similar to: glmer or not - glmer model specification

Hi, I have a dataset where the residual variance decreases with on one of the predictors (population size). Currently, the full model looks like this: prior<-list(R=list(V=1e-16, nu=-2),G1=list(V=diag(2), nu=2)) m<-MCMCglmm(response~poly(population size,2)*poly(other predictor,2)+time, random=~us(1+time):population, data=data, prior=prior) Basically, it's a random regression with

Hi, I want to analyse DNA sequence data (mtDNA) in R as in calculate Fst, Heterozygosity and such summary statistics. Package Adagenet converts the DNA sequence into retaining only retaining the polymorphic sites and then calcuates Fst.. but is there any other way to do this? I mean analyse the DNA sequence as it is.. and calculate the statistics? Thanks! [[alternative HTML version deleted]]

Sequenom has an odd format of calling a SNP genotype gg [1] "C" "GA" "A" "C" "C" "AG" "C" "C" "T" "G" homozygous A is called A and heterozygous is called AT The genetics package cannot handle the fact that some genotypes are declared with 2 letter while other are declared with only 1.

I'm trying to put two plots side-by-side in two panels. Since the y-axis of the two plots is the same, I want to leave out the y-axis of the second plot. And the two plots look quite separate from each other. Is there any way to have them stay closer to each other? I've tried to adjust mar option of par but it has a global effect as I see the left margin of the first plot will be

R-users, I'm seeking any suggestions on optimizing some code for speed. Here's the setup: the code below is part of a larger chunk that is calculating Fst values across loci and alleles. This chunk is designed to calculate the proportion ('p.a') of an allele ('a') at a locus in each population ('p') and the proportion of individuals heterozygous for that

I am involved in a study where, as in most of life, men demonstrate themselves to be recalcitrant. So while we have many probands and most of their mothers we only have about 50% of the trios being complete. I have been running tdt and trio.types. It appears as if it is ignoring the duos. Sometimes a duo can be informative. For instance Father ..missing Mother 1/2 Proband 1/1 This duo shows that

Hi all, This is really a stats question as much as an R question. I'm trying to do a joint scaling test (JST - see below) on some very oddly-distributed data and was wondering if anyone can suggest a good way of dealing with model violations and/or using R to evaluate how sensitive the model is to violations of the normality assumption. Here's a quick explanation of the analysis, the

Dear colleagues, working with the data frame below, trying to reverse two variables I the error message below. i searched through the help list but could not find any postings which could help me solve the situation. I tried attaching and detaching the data frame to no avail. Yours, Simon Kiss *DATA FRAME 'data.frame': 1569 obs. of 9 variables: $ equal : num 3 4 3 2 3 4 2 3 2 2 ...

Nicolas Kovacs wrote: > Le 08/03/2018 ? 11:30, hw a ?crit : >> The government says you must use squidguard to filter something? > > The law in France (Code P?nal, article 227-24) states that a public > network is not allowed to broadcast messages containing violence, > pornography or any content contrary to basic human dignity, which is > theoretically punishable with

Le 08/03/2018 ? 17:15, hw a ?crit : > But you aren?t broadcasting messages, or are you? > > If they mean something like "make data accessible", the only way to > be compliant with such a law is by not providing public access. How > do you distinguish between things that are contrary to basic human > dignity and things that aren?t, and how do you keep track of all >

Hello, I am using glmer() from lmer(lme4) to run generalized linear mixed models. I can't figure out how to extract the z values for the fixed effects that are reported using the summary function . Any help would be appreciated. Thanks, Spencer [[alternative HTML version deleted]]

Hello, I am using glmer() from lmer(lme4) to run generalized linear mixed models. However, I am having a problem extracting the standard errors for the fixed effects. I have used: summary(model)$coef fixed.effects(model) coef(model) to get out the parameter estimates, but do not seem able to extract the se's. Anybody have a solution? Thanks, John

Can anyone help with this - I have been running glmer code for linear mixed models on my works pc, and am now working on my laptop (which is vista) and when I load the lme4 package i get the message below and I cannot run any models - any one have any ideas? Emma trying URL 'http://cran.uk.r-project.org/bin/windows/contrib/2.8/Matrix_0.999375-21.zip' Error in download.file(url,

I would like to fit the following model: logit(p_{ij}) = \mu + a_i + b_j where a_i ~ N(0, \sigma_a^2) , b_j ~ N(0, \sigma_b^2) and \sigma_a = \sigma_b. Is it possible to fit a model with such a constraint on the variance components in glmer? -- View this message in context: http://r.789695.n4.nabble.com/glmer-question-tp4351829p4351829.html Sent from the R help mailing list archive at

Dear all, I am running glmer() in R. How can I retrieve the estimates of fixed effects and the variance of the random effects from the result? Thank you so much. Joe ___________________________________________________ ±zªº¥Í¬¡§Y®É³q ¡Ð ·¾³q¡B®T¼Ö¡B¥Í¬¡¡B¤u§@¤@¦¸·d©w¡I [[alternative HTML version deleted]]

Dear all, A am trying to fit a generalized linear mixed effects model with a binomial link function, my response data is binary, using the lme4 R package, for the glmer model but with the cauchit link function (CDF of Cauchy distribution), under the package this has not yet been coded and was wondering if anyone knew a way in which I could incorporate this link function into the code. Thankyou

dear R-users, I have to model counts where all counts above some threshold have been censored. In the same dataset I have too many zeroes for a Poisson or even a negative binomial distribution to make sense, so I would need a zero-inflated-censored negative binomial family for use in glmer (or glmmADMB?). That seems not to exist. my question is : how could I add a custom-built family of

Having briefly fallen for the notion that the negative.binomial family in MASS could be used in glmer, I want to use these lists for a sanity check on my final (?) plans. I want to use glmer for logistic regression and for poisson regression on a data set of 10,000 items. There will be two crossed random effects. For the logistic regression, I want odds ratios with confidence intervals.For the

Hello, I originally posted this on the stats stack exchange site, but given its focus on R software, it was removed -- so I figured I'd post here. I'm having trouble interpreting a change in effect direction and significance when I add an interaction term to my glmer() model. *Part 1* I ran an experiment in which participants made categorical decisions (out of two categories) in one of

Full_Name: susscorfa Version: 2.7.1 OS: ubuntu Submission from: (NULL) (129.125.177.31) Incorrect implementation of the grouping variable in the function glmer crashes R a small example: require(lme4); a<-data.frame(b=rpois(1000,10), c=gl(20,50), d=rnorm(1000,3), e=rnorm(1000,5), f=rnorm(1000,2)+5); glmer(b~d+f|c+(e), family=poisson, data=a) It crashes R on debian linux (2 independant