similar to: bootstrap for nlme

Dear All, I would like to use a nonparametric bootstrap to calculate the confidence intervals for the 5% and 95% quantiles using boot.ci. As you know, boot.ci requires the use of boot to generate bootstrap replicates for my statistic. However this last function doesn't work in my case because I am missing something. Here is an example y <- rnorm(100) Quantile <-

Hi, Let me start my saying that I am new to R hence my grasp of the appropriate used of R coding is undoubtedly way behind many on this forum. I am trying to use boostrapping to derive errors around my parameter estimate for the fixed effects in the following model. It is simply estimating the number of times an animal might cross a road based on the road's distance from a stream. I have

Hi, I'm having trouble understanding how to construct a random number generator for a parametric bootstrap. My aim is to bootstrap a Likelihood Ratio statistic (under the null) for a linear model. The function at this point is given by boot.test.n01 <- function(data, indeces, maxit=20) { y1 <- fit1+se(e2)*rnorm(314) mod1 <- glm(y1 ~ X1-1, maxit=maxit) y2 <-

Dear all, &nbsp; Is there a function to calculate the&nbsp; prediction intervals for random effects in non-linear mixed models? I found a way to do it for linear mixed models but not for non-linear&nbsp;mixed one. &nbsp; Many thnaks &nbsp; Bernard &nbsp; &nbsp; _____________________________________________________________________________ o.fr [[alternative

Hello. I am working on an analysis involving the nonlinear mixed model function (nlme) in R. The data consist of measures of carbon fixation by leaves as a function of light intensity and the parametric function (standard in this area because it has a biological interpretation) is a non-rectangular hyperbola. I cannot get the nonlinear mixed model (nlme) function to converge cleanly. I am

I've been using SAS PROC MULTTEST to perform multiple comparisons on data that are not normally distributed by using the stepdown bootstrap procedures of Westfall and Young (1993). According to the SAS manual, "the bootstrap method creates pseudo-data sets by sampling observations with replacement from each within-stratum pool of observations. An entire data set is thus created, and

Dear List, While trying to define a customised CITATION file for a package, following R-exts, I realised that if I use only one 'citEntry' I got both a text description and a BibTex entry for the package, as for the 'nlme' package: -------------------------- citation('nlme') To cite package 'nlme' in publications use: Jose Pinheiro, Douglas Bates, Saikat

Tony Rossini sent me a copy of Jim's message to this list asking about nlme. I was surprised to find that I had not subscribed to this list. I thought I did but apparently not. I have now corrected that. Development of the nlme library (the name is from "nonlinear mixed-effects" but the library can also be used to fit general forms of linear mixed-effects models) is a continuing

Dear list, In a mixed model I selected I find a non positive definite random effects variance-covariance matrix G, where some parameters are estimated close to zero, and related confidence intervals are incredibly large. Since simplification of the random portion is not an option, for both interest in the parameters and significant increase in the model fit, I would like to collect

When I type library() to see what is installed the following list in RED comes up. Packages in library '/home/jason/R/i686-pc-linux-gnu-library/2.13': abind Combine multi-dimensional arrays aplpack Another Plot PACKage: stem.leaf, bagplot, faces, spin3R, and some slider functions biglm bounded memory linear and

The Rows function which is called from plot.compareFits in the nlme package is not found. > plot(compareFits(coef(bp.model3),coef(bp.model3M))) Error in plot.compareFits(compareFits(coef(bp.model3), coef(bp.model3M))) : couldn't find function "Rows" > Can I find it elswhere? Have I missed a required package? Thanks Ross Darnell > library(help=nlme) nlme

I would like to document my findings (with a potential FIX) regarding the issue of calculation of the degrees of freedom with nlme(). The program given at the bottom of this email generates and fit 20 data sets with a mixed-effects LINEAR model, but using the function nlme() instead of lme(). In each case, the correct number of degrees of freedom for the intercept parameter is 12. However, in

Hi, I applied a linear mixed effect model in my data using the nlme package. lme2<-lme(distance~temperature*condition, random=~+1|trial, data) and then anova. I want to ask if it is posible to get the least squares means for the interaction effect and the corresponding 95%ci. And then plot this values. Thank you Panagiotis -- View this message in context:

Dear R-experts, I have a problem to formulate non linear mixed effects when more than one explanatory variable is present. I'm using R 1.1.1 under Linux. The version number of the nlme library is 3.1-7. "help(nlme)" says that among several possibilities it is possible to code fixed effects according to fixed = a1 + a2 + ... + an ~ X1 + X2 + ... + Xm where the left hand side

Hi I am running some linear and non-linear mixed effect models and would like to do some planned contrasts (a priori contrasts) I have looked in the help and in many forums and it seems possible to do so but don't understand how to write the function and I couldn't find an example in Pinheiro and Bates. lme {nlme} has a contrasts argument but I can't understand how to code it.

Dear R list members, I've turned up a strange discrepancy between results obtained from the lme function in the nlme package in R and results obtained with lme in S-PLUS. I'm using version 3.1-24 of nlme in R 1.4.1 under Windows 2000, and both S-PLUS 2000 and 6.0, again under Windows 2000. I've noticed discrepancies in a couple of instances. Here's one, using data from Bryk

Readers, Using version 251 I tried the following command: lm(y~a+b,data=datafile) Resulting in, inter alia: ... coefficients (intercept) a 1.2 3.4 Packages installed: acepack ace() and avas() for selecting regression transformations adlift An adaptive lifting scheme algorithm akima Interpolation of irregularly spaced

Full_Name: Vaidotas Zemlys Version: 2.9.0 OS: Ubuntu 8.10 Submission from: (NULL) (213.197.173.50) Steps to reproduce the bug: library(nlme) data(Oxboys) qm=lmList(height~age|Subject,data=Oxboys) grid=with(Oxboys,expand.grid(age=seq(min(age),max(age),length=50),Subject=levels(Subject))) res <- predict(qm,grid,se=TRUE) Erreur dans if (pool) { : l'argument est de longueur nulle res <-

Hi I compouted a multiple linear regression with repeated measures on one explanatory variable: BOLD peak (blood oxygenation) as dependent variable, and as independent variables I have: -age.group (binaray:young(0)/old(1)) -and task-difficulty measured by means of the reaction-time 'rt'. For 'rt' I have repeated measurements, since each subject did 12 different tasks. -> so

Dear All, let E(Y | t, b, theta) be the conditional expectation of Y where b is a vector of random effects , theta is a vector of fixed effects and t is the time of observation (t =1,2, ...,n). I wonder if nlme has a function to compue E(Y | t, theta). I know that it can be done by approximating the integrals over b of E(Y | t, b, theta). But I am wondering if nlme has a function