similar to: weighted likelihood for lme

Peter, This is a highly structured text. Just for the discussion, I separate the building blocks, where (D) and (E) and (F) are new: BEGIN OF TEXT -------------------- (A) Non-?NULL? ?weights? can be used to indicate that different observations have different variances (with the values in ?weights? being inversely proportional to the variances); (B) or equivalently, when the elements of

Hi, I am getting some weird results here and I think I am missing something. I am trying to program a function that for a set of random variables drawn from uniform distributions plots that distribution of the second order statistic of the ordered variables. (ie I have n uniform distributions on [0, w_i] for w_i different w_j and i=1..n. I want to plot the distribution of the second order

Hola a todos, Estoy intentando resolver un problema de optimización con R con restricciones lineales, pero no consigo incluir dichas restricciones. Es decir, f<-function(w){ sd(...) # desviación típica de ciertos datos } optim(rep(1/2,8),fn = f,lower=0,upper=1,method='L-BFGS-B') # no se como incluir aquí las restricciones Las restricciones son: la suma de los w_i es 1 y todos los

Dear all, I am trying to use the 'glm' package as part of a semiparametric technique that involves weighting a likelihood in various ways, i.e. L(theta;data)=Sum_i=1,..,n (W_i)(log L(theta;data_i)) Where W_i can be a kernel weighting function, or W_i can be an indicator of 'non-missingness' divided by a propensity score. In a Monte Carlo exercise, the option glm(...,

I am more than 'a little disappointed' that you expect a detailed explanation of the problems with your 'bug' report, especially as you did not provide any explanation yourself as to your reasoning (nor did you provide any credentials nor references). Note that 1) Your report did not make clear that this was only relevant to prediction intervals, which are not commonly used.

Hello! I would like to use lmer() to fit data, which are some estimates and their standard errors i.e kind of a "meta" analysis. I wonder if weights argument is the right one to use to include uncertainty (standard errors) of "data" into the model. I would like to use lmer(), since I would like to have a "freedom" in modeling, if this is at all possible. For

Hi All, I am trying to code an R script which gives me the time varying parameters of the NIG and GH distributions. Further, becasue I think these these time varying parameters should be more responsive to more recent observations, I would like to include a weighted likelihood estimation proceedure where the observations have an exponentially decaying weighting rather than the equal weighting

I tried lrm in library(Design) but there is always some error message. Is this function really doing the weighted logistic regression as maximizing the following likelihood: \sum w_i*(y_i*\beta*x_i-log(1+exp(\beta*x_i))) Does anybody know a better way to fit this kind of model in R? FYI: one example of getting error message is like: > x=runif(10,0,3) > y=c(rep(0,5),rep(1,5)) >

I have a function written for Splus, when I run it in R I obtain get an error because the function has the elements "0.d0" and "2.d0". How can I change it to run in R? The function can be found in page 230 from http://www.stat.wisc.edu/~mchung/teaching/stat471/stat_computing.pdf Function is as follows: gauher <- function(n) {# Gauss-Hermite: returns x,w so that

Dear Listers, I got this message when compiling a package: * creating pgirmess-manual.tex ... OK * checking pgirmess-manual.text ... ERROR LaTex errors when creating DVI version. This typically indicates Rd problems. The message is quite explicit but I struggled a lot before understanding that the trouble comes from a single file "selMod.rd" among 44 topics. Even though I have

OK. We have now three suggestions to repair the text: - remove the text - add "not" at the beginning of the text - add at the end of the text a warning; something like: "Note that in this case the standard estimates of the parameters are in general not correct, and hence also the t values and the p value. Also the number of degrees of freedom is not correct. (The parameter

Hi list: This is my first time to post my question on the list. Thanks for your help. I am solving a quadratic programming using R. Here is my question: w = arg min 0.5*w'Mw - w'N s. t. sum(w) = 1; w>0 note: w is weight vector, each w_i must >=0, and the sum of w =1. Here is my R code: A <-matrix(c(2.26,1.26,1.12,1.12,2.27,1.13,1.12,1.13,2.2),3,3); B <-

Hello! I am using for logistic regression in survey data the svyglm procedure. I wondered how does the strata effect estimates SE (in addition to the weights given proportional to population size). I know that for simple regression measurements of each strata is assumed to have different variance. But in a logistic model this is not the case. Can anyone help me here? Thank you Ron [[alternative

Hi, I need to run a burdensome simulation. I'm using a Dell PowerEdge 2850, 3.8 Ghz CPU, 2 Gb RAM, Windows server 2003, 32 bit. The task manager indicates that Rgui.exe is using 25% of the CPU speed. There is no other process that is slowing down R. Does anyone know if it's a limitation of R? If so, can I set up the program for the CPU usage like I do for the memory usage? Is there a

Dear listers, Here we have a strange result we can hardly cope with. We want to compare a null mixed model with a mixed model with one independent variable. > lmmedt1<-lme(mediane~1, random=~1|site, na.action=na.omit, data=bdd2) > lmmedt9<-lme(mediane~log(0.0001+transat), random=~1|site, na.action=na.omit, data=bdd2) Using the Akaike Criterion and selMod of the package pgirmess

Dear all, does anyone know a command to shut 'lme' (nlme) up? :) I have a loop for(i in 1:M){ lme(..) } and for each "i" i get the warning message >Fewer observations than random effects in all level 1 groups >in: ... I know I'm using "fewer observations...", I just don't want to see the message printed on the screen during the loop. Thanks, Marco

Dear R developers, I am a little disappointed that my bug report only made it to the wishlist, with the argument: Well, it does not say it has. Only relevant to prediction intervals. predict.lm does calculate prediction intervals for linear models from weighted regression, so they should be correct, right? As far as I can see they are bound to be wrong in almost all cases, if no weights

Yes. Thank you; I should have quoted it. I suggest to remove this text or to add the word "not" at the beginning. Arie On Sun, Oct 8, 2017 at 4:38 PM, Viechtbauer Wolfgang (SP) <wolfgang.viechtbauer at maastrichtuniversity.nl> wrote: > Ah, I think you are referring to this part from ?lm: > > "(including the case that there are w_i observations equal to y_i and

Hello, I have a vector of samples x of length N. Associated with each sample x_i is a certain weight w_i. All the weights are in another vector w of the same length N. I have another vector of samples y of length n (small n). All these samples have equal weights 1/n. The ECDF of these samples is defined as for example at http://en.wikipedia.org/wiki/Empirical_distribution_function and I can

Muy buenas, Tengo la siguiente duda/problema, He optimizado con éxito un problema de este tipo: \sum f(x_i) donde f es una curva exponencial (función no lineal) sujeto a: a_i < x_i < b_i y \sum f(x_i) < Presupuesto Vamos, es repartir un presupuesto forzando a que inviertas como poco a_i y como mucho b_i para cada i Esto lo hecho correctamente usando el paquete: