From: Joris Meys <jorismeys_at_gmail.com>

Date: Wed, 02 Jun 2010 13:45:35 +0200

Date: Wed, 02 Jun 2010 13:45:35 +0200

You could also use the hurdle model from the pscl library, but this is not guaranteed to work better. It is not a zero-inflation model, but a two-component model that fits the zeros using eg a binomial and the rest of the counts using eg a poisson or a negative binomial.

Cheers

Joris

On Wed, Jun 2, 2010 at 12:56 PM, Strubbe Diederik <diederik.strubbe_at_ua.ac.be

*> wrote:
*

> Dear all,

*>
**> I am using gamlss (Package gamlss version 4.0-0, R version 2.10.1, Windows
**> XP Service Pack 3 on a HP EliteBook) to relate bird counts to habit
**> variables. However, most models fail because “the global deviance is
**> increasing” and I am not sure what causes this behaviour. The dataset
**> consists of counts of birds (duck) and 5 habit variables measured in the
**> field (n= 182). The dependent variable (the number of ducks
**> counted)’suffers’ from zero-inflation and overdisperion:
**>
**> > proportion_non_zero <- (sum(ifelse(data$duck == 0,0,1))/182)
**> > mean <- mean(data$duck)
**> > var <- var(data$duck)
**> > proportion_non_zero
**> [1] 0.1153846
**> > mean
**> [1] 1.906593
**> > var
**> [1] 37.35587
**>
**> (I have no idea how to simulate a zero-inflated overdispersed Poisson
**> variable, but the data used can be found at
**> http://www.ua.ac.be/main.aspx?c=diederik.strubbe&n=23519).
**>
**>
**> First, I create a (strong) pattern in the dataset by:
**> data$LFAP200 <- data$LFAP200 + (data$duck*data$duck)
**>
**> I try to analyze these data by fitting several possible distributions
**> (Poisson PO, zero-inflated Poisson ZIP, negative binomial type I and type II
**> NBI NBII and zero-inflated negative binomial ZINBI) while using cubic
**> splines with a df=3. The best fitting model will then be choses on the basis
**> of its AIC.
**>
**> However, these models frequently fail to converge, and I am not sure why,
**> and what to do about it. For example:
**>
**> > model_Poisson <- gamlss(duck ~ cs(HHCDI200,df=3) + cs(HHCDI1000,df=3) +
**> cs(HHHDI200,df=3) + cs(HHHDI1000,df=3) + cs(LFAP200,df=3),data=data,family=
**> PO)
**> GAMLSS-RS iteration 1: Global Deviance = 1350.623
**> GAMLSS-RS iteration 2: Global Deviance = 1350.623
**>
**> > model_ZIPoisson <- gamlss(duck ~ cs(HHCDI200,df=3) + cs(HHCDI1000,df=3) +
**> cs(HHHDI200,df=3) + cs(HHHDI1000,df=3) + cs(LFAP200,df=3),data=data,family=
**> ZIP)
**> GAMLSS-RS iteration 1: Global Deviance = 326.3819
**> GAMLSS-RS iteration 2: Global Deviance = 225.1232
**> GAMLSS-RS iteration 3: Global Deviance = 319.9663
**> Error in RS() : The global deviance is increasing
**> Try different steps for the parameters or the model maybe inappropriate
**> In addition: There were 14 warnings (use warnings() to see them)
**>
**> > model_NBI <- gamlss(duck ~ cs(HHCDI200,df=3) + cs(HHCDI1000,df=3) +
**> cs(HHHDI200,df=3) + cs(HHHDI1000,df=3) + cs(LFAP200,df=3),data=data,family=
**> NBI)
**> GAMLSS-RS iteration 1: Global Deviance = 291.8607
**> GAMLSS-RS iteration 2: Global Deviance = 291.3291
**> ######......######
**> GAMLSS-RS iteration 4: Global Deviance = 291.1135
**> GAMLSS-RS iteration 20: Global Deviance = 291.107
**> Warning message:
**> In RS() : Algorithm RS has not yet converged
**>
**> > model_NBII <- gamlss(duck ~ cs(HHCDI200,df=3) + cs(HHCDI1000,df=3) +
**> cs(HHHDI200,df=3) + cs(HHHDI1000,df=3) + cs(LFAP200,df=3),data=data,family=
**> NBII)
**> GAMLSS-RS iteration 1: Global Deviance = 284.5993
**> GAMLSS-RS iteration 2: Global Deviance = 281.9548
**> ######......