----------------------------------------------------------------------
Message: 1
Date: Mon, 16 Feb 2015 12:28:28 +0530
From: Rajendra Mohan panda <rmp.iit.kgp at gmail.com>
Cc: "r-sig-ecology at r-project.org" <r-sig-ecology at r-project.org>
Subject: Re: [R-sig-eco] Using multiple species data for gam
Message-ID:
<CAGTzHJv7GzSd6UjQH3Oa9Xn+PykLUAbrcZgzFLd=gocGbsudGQ at mail.gmail.com>
Content-Type: text/plain; charset="UTF-8"
Dear Dr Greg Guerin
Lot many thanks for the advise. I have tried the code (not with 1000sp)
successfully but I find some warning messages which I need some help
regarding the same. Mentioned here for your kind advise:
glmer(Response ~ Temp + Pptn + Moisture+Soil+Slope+Aspect+Altitude+(1 +
Temp + Pptn+
Moisture+Soil+Slope+Aspect+Altitude|Species),family=binomial(link="logit")
,
data = SP)
Generalized linear mixed model fit by maximum likelihood (Laplace
Approximation) ['glmerMod']
Family: binomial ( logit )
Formula: Response ~ Temp + Pptn + Moisture + Soil + Slope + Aspect +
Altitude +
(1 + Temp + Pptn + Moisture + Soil + Slope + Aspect + Altitude |
Species)
Data: SP
AIC BIC logLik deviance df.resid
470.1219 748.2537 -191.0610 382.1219 4066
Random effects:
Groups Name Std.Dev. Corr
Species (Intercept) 1.19992
Temp 0.77083 0.27
Pptn 0.07742 -0.91 -0.64
Moisture 0.30603 -0.50 -0.91 0.79
Soil 1.17142 -0.44 -0.93 0.75 0.99
Slope 0.67329 -0.39 -0.53 0.56 0.32 0.31
Aspect 0.21153 -0.90 -0.65 1.00 0.82 0.78 0.50
Altitude 0.24942 -0.92 -0.63 1.00 0.78 0.74 0.55 1.00
Number of obs: 4110, groups: Species, 5
Fixed Effects:
(Intercept) Temp Pptn Moisture Soil
Slope Aspect Altitude
-8.8215831 0.3879856 0.0002847 -0.0816050 -0.1431987
-0.6200528 0.0116070 -0.0003179
Warning messages:
1: Some predictor variables are on very different scales: consider
rescaling
2: In commonArgs(par, fn, control, environment()) :
maxfun < 10 * length(par)^2 is not recommended.
3: In (function (fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf,
:
failure to converge in 10000 evaluations
4: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 37.0466 (tol = 0.001,
component
1)
5: In checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge: degenerate Hessian with 7 negative
eigenvalues
When I run nlmer, I find the following error message for which I also need
some help also because my data are mostly non-linear.
Your guidance will be highly appreciated
With Best Regards
Rajendra M Panda
SWR, IIT Kharagpur, India
On Thu, Feb 12, 2015 at 5:08 AM, Greg Guerin <greg.guerin at adelaide.edu.au>
wrote:
Hello,
not sure if you are looking to run the GLM/GAMs individually but in one
run, or as a community composition type model to test main
drivers/correlates of combined species occurrences. If the latter,
another
option is a GLMM with species having random slope to allow responses to
differ. For this, you would need to stack the occurrence matrix into a
?long? format (a row for the presence/absence of each species in each
plot
with corresponding predictor variables and a field for species).
Response Species Temp Pptn
0 Sp1 30 1000
1 Sp2 30 1000
1 Sp3 30 1000
In lme4, something like:
lmer(Response ~ Temp + Pptn + (1 + Temp + Pptn|Species),
family=binomial(link="logit"), data)
An example with R code in the Appendix:
http://dx.doi.org/10.1111/jvs.12111
Greg
--
Dr Greg Guerin
Postdoctoral Fellow
School of Biological Sciences, Faculty of Science
The University of Adelaide
CRICOS Provider Number 00123M
-----------------------------------------------------------
IMPORTANT: This message may contain confidential or legally privileged
information. If you think it was sent to
you by mistake, please delete all copies and advise the sender. For the
purposes
of the SPAM Act 2003, this
email is authorised by The University of Adelaide.
----------------------------------------------------------------------
Message: 1
Date: Tue, 10 Feb 2015 09:28:14 -0700
From: Tim Meehan <tmeeha at gmail.com>
To: Rajendra Mohan panda <rmp.iit.kgp at gmail.com>
Cc: "r-sig-ecology at r-project.org" <r-sig-ecology at r-project.org>
Subject: Re: [R-sig-eco] Using multiple species data for gam
Message-ID:
<
CAMTWOzpv58RRX2ocgTCpXh1EPAcxzkSCGKHvdUsaytGhCJH8MQ at mail.gmail.com>
Content-Type: text/plain; charset="UTF-8"
If you want to do this in a glm framework, you might look into the
Dear All
I have >1000 species with presence and absence (0 or 1) values and
seven corresponding predictor variables. If I can run gam/glm for the
data
using all species data simultaneously vs predictors. Data are
in
columns against their GPS locations (see below). I know it is
to
do separately for each species.
Your kind response is highly appreciated.
Sites Sp1 Sp2 Sp3 Alt Temp Pptn Ft
1A 0 1 1 20 30 1000 Evergreen
With Best Regards
Rajendra M Panda
School of Water Resources
Indian Institute of Technology Kharagpur, India
[[alternative HTML version deleted]]