search for: qmatrix

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...

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 of 1s and 0s with indices of allowed misclassfications (diagonal is ignored) (required) inits, # initial valu...

...servation, for example, process snapshot / fully-observed histories / partially-observed histories such as death times. Users upgrading from previous versions of msm should note that some of the syntax for specifying models has changed. In particular, initial values are now specified in the "qmatrix" and "covinits" arguments instead of the inits argument, so that "qmatrix" is no longer a matrix of 0/1 indicators. As always, refer to the user guide "msm-manual.pdf" in the "doc" subdirectory for a full description of the methodology, and worked examp...

...servation, for example, process snapshot / fully-observed histories / partially-observed histories such as death times. Users upgrading from previous versions of msm should note that some of the syntax for specifying models has changed. In particular, initial values are now specified in the "qmatrix" and "covinits" arguments instead of the inits argument, so that "qmatrix" is no longer a matrix of 0/1 indicators. As always, refer to the user guide "msm-manual.pdf" in the "doc" subdirectory for a full description of the methodology, and worked examp...

...d in terms of what I have done but will explain nonetheless. I am using the msm.package and trying to specify my reference category for an outcome covariate. The following command line works: ## age of respondent - using year5a: categorical preg_fyear5a.msm<-msm(outcome~ipi, subject=id, data, qmatrix=crudemat, exacttimes=TRUE, covariates=~factor(year5a)) However, in the dataset (data) the reference category 0 is a moderately small cell, so I would like to change it to 1 which is much large. There are 5 categories in the covariates year5a (0,1,2,3,4). I have tried all sorts of approaches with...

...responsible for increasing the probability of outcome of factor 2 (P(2)) by 0.48 but that does not make sense given that DC_MAX_POSSIBLE_L1 is an integer ranging from -20 to +20. How do I interpret the results ? the original call was: msm_pc10 = msm( OUTCOME ~ TIME, data = myFrame, qmatrix = qmatrix ,hmodel = list (hmmDist_1,hmmDist_2) ,hcovariates = list ( ~ 1 + DC_MAX_POSSIBLE_L1, ~ 1 + DC_MAX_POSSIBLE_L1) ,control = list(reltol = 1e-03) ,method = "BFGS", use.deriv = FALSE, hessian=FALSE, analyticp...

...ay4.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 ~ duration, subject = SerialNo, data = Data, qmatrix = twoway4.q, ematrix = ematrix,death = 4, obstrue = firstobs, covariates = ~ sex + age, method = "BFGS", control = list(reltol = 1e-10, fnscale = 50000, trace=1,REPORT=1)) msm stops after only 100 iterations and fails to compute the Hessian (See the output below). Why is it stopping at...

...roblem is that when the last stage (stage 4 in my data) gets repeated it seems to give me a warning message. although, it picks only one observation whereas there might be other such cases in the data (printing only the first case?) > out1.msm2 <-msm(stage~years,subject=patient,data=mydata2,qmatrix=new.twoway9.q,con trol=list(trace=1,report=1),covariates=~black+tport11,constraint=list(bl ack=c(1,1,1),tport11=c(1,1,1))) ... Warning message: Absorbing - absorbing transition at observation 30 in: msm.check.model(msmdata$fromstate, msmdata$tostate, msmdata$obs, My data at observation around 30...