014-Aug-29 03:57 UTC

[R] Question regarding the discrepancy between count model parameter estimates between "pscl" and "MASS"

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!
 
???
 
???

peter dalgaard

2014-Aug-29 11:13 UTC

head link

[R] Question regarding the discrepancy between count model parameter estimates between "pscl" and "MASS"

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 <nlivingston at 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 at 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.mes at cbs.dk  Priv: PDalgd at gmail.com

R help - Aug 2014 - Question regarding the discrepancy between count model parameter estimates between "pscl" and "MASS"

[R] Question regarding the discrepancy between count model parameter estimates between "pscl" and "MASS"

[R] Question regarding the discrepancy between count model parameter estimates between "pscl" and "MASS"