similar to: prediction intervals in lme

How can I get prediction intervals from a mixed-effects model? Consider the following example: library(nlme) fm3 <- lme(distance ~ age*Sex, data = Orthodont, random = ~ 1) df3.1 <- with(Orthodont, data.frame(age=seq(5, 20, 5), Subject=rep(Subject[1], 4), Sex=rep(Sex[1], 4))) predict(fm3, df3.1, interval='prediction') # M01 M01

Hi R-users, >From the last week I've been working fitting a linear mixed model with random intercept and fixed shape (model4) for a data set with 37 individuals measured over time, using lme package. Results are at the end of this message. The outcome is score and the covariate is age. My question is: is possible (and how) to estimate both 95% confidence and prediction intervals for the

Hi, I wonder if predict.gam is supposed to work with family=negbin() definition? It seems to me that the values returned by type="response" are far off the observed values. Here is an example output from the negbin examples: > set.seed(3) > n<-400 > dat<-gamSim(1,n=n) > g<-exp(dat$f/5) > dat$y<-rnbinom(g,size=3,mu=g) >

For a given linear regression, I wish to find the 2-tailed t-dist probability that Y-hat <= newly observed values. I generate prediction intervals in predict() for plotting, but when I calculate my t-dist probabilities, they don't agree. I have researched the issues with variance of individual predictions and been advised to use the variance formula below (in the code). I presume my

Does lme prediction work correctly with poly() terms? In the following simulated example, the predictions are wildly off. Or am I doing something daft? Milk yield for five cows is measured weekly for 45 weeks. Yield is simulated as cubic function of weekno + random cow effect (on intercept) + residual error. I want to recover an estimate of the fixed curve. ############### library(nlme)

Many thanks for your very useful comments and suggestions. Renaud 2006/5/30, Prof Brian Ripley <ripley at stats.ox.ac.uk>: > On Tue, 30 May 2006, Prof Brian Ripley wrote: > > > This is not really a bug. See > > > > http://developer.r-project.org/model-fitting-functions.txt > > > > for how this is handled in other packages. All model-fitting in R used =

Dear Dieter, sorry for not being more specific. I would like to use R to get a prediction (with standard error) of the response in a mixed model at selected values of the fixed-effects factors. Hence, in a mixed model, say, for response body size with, say, fixed factors sex and age, I would like to get a prediction of size for each sex and at selected ages such as 5, 10, 15; and I want a SE

I fit a random effects linear model to data, and then tried to use it to predict, but I got this error: > predict(lmeObject, newdata, level=0) Error in eval(mCall$fixed)[-2] : object is not subsettable This is a new error for me. It still occurs if I change the level to 1 or if I change the data for prediction back to the original dataset to which the lme model was fitted. Can anyone help?

I have two questions related to plotting predicted values for a linear mixed model using xyplot: 1: With a groups= argument, I can't seem to get the key to appear inside the xyplot. (I have the Lattice book, but don't find an example that actually does this.) 2: With lme(), how can I generate confidence bands or prediction intervals around the fitted values? Once I get them, I'd

Full_Name: Renaud Lancelot Version: Version 2.3.0 (2006-04-24) OS: MS Windows XP Pro SP2 Submission from: (NULL) (82.239.219.108) I think there is a bug in predict.lme, when a polynomial generated by poly() is used as an explanatory variable, and a new data.frame is used for predictions. I guess this is related to * not * using, for predictions, the coefs used in constructing the orthogonal

This is a follow-up mail of "the effect of blocking on the size of confidence intervals - analysis using aov". In both mails I pursue the idea of using blocking factors in order to reduce the width of confidence intervals. My dataset comprises, a quantitative response variable, namely: "response", and three categorical eplanatory variables, namely: "method",

There has been some recent discussion on this list about the value of using intervals with lme() to check for whether a model is ill-defined. My question is, what else can drive very large confidence intervals for the variance components (or cause the error message "Error in intervals.lme(Object) : Cannot get confidence intervals on var-cov components: Non-positive definite approximate

Is the project on creating R GUIs using QT interfaces still on? Any plans of using PyQT Regards Ajay Ohri Websites- http://decisionstats.com http://dudeofdata.com Linkedin- www.linkedin.com/in/ajayohri On Tue, Nov 9, 2010 at 8:33 PM, Kari Ruohonen <kari.ruohonen at utu.fi> wrote: > Hi, > I wonder if someone could help. I needed to transfer (copy) a workspace > file that

R-helpers, I am using the package "drc" to fit a 4 parameter logistic model. When I use the predict function to get prediction on a new dataset, I am not getting the requested confidence or prediction intervals. Any idea what is going on? Here is code to reproduce the problem: --- library(drc) # Fit model to existing dataset in package spinach.model <- drm(SLOPE~DOSE, data =

Hi all, I was wondering if there is a function out there, or someone has written code for making confidence intervals around model averaged predictions (y~á+âx). The model average estimates are from the dRedging library? It seems a common thing but I can't seem to find one via the search engines Examples of the models are: fit1 <- glm(y~ dbh, family = binomial, data = data) fit2 <-

> I'm trying to use "predict" with a linear mixed-effects logistic > regression model fitted with nlmmPQL from the MASS library. > Unfortunately, I'm getting an error "non-conformable arguments" in > predict.lme, and I would like to understand why. I'd like to briefly describe how I ended up working around this problem. The issue is that predict.lme

I'm using R on Windows v2.0.1 with the nlme package (v3.1-53) and am finding some unexpected discrepancies in the output of intervals.lme and coef.lme. I've included a toy dataset at the end, but briefly, the data are longitudinal data from couples in marital therapy. Each spouse's relationship satisfaction is measured 4 times; I've fit both linear and quadratic models to the

Dear R-list. I have done a logistic glm using Age as explanatory variable for some allergic event. #the model model2d<-glm(formula=AEorSAEInfecBac~Age,family=binomial("logit"),data=emrisk) #predictions for age 30 and 60 preds<-predict(model2d,data.frame(Age=c(30,60)),se.fit=TRUE) # prediction interval

Hi I really hope someone can help me. I have just started to work with S-plus, and have not yet understood how it really works. I am now trying to fit a mixed effects model with lme. My goal is to compare four different groups, at several different time points, and I therefore would like to create confidence intervals for linear combinations of my estimated parameters (as I usually do with

I am trying to explore nlmer by running some nlme examples from Pinheiro & Bates (2000). I do not seem to find information how to specify fixed effects covariates to nlmer models. Specifically, I tried to run the "Carbon Dioxide Uptake" example from p. 368 onwards in the PB200 book. The model without fixed effects covariates runs well but how to tell nlmer to include Type and