Re: [R-sig-eco] Binomial GLMM or GAMM with random intercept and temporal correlation
Thanks for the responses. I am going to try modelling the binomial data with a covariate representing the previous value and see if that's sufficient. I haven't used INLA, I have some experience with JAGs but not sure where to start with modelling temporal structures there. I need to model both regularly space and regularly spaces correlation. For the gaussian data I have, I am using lme. Is it ok to select temporal correlation strucutres based on AIC between REML models. I am trying a number of correlation structures (e.g. corSpher, corExp, corAR1). Thanks Sam -- View this message in context: http://r-sig-ecology.471788.n2.nabble.com/Binomial-GLMM-or-GAMM-with-random-intercept-and-temporal-correlation-tp7579035p7579046.html Sent from the r-sig-ecology mailing list archive at Nabble.com. ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Manova and DFA analysis
Hi, Those aren't really R questions, nor are they questions we can answer without a great deal more information. Your very best course would be to set up an appointment with a local statistician who can help you. If that isn't an option for you, a statistics forum would be a better place to ask. Sarah On Wed, Sep 3, 2014 at 7:10 AM, Mohammed Almalki m11m...@hotmail.com wrote: Dear all, I am new user for R program and I am looking for somebody to help me with Manova and discriminant function analysis DFA . I have four measurement traits for bird species (weight, wing length, tarsus length and bill length) and I would like to test for differences in body size between males and females of this species. FIRST, I applied MANOVA using (weight, wing length, tarsus length and bill length) as dependent variables and sex as an independent variable. In order to identify the significance of sex differences for each dependent variable using this form: rm data1-read.csv(C:/Users/Desktop/CP/CP_NOMISS.csv) names (data1) attached(data1) head(data1) manova1 - manova (cbind (Weight, Wing.Length, Tarsus.Length, Bill.Length)~ as.factor (Sex), data=data1) summary (manova1) summary.aov(manova1) After that I got four tables (one table for each variable) My questions are: 1. Is what I did correct and enough to get Manova results? 2. what is the most important result can describe the difference is it F value or Pr(F) value 2. How I can describe the results in figure? SECOND, I applied discriminant function analysis (DFA) on the four morphological characters using the package MASS in order to identify the variable that differed most between males and females using this form: rm library (MASS) data1-read.csv(C:/Users/Desktop/CP/CP_NOMISS.csv) head(data1) attach (data1) data1 plot(data1[ ,c(2,3,4,5)], col=data1[ ,1]) data1.lda - lda(SEX~WG + WL + TL + BL, data=data1) data1.lda After that I got this result: Coefficients of linear discriminants: LD1 WG -0.001040297 WL -0.011554912 TL 0.030233583 BL 0.498226667 1.Is what I did enough to say the variable that differed most between males and females is BL 0.498. And does this difference is reliable? OR there are other steps I have to do. Please excuse the long email. Thank you very much in advance for any help you can provide. Best regards,Mohammed -- Sarah Goslee http://www.functionaldiversity.org ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] Standardising and transformation of explanatory/independent/predictor variables for multiple regression analysis
Dear R-sig-ecology, I have spent some time trawling the internet, and seem to come across slight conflicting advice regarding the standardisation and transformation of variables prior to multiple regression analysis (e.g. LM, LME/GLS, GLM, GLMM, GAM, GAMM). I searched the archives here and I don't think this is a repeat, but I apologise if it is. 1. I understand that standardisation (subtract mean and divide by standard deviation) is important within a Bayesian environment and when using programs such as Rjags. However within frequentist packages (e.g. lme4, MASS etc) under what (if any) circumstances is it necessary? 2. Are transformations (e.g. log, sqrt etc) necessary for non-normal (highly skewed) explanatory variables or where extreme/outliers are observed. Some literature says this is necessary, other say it is not. Is the current consensus that transformations are generally not required on predictor/explanatory variables? Thank you Sam [http://www.plymouth.ac.uk/images/email_footer.gif]http://www.plymouth.ac.uk/worldclass This email and any files with it are confidential and intended solely for the use of the recipient to whom it is addressed. If you are not the intended recipient then copying, distribution or other use of the information contained is strictly prohibited and you should not rely on it. If you have received this email in error please let the sender know immediately and delete it from your system(s). Internet emails are not necessarily secure. While we take every care, Plymouth University accepts no responsibility for viruses and it is your responsibility to scan emails and their attachments. Plymouth University does not accept responsibility for any changes made after it was sent. Nothing in this email or its attachments constitutes an order for goods or services unless accompanied by an official order form. [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
[R-sig-eco] Four available places on GLMM course in Banff
