Heather Major writes:
>
> A small correction to my original post: the arrival
> fixed effect is a number (a count) and ranges between 0 - 52.
>
> Hello, I am new to R and working to understand the programming
> language and how the different tests work. I've jumped a bit
discouraged), it's polite to mention that you're doing so (to save
people the trouble of answering if the question has already been
answered elsewhere).
cheers
Ben Bolker
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Dixon, Philip M [STAT] pdixon@... writes:
Katie, I second Ben's suggestion to use functions in the drc
library, not nls(). Not only do those functions model binomial or
overdispersed binomial data, but they provide estimates and
confidence intervals for the EC50 and any other EC, e.g. EC10.
Katie Harding katiemharding@... writes:
I have insect size measurements that I fit to a Weibull curve
to determine EC50. This is my model
fit:model=nls(y~SSweibull(x,asym,drop,lrc,pwr))
I need to use the untreated buffer samples as the maximum size
(the left most asymptope). If I put
peterhouk1 . peterhouk@... writes:
Greetings Mollie -
Sure, the first general approach without explicitly telling R of my
grouping factor (not sure if that makes a difference, but second example
below does this). My best guess is that the full model has significance,
but the random
and plot is the random variable
and you're testing for an interaction between year and plot.
You might want to try r-sig-mixed-mod...@r-project.org with this one.
(Do mention that you tried r-sig-ecology first)
Ben Bolker
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Lee, Laura laura.lee@... writes:
Hi all!
Is there a package or function to model data using
a k-truncated negative binomial distribution? I know the
truncated function in the 'aster' package can be used
to simulate data, but I need to model.
Thanks!
Laura
You could write a
this thread (which also suggests alternatives)
http://r.789695.n4.nabble.com/combining-P-values-using-Fisher-s-method-
td832459.html [URL broken]
or
http://finzi.psych.upenn.edu/R/library/MADAM/html/fisher.method.html
good luck
Ben Bolker
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, and a variety of references
on observation-level random effects, at
http://glmm.wikidot.com/faq#overdispersion
Ben Bolker
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[oops, cc'd to r-sig-mixed by mistake. Sorry. Re-posting to
r-sig-ecology.]
Original Message
Subject: Fwd: Re: [R-sig-eco] cozigam
Date: Wed, 13 Jun 2012 07:40:08 -0400
From: Ben Bolker bbol...@gmail.com
To: r-sig-mixed-mod...@r-project.org r-sig-mixed-mod...@r-project.org
at the new Zuur et al. book on zero-inflated models for
guidance?
thanks
On Wed, Jun 13, 2012 at 4:10 PM, Ben Bolker bbol...@gmail.com
mailto:bbol...@gmail.com wrote:
[cc'ing back to r-ecology]
Original Message
Subject:Re: [R-sig-eco] cozigam
Malin Pinsky malin.pinsky@... writes:
I'm having problems fitting a mixed-effects model for an ecological
meta-analysis, and I'm curious if anyone has advice. In particular,
it's pretty clear that the variance in the residuals increases with
the predicted mean, but my normal fixes don't seem
Shawn O'Neil oneil.shawnt@... writes:
I am running mixed effects models using the nlme package. I am working
with a dataset where spatial autocorrelation is inherent in the response
variable. I have tested each of the five correlation structures that are
available for mixed models in nlme,
Jakub Szymkowiak qbaszym@... writes:
Duncan, Rob, Peter - thanks a lot for suggestions about literature!
Peter - by MCMC methods I mean Markov Chain Monte Carlo ;)
I have great book by Ben Bolker - Ecological Models and Data in R, but I'm
searching for more examples of using MCMC
Lara R. Appleby 04 Lara.R.Appleby.04@... writes:
I've done a standard two way ANOVA using glm on the dependent
variable clutchsize with the two factors treatment (which has 3
levels called 1, 2, and 3) and species (which has two levels
called 1 and 2). Apparently there is no significant
idea to test your
objective function outside of mle2() first.
Ben Bolker
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Johannes Radinger JRadinger@... writes:
thank you all for you tips with predict, predict.lm etc.
