similar to: mixed model question

Dear list, I am currently writing up some of my R models in a more formal sense for a paper, and I am having trouble with the notation. Although this isn't really an 'R' question, it should help me to understand a bit better what I am actually doing when fitting my models! Using the analysis of co-variance example from MASS (fourth edition, p 142), what is the correct notation for the

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =

Hallo everybody, I'm new in r and I"ll appreciate some help! I have a matrix of nrow=30 and ncoll=54,and I would like to generate 50 simulations with tha same size of the matrix!!!That is to say that I want to generate 50 matrices -for my 50 simulations - with the same dimensions! I took my 1st matrix according to the formula that I want to implement: D<-mean_m + U_i*mat_DELTA

Hi, Being new to R I'm asking for some advice on how to optimize the performance of the following piece of code: > alpha_c <- function(lambda=600e-9,alpha_s=1e-14,N=400,spacing=1e-7){ > > k<-2*pi/lambda > ri<-c(0,0) # particle at the origin > x<-c(-N:N) > positions <- function(N) { > reps <- 2*N+1 > matrix(c(rep(-N:N, each = reps), rep(-N:N,

I am using the package nlme to fit a simple random effects (variance components model) with 3 parameters: overall mean (fixed effect), between subject variance (random) and within subject variance (random). I have 16 subjects with 1-4 obs per subject. I need a 3x3 variance-covariance matrix that includes all 3 parameters in order to compute the variance of a specific linear

Here is an example of the data.frame that I have, df<-data.frame("case"=rep(1:5,each=9),"id"=rep(1:9,times=5),"x"=round(runif(length(rep(1:5,each=9))))) "case" represents the cases, "id" the persons, and "x" is the binary state. I would like to know in how many cases any two persons a. both have "1", b. the first has

Hello, thank you very much for your previous answers about the C++ code. I am interested in the application of the Gibbs Sampler in the IRT models, so in the function MCMCirt1d and MCMCirtkd. I've found the C++ source codes, as you suggested, but I cannot find anything about the Gibbs Sampler. All the files are for the Metropolis algorithm. Maybe I am not able to read them very well, by the

I''m a physicist working on fusion energy and dabble in statistics only occasionally, so please excuse gaps in my statistical knowledge. I''d appreciate any help that a real statistics expert could provide. Most people in my field do only very simple statistics, and I am trying to extend some work on multivariate linear regression to account for significant between-group

my major is bioinformatics, and i'm trying to cluster ( agglomerate the closest pari of observations ) in R. i have already got my own similarities metric, but do not know how to clust it based on similarities instead of dissimilarities. since the help document of hierclust mentions the parameter "sim", which seems good to me, but it doesn't appear in the code of hierclust()

Dear useRs (called Frank Harrell, most likely), after having preached for years to my medical colleagues to be cautious with stepwise selection procedures, they chanted back asking for an alternative when using mixed models. There is a half dozen laXXX packages around for all types of linear models, but as far I see there is none for mixed models such as lme. Even boot.stepAIC (which I

On 16/02/18 15:28, Bert Gunter wrote: > This is really a statistical issue. What do you think the Intercept term > represents? See ?contrasts. > > Cheers, > Bert > > > > Bert Gunter > > "The trouble with having an open mind is that people keep coming along and > sticking things into it." > -- Opus (aka Berkeley Breathed in his "Bloom

Hi everyone, I want to fit the following mixed effect model Y_ij = b_0i + b_1i * (t_ij*grp_ij == 1) + b_2i * (t_ij*grp_ij == 2) + v_0i + v_1i*t_ij + e_ij with a different covariance matrix of random effects for each group. (Y is the response t is time grp is the group indicator b 's are fixed effects v 's are random effects) I know that this is possible in SAS

I want to maximize a conditional likelihood function that is basically logistic conditional on the number of successes within strata. What would be a good starting place for this? A complication is that the denominator includes a term that is the sum over all permutations. Although there is no time dimension to the problem, it's possible a degenerate use of the Cox proportional hazards

Hello, can anybody here explain what a Jonckheere-Terpstra test is and whether it is implemented in R? I just know it's a non-parametric test, otherwise I've no clue about it ;-( . Are there alternatives to this test? thanks for help, Arne

This is really a statistical issue. What do you think the Intercept term represents? See ?contrasts. Cheers, Bert Bert Gunter "The trouble with having an open mind is that people keep coming along and sticking things into it." -- Opus (aka Berkeley Breathed in his "Bloom County" comic strip ) On Thu, Feb 15, 2018 at 5:27 PM, Marc Girondot via R-help < r-help at

Dear R-er, I try to get the standard error of fitted parameters for factors with a glm, even the reference one: a <- runif(100) b <- sample(x=c("0", "1", "2"), size=100, replace = TRUE) df <- data.frame(A=a, B=b, stringsAsFactors = FALSE) g <- glm(a ~ b, data=df) summary(g)$coefficients # I don't get SE for the reference factor, here 0:

In a classical meta analysis model y_i = X_i * beta_i + e_i, data {y_i} are assumed to be independent effect sizes. However, I'm encountering the following two scenarios: (1) Each source has multiple effect sizes, thus {y_i} are not fully independent with each other. (2) Each source has multiple effect sizes, each of the effect size from a source can be categorized as one of a factor levels

suppose something like probability(passing test) is driven by 1. fixed effects -- sex 2. district effects - district funding 3. school effects - neighborhood income, racial composition, % two parent families, ... 4. class effects - teacher quality measurement, 5. individual random effects - IQ. how would such a model be setup in lmer? I can't find much discussion on the

Dear R users, I am trying to fit the following ANCOVA model in R2.4.0 Y_ij=mu+alpha_i+beta*(X_ij-X..)+epsilon_ij Particularly I am interested in obtaining estimates for mu, and the effects alpha_i I have this data (from the book Applied Linear Statistical Models by Neter et al (1996), page 1020) y<-c(38,43,24,39,38,32,36,38,31,45,27,21,33,34,28)

Hello, Could someone give me a hint about what might be the difference between running urppTest with Z-alpha and Z-tau in type=c("Z-alpha", "Z-tau")? Is this the underlying equation: delta_y(t) = mu + tau*timetrend+(1-rho)*y(t-1) + alpha_1*delta_y(t-1) + ... + alpha_k*delta_y(t-k) + error term ? I looked at Banerjee et al. mentioned in the fSeries documentation, but that