similar to: Simulating a VEC

Hi everyone, I'm using the Johansen framework to determine a VEC using package urca. I have estimated the corresponding VEC using likelihood ratio test for restrictions on alpha, beta or both and I have generated objects of the class cajo.test. Now I want to diagnostic tests in the model, like heteroskedasticity test, residuals normality and serial autocorrelation, but I cannot find the way

Dear Sir/Madam, I have used ca.jo in urca package to identify the cointegration and cajorls to estimate the vecm. Althought both return the coefficients for long run relationship (or ect1 in cajorls), I am unable to find the standard error and t statistics. I spend some weeks to search around. I did find some similar enquiries before and answer provided Prof. Pfaff is to use vec2var. However,

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'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'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

?kml? is an implementation of k-means for longitudinal data (or trajectories). This algorithm is able to deal with missing value and provides an easy way to re roll the algorithm several times, varying the starting conditions and/or the number of clusters looked for. KmL 1.1.1 addition: - 7 imputations methods for longitudinal data - Calculus of three qualities criterion (Calinski&Harabatz,

?kml? is an implementation of k-means for longitudinal data (or trajectories). This algorithm is able to deal with missing value and provides an easy way to re roll the algorithm several times, varying the starting conditions and/or the number of clusters looked for. KmL 1.1.1 addition: - 7 imputations methods for longitudinal data - Calculus of three qualities criterion (Calinski&Harabatz,

After searching multiple combinations of keywords over the past two days and downloading n R graphics tutorials, I have not been able to find anything online or in my R books about how to save multiple plot panes to PNG. Specifically, I am using the irf() function in the vars package to generate plots of Impulse Response Functions: > x.data <-

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 to everyone! I am working on my final year project about multivariate time series. There are three variables in the multivariate time series model. I have a few questions: 1. I used acf and pacf plot and find my variables are nonstationary. But in adf.test() and pp.test(), the data are stationary. why? 2.I use VAR to get a model. y is the matrix of data set and I have made a once

Hi everyone, I did a principal component analysis (princomp) on a dataset I have, I plotted the first two columns and labeled the points by their index number. I observed that most index sequences (points 50:65 for example) kind of form arc shape trajectories on the graph (even loops), some of those seem to converge at some particular location and those specific patterns just happen to have some

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

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

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

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)

Hi all, here's a proposal for a SoC project on wildlife movement patterns analysis. A MSc student is available to anwser the call, of course. The idea has already been briefly discussed with the mantainer of the adehabitat package and the AniMov project community. Short description Develop some add-on functions to use the adehabitat package to perform basic movement ecology analysis, in

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

Dear members, I estimated a vector error correction model (VECM) using the "ca.jo" function in package "urca". I need to simulate the estimated model using R. I am aware how to simulate a VAR(p) model. Since the VECM is in difference form, I can't modify the VAR simulation codes to VECM. May one help me in this regard please? Thanks Mamush [[alternative HTML version