Re: [R] Using MCMC sampling to estimate p values with a mixed model
Hi, Thank you for the replies. Yes I would say it does resemble a randomized block design in that each person completed 4 out of a possible 16 versions/conditions of the vignette. There was approximately 20 responses in each condition. The commands I have been using are; modela <-lmer(DV ~ IV*IV*IV*IV + Co-variate + Co-variate + (1|Subject)) anova(modela). I have been working out the denominator degrees of freedom myself at the minute, just for reporting. Do you mean I should use the df and look up in F critical value table rather than use pMCMC bbolker? Robert I see what you mean, some of my hypotheses are two tailed, so for these F < 1 is a signficant finding, just in the other direction. For my one tailed hypotheses then a value of less than 1 would not be signficant? Thanks again it is really helpful as most of the lecturers in my university are familiar with SPSS and use R very rarely. -- View this message in context: http://r.789695.n4.nabble.com/Using-MCMC-sampling-to-estimate-p-values-with-a-mixed-model-tp3606654p3611453.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.
Re: [R] Using MCMC sampling to estimate p values with a mixed model
If your alternative hypothesis is unequal variances (2-sided), both F < 1 and F > 1 are of interest, and rejection of the equal variance null can occur on either side. The usual ANOVA F test is 1-sided, with an alternative the numerator variance exceeds the denominator one, so this is perhaps why you are confused. At 02:40 PM 6/18/2011, Woodcock, Helena wrote: Hi everyone, Apologies if this is a silly question but I am a student and this is my first time using R so I am still trying to educate myself on commands, models e.t.c I have a mixed model with four dichotomous fixed factors and subject as a random factor (as each person completed four vignettes, with factors crossed across vignettes). I have run an lmer model and used the Monte Carlo method to see if there are any significant main effects or interactions. However, when I looked at the p values some are showing as significant although the F value is less than 1. Is it possible to have a significant effect with an F value below 1?. I have a sample size of 150 and have read that the pMCMC values can be anti-conservative so wonder if it is because my sample size may be too small?. Thank you for any help [[alternative HTML version deleted]] __ 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. Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: r...@lcfltd.com Least Cost Formulations, Ltd.URL: http://lcfltd.com/ 824 Timberlake Drive Tel: 757-467-0954 Virginia Beach, VA 23464-3239Fax: 757-467-2947 "Vere scire est per causas scire" __ 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] Using MCMC sampling to estimate p values with a mixed model
Woodcock, Helena liverpool.ac.uk> writes: > Apologies if this is a silly question but I am a student > and this is my first time using R so I am still trying to > educate myself on commands, models e.t.c > > I have a mixed model with four dichotomous fixed factors and > subject as a random factor (as each person > completed four vignettes, with factors crossed across vignettes). > > I have run an lmer model and used the Monte Carlo method to > see if there are any significant main effects or > interactions. However, when I looked at the p values > some are showing as significant although the F value > is less than 1. Is it possible to have a significant > effect with an F value below 1?. > > I have a sample size of 150 and have read that the > pMCMC values can be anti-conservative so wonder if it is > because my sample size may be too small?. > It's hard to know exactly without more details/seeing the data; it does sound suspicious. Unless there's something you haven't told us, it sounds like this model is fairly close to a classical experimental design (randomized block), so I would guess that the answers should (?) be fairly close to the classical ones. Have you tried fitting with lme (from the nlme package) and seeing what it guesses for denominator degrees of freedom, or working out appropriate denominator degrees of freedom yourself? I would recommend that you send follow-ups to r-sig-mixed-mod...@r-project.org ... __ 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] Using MCMC sampling to estimate p values with a mixed model
Hi everyone, Apologies if this is a silly question but I am a student and this is my first time using R so I am still trying to educate myself on commands, models e.t.c I have a mixed model with four dichotomous fixed factors and subject as a random factor (as each person completed four vignettes, with factors crossed across vignettes). I have run an lmer model and used the Monte Carlo method to see if there are any significant main effects or interactions. However, when I looked at the p values some are showing as significant although the F value is less than 1. Is it possible to have a significant effect with an F value below 1?. I have a sample size of 150 and have read that the pMCMC values can be anti-conservative so wonder if it is because my sample size may be too small?. Thank you for any help [[alternative HTML version deleted]] __ 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] Using MCMC sampling to estimate p values with a mixed model
Hi everyone, Apologies if this is a silly question but I am a student and this is my first time using R so I am still trying to educate myself on commands, models e.t.c I have a mixed model with four dichotomous fixed factors and subject as a random factor (as each person completed four vignettes, with factors crossed across vignettes). I have run an lmer model and used the Monte Carlo method to see if there are any significant main effects or interactions. However, when I looked at the p values some are showing as significant although the F value is less than 1. Is it possible to have a significant effect with an F value below 1?. I have a sample size of 150 and have read that the pMCMC values can be anti-conservative so wonder if it is because my sample size may be too small?. Thank you for any help -- View this message in context: http://r.789695.n4.nabble.com/Using-MCMC-sampling-to-estimate-p-values-with-a-mixed-model-tp3606654p3606654.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.
