similar to: Weighted Likelihood Estimation of NIG Dist.

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

Dear R users, I'm trying to fit a simple random intercept model with a fixed intercept. Suppose I want to assign a weight w_i to the i-th contribute to the log-likelihood, i.e. w_i * logLik_i where logLik_i is the log-likelihood for the i-th subject. I want to maximize the likelihood for N subjects Sum_i {w_i * logLik_i} Here is a simple example to reproduce

Dear wizaRds, I use the MASS::kde2d function to estimate density of the two first principal components. I do that to have a graphic visualisation of a "group structure" in my dataset. So far, no problem. But i would like to estimate that density using weights according to the COS?? values that tells me if my observation is well represented on the factorial plan 1-2. I would like to

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 can't get my head around this - can someone give me a pointer: I have a vector of values ('orig') representing areas and I want to calculate the amount contributed by each original vector values in a new vector ('new'). I find this hard to explain so here is a graphical explanation: # CODE START orig <- 10:1 #10 values with varying area new <-

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

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(...,

Dear metafor users, I am working on a meta-analysis of reliability and the correlation associations. I need some help about conducting categorical moderators variables. Questions 1: How to conduct the weighted ANOVAs assuming a mixed-effects model on the tranformed alpha coefficients/the tranformes correlation coefficients for the categorical moderator variables? Questions 2: How to

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 would suggest to post this to the (recently created) R-sig-meta-analysis mailing list. See: https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis Best, Wolfgang >-----Original Message----- >From: R-help [mailto:r-help-bounces at r-project.org] On Behalf Of Naike Wang >Sent: Monday, June 26, 2017 08:32 >To: R-help at r-project.org >Subject: [R] Classic fail-safe N >

Hi all, I was conducting a meta-analysis of single proportions(i.e. without a control group) using the metafor package. When I performed a classic fail-safe N, I noticed that the result (the number of missing studies that would bring p-value to the alpha, to be exact)was different than that I got in Comprehensive Meta-Analysis Version 2.0. I wonder why R and CMA got different results. *Below is

Dear R community, I'm using the ,metafor'-package by Wolfgang Viechtbauer (Version: 0.5-5) to calculate random-effects meta-analyses using Correlations and Sample Sizes as the raw data. (By the way: Really a nice piece of work, Wolfgang! Thanks heaps.) I specified the "rma.uni' function so that it looks like this: MAergebnis<-rma.uni(ri=PosOutc, ni=N,

Hi all, I was conducting a meta-analysis of single proportions(i.e. without a control group) using the metafor package. When I performed a classic fail-safe N, I noticed that the result (the number of missing studies that would bring p-value to the alpha, to be exact)was different than that I got in Comprehensive Meta-Analysis Version 2.0. I wonder why R and CMA got different results. *Below is

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:

My local R-devel version now has (in ?lm) 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); or equivalently, when the elements of ?weights? are positive integers w_i, that each response y_i is the mean of w_i unit-weight observations

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 users of metafor, I am working on a meta-analysis using the metafor package. I have a excel csv database that I am working with. I am interested in pooling the effect measures for a particular subgroup (European women) in this csv database. I am conducting both sub-group and meta-regression. In subgroup-analyses, I have stratified the database to create a separate csv file just for European

Hi all, I am not a very proficient R-user yet, so I hope I am not wasting people?s time. I want to run a linear mixed model with 3 random factors (A, B, C) where A and B are partially crossed and C is nested within B. I understand that this is not easily possible using lme but it might be using lmer. I encountered two problems when trying: Firstly, I can enter two random factors in lmer but

Bonjour, J'ai un petit souci avec mon petit couple rsync over ssh. Quand je fais un rsync d'une machine, et que celle-ci dispose d'une messagerie de type vpopmail, le backup m'indique ? la fin que certains fichiers n'ont pu ?tre transf?r?s. Ceci est du au fait que sachant que les services POP/SMTP sont actif, des fichiers ont ?t? supprim?s entre le temps ou la liste des