*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/
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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
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: highs...@highstat.com
URL: www.highstat.com
URL: www.brodgar.com
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