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I am new to mmlcr and am working on a latent class mixture model attempting to identify the trajectory and number of classes that best describes my data. I am able to find model parameters such as degrees of freedom, loglikelihood, and BIC. For example, here is a cubic 3-class model I am using. mmlcr1 <- mmlcr(outer = ~ 1 | ID, components = list(list(formula = Score ~ poly(wave,3), class =

Dear list, I have been comparing the outputs of two packages for latent class regression, namely 'flexmix', and 'mmlcr'. What I have noticed is that the flexmix package appears to come up with a much better fit than the mmlcr package (based on logLik, AIC, BIC, and visual inspection). Has anyone else observed such behaviour? Has anyone else been successful in using the mmlcr

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

Christophe Genolini <cgenolin at u-paris10.fr> wrote >Thanks for yours answers. So if I understand: > - Trajectories are continuous, the other are discrete. > - The difference between time series and longitudinal is that time >series are made at regular time whereas longitudinal are not ? > - Repeated measures are over a short period of time. > > >So if I measure

Hi Rosa, You pass a vector to ggplot, which expects a data.frame. I am sure you meant to do this: point7$y_point7 <- point7$beta0_7 + point7$beta1_7*point7$time + point7 $epsilon_7 ggplot(point7, aes(time, y_point7)) + geom_line() HTH Ulrik On Wed, 19 Jul 2017 at 20:37 Rosa Oliveira <rosita21 at gmail.com> wrote: > Hi everyone, > > I?m trying to do a spaghetti plot and I

Greetings I'm a relatively new R user and I'm trying to build a latent class model. I've used the 'R Site Search' and it appears there's not much dialogue on these packages On mmlcr, I've gotten it working, but not sure if I'm using it correctly. On flexmix, I can only seem to get results for one class. I'm attaching my code below - if anyone

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

The Institute for Social Research (ISR) and its Statistical Consulting Service (SCS) at York University are pleased to announce our Summer Program In Data Analysis (SPIDA) for 2012. The Program runs from May 24th to June 1st, 2012.This year?s Program focuses on the theory and practice of linear models and mixed [or multilevel] models, as they are applied to hierarchical and longitudinal data.

corresponding UNIX user (or dummy entry in the password file) for each NT user who you want to authenticate, otherwise they can't get on unless you have set up "guest ok =yes" and defined a username for "guest" to connect as . This works for us, my NT karen account gets matched to my Unix Karen account, new files I create from the PC side get assigned the correct Unix UID,

corresponding UNIX user (or dummy entry in the password file) for each NT user who you want to authenticate. This works for us, my NT karen account gets matched to my Unix Karen account, new files I create from the PC side get assigned the correct Unix UID, my login directory is shared via [HOMES] correctly, etc as long as I don't run winbindd. However, when I turn on winbindd, the NT

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 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

All: I am new to R and would like your help in identifying the appropriate process to follow in order to modify the output from an existing package. I've had difficulty finding an answer online, perhaps because I am using incorrect terminology. A package that I am using (mmlcr) invokes another package (multinom). An output of multinom is the standard errors, but this output is not

Hi the list. Is there a bug in mmlcr package ? The following code does not compile: mmlcrTest <- function(dataW){ dataL <- reshape(dataW,idvar="id",timevar="T",varying=list("T0","T1","T2"),direction="long",v.names="score") resultR <- mmlcr(outer= ~ 1 | id, components = list(list(formula =

Hi, I have a small dataset that I'm fitting a segmented regression using nls on. I get a step below minimum factor error, which I presume is because residual sum of square is still "not small enough" when steps in the parameter space is already below specified/default value. However, when I look at the trace, the convergence seems to have been reached. I initially thought I might

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either

Dear R-users, I'd like to announce the release of the new package JM (JM_0.1-0 available from CRAN) for the joint modelling of longitudinal and time-to-event data. The package has a single model-fitting function called jointModel(), which accepts as main arguments a linear mixed effects object fit returned by function lme() of package nlme, and a survival object fit returned by either

Hi all, I a semi-beginner with R and I am working with the plm package to examine a longitudinal dataset. Each individual in this dataset has a longitudinal weight for the probability that he or she remains in the sample. Unfortunately, I have not found an argument to use weights in the plm function? I tried ?weights=? like in standard lm or in nlme or lm4 but it does not work. I asked the

Dear R-help list members I gathered longitudinal data on fish behaviour which I try to analyse using a multi level model for change. Mostly, I am following Singer & Willett (2003), who provide also the S/R code for their examples in the book (e.g. http://www.ats.ucla.edu/stat/Splus/examples/alda/ch4.htm). Of course I am interested in change over time, but I am also very much interested in

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