similar to: MSM package and qmatrix

A major update of the "msm" package for continuous-time Markov and hidden Markov multi-state models is now available from CRAN. Hidden Markov models with general, continuous response distributions are now supported. These models are used for Markov processes which can only be observed through the value of some noisy marker. Censored states are now supported at any observation time.

A major update of the "msm" package for continuous-time Markov and hidden Markov multi-state models is now available from CRAN. Hidden Markov models with general, continuous response distributions are now supported. These models are used for Markov processes which can only be observed through the value of some noisy marker. Censored states are now supported at any observation time.

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 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 ~

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

(Oops first mistake was posting to the wrong area) I am not sure what is needed to be posted 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,

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

Cc: Rob Clark <robdclark at gmail.com> Signed-off-by: Emil Velikov <emil.l.velikov at gmail.com> --- include/uapi/drm/msm_drm.h | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/include/uapi/drm/msm_drm.h b/include/uapi/drm/msm_drm.h index 254d3e9..bf19d2c 100644 --- a/include/uapi/drm/msm_drm.h +++ b/include/uapi/drm/msm_drm.h @@ -20,6 +20,10 @@ #include

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

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

Greetings, I'm using MSM (mutli-state markov modeling) package to study the progression of fibrosis in U.S hepatitis C population. I find this is a very fascinating tool for an applied researcher like myself. I have a four stage progression only model without any absorbing stage, also assuming no misclassification error in the data for the time being. I also have a couple covariates in the

I am reading the comprehensive on-line documentation about msm. The positive side is that it seems it has been designed for biomedical statistics, like Clinical Trials. The bad side is that it does not seem to model observations sequences that are not independent but instead are autocorrelated, as it is my case. I did not find any mention to correlated observations therefore I assume the authors

Dear All, I?m using R package ?msm? to fit a multi state model to infection history data (counts of infections per month upto diagnosis of a particular disease (sink state is state 11). The observed transitions are as follows: to from 1 2 3 4 5 6 7 8 10 11 1 35192 3806 899 233 46 11 3 0 1 534 2 3801 790 249 69 15

windows XP R 2.6.0 I have tried to install the msm package several times. Each time the installation appears to work. I then go to PACKAGES->LOAD PACKAGE but the msm package does not appear in the SELECT ONE dialog box. Can someone suggest how I can get msm to run on my system? Thanks, John Confidentiality Statement: This email message, including any attachments, is for th...{{dropped:6}}

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

For all outputs except DSI we have a 1:1 relationship between connectors and encoders and the driver is relying on the atomic helpers: we can drop the custom ->best_encoder() and let the core call drm_atomic_helper_best_encoder() for us. Signed-off-by: Boris Brezillon <boris.brezillon at free-electrons.com> --- drivers/gpu/drm/msm/edp/edp_connector.c | 10 ----------

GEM object functions deprecate several similar callback interfaces in struct drm_driver. This patch replaces the per-driver callbacks with per-instance callbacks in msm. The only exception is gem_prime_mmap, which is non-trivial to convert. Signed-off-by: Thomas Zimmermann <tzimmermann at suse.de> --- drivers/gpu/drm/msm/msm_drv.c | 13 ------------- drivers/gpu/drm/msm/msm_drv.h | 1 -

Prepare for the coming implementation by GCC and Clang of the __counted_by attribute. Flexible array members annotated with __counted_by can have their accesses bounds-checked at run-time checking via CONFIG_UBSAN_BOUNDS (for array indexing) and CONFIG_FORTIFY_SOURCE (for strcpy/memcpy-family functions). As found with Coccinelle[1], add __counted_by for struct dpu_hw_intr. [1]

Prepare for the coming implementation by GCC and Clang of the __counted_by attribute. Flexible array members annotated with __counted_by can have their accesses bounds-checked at run-time checking via CONFIG_UBSAN_BOUNDS (for array indexing) and CONFIG_FORTIFY_SOURCE (for strcpy/memcpy-family functions). As found with Coccinelle[1], add __counted_by for struct dpu_hw_intr. [1]

Prepare for the coming implementation by GCC and Clang of the __counted_by attribute. Flexible array members annotated with __counted_by can have their accesses bounds-checked at run-time checking via CONFIG_UBSAN_BOUNDS (for array indexing) and CONFIG_FORTIFY_SOURCE (for strcpy/memcpy-family functions). As found with Coccinelle[1], add __counted_by for struct dpu_hw_intr. [1]