similar to: msm and pmatrix

Dear All, I would like to use the simprof function (clustsig package) but the available distances do not include Jaccard distance, which is the most appropriate for pres/abs community data. Here is the core of the function: > simprof function (data, num.expected = 1000, num.simulated = 999, method.cluster = "average", method.distance = "euclidean", method.transform =

All, We are researching approaches to parallel R with the end goal of running R in a distributed manner on a Linux cluster. We expect of course to do some work decomposing our problems to be task-parallel or data-parallel, but wouldn't mind getting an initial boost working with "embarrassingly parallel" code sections and one of the approaches below. Incidentally our environment

Hello I'm a population ecologist and use R for all my stats and modelling. Recently I have been using R to numerically solve integral projection models. This involves constructing several large matrices. The current code by Easterling (Size-specific sensitivity: Applying a new structured population model. Ecology, 2000, 81, 694-708) uses nested loops to construct the matrices. To speed up the

Dear R Users, I have the following glm, which I am running several times in a loop (I am not including the full code): reduced_model <- NULL; full_model <- NULL; reduced_model <- try(glm.fit(X4,n,family=poisson(link="log"))) full_model <- try(glm.fit(X5,n,family=poisson(link="log"))); On some occasions, an error is produced, which is why I have attempted to work

Dear all, Currently I am trying to integrate a function which depends on four variables, two of which are given, one is given in the integrate function, so there is one variable to integrate on. The code is as follows: Pmatrix = function(th) { P = matrix(nrow=6, ncol=6, data=0) P[1,1] = P[2,1]=P[3,2]=P[4,3]=P[5,4]=P[6,5]= exp(-th) P[,6] = 1-exp(-th) return(P)} lim.verd =

Hi, I use nnet for my classification problem and have a problem concerning the calculation of the final value for my validation data.(nnet only calculates the final value for the training data). I made my own final value formula (for the training data I get the same value as nnet): # prob-matrix pmatrix <- cat*fittedValues tmp <- rowSums(pmatrix) # -log likelihood

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

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.

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

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

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

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)

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 -

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