similar to: Mean and individual growth curve trajectories

Hi all, I am trying to plot the fitted trajectories for each individual from an individual growth model (fit with a linear mixed effects model in lme). How can I plot each person's trajectory in the *same* panel, along with the mean-level trajectory? Below is an image of a plot similar to what I'm trying to create (from: http://jpepsy.oxfordjournals.org/content/31/10/1002/F6.large.jpg):

I was trying to save a data frame to an excel file using the following command: write.table(myData, file="myData.csv",sep=",", row.names=F) The command works for some data frames, but for other data frames, I get the following error: Error in if (inherits(X[[j]], "data.frame") && ncol(xj) > 1L) X[[j]] <- as.matrix(X[[j]]) : missing value where

Hello. I have 5 measurement points, my dependent variable is ordinal (0 - 3), and I want to visualize my data. I'm pretty new to R. What I want is to find out whether people with different baseline covariates have different trajectories, so I want a plot with the means trajectory of my dependent variable (the individual points do not make a lot of sense in ordinal data) on each measurement

I just upgraded my version of R from 2.8.1 to 2.9.0. After doing so, I now have problems using the package installer to reinstall all my packages. Some download and install without problems, but for others, I get the spinning pinwheel and after even 10 minutes, nothing is downloaded. R stops responding. At first, I had not deleted the old version of 2.8.1 before installing 2.9.0. However, I

I have a data set similar to the following: Color Score RED 10 RED 13 RED 12 WHITE 22 WHITE 27 WHITE 25 BLUE 18 BLUE 17 BLUE 16 and I am trying to to select just the values of Color that are equal to RED or WHITE, excluding the BLUE. I've tried the following: myComp1<-subset(dataset, Color =="RED" | Color == "WHITE")

Is it possible to change the x-axis values in a histogram to reflect binned values? Here are my data: histexample<-c(6,7,7,8,8,8,9,9,9,9,9,10,10,10,10,10,10,10,11,11,11,11,11,11,12,12,12,12,13,13,13,14,14,14,15,16) hist(histexample) Now, I'll bin pairs of adjacent values together (e.g., 5-6, 7-8, 9-10, 11-12, 13-14, 15-16) using the following bins<-c(4.5,6.5,8.5,10.5,12.5,14.5,16.5)

I'm fitting a lme growth curve model with two predictors and their interaction as predictors. The multilevel model is nested so that level 1 is time within the individual, and level 2 is the individual. I would like to plot the mean group-level trajectories at plus and minus 1 SD from the mean of the main effects composing the interaction term. Thus, the plot should have 4 lines (mean

I am trying to reshape data that are in the wide format into the long format. The design is a repeated-measures design, which combined 3 levels of shape (circle, square, triangle) with 3 levels of color (Blue, Red, Green), for a total of 9 variables. The wide data look like this (sorry I couldn't get the columns to line up quite right: Subject CircleBlue CircleRed CircleGreen SquareBlue

Hi there, I want to make trajectory plots for data as follows: ID time y 1 1 1.4 1 2 2.0 1 3 2.5 2 1.5 2.3 2 4 4.5 2 5.5 1.6 2 6 2.0 ... That is, I will plot a growth curve for each subject ID, with y in the y axis, and time in the x axis. I would like to have all growth curves in the same plot. Is there

Hi   I am using following code to produce a xyplot for some longitudinal data. There are 2 panels. It produced all longitudinal trajectories with mean profile. But since the dataset it very large plot looks very messy. I want to show, say 10 randomly selected individual longitudinal trajectories together with mean profile for entire dataset. Could any help me to alter the following code to do

Dear List: I have a data set with over 7000 students with about 4 observations over time per student. I want to examine the within-group fits of a random sample of this group as it takes forever to compute and draw all 7000 regressions. Here is the code I have used so far. >group<-groupedData(math~year|childid, data=scores) >group.list<-lmList(group)

Dear all, I have a problem where I must represent points with XYZ coordinates changing over time. I will do a number of operations on this data such as calculating the YZ-projection distance of the points to the origin over time, the frequency spectrum of the X-T data etc. I am trying to find a good way of representing this data with an appropriate data structure. It appears like

hello i have a mixed effect model which gives slope and intercept terms for 6 groups (diagnosis (3 levels) by risk group(2 levels)). the fixed part of the model is -- brain volume ~ Diagnosis + Risk Group + (Risk Group * age : Diagnosis) - 1 thus allowing risk group age/slope terms to vary within diagnosis and omitting a nonsignificant diagnosis by risk group intercept (age was centered)

Hello, I'm developing an ordination using metaMDS (package vegan). The analysis identifies 3 distinct groups that I'd like to define by either adding circles or ellipses to help identify the groups. The data set is a spatial temporal data base depicting change in each of 4 areas over 3 time periods. I can add lines that would link each of the time - x - area trajectories, but

I'd like to log all console I/O for an entire session while maintaining normal console I/O. I'm working in Windows98, RGui, R1.1.1. The sink() function is not the answer since it diverts output from the console. In the RFAQ I see references to "...save the contents of the interaction buffer to a file..." and in the R-Help Archive "...just save the console output...". I

Hi all, I am having issues comparing models with lmer. As an example, when I run the code below the model summaries (AIC, BIC, loglik) differ between the summary() and anova() commands. Can anyone clear up what's wrong? Thank you! Darren Ward library(lme4) data(sleepstudy) fm1<-lmer(Reaction ~ Days + (1|Subject), sleepstudy) summary(fm1) fm2<-lmer(Reaction ~ Days +

I wonder if someone can help me understand some counterintuitive simulation results. Below please find 12 lines of R code that theoretically, to the best of my understanding, should produce essentially a flat line with no discernable pattern. Instead, I see an initial dramatic drop followed by a slow rise to an asymptote. The simulation computes the mean of 20,000 simulated trajectories

Full_Name: Dale Barr Version: 2.5.1 (patched) OS: Ubuntu linux x86_64 Submission from: (NULL) (138.23.70.108) When RMySQL is loaded in before lme4, the summary() function for lmer objects in the lme4 packages produces the following error: Error in printMer(object) : no slot of name "status" for this object of class "table" When RMySQL is loaded AFTER lme4, however, no such

Dear all, I found a problem with 'lme4'. Basically, once you load the package 'aod' (Analysis of Overdispersed Data), the functions 'lmer' and 'glmer' don't work anymore: library(lme4) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data

Hi, For some reason do.call on anova fails if the models are named lmer objects. Consider the following example: library(lme4) models <- list( lmer(Reaction ~ Days + (1| Subject), sleepstudy), lmer(Reaction ~ Days + (Days | Subject), sleepstudy)) # # models is an unnamed list, do.call works (although with warning): do.call(anova, models) # # after labeling the models, do.call gives an