On 29/08/2014 17:46, Nick Livingston wrote:
Thank you for your responses.
Since my previous attempt to manually truncate my DV didn't work, I'm very interested in
trying again using the zerotrun() function. However, I attempted to install
"countreg" but received the following notification:
Warning in install.packages :
unable to access index for repository
http://R-Forge.R-project.org/bin/macosx/contrib/3.0
package ‘countreg’ is available as a source package but not as a
binary
Warning in install.packages :
package ‘countreg’ is not available (for R version 3.0.3)
I received the same message when attempting to install it in version 3.1.0, and
the latest version, 3.1.1. Am I missing something?
As the package does not contain compiled code, you can simply install
from the sources. (I just did so in the GUI: just untick 'binary'.)
Thank you again. I appreciate your input.
-Nick
--------------------------------------------
On Fri, 8/29/14, Achim Zeileis <achim.zeil...@uibk.ac.at> wrote:
Subject: Re: [R] Question regarding the discrepancy between count model parameter estimates
between "pscl" and "MASS"
To: "peter dalgaard" <pda...@gmail.com>
Cc: "Nick Livingston" <nlivings...@ymail.com>, r-help@r-project.org
Date: Friday, August 29, 2014, 5:26 AM
On Fri, 29 Aug 2014,
peter dalgaard wrote:
>
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.
Yes. You can replicate the truncated count part
of the hurdle model with
the zerotrunc()
function from the "countreg" package. The package
is not
yet on CRAN but can be easily
installed from R-Forge.
> -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.
>
______________________________________________
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.
--
Brian D. Ripley, rip...@stats.ox.ac.uk
Emeritus Professor of Applied Statistics, University of Oxford
1 South Parks Road, Oxford OX1 3TG, UK
______________________________________________
R-help@r-project.org mailing list
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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