similar to: existing package (mmlcr) modification -- appropriate process?

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 =

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

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 =

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

Hello, I have a time series of data as a data.frame. Occasionally there is one or more days missing (e.g. data available for days 2, 3, 4, 8, 9, 10 --> missing days between 4 and 8). The experimental time information can be found in the 2nd column of "data". I would like to have a continuous time line with one time point per day. Therefore I try to insert lines for the missing

Dear R Developers, the following CRAN packages do not cleanly pass R CMD check for quite some time now and did not have any updates since the time given. Several attempts by the CRAN admins to contact the package maintainers had no success. norm, 1.0-9, 2002-05-07, WARN sound, 0.6, 2002-08-31, WARN Bhat, 0.9-07, 2003-11-04, WARN HI, 0.1, 2003-11-04, WARN mmlcr, 1.3.2, 2003-11-15, WARN mvnmle,

Dear list, I would like to fit a negative binomial regression model as described in "Byers AL, Allore H, Gill TM, Peduzzi PN., Application of negative binomial modeling for discrete outcomes: a case study in aging research. J Clin Epidemiol. 2003 Jun;56(6):559-64" to my data in which the response is count data. There are also 10 predictors that are count data, and I have also 3

Dear List, Does anyone have experience using mmlcr Package? I am working on a longitudinal dataset. The model fitting is fine. However, I had a problem with the command "plot.mmlcr". It seems to me it can not plot a very nice trajectory plot for longitudinal dataset. I wonder whether somebody can share the experience, i.e. how to make a nice trajectory plot for longitudinal data.

Dear All: I am interested in multinomial logit models (function multinon, library nnet) but I'm having troubles in choose whether to define the predictors as factors or not. I had posted earlier this example (thanks for the reply ronggui): worms<- data.frame(year= rep(2000:2004, c(3,3,3,3,3)),age=rep(1:3,5),

If one estimates a model using multinom, is it possible to perform the omnibus LR test ( the analogue to omnibus F in linear models ) using the output from multinom ? The residual deviance is there but I was hoping I could somehow pull out the deviance based on just using an intercept ? Sample code is below from the CAR book but I wasn't sure how to do it based on that example. Thanks

Dear R users, The nnet package includes the multinom method for the confint function. The R Help file (?confint) for the generic function in the stats package and the help files for the glm and nls methods in the MASS package indicate that one can use the "parm" argument as "a specification of which parameters are to be given confidence intervals, either a vector of numbers or

Hello, I am working with s.multinom() from the ade4package. I tried to plot the results of my %PCA (via the function dudi.pca()) followed by a MCOA (mcoa()). But when I give my variables to s.multinom() I get the following Error message: Error in FUN(1:14[[1L]], ...) : number 1 profile without data I know that it has to do with one (or two?) of the first arguments (kpca,sglmarkerfrq) but I just

I have RedHat linux 9 with R 1.8. I'm estimating models with multinom with a dependent variable that has 3 different values. Sometimes the models run fine and I can understand the results. Sometimes when I put in another variable, I see an indication that the estimation did work, but then I can't get the summary method to work. It's like this: > votemn1 <-

I am trying to create a LaTex table based on a multinom (nnet) object using the stargazer command. I have created a small data frame to demonstration the problem: data <- data.frame(age=1:21, hight=20:40, ed=c(1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3,1,2,3)) data$ed <- as.factor(data$ed) I then make a multinomial model using the command multinom from the nnet package: model <- multinom(ed ~

Hello, I apologize if this seems like an obvious question, but I have been looking everywhere and have yet to find an answer. I am doing a multinomial regression with multinom() in the nnet package. I have a 3 level ordered response (ordered()) variable and 4 predictors, 3 of which are numerical and one which is an ordered factor (also ordered()) with 5 levels (a, b, c, d, e). My question is in

Hello List and thanks in advance for all of your help, I am trying implement a permutation test of a multinomial logistic regression ('multinom' within the nnet package). In the end I want to compare the parameter estimate from my data to the distribution of randomized parameter estimates. I have figured out how to permute my dependent variable (MNNUM) x number of times, apply

Hi, I am searching for methods to compare regression models with an ordered categorical response variable (polr versus multinom). The pattern of predictions of both methods (using the same predictor variables) is quite different and the AIC is smaller for the multinom approach. I guess polr has more strict premises for the structure of the response variable, which methods can be used to test for

Is anyone aware of a way to check whether multinom has converged by checking a component of the output ? I'm not familar with nnet but, since multinom calls nnet , maybe there is an extra argument once can send to multinom to capture this information. Thanks.

Dear all, I'm fitting a multinom function to my dataset (multinom(outcome~age+K+D)) and I need to present my results on a poster. Does someone know a nice way of doing that? I think I saw in an archive that you cannot plot a multinom.object, is it true? Thank you by advance for your help, Cheers Camille

Hi, I am trying to specify a multinomial logit model using the multinom function from the nnet package. Now I add another independent variable and it halves the AIC as given by summary(multinom()). But when I call Anova(multinom()) from the car package, it tells me that this added variable is insignificant (Pr(>Chisq)=0.39). Thus, the improved AIC suggests to keep the variable but the Anova