similar to: Confidence Intervals for Mixed Effects

This is not a bug report but didn't see another way to ask a question. For the approximate t-test assuming unequal variances, the R docs cite Welch's method for the df of the approximating distribution. I have several methods books, and they all uses Satterthwaite's method. Why does R use Welch's method where can I learn about Welch's method? Sincerely, David Allen

Dear R experts, I was wondering if there are any R functions that give the tail area of a sum of chisquare distributions of the type: a_1 X_1 + a_2 X_2 where a_1 and a_2 are constants and X_1 and X_2 are independent chi-square variables with different degrees of freedom. Thanks, Klaus -- "Feel free" - 5 GB Mailbox, 50 FreeSMS/Monat ...

I am analyzing a dataset on the effects of six pesticides on population growth rate of a predatory mite. The response variable is the population growth rate of the mite (ranges from negative to positive) and the exploratory variable is a categorical variable (treatment). The experiment was blocked in time (3 blocks / replicates per block) and it is unbalanced - at least 1 replicate per block. I am

Hi R community! I've fitted three mixed-effects regression models to a thousand bootstrap samples (case-resampling regression) using the lme4 package in a custom-built for-loop. The only output I saved were the inferential statistics for my fixed and random effects. I did not save any output related to the performance to the machine learning algorithm used to fit the models (REML=FALSE).

Hello and sorry to disturb. I'm trying to plot the confidence intervals for the fixed effects of a lme. I want to obtain graphically, if it is possible, a bar with Estimate, upper and lower CI for each level of the factors. I know how to do for a lm model but for a lme one, I tried with plot(intervals(...)) and plot(ci(...)) from the gmodels package but it doesn't work well. Thanks

HI, I am using lmer() for a simple mixed effects model. The model is of the form logit(y)~ x + (1|z), where x is an indicator variable and z a multi-level factor. I would like an estimate of the response variable (either y or logit y) with an associated confidence interval for a given value of x. There does not appear to be a predict function written for lmer(). The output for the fixed

Dear All: Significance Analysis of Microarrays(SAM) As we know sam do multiple t.test as following ## Default S3 method: t.test(x, y = NULL, alternative = c("two.sided", "less", "greater"),mu = 0, paired = FALSE, var.equal = FALSE,conf.level = 0.95, ...) var.equal: a logical variable indicating whether to treat the two variances as being equal. If 'TRUE'

Hi All, I'm taking an Experimental Design course this semester, and have spent many long hours trying to coax the professor's SAS examples into something that will work in R (I'd prefer that the things I learn not be tied to a license). It's been a long semester in that regard. One thing that has really frustrated me is that lme has an extremely counterintuitive way for

>>>>> "TL" == Thomas Lumley <tlumley@u.washington.edu> >>>>> on Sun, 6 Oct 2002 09:19:27 -0700 (PDT) writes: TL> On Sat, 5 Oct 2002 roxburg@kih.net wrote: >> This is not a bug report but didn't see another way to >> ask a question. TL> Well, you could try the r-help or r-devel mailing lists >> For

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has been coded as 0 for age 12, 1 for age 15 and 2 for age 18. I am currently using a time varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, to examine whether trajectory/variation

I am running a multilevel growth curve model to examine predictors of social anhedonia (SA) trajectory through ages 12, 15 and 18. SA is a continuous numeric variable. The age variable (Index1) has been coded as 0 for age 12, 1 for age 15 and 2 for age 18. I am currently using a time varying predictor, stress (LSI), which was measured at ages 12, 15 and 18, to examine whether trajectory/variation

I am trying to do a forecasting exercise for a series, x. My forecast model consists of the following I first regress log(x) on time and dummy variables for each month. lm(log(x) ~ time + monthly dummies) I then use predict() to obtain a prediction for the next year. I then fit an AR(6)/AR(12) model on the residuals of the regression. I use predict() here also to obtain the prediction for the

I'm using read.csv to read in a csv file containing '#' characters. For example, say I'm reading the following file (test.csv): var1,var2,var3 a,b,c d,e#,f g,h,i It outputs: > read.csv("Raw Data\\test.csv") var1 var2 var3 1 a b c 2 d e 3 g h i Warning message: incomplete final line found by readTableHeader on 'Raw Data\test.csv'

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

Dear all, I am using the Instrumental Variable approach to estimate the causal effects of TWO endogenous variables in a Mendelian Randomization study. As long as point estimation is concerned, I have no problem: both "ivreg" in library "AER" and "tsls" in library "sem" do the job perfectly. The problems begin when I try to obtain confidence intervals for

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

R 2.3 Windows XP I am trying to understand lme. My aim is to run a random effects regression in which the intercept and jweek are random effects. I am comparing output from SAS PROC MIXED with output from R. The point estimates and the SEs are the same, however the DFs and the p values are different. I am clearly doing something wrong in my R code. I would appreciate any suggestions of how I can

Hi, I'm trying to work out plotting effect confidence intervals for a mixed effects design. For example, when measuring heights over age one will get two kinds of confidence intervals from the resulting model (using intervals in lme), a broad inference interval from the random intercept, and a narrow inference interval about the fixed effect slope. I've been considering what

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",

I want to compute confidence intervals for the random effect estimates for each subject. From checking on postings, this is what I cobbled together using Orthodont data.frame as an example. There was some discussion of how to properly access lmer slots and bVar, but I'm not sure I understood. Is the approach shown below correct? Rick B. # Orthodont is from nlme (can't have both nlme and