Alain-

Thanks again for your reply. Yes, the offset for effort is only in the count part of the model. Sorry I wasn't clear about why I was using 'sum'...my effort data set contains records of trips with the effort given for each trip. I thought using sum would get me the total number of turtles predicted for the new effort data. I did also log-transform the effort in the new effort data before applying predict (correct?). I will try the manual method as you recommended to help me understand the code.

Thanks,

Laura

From: Alain Zuur [via R] [mailto:ml-node+s789695n4636025h47@n4.nabble.com]
Sent: Tuesday, July 10, 2012 1:52 PM
To: Lee, Laura
Subject: Re: Predicted values for zero-inflated Poisson

*Laura Lee* laura.lee at ncdenr.gov
<mailto:r-help%40r-project.org?Subject=Re%3A%20%5BR%5D%20Predicted%20values%20for%20zero-inflated%20Poisson&In-Reply-To=%3C1341937636301-4636016.post%40n4.nabble.com%3E>
/Tue Jul 10 18:27:16 CEST 2012/

------------------------------------------------------------------------

I want to predict the number of turtles for different levels of effort and
combinations of covariates. So, for my dataset from which I built the model,
would I compare sum(predict(ZIP,type="response")) to the observed bycatch to
compare numbers? In order to predict for the new data (called effort), would
I use sum(predict(ZIP,newdata=effort,type="response"))? I want to be certain
I am understanding the coding--this is my first time using the predict
function.

Thanks,

Laura




Laura
Why do you use the sum? If you use:

PredY <- predict(ZIP, type = "response") then you have predicted values for each of the rows in your effort data frame.
Job done.

You have an offset in your model, isn't it? You will need to choose values for this in the data frame effort as well.
Also double check that the offset is only in the count part....at least that is what I would do.
Note that using an offset means that you assume that if sampling effort is doubled, your fish (?) numbers double.




If you fully want to understand what predict is doing, try to do it manually. Below is R code from Chapter 7 (Zero Inflation and GLMM with R)






M3 <- zeroinfl(ParrotFish ~ Depth + Slope + SQDistRck + DistSed + Swell + Chla + SST,
                dist = "poisson", link = "logit",
                data = PF2)

Betas.logistic <- coef(M3, model = "zero")
X.logistic     <- model.matrix(M3, model = "zero")
eta.logistic   <- X.logistic %*% Betas.logistic
p              <- exp(eta.logistic) / (1 + exp(eta.logistic))

Betas.log      <- coef(M3, model = "count")
X.log          <- model.matrix(M3, model = "count")
eta.log        <- X.log %*% Betas.log
mu             <- exp(eta.log)

ExpY       <-  mu * (1 - p)
VarY       <- (1 - p) * (mu + p * mu^2)



Instead of using model.matrix(M3), you could specify your own data frame with covariates.
Your effort. Something like:

M4 <- zeroinfl(ParrotFish ~ Depth + Slope   | SST,
                dist = "poisson", link = "logit",
                data = PF2)

betapois <- coef(M4, model = "count")
betaBin  <- coef(M4, model = "zero")

MyDataPois <- data.frame(Depth = blah blah,
                          Slope = Blah blah)
MyDataBin  <- data.frame(SST =  blah)

Xpois <- model.matrix(~ blah blah, data = MyDataPois)
Xbin  <- model.matrix(~ blah blah, data = MyDataBin)

eta.Pois <- Xpois %*% betapois
eta.Bin <- blah blah

mu = blah blah
pi = blah blah

ExpY = ...


Doing it like this means you fully understand it..:-)


Alain




--

Dr. Alain F. Zuur
First author of:

1. Analysing Ecological Data (2007).
Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p.
URL: www.springer.com/0-387-45967-7<http://www.springer.com/0-387-45967-7>


2. Mixed effects models and extensions in ecology with R. (2009).
Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer.
http://www.springer.com/life+sci/ecology/book/978-0-387-87457-9


3. A Beginner's Guide to R (2009).
Zuur, AF, Ieno, EN, Meesters, EHWG. Springer
http://www.springer.com/statistics/computational/book/978-0-387-93836-3


4. Zero Inflated Models and Generalized Linear Mixed Models with R. (2012) Zuur, Saveliev, Ieno.
http://www.highstat.com/book4.htm

Other books: http://www.highstat.com/books.htm


Statistical consultancy, courses, data analysis and software
Highland Statistics Ltd.
6 Laverock road
UK - AB41 6FN Newburgh
Tel: 0044 1358 788177
Email: [hidden email]</user/SendEmail.jtp?type=node&node=4636025&i=0>
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
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Dr. Alain F. Zuur
First author of:

1. Analysing Ecological Data (2007).
Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p.
URL: www.springer.com/0-387-45967-7<http://www.springer.com/0-387-45967-7>


2. Mixed effects models and extensions in ecology with R. (2009).
Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer.
http://www.springer.com/life+sci/ecology/book/978-0-387-87457-9


3. A Beginner's Guide to R (2009).
Zuur, AF, Ieno, EN, Meesters, EHWG. Springer
http://www.springer.com/statistics/computational/book/978-0-387-93836-3


Other books: http://www.highstat.com/books.htm


Statistical consultancy, courses, data analysis and software
Highland Statistics Ltd.
6 Laverock road
UK - AB41 6FN Newburgh
Tel: 0044 1358 788177
Email: highstat@highstat.com<mailto:highstat@highstat.com>
URL: www.highstat.com<http://www.highstat.com>
URL: www.brodgar.com<http://www.brodgar.com>

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-----
Laura M. Lee
Senior Stock Assessment Scientist
North Carolina Division of Marine Fisheries
E-Mail: laura.lee@ncdenr.gov
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