similar to: R Package for Longitudinal Binary Response Data?

Dear List,   I have some longitudinal data, each patient was followed at times 0, 12, 16, 24 weeks and measure severity of a illness (0-worse, 1-same, 2-better). So, longitudinal response is categorical.  I was wondering whether lmer in R can fit a model for this type of data. If so, how we code? Or any other function in R that can fit this type of longitudinal data? Any suggestion would be

Hello, markdown (Python implementation) seems to render:  to <img src="pfad/und/eine" title="lange/url zum bild.jpg" alt="alternativer text"/> breaking the URL at the whitespace. AFAIK whitespaces are ok in URLs so why is it breaking up the URL here? Thanks, Florian -- I'm using an

Hello i like to give a windows group (W2K3-AD) permissions to use a share read/write on a Samba domain member server. Therefore if have added the Samba server to the domain without problem and created a share like this: [bild] comment = Some Comment path = /data/bild admin users = root force user = smbuser force group = sambashare valid users = DOMAIN\w-user1

Hi, I'm just starting out with Sweave, and I can't get a plot(linmod) to display all four plots: << bild >>= x1 <- runif(100) x2 <- rexp(100) y <- 3 + 4*x1 + 5*x2 + rnorm(100) mod <- lm(y~x1+x2) plot(mod) @ Some Text <<fig=TRUE>>= <<bild>> @ This plots only the first image of the four-page plot.lm() result. I don't want to use

Hi again! Following up my previous posting below (to which no response as yet), I have located a report which situates this type of question in a longitudinal modelling context. http://www4.stat.ncsu.edu/~dzhang2/paper/glm.ps Generalized Linear Models with Longitudinal Covariates Daowen Zhang & Xihong Lin (This work seems to originally date from around 1999). They consider an outcome Y,

Hi, I think I have configured everything correctly but for some reason I can?t get a list of the shared mailboxes to show up. When I run: doveadm acl debug -u m.markov Shared/d.marteva/INBOX doveadm(root): Debug: Skipping module doveadm_fts_plugin, because dlopen() failed: /usr/lib/dovecot/modules/doveadm/lib20_doveadm_fts_plugin.so: undefined symbol: fts_backend_rescan (this is usually

Hi all, I was hoping somebody may know of a function for simulating a large binary sequence (length >10 million) using a (1st order) markov model with known (2x2) transition matrix. It needs to be reasonably fast. I have tried the following; mc<-function(sq,P){ s<-c() x<-row.names(P) n<-length(sq) p1<-sum(sq)/n s[1] <- rbinom(1,1,p1); for ( i in 2:n){ s[i]

Dear Oscar,   Thanks for your help.It's so nice of you to explain this package to me.   Best Regards,   James LAN 发件人： Oscar Rueda [via R] <ml-node+s789695n4431468h14@n4.nabble.com> 收件人： monkeylan <lanjinchi@yahoo.com.cn> 发送日期： 2012年2月29日, 星期三, 下午 9:21 主题: Re: Bayesian Hidden Markov Models Dear James, The distances are normalized between zero and 1, so in your case all of

First I would like to say thank you for taking the time to read it.Here is my problem. I am running a lmer analysis for binary longitudinal (repeated measures) data. Basically, I manipulated fruits and vegetation to two levels each(present and absent) and I am trying to access how these factors affect mice foraging behavior. The design consist of 12 plots, divided in 3 blocks. So each block

I don?t seem to have that install on my Debian machine. But I have done this: 1 LIST "" % * LIST (\HasNoChildren) "/" confirmed-spam * LIST (\HasChildren) "/" Archive * LIST (\HasNoChildren \Sent) "/" Sent * LIST (\HasChildren \Trash) "/" Trash * LIST (\HasNoChildren) "/" Spam * LIST (\HasChildren) "/" Archives * LIST

Good day Sir/Ma'am! This is Alyssa Fatmah S. Mastura taking up Master of Science in Statistics at Mindanao State University-Iligan Institute Technology (MSU-IIT), Philippines. I am currently working on my master's thesis titled "Comparing the Three Estimation Methods for the Four Parametric Logistic (4PL) Item Response Model". While I am looking for a package about Markov chain

Thank you for your prompt answer. The breathing signal observations are the amplitude values as a function of time and phase. According to our model the hidden states are the different breathing types. Subjects, whose respiratiion process is regular, are likely to breathe, keeping the same cycle pattern/type, for many consecutive cycles. therefore dwelling in the same hidden state. The more

Hey Guys, i am kind of confused. Under windows the system() function works great, but not with my mac. I have two questions: 1) What do i have to change. Using packages which require system or eval(parse() everything is fine, but when i try it myself "sh: cmd: command not found" Under windows i use e.g system('cmd /c copy "bild.jpg"',intern=FALSE ) 2) I really love

Dear Users, i have this time series, the tree lines means different level, i would use a Markov switching model with two states to modelling this time series. i would obtain the relative transition matrix (2X2) the first state is above the value of 23.65 (the higher line) the second state is below the value of 23.65 You can ignore the other two lines

Hi Folks, I'd be grateful for suggestions about approaching the following kind of data. I'm not sure what general class of models it is best situated in (that's just my ignorance), and in particular if anyone could point me to case studies associated with an R approach that would be most useful. Suppose I have data of the following kind. Each "subject" is observed at say 4

Greetings fellow R entusiasts! We have some problems converting a computer routine written initially for Gauss to estimate a Markov Regime Switching analysis with Time Varying Transition Probability. The source code in Gauss is here: http://www.econ.washington.edu/user/cnelson/markov/programs/hmt_tvp.opt We have converted the code to R, and it's running without errors, but we have some

Hi, I am looking for a package to estimate regime switching models (states following a markov chain). I found packages for Hidden Markov Models but I am looking for something a little different: In the HMM the conditional distribution of the observations (give the state) is a known distribution (normal or others), while the package I need should allow to set a conditional distribution (given the

Aki, you were right. It was at /usr/lib/dovecot Here is the output: root at mail:~# /usr/lib/dovecot/imap -u m.markov * PREAUTH [CAPABILITY IMAP4rev1 LITERAL+ SASL-IR LOGIN-REFERRALS ID ENABLE IDLE SORT SORT=DISPLAY THREAD=REFERENCES THREAD=REFS THREAD=ORDEREDSUBJECT MULTIAPPEND URL-PARTIAL CATENATE UNSELECT CHILDREN NAMESPACE UIDPLUS LIST-EXTENDED I18NLEVEL=1 CONDSTORE QRESYNC ESEARCH ESORT

Dear R users, I have been trying to obtain the MLE of the following model state 0: y_t = 2 + 0.5 * y_{t-1} + e_t state 1: y_t = 0.5 + 0.9 * y_{t-1} + e_t where e_t ~ iidN(0,1) transition probability between states is 0.2 I've generated some fake data and tried to estimate the parameters using the constrOptim() function but I can't get sensible answers using it. I've tried using

Hello, I'm writing about msm. It may be that consistent users of Markov models have a good idea as to what constitutes workable data for a model. I think of general rules, in basic statistical studies where n is limited to exclude fairly precise figures in the lower range. On the other hand Markov models don't seem to be often enough used for parameters to be as well laid out. I also