similar to: msm version 0.5 released

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

Hello Chris, I am trying to fit a 4 state multi-state model using hidden markov routine in the msm package. 1. initial parameters: twoway4.q <- rbind(c(0, 0.25, 0, 0.25), c(0.166, 0, 0.166, 0.166), c(0, 0.25, 0, 0.25), c(0, 0, 0, 0)) ematrix <- rbind( c(0, 0.01, 0, 0), c(0.01, 0, 0.01,0), c(0, 0.1, 0, 0), c(0, 0, 0, 0)) 2. the model: msm_covariates_sexandage <- msm(state ~

Hello, I'm wondering if anyone has used the msm package to compute the steady state probabilities for a Markov model? thanks, Russell

Hi R-helpers, I am having a problem understanding how to construct the qmatrix, i have read the help menu for msm but I am still a bit lost. Below is an example of one of my transition matrices: statetable.msm(BEH, ID, data = A1) to from 1 2 3 4 5 23 200 201 203 999 1 86 11 2 20 1 9 3 11 1 22 2 18 4 4 4 0 1 1 1 0 1 3 5 2 0 5 0 0 2 0 0 3

Hi, I have a simple question on interpreting output results from your msm package in R when using a categorical model with hcovariates i.e. covariates on the parameters of the emission model. The interpretation is straightforward for normal distributions, etc. but when using categorical distributions I get results like these: prob P(1)

Hello After (simple random cluster) resampling with replacement I ran MSM function and I'm getting the following warning message ,which I'm not sure why. I don't have any absorbing stage set up in my MSM model. I have a 4 stage uni-directional MSM model. The only thing I see might be a problem is that when the last stage (stage 4 in my data) gets repeated it seems to give me a warning

Hi All, I have some data where the dependent variable is a score, low (1:3) or high (8:9), and the independent variables are 21 genotypic markers. I'm fitting a logistic regression on the whole dataset after transforming the score to 0/1 and normal linear regression on the high and low subsets. I all cases I have a numer of cases of data 'duplications', i.e. different

Hi All, is there a way of predicting memory usage? I need to build an array of 86000 by 2500 numbers (or I might create a list of 2 by 2500 arrays 43000 long). How much memory should I expect to use/need? Cheers, Fede -- Federico C. F. Calboli Department of Epidemiology and Public Health Imperial College, St. Mary's Campus Norfolk Place, London W2 1PG Tel +44 (0)20 75941602 Fax +44

Hello, I'm working with a dynamic system that I've started to analyse using msm(I've emailed to chris, orignator of the program, separately, but maybe he's on holiday). The data is obtained from a large cohort of students and consists of a model of learning states that students pass through over a period of one school year. As analyzed with the msm program, the data shows 'no

Dear All, I will soon be working with NIH and possibly FDA. Will I be able to use R or will I be forced to use SAS? Cheers, Federico -- Federico C. F. Calboli Department of Epidemiology and Public Health Imperial College, St. Mary's Campus Norfolk Place, London W2 1PG Tel +44 (0)20 75941602 Fax +44 (0)20 75943193 f.calboli [.a.t] imperial.ac.uk f.calboli [.a.t] gmail.com

I have code and documentation that don't match, but R CMD check didn't flag it. in mspath.R mspath <- function(formula, # formula with observed Markov states ~ observation times (required) qmatrix, # matrix of 1s and 0s with indices of allowed transitions (diagonal is ignored) (required) misc = FALSE, ematrix = NULL, # matrix

Dear All, just a quick example: > x = 1:25 > x[12] = NA > x [1] 1 2 3 4 5 6 7 8 9 10 11 NA 13 14 15 16 17 18 19 20 21 22 23 24 25 > y = x[x<10] > y [1] 1 2 3 4 5 6 7 8 9 NA Is there any way of NOT getting NA for y = x[x<10]? Similarly > y = x[x<15] > y [1] 1 2 3 4 5 6 7 8 9 10 11 NA 13 14 How do I get rid of the NA (not post

Hi All, a colleague wants to calculate the Fisher information matrix for a model he wrote (not in R). He can easily get the neg-log-likelihood and the best fit parameters at the minimum. He can also get negLLs for other parameter values too. Given these data, is there a way in R to calculate the Fisher information matrix? Best, Federico -- Federico C. F. Calboli Department of Epidemiology

Hi All, is there an equivalent of the Unix command 'less' (or 'more'), so I can look at what's inside a data.frame or a matrix without having it printed out on console? I am using R on Debian Linux and Mac OS 10.4.5 Cheers, F -- Federico C. F. Calboli Department of Epidemiology and Public Health Imperial College, St Mary's Campus Norfolk Place, London W2 1PG Tel +44

Hi everyone, I would like to find out when and where exactly I get the following warning in a piece of code I've written: Error in "[<-"(`*tmp*`, iseq, value = numeric(0)) : nothing to replace with The code is a for () loop performing a somewhat trivial calculation, modulated by a number of logical if(){} else(){} conditions, involving the creation of a number of

Hi All, does anyone ever import old SPSS files in a sl3 format? read.spss('file.sl3') does not seem to work... it's not recognised as a supported SPSS format at all. Best, Fede -- Federico C. F. Calboli Department of Epidemiology and Public Health Imperial College, St. Mary's Campus Norfolk Place, London W2 1PG Tel +44 (0)20 75941602 Fax +44 (0)20 75943193 f.calboli

Hi All, is there a reasonably simple way of using a black and white chequer/checker board pattern as a colour: barplot(mydata, col = c('red', 'blue' 'checkerboard')) ? BW F -- Federico C. F. Calboli Neuroepidemiology and Ageing Research Imperial College, St. Mary's Campus Norfolk Place, London W2 1PG Tel +44 (0)20 75941602 Fax +44 (0)20 75943193 f.calboli

Hi All, I'm having a problem with barplot: mydata [1,] 2 108 0 0 0 1 3 0 0 0 0 0 7 18 3 4 8 20 26 20 19 7 1 1 mycol = c(rep('yellow', 2), rep('white', 3), rep('orange',2), rep('white', 5), rep('orange',3), rep('red',9)) barplot(mydata, col = mycol) gives me an uniformly yellow barplot. How do I solve this? bw Federico

Hi, I am subsetting a matrix thus: test [,1] [,2] [,3] [1,] 1 7 13 [2,] 2 8 14 [3,] 3 9 15 [4,] 4 10 16 [5,] 5 11 17 [6,] 6 12 18 test[cbind(c(1,3,5), c(2,1,3))] [1] 7 3 17 This works fine, and is the equivalent of c(test[1,2], test[3,1], test[5,3]). cbind(c(1,3,5), c(2,1,3)) would obviously look like: [,1] [,2] [1,] 1 2 [2,]

Hi All, is there a function in R that allows me to work with fractions without transforming them to floats (or whatever) in between? Something that would calculate something like: (1/2 + 1/8) * 1/2 = 5/16 without ever transforming to 0.5 and 0.125? Best, Federico -- Federico C. F. Calboli Department of Epidemiology and Public Health Imperial College, St Mary's Campus Norfolk Place,