[R-sig-eco] zero-truncated Poisson

Peter Solymos solymos at ualberta.ca
Sat Apr 9 09:07:15 CEST 2011


Jeff,

The zero truncated Poisson distribution can be described as a
probability function conditional on Y>0:

Pr(Y=y|Y>0) = Pr(Y=y) / (1-Pr(Y=0)), y=1,2,3,...

This leads to this function 'nll' for the negative log-likelihood:

nll <- function(theta, y, X) {
    lambda <- exp(drop(X %*% theta))
    -sum(dpois(y, lambda, log=TRUE) - log(1-exp(-lambda)))
}

where theta is the vector of parameters, y is the observation vector,
X is the design matrix.

A simple simulation is useful to check that the theory works. I used
'optim' with the default Nelder-Mead simplex algorithm, initial values
are based in non-truncated Poisson estimates to speed up convergence:

theta <- c(1, 2)
X <- model.matrix(~rnorm(100))
y <- rpois(100,exp(X %*% theta))
X <- X[y>0,]
y <- y[y>0]
inits <- coef(glm(y ~ X-1, family=poisson))
res <- optim(par=inits, fn=nll, y=y, X=X)
cbind(theta=theta, theta.hat=res$par)

Here I used the data you provided, note that 'hessian=TRUE' for some
subsequent SE calculations:

inits <- coef(glm(taken ~ mass + year, blja, family=poisson))
y <- blja$taken
X <- model.matrix(~ mass + year, blja)
res <- optim(par=inits, fn=nll, y=y, X=X, hessian=TRUE)

Vector of parameters that minimize 'nll' (maximize the log-likelihood)
given the data:

cf <- res$par

Standard error (note that due to minimization, I used the Hessian
instead of -H):

se <- sqrt(diag(solve(res$hessian)))

z and p values and the final output table:

zstat <- cf/se
pval <- 2 * pnorm(-abs(zstat))
out <- cbind(cf, se, zstat, pval)
colnames(out) <- c("Estimate", "Std. Error", "z value", "Pr(>|z|)")

printCoefmat(out, digits = 4, signif.legend = FALSE)

For your data set, this gives:

            Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.76392    0.10659  -7.167 7.66e-13 ***
mass         0.24008    0.01262  19.028  < 2e-16 ***
yeartwo     -0.29006    0.11974  -2.423   0.0154 *

Cheers,

Peter

Péter Sólymos
Alberta Biodiversity Monitoring Institute
and Boreal Avian Modelling project
Department of Biological Sciences
CW 405, Biological Sciences Bldg
University of Alberta
Edmonton, Alberta, T6G 2E9, Canada
Phone: 780.492.8534
Fax: 780.492.7635
email <- paste("solymos", "ualberta.ca", sep = "@")
http://www.abmi.ca
http://www.borealbirds.ca
http://sites.google.com/site/psolymos



