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Does anyone out in Fish-Sci land know of anyone conducting or who plans on conducting research on the two dam removals on the Penobscot River in Maine scheduled for 2007? I'm looking for a PhD opportunity looking at the ecological effects of these dam removals.
Thanks in advance,
Joe
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Joseph Schwartz
Pollution Control Coordinator
City of Carrollton
972-466-3533
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-----Original Message-----
From: Scientific forum on fish and fisheries
[mailto:[log in to unmask]]On Behalf Of Alberto Murta
Sent: Tuesday, June 22, 2004 11:40 AM
To: [log in to unmask]
Subject: Re: fisheries data analysis
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On Tuesday 22 June 2004 13:32, you wrote:
>
> I'm definitely no salesman for SPSS but it can also cope with more than one
> dependent variable (multivariate GLM). Do be cautious however, that the
> data (the dependent variable,s) approximates a normal distribution (check
> it), though you can transform it or apply a weighted least squares weight
> which might help, though won't work miracles.
Generalized linear models were developed precisely for data that does _not_
follow a Normal distribution (such as Poisson, or binomial)
>
> Only factors (categories) are compared as interactions (a, b, a*b etc) so
> it may be better having the data in this form rather than as co-variables,
> though you might loose precision.
If a variable is continuous (not a factor), then should be treated as such
>SPSS has a bewildering array of outputs
> but an important test is the homogeneity test (Levene's test of equality of
> error variance). If P < 0.05 then the model is invalid and you'll have to
> reassess your list of factors and co-variables. GLM is a parametric test
> and the usual assumptions of these must be employed.
>
> Post hoc testing should be done to check the form that significant
> interactions take. I usually do this by a suitable test between the
> dependent variable and the factor (anova etc) or co-variable (regression)
> responsible. These post hoc test have their own assumptions which you must
> note, too.
>
> Finally:
> GLM is a very powerful tool which can cope with a wide variety of data
> types, though the initial model must be valid.
>
Your message is full of misunderstandings about GLM. I strongly advise you to
stop using SPSS and use R instead (and read a good introductory book on GLM,
such as Dobson, A. J. (1990) An Introduction to Generalized Linear Models.
London: Chapman and Hall.)
The problem of software like SPSS is that it seems so easy to do everything,
that people never realise what they know (or don't know). Software like R is
more demanding about the theoretical knowledge of the user.
> Stephen Cotterell
>
> -Stephen Cotterell
> -Rm 4, 22 Portland Square
> -School of Earth, Ocean and Environmental Science
> -University of Plymouth
> -Drake Circus
> -PLYMOUTH
> -PL4 8AA
> -T: +44 (0) 1752 232411
> -F: +44 (0) 1752 232406
> -E: [log in to unmask]
>
>
> -----Original Message-----
> From: Scientific forum on fish and fisheries
> [mailto:[log in to unmask]]On Behalf Of Antonio Olinto
> Sent: 22 June 2004 12:52
> To: [log in to unmask]
> Subject: Re: fisheries data analysis
>
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>
> Dear Abad,
>
> Another statistic tool to access the magnitude of the effects of your
> variables is the analysis of deviance, given in the generalized linear
> models. In GLM you can use both category and continuous data, and also you
> can choose distributions other than gaussian, like gamma.
>
> There's an example in Quinn & Deriso 1999 (Quantitative Fish Dymamics). See
> pages 18 to 25. They check the effects of year, area and depth on CPUE. You
> can do the same with shell height. In the example they use normal
> distribution, log link function and all variables are categories. Depending
> on your data, you can improve this.
>
> I use the program R (www.r-project.org), it's a freeware and runs on
> different plataforms. If you want I can send you a pdf demostrating how to
> solve Quinn & Deriso's example in R.
>
> Best regards,
>
> Antonio Olinto Avila da Silva
> Sao Paulo Fisheries Institute
> Brazil
>
> Citando Antonio Hervas Abad <[log in to unmask]>:
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> >
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> >
> > I'm working with scallop (pecten maximus) stock assessment in Ireland.
> > One of the stock assessment procedures that i am carrying on is a
> > sampling programm from the landings. Shell height is recoreded from each
> > landing. After several measurements from differents vessels, differents
> > fishing areas and different time of the year i want to study the effect
> > of vessels, season and location, on the scallop size structure obtained
> > from the sampling. I understand that analysis of variance (ANOVA or
> > MANOVA) is the method to use for this study but maybe some of you has
> > experience working with similars studies and can advice to me of which
> > statistic analysis is the more suitable.
> >
> > Thanks very much
> >
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> -------------------------------------------------
> WebMail Bignet - O seu provedor do litoral
> www.bignet.com.br
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--
Alberto G. Murta
Institute for Agriculture and Fisheries Research (INIAP-IPIMAR)
Av. Brasilia, 1449-006 Lisboa, Portugal | Phone: +351 213027062
Fax:+351 213015948 | http://ipimar-iniap.ipimar.pt/pelagicos/
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