similar to: help to make a map on R

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

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 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):

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

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, 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

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

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 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 currently trying to find a method to interpolate or smooth data that represent a trajectory in space. For example, I have an ordered (=time) set of (x,y) tuples which constitute a path in a 2D space. Is there a way using R to interpolate between these points in a way similar to spline interpolation so that I get a smooth path in space? Greetings, Dieter -- Dieter Vanderelst

>If you are looking at radioactive decay maybe but how often do >you actually see exponential KM curves in real life? Exponential curves are rare. But proportional hazards does not imply exponential. > A trial design could in fact try to get all the control sample to "event" at the same time if enough was known about prognostic factors and natural trajectory You are a

Dear all, I'm attempting to use a piecewise regression to model the trajectory of reproductive traits with age in a longitudinal data set using a mixed model framework. The aim is to find three slopes and two points- the slope from low performance in early age to a point of high performance in middle age, the slope (may be 0) of the plateau from the start of high performance to the

Hi All, if memory serves me well I recall some paper comparing the relative success in getting mainstream acceptance (as mainstream as statistics can be) of both R and Octave. I remember vaguely that the fact the development strategies (core team vs one main developer) played a major role in the relative success of the two programs. I tried to find this paper, but my goggle skills are failing

I've written a very simple bit of code to plot a trajectory using rgl: x <- (c(0,-5.947,-11.496,-16.665,-21.474,-25.947,-30.116,-34.017,-37.684,-41.148,-44.435,-47.568,-50.567,-53.448,-56.223,-58.906)) y <- (c(0,33.729,65.198,94.514,121.784,147.151,170.797,192.920,213.717,233.360,252.003,269.774,286.781,303.117,318.858,334.071)) z <-

Hi all, I've fit a mixed linear model to some longitudinal data. I'm interested in the differences in patterns of decrease in the dependent variable according to group status, and my hypothesis particularly predicts a difference between the groups in trajectory of change at between specific ages. The data shows a significant interaction between group and the linear and quadratic effects

Hi everyone, I?m trying to do a spaghetti plot and I know I?m doing all wrong, It must be. What I need: 15 subjects, each with measurements over 5 different times (t1, ..., t5), and the variable that I need to represent in the spaguetti plot is given by: PCR = b0 + b1 * ti + epsilon B0, - baseline of each subject B1 - trajectory of each subject over time (so multiply by t) Epsilon - error

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, This is a question regarding technique rather than an R specific issue. I have been asked to evaluate a 30+ year long term continuous survey of bird presence/absence data that has an associated ocular estimate of the vegetation community percent coverage. The data are organized by subpopulations (5), and by year ( 1991 - present). We are interested in gaining understanding on whether bird