From summary(lm-model), I can get the Estimate and the Standard
Error for intercept and slopes. With confint I can get confidence
intervals (?) for these parameters for different levels.
I want
lgj200306 lgj200306@... writes:
Thaks David very much, but how can I
improve my model? Should I change my likelihood function
or do some thing else?
Best wishes for all list members!
Your choices are:
(1) if the final result looks sensible, and none of the final
predicted values
Chris Howden chris@... writes:
U could try the predict function with se.fit=true. I believe this
should give u the predicted score and se and u can calculate CI from
there.
U'll have to create an input matrix with the score u want to predict for.
Unfortunately, predict() doesn't
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On 05/19/2011 06:24 PM, Fred Takahashi wrote:
Ben and Ruben, thank you very much for the suggestions. I started
the “homework” and I had the impression that I can forget to find a
GLMM implementation with Tweedie distribution (in a preliminar GLM,
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On 05/19/2011 02:41 PM, Stolen, D Eric (KSC-IHA-4400)[Innovative Health
Applications LLC] wrote:
Hello; I am working on a logistic regression model in which I have
quasi-complete separation on an explanatory variable (see table
below). The
If this isn't already answered:
I don't quite understand the question: what do you mean by do a
complete data set from an object in R? What do you mean by the
subsetting is dangerous ... as you need to specify the levels for all
your factors again?
(What do your 3000 columns of data
+year),
data=subset(blja,mass10),
start=list(logmu=1))
It also changes the estimate of the mass effect considerably:
confint(m1)
confint(m2)
Ben Bolker
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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, :
On 11-04-03 11:05 PM, Jane Shevtsov wrote:
Thanks, but that doesn't quite work. Using your toy example gives an
error about variable lengths.
z - matrix(1:9,nrow=3,dimnames=list(1:3,4:6))
z.melt - melt(z)
z.melt
X1 X2 value
1 1 4 1
2 2 4 2
3 3 4 3
4 1 5 4
5
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On 11-04-02 10:39 PM, Jane Shevtsov wrote:
I have data in matrix form from a parameter scan of a food web
simulation. (The matrix entry M[i,j] gives the variable of interest
for a web size i and connectance j.) How would I find an equation
, but if the phase of the
cycle is consistent through the whole time period you could add a
sinusoidal component (with known phase and period) to the model ...
Ben Bolker
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On 03/17/2011 06:27 PM, Gabe Strain wrote:
Hi all, I am relatively new to R and am having problems modelling my
data. I sampled a single salamander species along streams (Sites,
random effect, n=16) over five months (fixed effect) with three
methods (TRMT, fixed effect).
Philosophically,
On 11-01-20 12:24 PM, Iannone, Basil wrote:
Dear R users,
I am having a problem with interpreting anova results of a linear mixed
effects model.
The data I am analyzing is from study that was set up as a randomized
block design with two factors at two levels. The model is:
m1 -
On 11-01-06 12:16 PM, Jarrett Byrnes wrote:
Ah, so, you have factors that are no longer present in the data. I
use a quick function I wrote called purgef to get rid of zombie
factors.
purgef-function(x) lapply(x, function(x) x[drop = TRUE])
So, here, before your boxplot
dat$ageclass
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Works for me?
occ - c(2,5,3)
n1 - nullModel.spsite(occ, iter=5, plots=10)
n2 - nullModel.spsite(occ, iter=5, plots=10)
n1[,,1]
n2[,,1]
all(n1[,,1]==n2[,,1]) ## FALSE
Can you show an example that doesn't work?
(This is with R 2.12.1, but
treatment of multi-level modeling in general] or (3) my
book [not as specific to Bayes/WinBUGS, but long on general explanation]
for tackling your real problem.
good luck ...
Ben Bolker
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that this can be done using an object/approach called 'copulas' but
this is a whole new can of worms which I have not opened myself ...
Ben Bolker
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before I waste time doing it, has anyone written code that uses
Rgraphviz to translate demographic (Leslie/Lefkovitch) matrices into
state diagrams? Can't find anything on the web ...
