similar to: identical results with PQL and Laplace options in lmer function (package lme4)

This is a summary and extension of the thread "GLMM (lme4) vs. glmmPQL output" http://maths.newcastle.edu.au/~rking/R/help/04/01/0180.html In the new revision (#Version: 0.4-7) of lme4 the standard errors are close to those of the 4 other methods. Thanks to Douglas Bates, Saikat DebRoy for the revision, and to G?ran Brostr?m who run a simulation. In response to my first posting, Prof.

Hi, i have many variables for in example 4weeks and want to do aggregations, like mean standard , deviation etc.. With mean it works but how i can calculate the standard deviation for the 4weeks and for every ID. many thanks & regards, christian week1 <- grep("(_PRO_001)",names(dmx3),perl=T) week1table <- subset(dmx3,select=c(ID,week1)) week2 <-

Dear Lest, I have a two-variable data frame as follows (the time peirod of the actual data set is 10 years): Date Amount 1 6/1/2007 1 2 6/1/2007 1 3 6/4/2007 2 4 6/5/2007 2 5 6/11/2007 3 6 6/12/2007 3 7 6/12/2007 3 8 6/13/2007 3 9 6/13/2007 3 10 6/18/2007 4 11 6/18/2007 4 12 6/25/2007 5 13 6/28/2007 5

I apologise for writing again about the problem with using stepAIC + multinom, but I think the reason why I had it in the first place is perhaps there may be a bug in either stepAIC or multinom. Just to repeat the problem, I have 126 variables and 99 cases. I don't know if the large number of variables could be the problem. Of couse the reason for doing a stepwise method is to reduce this

The partial R-square (or coefficient of partial determination, or squared partial correlation coefficients) measures the marginal contribution of one explanatory variable when all others are already included in multiple linear regression model. The following link has very clear explanations on partial and semi-partial correlation: http://www.psy.jhu.edu/~ashelton/courses/stats315/week2.pdf In

Dear Rusers, I have used R,S-PLUS and SAS to analyze the sample data "bacteria" in MASS package. Their results are listed below. I have three questions, anybody can give me possible answers? Q1:From the results, we see that R get 'NAs'for AIC,BIC and logLik, while S-PLUS8.0 gave the exact values for them. Why? I had thought that R should give the same results as SPLUS here.

Hello! Just would like to make sure I am not doing something wrong. I am running an OLS regression. I have several subgroups in the data set (locations) - and in each location I have weekly data for 2 years - on my DV and on all predictors. Looks like this: location week DV Predictor1 Predictor 2 location1 week1 xxx xxxxxxx xxxxxxxxx location1 week2 xxx xxxxxxx xxxxxxxxx . .

Dear all, I have a problem with an Rd file containing French accentuated characters. I have uploaded the file at http://filex.cirad.fr/get?k=cjW7lImMaNC6Ci2vX0H I have declared Encoding: latin1 in the package DESCRIPTION file and I have added \encoding{latin1} in the header of the Rd file. When I compile the package manual, I have LaTeX errors: ! Package inputenc Error: Unicode char \u8:?F

Hello I try to find probability values of some predictor combinations using logistic reg. in response level. Firstly I found coefficients by glm function. Then I followed two ways to get probability values: 1- probility <- exp(X0+bX1+cX2+...)/((1+exp(X0+bX1+cX2+...)) 2- probility <- predict(glm.obj,type="resp") Should have these two given same result ? if so, I did not have. Why

Hi All, I have a time series data with two continuous variables (say Var1 and Var2) for 4 years (***not continuous, do have some breaks because of missing data***). Something like this: Date Var1 Var2 12/01/2004 7 0 12/01/2004 0 0 12/01/2004 0 7 12/01/2004 7 0 12/01/2004 0 7 12/01/2004 0 7 12/02/2004 0 0 ... I need to find out weekly average of var1 and var2, so that I end up with data like:

Dear R community, I performed a generalized linear mixed model using glmmPQL (MASS library) to analyse my data i.e : y is the response with a poisson distribution, t and Trait are the independent variables which are continuous and categorical (3 categories C, M and F) respectively, ind is the random variable. mydata<-glmmPQL(y~t+Trait,random=~1|ind,family=poisson,data=tab) Do you think it

Dear R community, I have two questions regarding fitting GLMM using maximum likelihood method. The first one arises from trying repeat an analysis in the Breslow and Clayton 1993 JASA paper. Model 3 of the epileptic dataset has two random effects, one subject specific, and one observation specific. Thus if we count random effects, there are more parameters than observations. When I try to run the

> library(MASS) > attach(bacteria) > table(y) y n y 43 177 > y<-1*(y=="y") > table(y,trt) trt y placebo drug drug+ 0 12 18 13 1 84 44 49 > library(lme4) > model1<-lmer(y~trt+(week|ID),family=binomial,method="PQL") Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of

I am analysing binomial data using a generalised mixed effects model. I understand that if I use glmmPQL it is not appropriate to compare AIC values to obtain a minimum adequate model. I am assuming that this means it is also inappropriate to use AIC values from lmer since, when analysing binomial data, lmer also uses PQL methods. However, I wasn't sure so please could somebody clarify

I've created an SVM in R using the kernlab package, however it's running incredibly slow (20,000 predictions takes ~45 seconds on win64 R distribution). CPU is running at 25% and RAM utilization is a mere 17% ... it's not a hardware bottleneck. Similar calculations using data mining algorithms in SQL Server analysis services run about 40x faster. Through trial and error, we

Does anyone have any R code that shows how to do a Laplace Approximation? I know there are a variety of these numerical approximation algorithms and I'm pretty open at this point, I'm just curious how it's approximated in R code. I have seen some functions in packages, but I think they all call C code, and am looking for an example in R only. Thanks. -- View this message in

Is there some package out there that implements functions for reliability analysis, especially for software reliability? In particular, I'm looking for: * Laplace factor (Cox & Lewis 1978) * Goel-Okumoto fitting Thanks in advance, -Ekr -- [Eric Rescorla ekr at rtfm.com]

Can anyone show how to calculate a multivariate Laplace density? Thanks. -- View this message in context: http://r.789695.n4.nabble.com/Multivariate-Laplace-density-tp3864072p3864072.html Sent from the R help mailing list archive at Nabble.com.

Hello, <http://tolstoy.newcastle.edu.au/R/help/04/07/0312.html#0313qlink1> Could anybody tell me how to generate discrete Laplacian distribution? I need to sample uma discretised Laplacian density like this: J( g -> g´) ~ exp (-lambda | g´ - g |) g in {0,…, gmax} Thanks, Nicolette [[alternative HTML version deleted]]

Hi, I have to draw samples from an asymmetric-Laplace-Normal distribution: f(u|y, x, beta, phi, sigma, tau) \propto exp( - sum( ( abs(lo) + (2*tau-1)*lo )/(2*sigma) ) - 0.5/phi*u^2), where lo = (y - x*beta) and y=(y_1, ..., y_n), x=(x_1, ..., x_n) -- sorry for this huge formula -- A WinBUGS Gibbs sampler and the HI package arms sampler were used with the same initial data for all parameters. I