similar to: MCMCglmm with cross-classified random effects

Hi all: I get the following error message that I am not able to resolve. Error in if (const(t, min(1e-08, mean(t)/1e+06))) { : missing value where TRUE/FALSE needed It appears right before the last data.frame statement. Below is the program that simulates data from one way random effects model and then computes normality and bootstrap confidence interval for

R-users E-mail: r-help@r-project.org Hi! R-users. http://finzi.psych.upenn.edu/R/library/mvpart/html/xpred.rpart.html says: data(car.test.frame) fit <- rpart(Mileage ~ Weight, car.test.frame) xmat <- xpred.rpart(fit) xerr <- (xmat - car.test.frame$Mileage)^2 apply(xerr, 2, sum) # cross-validated error estimate # approx same result as rel. error from printcp(fit) apply(xerr, 2,

hi all, I want to move some routing tasks out of the router and into the controller. The goal is to make Merb feel less like mod_rewrite and give the user more control at the controller. The new Router is simple: it takes the path_info (not the whole request) then outputs a controller class and some parameters from the path matching. The rest of the routing would be done at the controller level.

Hi All, The data set that I have is a cluster data, and I want to run a HLM mixed model with multi-level response. Here is my data set: response: - Level (num: 1, 2, 3, 4, 5 - 5 levels) Covariates: - Type (Factor: A, B, C - 3 levels) - yr (num: 2006, 2007, ...) - Male (num: 0=not Male, 1=Male - 2 levels) - Ethnicity (Factor: A, B, H, ..., - 7 levels) - ELL (num: 0, 1, - 2

> On Mar 22, 2018, at 1:31 PM, Michelle Kline <michelle.ann.kline at gmail.com> wrote: > > Hi, > > Thanks in advance for any help on this question. I'm running multinomial > models using the MCMCglmm package. The models have 5 outcome variables > (each with count data), and an additional two random effects built into the > models. The issue is that when I use

Hi David, Thanks for your comment. I haven't posted the data because they are unpublished and include human subjects so there are issues with sharing on a list serv, but I thought perhaps someone had encountered a similar problem and would already know the answer. I will reconsider whether my University's ethics approval would allow me to post the data and update the question if I think

Hi, Thanks in advance for any help on this question. I'm running multinomial models using the MCMCglmm package. The models have 5 outcome variables (each with count data), and an additional two random effects built into the models. The issue is that when I use the following code, the summary only gives me results for four of the outcome variables. Here is the code for my model: m3.random

Hello, I need some advice how to debug this issue ... Recently I got a new mainboard for a router, it's a Gigabyte GA-H77N-WIFI with two onboard re(4) NICs. The problem is that re0 works fine and re1 doesn't: It doesn't receive any packets. Tcpdump displays all outgoing packets, but no incoming ones on re1. Ifconfig shows the link correctly (100 or 1000 Mbit, depending on where I

Dear R-Helpers, I am having troubles with installing with MCMCglmm package and I get the following error with a package "Matrix" Warning in library(pkg, character.only = TRUE, logical.return = TRUE, lib.loc = lib.loc) : there is no package called 'Matrix' Error: package 'Matrix' could not be loaded Execution halted ERROR: lazy loading failed for package

Dear all, I would like to add mixed effects in a multinomial model and I am trying to use MCMCglmm for that. The main problem I face: my data set consits of a trapping data set, where the observation at eah trap (1 or 0 for each species) have been aggregated per traplines. Therefore we have a proportion of presence/absence for each species per trapline. ex: ID_line mesh habitat Apsy Mygl

Thanks for your answers Stephen and Ben, I hope I am posting on the correct list now. I managed so far to run the multinomial model with random effect with the following command: MCMCglmm(fixed=cbind(Apsy,Mygl,Crle,Crru,Miag,empty) ~ habitat:trait,random=~idh(trait):mesh,family="multinomial12", data=dataA,rcov=~trait:units) (where multiple responses are different species, Habitat

I have an Asus P5Q-WS running FreeBSD 7 and the onboard Realtek NIC's are not being detected. No Kernel modifications as this is a fresh install. pciconf -lv : re0@pci0:3:0:0: class=0x020000 card=0x82c61043 chip=0x816810ec rev=0x02 hdr=0x00 vendor = 'Realtek Semiconductor' device = 'RTL8168/8111 PCI-E Gigabit Ethernet NIC' class = network

Hi R-users: Can anyone give an example of giving starting values for MCMCglmm? I can't find any anywhere. I have 1 random effect (physicians, and there are 50 of them) and family="ordinal"? How can I specify starting values for my fixed effects? It doesn't seem to have the option to do so. Thanks, Ping

1. (Mainly) Statistical issues are generally off topic on this list. You might want to try the r-sig-mixed-models list instead. 2. However, I think a better answer is to seek local statistical expertise in order to have an extended discussion about your research intent in order to avoid producing yet more irreproducible psychological research. Cheers, Bert Bert Gunter "The trouble with

Dear R users, I?d like to pose aquestion about pMCMC and HDP. I have performed a mixed logistic regression by MCMCglmm (a very good package) obtaining the following results: Iterations = 250001:799901 Thinning interval = 100 Sample size = 5500 DIC: 10.17416 G-structure: ~ID_an post.mean l-95% CI u-95% CIeff.samp ID_an 0.7023 0.0001367 3.678 2126 R-structure: ~units post.mean l-95%

Hi all, I previously emailed about a multinomial model, and after seeking some additional help, realized that since my response/outcome variables are not mutually exclusive, I need to use a multi-response model that is *not* multinomial. I'm now trying to figure out how to specify the priors on the multi-response model. Any help would be much appreciated. My data look like this: X

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

Dear MCMCglmm users, I am currently struggling with the specification of a proper prior and model formula for a multi-response MCMCglmm with two of the three response variables being Gaussian and the third being za-poisson. The model includes several fixed effects and three nested random effects. In general, I would prefer to fit a model with a fixed effect of trait and suppressed intercept for

Dear Jarrod, I have a data set where residual have a heavy-tailed distribution with some extreme residual values and consequently the distribution deviates from the Gaussian one. Is it possible to include an skewed-normal density for the residual in MCMCglmm package? I have done the analysis of this data with both ASReml & MCMCglmm. The results are similar and outcome from MCMCglmm

I have a question for users of MCMCglmm that have experience implementing the zero-inflated poisson model. I find that the documentation, and previous questions, do not offer a lot of clear guidance on specifying and interpreting the zipoisson model. In particular, I see a lot of zero-inflated poisson examples that use the at.level(trait, x):variableName syntax. Specifically, the MCMCglmm