On Fri, Apr 8, 2011 at 6:40 PM, Stratford, Jeffrey
<jeffrey.stratford at wilkes.edu> wrote:
> Thanks Ben,
>
> That didn't do the trick. I checked all the classes
>
>    year      size  distance     taken      mass
>  "factor"  "factor" "numeric" "integer" "numeric"
>
> And used the notation that you suggested and I did get the same error.
>
> Just so it's not such a black box for me, what makes vector GLMs
> different from "ordinary" GLMs?
>
> Thanks,
>
> Jeff
>
> Here's all the data.
>
> structure(list(year = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label =
> c("one",
> "two"), class = "factor"), size = structure(c(2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
> 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L), .Label = c("large", "small"), class = "factor"), distance =
> c(30.8735,
> 121.505, 46.055, 46.055, 46.055, 9.343, 46.055, 46.055, 46.055,
> 85.271, 85.271, 85.271, 85.271, 85.271, 85.271, 30.8735, 30.8735,
> 9.343, 9.343, 9.343, 9.343, 9.343, 9.343, 9.343, 152.717, 152.717,
> 152.717, 152.717, 152.717, 152.717, 152.717, 46.055, 46.055,
> 152.717, 152.717, 20.698, 20.698, 17.217, 17.217, 9.343, 9.343,
> 17.217, 46.055, 152.717, 46.055, 17.217, 152.717, 17.217, 17.217,
> 46.055, 30.8735, 30.8735, 30.8735, 5.69, 30.8735, 17.217, 30.8735,
> 30.8735, 30.8735, 30.8735, 30.8735, 9.343, 9.343, 17.217, 17.217,
> 17.217, 17.217, 17.217, 17.217, 9.343, 9.343, 17.217, 17.217,
> 17.217, 20.698, 17.217, 17.217, 17.217, 5.42, 17.217, 17.217,
> 17.217, 9.343, 30.8735, 30.8735, 30.8735, 9.343, 9.343, 9.343,
> 9.343, 9.343, 9.343, 9.343, 9.343, 9.343, 9.343, 9.343, 9.343,
> 17.217, 9.343, 9.343, 9.343, 9.343, 9.343, 9.343, 9.343, 9.343,
> 30.8735, 30.8735, 17.217, 17.217, 46.055, 36.239, 17.217, 17.217,
> 17.217, 46.055, 30.8735, 30.8735, 17.217, 17.217, 17.217, 121.505,
> 152.717, 152.717, 17.217, 152.717, 121.505, 121.505, 121.505,
> 121.505, 121.505, 9.343, 121.505, 9.343, 121.505, 121.505, 30.8735,
> 121.505, 17.217, 17.217, 17.217, 9.343, 30.8735, 85.271, 85.271,
> 85.271, 85.271, 85.271, 85.271, 9.343, 85.271, 85.271, 85.271,
> 85.271, 20.698, 9.343, 30.8735, 17.217, 20.698, 30.8735, 17.217,
> 85.271, 121.505, 121.505, 85.271, 85.271, 85.271, 85.271, 121.505,
> 121.505, NA, 121.505, 9.343, 9.343, 9.343, 9.343, 9.343, 9.343,
> 9.343, 30.8735, 17.217, 9.343, 9.343, 9.343, 30.8735, 30.8735,
> 30.8735, 9.343, 9.343, 30.8735, 17.217, 5.42, 17.217, 85.271,
> 85.271, 30.8735, 30.8735, 30.8735, 30.8735, 30.8735, 30.8735,
> 17.217, 85.271, 17.217, 30.8735, 30.8735, 85.271, 30.8735, 30.8735,
> 85.271, 30.8735, 30.8735, 30.8735, 30.8735, 30.8735, 30.8735,
> 17.217, 30.8735, 9.343, 30.8735, 30.8735, 30.8735, 30.8735, 9.343,
> 30.8735, 30.8735, 17.217, 9.343, 9.343, 9.343, 85.271, 30.8735,
> 30.8735, 46.055, 5.69, 85.271, 85.271, 17.217, 46.055, 85.271,
> 30.8735, 85.271, 46.055, 121.505, 121.505, 121.505, 17.217, 17.217,
