similar to: Looking to maximize a conditional likelihood

I am trying to fit a linear mixed model of the form y_ij = X_ij \beta + delta_i + e_ij where e_ij ~N(0,s^2_ij) with s_ij known and delta_i~N(0,tau^2) I looked at the ecme routine in package:pan, but this routine does not allow for different Vi (variance covariance matrix of the e_i vector) matrices for each cluster. Is there an easy way to fit this model in R or should I bite the bullet and

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 =

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)

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

Hi, I want to calculate expected likelihood over a 2D discrete distribution, something like \sum_k \sum_l p_{kl} L(v_k, v_l) It is trivial to write a cycle like for(k in 1:K) for(l in 1:L) sum <- sum + p[k,l]*L(v[k], v[l]) But is there a more clever way for R? Best wishes, Ott

I have a question about constructing the likelihood function where there is censoring at level 1 in a two-level random effects sum. In a conventional solution, the likelihood function is constructed using the density for failures and the survivor function for (in this case, right) censored results. Within (for example) an R environment, this is easy to do and gives the same solution as survreg

Dear R users, I have a couple of linear model related questions. 1) How do I produce a fixed effect linear model using lme? I saw somewhere (this may be Splus documentation since I use Splus and R interchangeably) that using lme(...,random= ~ -1 | groups,...) works, but it gives the same as lme(...,random= ~ 1 | groups,...), ie. fits a random effect intercept term. The reason why I want to do

Hello R-help, I need to compute matrices of first derivatives of a covariance matrix C with entries given by c_ij=theta*exp(-0.5* sum(eta*(x[i,]-x[j,])^2)), wrt to elements of eta, a m-dimensional vector of parameters, given a n*m data matrix x. So far, I have been computing matrices for each parameter (given by par[index]) analytically, using the following kmatder<- function(x, par, index) {

Hi, Suppose I have a multivariate response Y (n x k) obtained at a set of predictors X (n x p). I would like to perform a linear regression taking into consideration the covariance structure of Y within each unit - this would be represented by a specified matrix V (k x k), assumed to be the same across units. How do I use "lm" to do this? One approach that I was thinking of

Hi Is there any way to fit a bivaraite longitudinal mixed model using R. I have a data set with col names resp1 (Y_ij1), resp2 (Y_ij2), timepts (t_ij), unit(i) j=1,2,..,m and i=1,2,..n. I want to fit the following two models Model 1 Y_ij1, Y_ij2 | U_i = u_i ~ N(alpha + u_i + beta1*t_ij, Sigma) U_i ~ iid N(0, sigu^2) Sigma = bivariate AR structure alpha and beta are vectors of order 2.

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

Hi all, I am working for nest box occupancy data for birds and would like to construct a Markov transition matrix, to derive transition probabilities for ALL years of the study (not separate sets of transition probabilities for each time step). The actual dataset I'm working with is 125 boxes over 14 years that can be occupied by 7 different species, though I have provided a slimmed down

Dear list, I'm trying to fit a multilevel (mixed-effects) model using the lme function (package nlme) in R 2.4.0. As a mixed-effects newbie I'm neither sure about the modeling nor the correct R syntax. My data is structured as follows: For each subject, a quantity Y is measured at a number (>= 2) of time points. Moreover, at time point 0 ("baseline"), a quantity X is

I would like to fit a hierarchical regression model from Witte et al. (1994; see reference below). It's a logistic regression of a health outcome on quntities of food intake; the linear predictor has the form, X*beta + W*gamma, where X is a matrix of consumption of 82 foods (i.e., the rows of X represent people in the study, the columns represent different foods, and X_ij is the amount of

Hi, I have the following type of data: 86 subjects in three independent groups (high power vs low power vs control). Each subject solves 8 reasoning problems of two kinds: conflict problems and noconflict problems. I measure accuracy in solving the reasoning problems. To summarize: binary response, 1 within subject var (TYPE), 1 between subject var (POWER). I wanted to fit the following model:

I am trying to make a x-axis and y-axis titles with both a special character and a subscript. I am not being able to do this. I think its just a placing of my parenthesis, but I've tried (seemingly) everything. Even more, when I try the blog users code it works. Is it because I?m using longitudinal data? Even more. Is it possible to colour each one of the 15 lines with a different

Hi, everyone, Before everything, thanks. Lots of thanks ;)!!!! I don?t think you understood everything I need to do. I want to write t_i instead of "Day in ICU? [i subscript for t] and y_ij instead of "CRP (mg/dL)? [ij superscript for y]. The label of the axis? :( Can you help me on that task? Thanks!!!!! Best, Rosa Oliveira > On 31 Jul 2017, at 10:28, Martin Maechler

Hi Keep your messages coppied to R helplist, others could give you answers too. See in line From: Rosa Oliveira [mailto:rosita21 at gmail.com] Sent: Tuesday, August 1, 2017 4:38 PM To: PIKAL Petr <petr.pikal at precheza.cz> Subject: Re: [R] Superscript and subscrib R for legend x-axis and y-axis and colour different subjects in longitudinal data with different colours Hi Pikal, I looked

Dear R users, I am looking for a reference to an algorithm for estimation of sample quantiles which does not require bringing the whole data into memory (more precisely its memory complexity should be much less than linear, ideally constant). I realize that such an algorithm can only be approximate and actually quite wrong for some samples, but that's fine with me. Thank you, Vadim

>>>>> PIKAL Petr <petr.pikal at precheza.cz> >>>>> on Mon, 31 Jul 2017 09:11:18 +0000 writes: > Hi Martin see in line >> -----Original Message----- From: Martin Maechler >> [mailto:maechler at stat.math.ethz.ch] Sent: Monday, July >> 31, 2017 10:52 AM To: PIKAL Petr <petr.pikal at precheza.cz> >> Cc: