# [R] Generalized nonlinear mixed model function?

From: Phillip J van Mantgem <pvanmantgem_at_usgs.gov>
Date: Wed, 13 Feb 2008 11:18:59 -0800

(Sorry for the re-posting. My first attempt failed.)

From my reading it seems that nlme from the nlme package can fit nonlinear mixed models, while lmer from the lme4 package can fit generalized linear mixed models.

One alternative I’ve found is gnlmix from the repeated package, although this only allows for a single random effect.

Is there anything else out there that I have missed?

Thanks,
Phil

Phillip van Mantgem
USGS Western Ecological Research Center Sequoia and Kings Canyon Field Station
47050 Generals Highway #4
Three Rivers, CA 93271-9651 USA

The motivating problem is estimating average trends in forest mortality rates with unequally spaced census intervals. The census interval has an exponential effect on survival (i.e., p = annual survival, year.interval = census interval, and p^year.interval; annual morality = 1 - p), leading me to use a nonlinear model. Our data are composed of counts of live and dead trees, so I’ll need to use a binomial or poisson model.

Our data look like the following...

# plot identifier (random effect)
# eventually I’ll need to add another random term for species identity
plot <- rep(c("A", "B", "C"), each = 3)

# census identifier

census <- rep(1:3, 3)

# year of census

year <- c(1960, 1989, 2004, 1960, 1989, 2004, 1955, 1989, 2004)

# interval between census years
year.interval <- c(NA, 29, 15, NA, 29, 15, NA, 34, 15)

# count of live trees

n.live <- c(1509, 1249, 1106, 1986, 1616, 1383, 3529, 2831, 2511)

n.dead <- c(NA, 260, 143, NA, 370, 233, NA, 698, 320)

forest.mort <- data.frame(plot, census, year, year.interval, n.live, n.dead)

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