Re: [R] output too large to display all
Dear All Thanks for the suggestions. Mark's suggestion to specify corr=FALSE did the job and removed the reams of correlations that were being outputted from the model and using up all the output space. Thanks Christine --On 06 July 2009 12:44 -0600 Lyman, Mark mark.ly...@atk.com wrote: Take a look at the print method for the mer class, class?mer. I believe setting the correlation argument to FALSE will give you what you want. See the examples. Mark Lyman, Statistician Engineering Systems Integration, ATK Hi R Users, Hopefully a very simple solution, but I am stumped nevertheless. I am running glmer in which the output is too large so that not all the correlations are displayed. I expanded the max.print as recommended on this website. However, this still does not allow me to see the relevant information regarding the model fit (AIC etc), random and fixed effects. I have not been able to find any similar posts. I would be very grateful if someone could specify what I need to state in order to view all the results generated from the model. Many thanks in advance, Christine __ R-help at r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] output too large to display all
Hi R Users, Hopefully a very simple solution, but I am stumped nevertheless. I am running glmer in which the output is too large so that not all the correlations are displayed. I expanded the max.print as recommended on this website. However, this still does not allow me to see the relevant information regarding the model fit (AIC etc), random and fixed effects. I have not been able to find any similar posts. I would be very grateful if someone could specify what I need to state in order to view all the results generated from the model. Many thanks in advance, Christine __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Replacing 0s with NA
Hello I have a dataset in which I would like to replace 0s with NAs. There is a lot of information on how to replace NAs with 0, but I have struggled to find anything with regards to doing the reverse. Any recommendations would be great. Cheers Christine __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Random effects aov
Dear All I have a repeated measures design in which abundance was measured repeatedly over 10 months in three treatments (Tortoise A; Tortoise B and control) established in 6 blocks, i.e. crossed fixed effects. My original design incorporated two tortoises per treatment, however as fieldwork goes I ended up losing some animals. Rather than lose a couple of enclosures in the analysis and have to do a lmer, I thought I could include tortoise weight as an explanatory variable. For my treatments, tortoise weight in the control always = 0, while in general Tortoise A is twice as large as Tortoise B except when I lost animals. Is this the correct model? aov(Tel.ab~Tort.W+Treatment*Month+Error(Month/Block)) Or should tortoise weight be nested in Treatment, i.e not included as a fixed factor but including the fact that tortoises species may have an effect? I am utterly confused now as to whether that should be the case as to some extent Tort.W and Treatment are correlated. Any help would be much appreciated. Many thanks Christine -- Christine Griffiths School of Biological Sciences University of Bristol Woodland Road Bristol BS8 1UG Tel: 0117 9287593 Fax 0117 925 7374 christine.griffi...@bristol.ac.uk http://www.bio.bris.ac.uk/research/mammal/tortoises.html __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Overdispersion using repeated measures lmer
Thanks. I did try using quasipoisson and a negative binomial error but am unsure of the degree of overdispersion and whether it is simply due to missing values. I am investigating to see if I can replace these missing values so that I can have a balanced orthogonal design and use lme or aov instead which is easier to interpret. Any ideas on whether it is feasible to replace missing values for a small dataset with repeated measures? I have 6 blocks with 3 treatments sampled over 10 months. Two blocks are missing one treatment, albeit a different one. Also any suggestions about how I would go about this would be much appreciated. I am also unsure of whether my random effects (Month|Block) for repeated measures with random slope and intercept is correct and whether (1|Month) + (1|Block) represents repeated measures. Any confirmation would be great. Cheers