search for: x_ij

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 =

...rds model (in the survival package of 1.7) will do what I want. Here's a little more detail on the function, though this is still quite terse. Pseudo Tex notation: We have cases in clusters. y_ij is the outcome (0 or 1) for the j'th case in the i'th cluster It has vector covariates X_ij. c_ij is a transform of y_ij and is in (0, 1) (it's actually the probability of being in cluster j). It may be specified a priori or to be estimated. Maximize the product over i of c_ij exp(sum_j y_ij X_ij b) ---------------------------- sum_K c_ij exp(sum_K y_ij X_ij b) Where sum_K means w...

...ts a random effect intercept term. The reason why I want to do this is test for the significance of the random effect intercept term. anova( , ) does not work for an lm model and lme model together. 2) Is there some nice way of handling linear models which are of the form response_ij = a_i + b_i x_ij + \epsilon_ij where a_i and b_i are fixed effects, x_ij is given (continuous) data, \epsilon_ij ~ N(0, \sigma^2), and the i's range over some group? This is basically a group of regression models, but I want them handled as one unit for the purposes of estimation of \sigma^2 etc. I know that...

..., assumed to be the same across units. How do I use "lm" to do this? One approach that I was thinking of is as follows: Flatten Y to a vector, say, Yvec (n*k x 1). Create Xvec (n*k, p*k) such that it is made up of block matrices Bij (k x k), where Bij is a diagonal matrix with X_ij as the diagonal (i = 1,.n, and j = 1,.,p). Now I can use "lm" in a univariate mode to regress Yvec against Xvec, with covariance matrix Vvec (n*k x n*k). Vvec is a block-diagonal matrix with blocks of V along the diagonal. This seems like a valid approach, but I still don't know ho...

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...

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

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) x<-c(21,34,23,26,26,29,22,29,30,28,...

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

...ee 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 food j eaten by person i); and W is a matrix of some other predictors (sex, age, ...). The second stage of the model is a regression of X on some food-level predictors. Is it possible to fit this model in (the current version of) lmer()? The challenge is that the persons are...

...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 even if it is a little heavy. But where there is an hierarchical situation, we need to consider the contributions at level 2. y_ij=X_ij.beta'+err2_i+err1_ij If all the units at level 1 for a given level 2 are censored, then the information we have for the level 2 is itself censored and we should presumably use the survivor function. Conversely if none of the units at level 1 are censored, then the information at level 2 is co...

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