> 25.508, 9.343, 17.217, 17.217, 9.343, 17.217, 9.343, 17.217,
> 34.35, 34.35, 46.055, 46.055, 9.343, 20.698, 17.217, 27.25, 20.54,
> 27.25, 20.698, 17.217, 20.698, 20.698, 15, 15, 8.35, 13.63, 20.34,
> 17.217, 5.69, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 7.5, 30,
> 17.217, 5, 5, 5, 7.93, 7.93, 7.93, 5, 7.71, 5, 17.217, 8.175,
> 8.175, 6.69, 6.69, 6.69, 10.875, 5.345, 5.345, 5.345, 5.345,
> 3.54, 10.755, 10.755, 10.755, 19.61, 20.145, 20.145, 20.145,
> 10.34, 5.35, 6.34, 10.34, 5.35, 10.34, 9.343, 9.343, 9.343, 9.343,
> 137.111, 17.217, 137.111, 17.217, 137.111, 137.111, 17.217, 46.055,
> 46.055, 17.217, 17.77, 20.54, 17.217, 17.217, 20.54, 11.75, 11.75,
> 17.217, 56.89, 20.54, 20.54, 55, 75, 75, 75, 19.61, 19.61, 19.61,
> 19.61, 19.61, 25.508, 20.698, 5.42, 5.42, 19.61, 19.61, 19.61,
> 20.698, 5.42, 25.508, 5.42, 17.217, 16.92, 9.343, 9.343, 19.61,
> 9.343, 19.61, 19.61, 16.92, 16.92, 16.92, 5.42, 5.42, 19.61,
> 5.42, 9.343, 19.61, 5.42, 5.42, 19.61, 9.343, 46.055, 17.217,
> 5.42, 19.61, 5.69, 19.61, 17.217, 9.343, 5.42, 19.61, 19.61,
> 17.217, 35.508, 5.42, 5.42, 5.42, 19.61, 17.217, 5.69, 19.61,
> 19.61, 8.35, 17.217, 5.69, 19.61, 5.69, 5.69, 17.217, 19.61,
> 16.92, 19.61, 17.217, 9.343, 5.42, 17.217, 17.217, 9.343, 33.456,
> 17.217, 46.055, 137.11, 56.89, 54.25, 17.217, 56.89, 55, 17.217,
> 46.055, 20.698, 46.055, 54.25, 27.25, 53.5, 5.69, 53.5, 20.698,
> 20.698, 5.69, 17.217, 17.217, 17.217, 17.217, 17.217, 5.69, 17.217,
> 17.217, 17.217, 11.75, 17.217, 5.69, 11.75, 11.75, 5.42, 5.42,
> 17.217, 5.69, 20.54, 11.75, 5.69, 11.75, 17.217, 121.505, 121.505,
> 121.505, 11.75, 5.69, 11.75, 11.75, 20.698, 121.505, 17.217,
> 11.75, 17.217, 5.42, 17.217, 17.217, 5.42, 5.69, 121.505, 17.217,
> 5.42, 46.055, 5.69, 20.698, 46.055, 15.86, 15.86, 5.69, 5.69,
> 11.75, 5.42, 46.055, 5.42, 10.349, 121.505, 15.86, 25.508, 17.217,
> 17.217, 11.75, 17.217, 17.217, 17.217, 17.217, 17.217, 17.217,
> 46.055, 17.217, 17.217, 17.217, 17.217, 17.217, 5.42, 17.217,
> 17.217, 17.217, 9.343, 85.271, 46.055, 17.217, 17.217, 46.055,
> 25.508, 25.508, 25.508, 20.698, 19.61, 11.75, 11.75, 11.75, 20.698,
> 11.75, 11.75, 20.45, 11.75, 20.45, 20.45, 7.5, 11.75, 11.75,
> 20.45), taken = c(10L, 2L, 12L, 1L, 4L, 1L, 10L, 3L, 5L, 5L,
> 1L, 5L, 3L, 3L, 5L, 2L, 4L, 2L, 1L, 4L, 1L, 1L, 2L, 1L, 3L, 1L,
> 3L, 2L, 3L, 2L, 2L, 1L, 4L, 1L, 5L, 2L, 2L, 2L, 2L, 2L, 1L, 1L,
> 1L, 1L, 1L, 1L, 3L, 1L, 3L, 2L, 2L, 3L, 2L, 3L, 1L, 2L, 3L, 2L,
> 2L, 2L, 1L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 2L, 1L,
> 4L, 2L, 3L, 2L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 1L, 2L, 2L,
> 2L, 1L, 2L, 3L, 1L, 1L, 1L, 3L, 1L, 1L, 1L, 1L, 1L, 3L, 4L, 2L,
> 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 2L, 3L, 2L, 1L, 1L, 5L, 1L, 1L,
> 5L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 1L, 3L, 1L, 2L, 2L, 2L,
> 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 2L,
> 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L,