Ben Bolker
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library(Rgraphviz)
X - matrix(c(0, 0.1, 1.5,
1, 0, 0,
0, 0.4, 0.2),
byrow=TRUE,
nrow=3)
dimnames(X) - list(c(J,A1,A2),c(J,A1,A2))
X
Xg - as(t(X),graphNEL)
plot(Xg,recipEdges=distinct)
ew - as.character(unlist(edgeWeights(Xg)))
ew -
other way
where there is no natural denominator, I would either (sigh) use
transformations or look into beta regression.
I'd be interested to hear other opinions.
good luck,
Ben Bolker
Boughton, Elizabeth H., Pedro F. Quintana-Ascencio, and Patrick J.
Bohlen. 2010. Refuge effects of Juncus
for
autocorrelation,
nonlinear trends, etc.), I don't know that it would be worth it except
for the
purposes of learning how to do it.
Ben Bolker
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On 10-09-20 12:10 PM, Conrado Galdino wrote:
Dear all,
I would like to know how to do a 'symmetrical dot density graphic'
(example image in the link http://imgfrog.com/?v=2fP0.png ) with
R. I tried hard but with no success. If some one can help
On 10-09-09 03:52 PM, Jorge Velasquez wrote:
Hi there,
Is anyone aware of a method I could use to calculate AIC values for
models obtained using the thin plate splines function (Tps) from the
package fields?
Thanks,
Jorge
Don't know, but does the mgcv package also fit the model you
in ecology
with R. Chapter 11 Zero-Truncated and Zero-Inflated Models for Count Data.
Am 31/08/2010 11:21, schrieb Ben Bolker:
A few comments:
are the habitats replicated? If not, you have a fairly serious
experimental design problem -- you can't statistically distinguish
between the measured
Chris Mcowen chrismco...@... writes:
Hi Philip,
Thanks very much for this, i was completely unaware. I have read various
papers using lmer to calculate the
AIC statistic and none have mentioned this?
I have just run through a random section of my models with this correction,
however
hypotheses about which factors are important**, simply fit
the full model and base your inference on the estimates and confidence
intervals from the full model.
good luck,
Ben Bolker
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On 10-08-04 01:13 PM, Chris Mcowen wrote:
Hi Ben,
That is great thanks.
whether you select models via p-value or AIC *should* be based on whether you
are trying to test hypotheses or make predictions
I have 7 factors of which 5 have been shown, theoretically and empirically, to
,
and i have never used a model to predict unknown values. But i am sure i will
come to it if read around!
Thanks for all your help, it is greatly appreciated
On 4 Aug 2010, at 20:09, Ben Bolker wrote:
On 10-08-04 01:13 PM, Chris Mcowen wrote:
Hi Ben,
That is great thanks.
whether
to get the book by Zuur et al on mixed models in ecology.
Ben Bolker
On Fri, Jul 30, 2010 at 11:49 AM, Javier Martinez
javi.martinez.lo...@gmail.com wrote:
Hello again Mr. Bolker,
I have now tried glmmPQL and look very promising because I have in
fact the expected results. Since I do not get
to figure out AICc?
Great question. I think I would probably go with 32, but I'd like to
hear other opinions.
Ben Bolker
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?
Thanks,
Caitlin
Please don't cross-post if you can avoid it (you just posted an almost
identical query to r-sig-mixed-models, which was probably a better
venue) ...
cheers
Ben Bolker
--
Ben Bolker
Associate professor, Biology Dep't, Univ. of Florida
bol...@ufl.edu / www.zoology.ufl.edu
throw everything into the models), but that data-driven
model selection has some very fundamental problems ...
cheers
Ben Bolker
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.
means / standard errors ...
good luck,
Ben Bolker
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the fitted model many times
and calculate the summary statistics for the simulated data
(which represent the null hypothesis that the data really do
come from the fitted model) and see where your observed
statistic falls in the distribution.)
~cheers
~ Ben Bolker
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A couple more suggestions:
Mark Bravington's debug package (mvbdebug, available
on CRAN) can be a more powerful debugging tool if you
want to do a more graphical stepping-through your code;
a cruder but effective technique is to put a cat()
statement into your objective function, e.g.
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