There are four remaining places on the following course: Course: Introduction to MCMC, Linear mixed effects models and GLMM with R When: 22-26 September, 2014 Where: Parks Canada, Banff, Canada Flyer: http://www.highstat.com/Courses/Flyer2014_09Banff.pdf Course website: http://www.highstat.com/statscourse.htm Kind regards, Alain Zuur -- Dr. Alain F. Zuur First author of: 1. Beginner's Guide to GAMM with R (2014). 2. Beginner's Guide to GLM and GLMM with R (2013). 3. Beginner's Guide to GAM with R (2012). 4. Zero Inflated Models and GLMM with R (2012). 5. A Beginner's Guide to R (2009). 6. Mixed effects models and extensions in ecology with R (2009). 7. Analysing Ecological Data (2007). Highland Statistics Ltd. 9 St Clair Wynd UK - AB41 6DZ Newburgh Tel: 0044 1358 788177 Email: highs...@highstat.com URL: www.highstat.com ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
Re: [R-sig-eco] Standardising and transformation of explanatory/independent/predictor variables for multiple regression analysis
Dear Sam, I hear your concern and I sympathise. The reason for the conflicting advice, in my opinion, is partly historical and partly due to academic heredity. When people first started doing statistical analyses, they didn't have computers and all calculations had to be done by hand. This, coupled with a statistical theory in its infancy, limited the choice of analysis methods. The result was the pragmatic approach of altering-your-data-to-fit-the-method. There still is, of course, some good reasons to do this, but only sometimes. Now to answer your questions. Standardisation of covariates doesn't have inferential benefits. That is the model you fit will still be the same irrespectively. If you transform your covariates (by a non-linear transformation) then the model will change. The reason for standardising is to avoid computational issues (like numerical underflow and overflow) and some believe it helps to place priors on in a Bayesian analysis. The reason for transforming is quite different. It is done when you believe that the scale of the covariate is different to that measured. When fitting smooths (GAM(M)s) then the scale shouldn't matter so much anyway, but there still will be some dependence through the location of knots and the distance between points in covariate space. Observations with outlying covariates are likely to have high leverage (they have an excessive amount of influence on the analysis result). Some would argue that you should transform these covariates to account for them. I would only transform if I thought the scale was wrong, or there were other (larger) issues with the data/analysis. In preference, I would try to do an analysis that reduced the influence of these covariate values. The extreme case is to remove that observation altogether (assume that the observation actually comes from a different sampling frame than you are interested in). A less extreme approach would be to down-weight the observation, or use bootstrap, or resistant/robust methods. These are just suggestions that I'm not overly familiar with. I have used them before but I need to look them up each time). I hope that this helps, Scott On 04/09/14 03:34, Samantha Cox wrote: Dear R-sig-ecology, I have spent some time trawling the internet, and seem to come across slight conflicting advice regarding the standardisation and transformation of variables prior to multiple regression analysis (e.g. LM, LME/GLS, GLM, GLMM, GAM, GAMM). I searched the archives here and I don't think this is a repeat, but I apologise if it is. 1. I understand that standardisation (subtract mean and divide by standard deviation) is important within a Bayesian environment and when using programs such as Rjags. However within frequentist packages (e.g. lme4, MASS etc) under what (if any) circumstances is it necessary? 2. Are transformations (e.g. log, sqrt etc) necessary for non-normal (highly skewed) explanatory variables or where extreme/outliers are observed. Some literature says this is necessary, other say it is not. Is the current consensus that transformations are generally not required on predictor/explanatory variables? Thank you Sam [http://www.plymouth.ac.uk/images/email_footer.gif]http://www.plymouth.ac.uk/worldclass This email and any files with it are confidential and intended solely for the use of the recipient to whom it is addressed. If you are not the intended recipient then copying, distribution or other use of the information contained is strictly prohibited and you should not rely on it. If you have received this email in error please let the sender know immediately and delete it from your system(s). Internet emails are not necessarily secure. While we take every care, Plymouth University accepts no responsibility for viruses and it is your responsibility to scan emails and their attachments. Plymouth University does not accept responsibility for any changes made after it was sent. Nothing in this email or its attachments constitutes an order for goods or services unless accompanied by an official order form. [[alternative HTML version deleted]] ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology -- Scott Foster CSIRO E scott.fos...@csiro.au T +61 3 6232 5178 Postal address: CSIRO Marine Laboratories, GPO Box 1538, Hobart TAS 7001 Street Address: CSIRO, Castray Esplanade, Hobart Tas 7001, Australia www.csiro.au ___ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
