search for: agq

...oy you with this problem but I could not deal with the problem with the help of books, internet or friends. So my hope is that you would be so kind and you find some minutes to look through one of my examples. I would be deeply greatful. My R script: library (nlme) #Datei laden randomInterceptDIQAGQ <- lme(NoteD ~ IQ + AGQ, data = Gind, random = ~1|Klnr, method = "ML", na.action = na.exclude) summary (randomInterceptDIQAGQ) intervals (randomInterceptDIQAGQ) my Output: Final model, : 2 predictors, no RandomSlope > randomInterceptDIQAGQ <- lme(NoteD ~ IQ + AGQ, data = Gind...

...random effects model for repeated measures semi-continous data with a cluster at 0. I use the "model y ~ general(loglike)" statement in Proc NLMIXED, so I can specify a general log likelihood function constructed by SAS programming statements. Then the likelihood can be maximized by AGQ. Is there a similar generic AGQ function in R to let me write explicitly the log likelihood and then maximize it accordingly? Can nlme do the work? Thanks! Lei Liu Assistant Professor Division of Biostatistics and Epidemiology Department of Public Health Sciences School of Medicine University of...

...) 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 ?Laplace?, ?AGQ?

...seem to be making good progress on the P5600 scheduler using the new machine model but I've got a few questions about it. How would you represent an instruction that splits into two micro-ops and is dispatched to two different reservation stations? For example, I have two reservation stations (AGQ and FPQ). An FPU load instruction is split into a load micro-op which is dispatched to AGQ and a writeback micro-op which is dispatched to FPQ. The AGQ micro-op is issued to a four-cycle latency pipeline called LDST. Three cycles after issue, the LDST pipeline wakes up the FPQ micro-op, which write...

The freely available R package glmmADMB can do Adaptive Gaussian Quadrature for this type of model, since it is built using AD Model Builder's random effects module which incorporates this feature. There is now a beta version of the software for people using R on the Mac intel platform. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Cheers, Dave -- David A. Fournier

...eally good questions. For future reference, I might provide better examples if you attach what you have so far for the model. How would you represent an instruction that splits into two micro-ops and is dispatched to two different reservation stations? For example, I have two reservation stations (AGQ and FPQ). An FPU load instruction is split into a load micro-op which is dispatched to AGQ and a writeback micro-op which is dispatched to FPQ. The AGQ micro-op is issued to a four-cycle latency pipeline called LDST. Three cycles after issue, the LDST pipeline wakes up the FPQ micro-op, which write...

...stimates provided by Agresti (2002, Categorical data analysis, Wiley) using abortion data in table 10.13 (estimates provided in table 12.3 p. 505). I fit the same model using these three commands: a1 <- lmer(resp ~ sex + option1 + option2 + (1|id), data=abort,family=binomial, method = c("AGQ")) a2 <- lmer(resp ~ sex + option1 + option2 + (1|id), data=abort,family=binomial, method = c("Laplace")) a3 <- lmer(resp ~ sex + option1 + option2 + (1|id), data=abort,family=binomial, method = c("PQL")) All three methods provide the exact same estimates (which sh...

...er library(lme4) n <- 120 x1 <- runif(n, -4, 4) x2 <- sample(0:1, n, TRUE) z <- rnorm(n) id <- 1:n N <- sample(20:200, n, TRUE) y <- rbinom(n, N, plogis(0.1 + 0.2 * x1 - 0.5 * x2 + 1.5 * z)) m1 <- lmer(cbind(y, N - y) ~ x1 + x2 + (1 | id), family = binomial, method = "AGQ") m1 how to extract the p-value for 'x2' from object m1? Thanks in advance for any hint, tokas __________________________________________ Just $16.99/mo. or less. dsl.yahoo.com

...placian approximations): > mod2 <- lmer(yb ~ reg*nutrient*amd + + (1|rack) + (1|status) + + (1|popu) + (1|popu:amd) + + (1|gen) + (1|gen:nutrient) + (1|gen:amd) + + (1|gen:nutrient:amd), + data=datnm, family=binomial, method="AGQ") Error in lmer(yb ~ reg * nutrient * amd + (1 | rack) + (1 | status) + : method = "AGQ" not yet implemented for supernodal representation I would really appreciate any and all thoughts or leads. Cheers, Hank Stevens > version _ platform powerpc-apple-d...