Christine Christine Griffiths-2 wrote: Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My fixed effects are treatment and Month, and my random effects are Block which was repeated sampled. My model is: Model-lmer(Count~Treatment*Month+(Month|Block),data=dataset,family=poisson(link=sqrt)) Is this the only way in which I can specify my random effects? I.e. can I specify them as: (1|Block)+(1|Month)? When I run this model, I do not get any residuals in the error term or estimated scale parameters and so do not know how to check if I have overdispersion. Below is the output I obtained. Generalized linear mixed model fit by the Laplace approximation Formula: Count ~ Treatment * Month + (Month | Block) Data: dataset AIC BIC logLik deviance 310.9 338.5 -146.4292.9 Random effects: Groups NameVariance Std.Dev. Corr Block (Intercept) 0.06882396 0.262343 Month 0.00011693 0.010813 1.000 Number of obs: 160, groups: Block, 6 Fixed effects: Estimate Std. Error z value Pr(|z|) (Intercept) 1.624030 0.175827 9.237 2e-16 *** Treatment2.Radiata0.150957 0.207435 0.728 0.466777 Treatment3.Aldabra -0.005458 0.207435 -0.026 0.979009 Month-0.079955 0.022903 -3.491 0.000481 *** Treatment2.Radiata:Month 0.048868 0.033340 1.466 0.142717 Treatment3.Aldabra:Month 0.077697 0.033340 2.330 0.019781 * --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Correlation of Fixed Effects: (Intr) Trt2.R Trt3.A Month T2.R:M Trtmnt2.Rdt -0.533 Trtmnt3.Ald -0.533 0.450 Month -0.572 0.585 0.585 Trtmnt2.R:M 0.474 -0.882 -0.402 -0.661 Trtmnt3.A:M 0.474 -0.402 -0.882 -0.661 0.454 Any advice on how to account for overdispersion would be much appreciated. Many thanks in advance Christine -- Christine Griffiths School of Biological Sciences University of Bristol Woodland Road Bristol BS8 1UG Tel: 0117 9287593 Fax 0117 925 7374 christine.griffi...@bristol.ac.uk http://www.bio.bris.ac.uk/research/mammal/tortoises.html __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- View this message in context: http://www.nabble.com/Overdispersion-using-repeated-measures-lmer-tp23595955p23612349.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] Overdispersion using repeated measures lmer
Dear All I am trying to do a repeated measures analysis using lmer and have a number of issues. I have non-orthogonal, unbalanced data. Count data was obtained over 10 months for three treatments, which were arranged into 6 blocks. Treatment is not nested in Block but crossed, as I originally designed an orthogonal, balanced experiment but subsequently lost a treatment from 2 blocks. My fixed effects are treatment and Month, and my random effects are Block which was repeated sampled. My model is: Model-lmer(Count~Treatment*Month+(Month|Block),data=dataset,family=poisson(link=sqrt)) Is this the only way in which I can specify my random effects? I.e. can I specify them as: (1|Block)+(1|Month)? When I run this model, I do not get any residuals in the error term or estimated scale parameters and so do not know how to check if I have overdispersion. Below is the output I obtained. Generalized linear mixed model fit by the Laplace approximation Formula: Count ~ Treatment * Month + (Month | Block) Data: dataset AIC BIC logLik deviance 310.9 338.5 -146.4292.9 Random effects: Groups NameVariance Std.Dev. Corr Block (Intercept) 0.06882396 0.262343 Month 0.00011693 0.010813 1.000 Number of obs: 160, groups: Block, 6 Fixed effects: Estimate Std. Error z value Pr(|z|) (Intercept) 1.624030 0.175827 9.237 2e-16 *** Treatment2.Radiata0.150957 0.207435 0.728 0.466777 Treatment3.Aldabra -0.005458 0.207435 -0.026 0.979009 Month-0.079955 0.022903 -3.491 0.000481 *** Treatment2.Radiata:Month 0.048868 0.033340 1.466 0.142717 Treatment3.Aldabra:Month 0.077697 0.033340 2.330 0.019781 * --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Correlation of Fixed Effects: (Intr) Trt2.R Trt3.A Month T2.R:M Trtmnt2.Rdt -0.533 Trtmnt3.Ald -0.533 0.450 Month -0.572 0.585 0.585 Trtmnt2.R:M 0.474 -0.882 -0.402 -0.661 Trtmnt3.A:M 0.474 -0.402 -0.882 -0.661 0.454 Any advice on how to account for overdispersion would be much appreciated. Many thanks in advance Christine -- Christine Griffiths School of Biological Sciences University of Bristol Woodland Road Bristol BS8 1UG Tel: 0117 9287593 Fax 0117 925 7374 christine.griffi...@bristol.ac.uk http://www.bio.bris.ac.uk/research/mammal/tortoises.html __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
