similar to: Simple mean trajectory (ordinal variable)

I'm trying to understand how to plot individual growth curve trajectories, with the overall mean trajectory superimposed (preferably in a slightly thicker line, maybe in black) over the individual trajectories. Using the sleepstudy data in lme4, here is the code I have so far: library(lme4) library(lattice) xyplot(Reaction ~ Days, data = sleepstudy, group = Subject, type = 'l')

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 have two regressions to perform - one with a metric DV (-3 to 3), the other with an ordered DV (0,1,2,3). Neither normal distribution not homoscedasticity is given. I have a two questions: (1) Some sources say robust regression take care of both lack of normal distribution and heteroscedasticity, while others say only of normal distribution. What is true? (2) Are there ways of using robust

Hi, I'm trying to read some trajectory files (text files) which have the form: # trial n # t X Y 0 1 2 0.2 1 3 0.4 1.2 4 ... # trial n+1 # t X Y 0 1 2 0.2 1.3 3.3 0.4 1.5 5 ... ... where the symbol # means that the line is not a numeric value, but I still need to read the number of the trial (n=1, 2, etc.). I

dear helpers, I wonder if there is a way to read a molecular dynamic trajectory file ( binary file) produced by CHARMM into R. Something like that in matlab. Actually this will save tremendous effort in post processing. best regards karim

Hello, I'm attempting to create a smooth, 3D plot of a trajectory (rather than the cloud or wireframe functions). I would rather the individual data points not be visible. I've had no luck finding this on the graphics or help pages. Thank you in advance. Chris Some example data, just in case: 25 32 40 12 25 32 2 12 25 2 2 12 20 2 2 1 20 2 6 1 20 5 6 1 5 5 6

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 all, I need a little help for construct an state's map on R. The first problem is to get the data. I have a datafile of longitude and latitude in the follow format: trajectory latitude longtude T -22.045618 -51.287056 T -22.067078 -51.265888 T -22.067039 -51.207249 T -22.059690 -48.089695 T -22.075529 -48.074608 T -22.072460 -48.044472 T -22.062767 -48.298473 T -22.077349

Hello, I have many hundred variables in my longitudinal dataset and lots of missings. In order to plot data I need to remove missings. If I do > data <- na.omit(data) that will reduce my dataset to 2% of its original size ;) So I only need to listwise delete missings on 3 variables (the ones I am plotting). data$variable1 <-na.omit(data$variable1) does not work. Thank you

Dear all, Fernando Colchero, Owen Jones and Maren Rebke, Max Plank Institute for Demographic Research, present BaSTA -Bayesian Survival Trajectory Analysis. Fernando, Owen and Maren have put together this beautiful video exploring research on ageing and and how to deal with incomplete data. To install BaSTA http://basta.r-forge.r-project.org BaSTA users mailing list,

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 simulate the trajectory of the pension assets of one person. In C-like syntax, it looks like this: daily.wage.growth = 1.001 # deterministic contribution.rate = 0.08 # deterministic 8% Wage = 10 # initial Asset = 0 # initial for (10,000 days) { Asset += contribution.rate * Wage

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

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

Hello, Does anyone know how to make "movies" in R by making a sequence of plots? I'd like to animate a long trajectory for exploratory purposes only, without creating a bunch of image files and then using another program to string them together. In Splus I would do this using double.buffer() to eliminate the flickering caused by replotting. For instance, with a 2-D trajectory

Greetings, I have the following problem: I want to compare a "parameter trajectory", i.e. a series of real numbers (representing equidistant samples of a time-varying parameter) produced by some "model", to a reference trajectory, measured from the real world, in order to get a rating of how good the model that produced the first trajectory is. Ok, so I use the RMS of the

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

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

Dear all, I have just uploaded 4 new packages on CRAN, which are on the long term designed to replace the "old" package adehabitat: * adehabitatMA: functions to perform spatial operations (morphology, buffer, etc.) * adehabitatHS: functions for the analysis of habitat selection by wildlife * adehabitatHR: functions for home range estimation of animals * adehabitatLT: functions for