> 1L, 2L, 1L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L, 1L,
> 1L, 3L, 1L, 1L, 1L, 2L, 1L, 2L, 3L, 1L, 2L, 1L, 2L, 2L, 2L, 2L,
> 2L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
> 1L, 1L, 1L, 1L, 1L, 2L, 2L, 3L, 2L, 1L, 2L, 2L, 2L, 1L, 3L, 2L,
> 2L, 3L, 3L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 3L, 2L, 4L,
> 2L, 2L, 2L, 1L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 3L, 3L, 2L, 2L, 4L,
> 1L, 2L, 4L, 3L, 3L, 4L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 3L, 1L, 1L,
> 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 3L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L,
> 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
> 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 3L, 2L, 3L, 2L, 3L, 1L, 2L,
> 2L, 1L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 2L, 1L, 2L), mass = c(13.8758,
> 2.77516, 16.65096, 1.38758, 5.55032, 1.38758, 13.8758, 4.16274,
> 6.9379, 6.9379, 1.38758, 6.9379, 4.16274, 4.16274, 6.9379, 2.77516,
> 5.55032, 2.77516, 1.38758, 5.55032, 1.38758, 1.38758, 2.77516,
> 1.38758, 4.16274, 1.38758, 4.16274, 2.77516, 4.16274, 2.77516,
> 2.77516, 1.38758, 5.55032, 1.38758, 6.9379, 2.77516, 2.77516,
> 5.49268, 5.49268, 5.49268, 2.74634, 2.74634, 2.74634, 2.74634,
> 2.74634, 2.74634, 8.23902, 1.295, 3.885, 2.59, 2.59, 3.885, 2.59,
> 3.885, 1.295, 2.59, 3.885, 2.59, 2.59, 2.59, 1.295, 2.59, 2.59,
> 2.59, 1.295, 1.295, 1.295, 1.295, 1.295, 1.295, 2.59, 3.885,
> 2.59, 1.295, 5.18, 2.59, 3.885, 2.59, 1.295, 2.59, 1.295, 1.295,
> 1.295, 2.59, 2.59, 2.59, 2.59, 1.295, 2.59, 2.59, 2.59, 1.295,
> 2.59, 3.885, 1.295, 1.295, 1.295, 3.885, 1.295, 1.295, 1.295,
> 1.295, 1.295, 3.885, 5.18, 2.59, 1.295, 1.4429, 2.8858, 1.4429,
> 1.4429, 2.8858, 1.4429, 1.4429, 2.8858, 4.3287, 2.8858, 1.4429,
> 1.4429, 7.2145, 1.4429, 1.4429, 7.2145, 1.4429, 1.4429, 1.4429,
> 1.4429, 1.4429, 2.8858, 4.3287, 1.4429, 1.4429, 1.4429, 4.3287,
> 1.4429, 2.8858, 2.8858, 2.8858, 1.4429, 2.877, 2.877, 2.877,
> 2.877, 5.754, 2.877, 2.877, 2.877, 2.877, 2.877, 2.877, 8.631,
> 2.877, 2.877, 5.754, 2.877, 2.877, 2.877, 2.877, 5.754, 2.877,
> 2.877, 2.877, 2.877, 5.754, 5.754, 2.877, 2.877, 2.877, 2.877,
> 2.877, 2.877, 1.3719, 1.3719, 1.3719, 1.3719, 1.3719, 2.7438,
> 1.3719, 2.7438, 1.3719, 1.3719, 2.7438, 2.7438, 1.3719, 1.3719,
> 1.3719, 1.3719, 2.7438, 1.3719, 1.3719, 2.7438, 1.3719, 2.7438,
> 1.3719, 1.3719, 1.3719, 1.3719, 1.3719, 4.1157, 1.3719, 1.3719,
> 1.3719, 1.3719, 4.1157, 1.3719, 1.3719, 1.3719, 2.7438, 1.3719,
> 2.7438, 4.1157, 1.3719, 2.7438, 1.3719, 2.7438, 2.7438, 2.7438,
> 2.7438, 2.7438, 1.3719, 1.3719, 2.7438, 2.7438, 1.3719, 1.3719,
> 1.3719, 2.8316, 2.8316, 2.8316, 2.8316, 2.8316, 2.8316, 2.8316,
> 2.8316, 2.8316, 2.8316, 2.8316, 2.8316, 2.8316, 2.8316, 2.8316,
> 2.8316, 2.8316, 2.8316, 2.8316, 2.8316, 2.8316, 1.17028, 2.34056,
> 1.17028, 1.17028, 1.17028, 1.17028, 1.17028, 1.17028, 2.34056,
