similar to: lognormal sampleing using covariance matrix

Hi, Does anyone know how to extract fixed effects SE values from generalized linear mixed models estimated using the lmer function in the lme4 library? I searched attributes and structure with no luck. Thanks Frank A. La Sorte, Ph.D. Department of Fisheries and Wildlife Sciences University of Missouri Columbia, MO 65211 USA

For a Monte Carlo study I need to extract from an lme model the estimated standard deviation of a random effect and store it in a vector. If I do a print() or summary() on the model, the number I need is displayed in the Console [it's the 0.1590195 in the output below] >print(fit) >Linear mixed-effects model fit by maximum likelihood > Data: datag2 > Log-likelihood:

Hi, I have written the following code which works fine step<-5 numSim<-15 N<-double(numSim) A<-double(numSim) F<-double(numSim) M<-double(numSim) genx<-double(numSim) for (i in 1:numSim) { N[i]<-20 PN<-(runif(N[i], 0, 1)) A[i]<-sum(ifelse(PN>0.2, 1, 0)) PF<- runif((A[i]*0.5), 0, 1) F[i]<-sum(ifelse(PF>0.2, 1, 0)) PM<-

Hello. I have the following object which is a list of length NumSim with each entry being a matrix of dimensions Ncurvas x 3: dW = replicate(NumSim,cbind(rnorm(Ncurvas),rnorm(Ncurvas),rnorm(Ncurvas)),simplify=F) I would like to transform it into an array of dimension Ncurvas x 3 x NumSim. Does anybody does how to do this? or how to generate directly and array composed of independent random

Dear all, I am currently working on a function in which I would like to avoid using the command sample(). Therefore, I am now trying to make a for loop that does the same thing as the in built function sample does: Rearranging the items of a object randomly. So, the output I want to you get is the same as sample() would give me: e.g.: data <- c(5,4,6,7,8) sample(data) > data <-

Hi, I have tried to use uniroot to solve a value (value a in my function) that gives f=0, and I repeat this process for 10000 times(stimulations). However error occures from the 4625th stimulation - Error in uniroot(f, c(0, 2), maxiter = 1000, tol = 0.001) : f() values at end points not of opposite sign I have also tried interval of (lower=min(U), upper=max(U)) and it won't work as well.

Hello, Hela wrote : My problem in a few words is as folow: I used the fCopulae packages because i have 2 series which are already transformed in the uniform domain (the space of the copulas functions) and i estimated with type archmList() from 1 to 22, but i don't know their names:for exemple the type=4 is the Gumbel Copula...and for the others i can't have any idea about how can i find

I submitted this to rmetrics help list as well... I've been using R-2.4 with Rmetrics successfuly for a year or two. I recently moved to Ubuntu Linux 8.04, loaded R via apt-get install R-base etc. etc... then in the R interface i ran install.packages("fMultivar"), chose the CRAN mirror and it loaded fMultivar and all the required dependecies. However, when I try to run the same

Dear all R users, Please forgive me if my question is too trivial. Suppose I have two variables, (x,y) which is log-normally distributed with expected value (mu1, mu2) and some variance-covariance matrix. Now I want to draw a random sample of size 1000 from this distribution. Is there any function available to do this? Thanks and regards, Megh

Hello all, I'd like to calculate the mean correlation within a cluster and understand if it's significantly >0. I'm using packages 'geomorph' and 'paleomorph'. #Simulate an array A <- array ( rep ( 1 : 36 , by = 4 ), dim = c ( 12 , 3 , 4 )) #Load 'geomorph' package and superimpose coordinates test.gpa <- gpagen ( A , print.progress = FALSE ) #Load

Hi, I created a matrix of correlation coefficients and their respective scatterplots using the commands hetcor() and panel.cor(). Can I edit this matrix? I would like to have the scale values display at the top, as opposed to the alternating top-bottom scale approach. Also, I would like to increase the font size of the graphic for print purposes. Any help on how to accomplish this would be

HI Every one Could some one provide me definitions of following bivariate distributions gamma, exponencial, Weibull, half-normal , Rayleigh, Erlang,chi-square thanks A.S. Qureshi

Folks, I have been using the VAR {vars} program to find a fit for the following bi-variate time series (subset): bivariateTS<-structure(c(0.950415958293559, 0.96077848972081, 0.964348957109053, 0.967852884998915, 0.967773510751625, 0.970342843688257, 0.97613937178359, 0.980118627997436, 0.987059493773907, 0.99536830931504, 1.00622672085718, 1.01198013845981, 1.01866618122606,

Can anyone advise me on some pratical papers or books On Gaussian Copulas? Anything in the genre of Copulas Dummies Would be a help. As simpe, and approachable with minimal pedantic style. Thanks, Neil -------------------------------------------------------- This information is being sent at the recipient's reques...{{dropped:16}}

Hi,   I use fCopulae package to draw different graphs of univariate and bivariate skew t.  But the plots titles overlap.  I tried using cex.main, font.main to adjust the size but they still overlaps.  Here is my code: par(mfrow = c(3, 1)) mu = 0 Omega = 1 alpha1 = 0 alpha2 = 1.5 alpha3 = 2 alpha4 = 0.5 Z1 = matrix(dmvst(x, 1, mu, Omega, alpha1, df = Inf), length(x)) Z2 = matrix(dmvst(x, 1, mu,

Hi, I conduct a panel data estimation and obtain estimators for two of the coefficients beta1 and beta2. R tells me the mean and covariance of the distribution of (beta1, beta2). Now I would like to find the distribution of the quotient beta1/beta2, and one way to do it is to simulate via the joint distribution (beta1, beta2), where both beta1 and beta2 follow t distribution. How could we

I'm in the process of converting some S3 methods to S4 methods. I have this function : setGeneric("enrichmentCalc", function(rs, organism, seqLen, ...){standardGeneric("enrichmentCalc")}) setMethod("enrichmentCalc", c("GenomeDataList", "BSgenome"), function(rs, organism, seqLen, ...) { ... ... ... })

I have a set of data that is not normally distributed and for which I need to build a model. So, I tried the lrm function from the design-package. The first run went well, and I got the following results: Wald Statistics Response: RVCL2PROC.mott Factor Chi-Square d.f. P TTV.mott (Factor+Higher Order Factors) 69.01 4

Dear List, I am trying to fit Truncated Lognormal to a data set that is 'truncated' from above a certain value, say, 0.01. Below is what I was able to come up with. I would appreciate it if you could review and make any necessary changes. # This is modified off the code for 'dtnorm' of library(msm). dtlnorm <- function (n, mean = 0, sd = 1, lower = -Inf, upper = Inf) {

Hi everyone I am attempting to estimate a model with a frailty effect distributed as a lognormal variable.I am using the following code: frailtyPenal(formula, data, ..., RandDist = "LogN") I get the following error message: Error in frailtyPenal(Surv(,) ~ + , : unused argument(s) (RandDist = "") What can I do? Thanks for the help [[alternative HTML version deleted]]