######
**> GAMLSS-RS iteration 5: Global Deviance = 280.7311
**> GAMLSS-RS iteration 15: Global Deviance = 280.6343
**>
**> > model_ZINBI <- gamlss(duck ~ cs(HHCDI200,df=3) + cs(HHCDI1000,df=3) +
**> cs(HHHDI200,df=3) + cs(HHHDI1000,df=3) + cs(LFAP200,df=3),data=data,family=
**> ZINBI)
**> GAMLSS-RS iteration 1: Global Deviance = 1672.234
**> GAMLSS-RS iteration 2: Global Deviance = 544.742
**> GAMLSS-RS iteration 3: Global Deviance = 598.9939
**> Error in RS() : The global deviance is increasing
**> Try different steps for the parameters or the model maybe inappropriate
**>
**>
**> Thus, in this case, only the Poisson (PO) and Negative Binomial type I
**> (NBI)converge whereas all other models fail…
**>
**> My first approach was to omit the smoothing factors for each model, or
**> further reduce the number of variables but this does not solve the problem
**> and most models fail, often yielding a “Error in RS() : The global deviance
**> is increasing” message.
**>
**> I would think that, given the fact that the dependent variable is
**> zero-inflated and overdispersed, that the Zero-Inflated Negative Binomial
**> (ZINBI) distribution would be the best fit, but the ZINBI even fails in the
**> following very simple examples.
**>
**> > model_ZINBI <- gamlss(duck ~ cs(LFAP200,df=3),data=data,family= ZINBI)
**> GAMLSS-RS iteration 1: Global Deviance = 3508.533
**> GAMLSS-RS iteration 2: Global Deviance = 1117.121
**> GAMLSS-RS iteration 3: Global Deviance = 652.5771
**> GAMLSS-RS iteration 4: Global Deviance = 632.8885
**> GAMLSS-RS iteration 5: Global Deviance = 645.1169
**> Error in RS() : The global deviance is increasing
**> Try different steps for the parameters or the model maybe inappropriate
**>
**> > model_ZINBI <- gamlss(duck ~ LFAP200,data=data,family= ZINBI)
**> GAMLSS-RS iteration 1: Global Deviance = 3831.864
**> GAMLSS-RS iteration 2: Global Deviance = 1174.605
**> GAMLSS-RS iteration 3: Global Deviance = 562.5428
**> GAMLSS-RS iteration 4: Global Deviance = 344.0637
**> GAMLSS-RS iteration 5: Global Deviance = 1779.018
**> Error in RS() : The global deviance is increasing
**> Try different steps for the parameters or the model maybe inappropriate
**>
**>
**>
**> Any suggestions on how to proceed with this?
**>
**> Many thanks in advance,
**>
**>
**> Diederik
**>
**>
**> Diederik Strubbe
**> Evolutionary Ecology Group
**> Department of Biology
**> University of Antwerp
**> Groenenborgerlaan 171
**> 2020 Antwerpen, Belgium
**> tel: +32 3 265 3464
**>
**>
**> [[alternative HTML version deleted]]
**>
**>
**> ______________________________________________
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**> PLEASE do read the posting guide
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**>
**>
*

-- Joris Meys Statistical Consultant Ghent University Faculty of Bioscience Engineering Department of Applied mathematics, biometrics and process control Coupure Links 653 B-9000 Gent tel : +32 9 264 59 87 Joris.Meys_at_Ugent.be ------------------------------- Disclaimer : http://helpdesk.ugent.be/e-maildisclaimer.php [[alternative HTML version deleted]]Received on Wed 02 Jun 2010 - 11:49:44 GMT______________________________________________ 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.

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