> 2.34056, 3.51084, 2.34056, 1.17028, 2.34056, 2.34056, 2.34056,
> 1.17028, 3.51084, 2.34056, 2.34056, 3.51084, 3.51084, 2.34056,
> 1.17028, 2.03456, 4.06912, 2.03456, 2.03456, 2.03456, 4.06912,
> 2.03456, 2.03456, 2.03456, 2.03456, 2.03456, 2.03456, 2.03456,
> 2.03456, 2.03456, 2.03456, 2.03456, 2.03456, 2.03456, 2.03456,
> 2.03456, 2.03456, 2.03456, 2.03456, 2.03456, 2.03456, 2.03456,
> 2.03456, 2.03456, 2.03456, 2.03456, 2.03456, 2.03456, 2.03456,
> 2.03456, 2.03456, 2.03456, 2.03456, 2.03456, 2.03456, 2.03456,
> 2.03456, 2.03456, 2.03456, 2.03456, 4.06912, 2.03456, 2.03456,
> 2.03456, 2.03456, 2.03456, 2.03456, 1.189102041, 1.189102041,
> 1.189102041, 2.378204082, 3.567306123, 2.378204082, 4.756408164,
> 2.378204082, 2.378204082, 2.378204082, 1.189102041, 1.189102041,
> 1.189102041, 1.189102041, 2.378204082, 1.189102041, 2.378204082,
> 1.189102041, 3.567306123, 3.567306123, 2.378204082, 2.378204082,
> 4.756408164, 1.189102041, 2.378204082, 4.756408164, 3.567306123,
> 3.567306123, 4.756408164, 2.45232, 2.45232, 2.45232, 2.45232,
> 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 4.90464, 2.45232,
> 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 2.45232,
> 2.45232, 4.90464, 4.90464, 2.45232, 2.45232, 2.45232, 2.45232,
> 2.45232, 2.45232, 2.45232, 2.45232, 4.90464, 2.45232, 2.45232,
> 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 2.45232,
> 7.35696, 2.45232, 2.45232, 2.45232, 4.90464, 2.45232, 2.45232,
> 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 4.90464,
> 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 2.45232,
> 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 4.90464, 2.45232,
> 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 2.45232, 2.45232,
> 2.64516, 1.76344, 1.76344, 1.76344, 2.64516, 2.64516, 1.76344,
> 1.76344, 1.76344, 2.64516, 1.76344, 1.76344, 1.76344, 1.76344,
> 1.76344, 1.76344, 2.64516, 2.64516, 0.88172, 0.88172, 2.46532,
> 2.46532, 2.46532, 2.46532, 2.46532, 2.46532, 2.46532, 2.46532,
> 2.46532, 2.46532, 2.46532, 2.46532, 2.46532, 2.46532, 2.46532,
> 2.46532, 2.46532, 4.93064, 2.46532, 2.46532, 2.46532, 2.46532,
> 2.46532, 2.46532, 2.46532, 2.46532, 2.46532, 2.46532, 2.46532,
> 2.46532, 4.93064, 2.46532, 2.46532, 2.46532, 2.46532, 2.46532,
> 4.93064, 2.46532, 2.46532, 2.46532, 2.46532, 2.46532, 2.46532,
> 2.46532, 2.46532, 4.93064, 2.46532, 2.46532, 4.93064, 2.46532,
> 2.46532, 2.46532, 4.93064, 4.93064, 2.46532, 4.93064, 2.46532,
> 2.46532, 2.46532, 2.46532, 2.46532, 2.46532, 2.46532, 2.28446939,
> 2.28446939, 2.28446939, 2.28446939, 2.28446939, 2.28446939, 2.28446939,
> 2.28446939, 2.28446939, 2.28446939, 2.28446939, 2.28446939, 2.28446939,
> 2.28446939, 2.28446939, 2.28446939, 2.28446939, 2.28446939, 2.28446939,
> 2.28446939, 4.56893878, 4.568938778, 6.853408167, 4.56893878,
> 6.85340817, 4.56893878, 6.85340817, 2.28446939, 4.56893878, 4.568938778,
>
> 2.284469389, 4.56893878, 6.85340817, 4.56893878, 4.56893878,