I'm getting the following error in a script: "Error: could not find function "lmer."??? I'm wondering of my lme4 package is installed incorrectly.? Can someone tell me the installation procedure?? I looked at the support docs but couldn't translate that into anything that would work.

..."0.9-5" "1.58" "0.9-5" "0.9-5" and the modified lmer code (sourced in the above code) is: lmer1 <- function (formula, data, family = gaussian, method = c("REML", "ML", "PQL", "Laplace", "AGQ"), control = list(), start = NULL, subset, weights, na.action, offset, contrasts = NULL, model = TRUE, matDimI = matDim, rand_matI = rand_mat, ...) { method <- match.arg(method) formula <- as.formula(formula) if (length(formula) < 3) stop("formul...

...e3 -0.7533085 0.5674532 294 -1.3275253 0.1854 x1:type4 -0.0549616 0.5777216 294 -0.0951351 0.9243 etc. However, there seems to be no route to extract the corresponding information from the lmer model:- > lmer.fit=try(lmer(score~x*type+(1|subject), data=df, family=binomial, method='AGQ'),TRUE) > summary(lmer.fit) Generalized linear mixed model fit using AGQ Formula: score ~ x * type + (1 | subject) Data: df Family: binomial(logit link) AIC BIC logLik deviance 510.2616 550.1762 -245.1308 490.2616 Random effects: Groups Name Variance Std...

...: msMaxIter; maxIter... but I still get the message. Should I bother about it? If yes, what should I do to not get the message? Thank you all in advance for you answers. PS: My model writing is for example Fm<-lmer(alive~factor(sex)+mass+parasite+Latitude+(1|ID), family=binomial, method="AGQ", data=donnee). Where "parasite" is presence/absence (0 or 1) and ID is the station identity where I captured 0 to 20 specimens, each being alive or dead, having a sex, a mass, a presence/absence of parasite. Latitude is given at the station level. Date is given at the specimen leve...

...in this model represent 0.1051 on my machine. Does anyone have an advice how to handle such problems? I find the tolerance needed to achieve reasonable deviances rather high, and makes me not too confident about the estimates and the model. Using the other methods, ("Laplace" or "AGQ") did not help. My system is windows 2000, > version _ platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 2 minor 2.0 year 2005 month 10 day 06 svn rev 35749 language R Thanks Ivar Herfindal By the way, great thanks...

Hi! I get the following message trying to install the matrix pakcage, can anyone help me please? trying URL `http://cran.r-project.org/bin/windows/contrib/2.0/Matrix_0.95-5.zip' Error in download.file(url, destfile, method, mode = "wb") : cannot open URL `http://cran.r-project.org/bin/windows/contrib/2.0/Matrix_0.95-5.zip' In addition: Warning message: cannot open: HTTP

...4 treatments, so 'individual' is a random factor We came up with this model: model1<-lmer(cbind(ExpTree,Total visits-ExpTree)~ Treat +(1|Ind),method="ML" , family=binomial, data=r)) However, the error we get is this: Error in match.arg(method, c("Laplace", "AGQ")) : 'arg' should be one of ?Laplace?, ?AGQ? HELP! -- View this message in context: http://www.nabble.com/Mixed-effects-model-with-binomial-errors----problem-tp19413327p19413327.html Sent from the R help mailing list archive at Nabble.com.

Hi all, Does anybody know any function that performs gaussian adapative quadrature integration of univariate functions? Thanks in advance, Regards, Caio __________________________________________________ [[alternative HTML version deleted]]

Hello all, I'm attempting to fit a generalized linear mixed-effects model using lmer (R v 2.6.0, lmer 0.99875-9, Mac OS X 10.4.10) using the call: vidusLMER1 <- lmer(jail ~ visit + gender + house + cokefreq + cracfreq + herofreq + borcur + comc + (1 | code), data = vidusGD, family = binomial, correlation = corCompSymm(form = 1 | ID), method = "ML") Although the model fits, the

Dear All, I have the following error message when I fitted lmer to a binary data with the "AGQ" option: Error in family$mu.eta(eta) : NAs are not allowed in subscripted assignments In addition: Warning message: IRLS iterations for PQL did not converge Any help? Thanks in advance, Abderrahim [[alternative HTML version deleted]]

To whom it may concern: I am using lmer for a statistical model that includes non-normally distributed data and random effects. I used this same function in the most recent version of R as of fall 2005, and have re-done some of the same analyses using all of the same files, but with the newest version of R (2.2.1). I get answers that are not exactly the same (although I do get the same