> 6.85340817, 4.56893878, 2.28446939, 4.56893878, 2.28446939, 4.56893878
> )), .Names = c("year", "size", "distance", "taken", "mass"), class =
> "data.frame", row.names = c(NA,
> -550L))
>
> -----Original Message-----
> From: Ben Bolker [mailto:bbolker at gmail.com]
> Sent: Friday, April 08, 2011 4:15 PM
> To: Stratford, Jeffrey
> Cc: r-sig-ecology at r-project.org
> Subject: Re: [R-sig-eco] zero-truncated Poisson
>
> -----BEGIN PGP SIGNED MESSAGE-----
> Hash: SHA1
>
> On 04/08/2011 04:10 PM, Stratford, Jeffrey wrote:
>> Hi everyone,
>>
>> I'm attempting to run a zero-truncated Poisson using VGAM and I run
> into
>> this error:
>>
>> Error in if ((temp <- sum(wz[, 1:M, drop = FALSE] < wzepsilon)))
>> warning(paste(temp,  :
>>   argument is not interpretable as logical
>>
>> Here's the code I'm using
>>
>> blja <- read.csv("g:\\blja\\blja.csv", header=T)
>> library(VGAM)
>> glm1 <- vglm(blja$taken ~ blja$mass + blja$year,family=pospoisson,
>> data=blja)
>
>  Just a *very* quick suggestion: try
>
> glm1 <- vglm(taken ~ mass + year,family=pospoisson, data=blja)
>
>  and see if that helps.
>
>  Also try summary() on your data (and/or sapply(blja,class))
> to see if your data types are what you think they are (hint: R
> often converts numeric data to factors if it has glitches in it)
>
>  If neither of those helps, could you use dput() to post your data (or
> a subset thereof) in an easier-to-use format?
>
>>
>> We're looking at the number of acorns taken by jays using year (two
>> years) and how much (mass) they're taking.
>>
>> Our data looks like this:
>>
>> year  size    distance        taken   mass
>> one   small   30.8735 10      13.8758
>> one   small   121.505 2       2.77516
>> one   small   46.055  12      16.65096
>> one   small   46.055  1       1.38758
>> one   small   46.055  4       5.55032
>> one   small   9.343   1       1.38758
>> one   small   46.055  10      13.8758
>> one   small   46.055  3       4.16274
>> one   small   46.055  5       6.9379
>> one   small   85.271  5       6.9379
>> one   small   85.271  1       1.38758
>> one   small   85.271  5       6.9379
>> one   small   85.271  3       4.16274
>> ...
>>
>> Any suggestions (and alternatives!) would be appreciated!
>>
>> Thanks a bunch,
>>
>> Jeff
>>
>> ************************************
>> Dr. Jeffrey A. Stratford
>> Department of Health and Biological Sciences
>> 84 W. South Street
>> Wilkes University, PA 18766
>> jeffrey.stratford at wilkes.edu
>> 570-408-4761 (office)
>> 570-332-2942 (cell)
>> http://web.wilkes.edu/jeffrey.stratford/
>> ************************************
>>
>>
>>
>>       [[alternative HTML version deleted]]
>>
>> _______________________________________________
>> R-sig-ecology mailing list
>> R-sig-ecology at r-project.org
>> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
>
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