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)                0.8597657482

P(2)                0.0001943636

P(3)                0.0042761544

P(4)                0.0002781397

P(5)                0.0005549986

P(6)                0.1349305955

DC_MAX_POSSIBLE_L1  0.4804758829

DC_MAX_POSSIBLE_L1  0.4216246143

DC_MAX_POSSIBLE_L1 -0.0552074714

DC_MAX_POSSIBLE_L1  0.1899573843

DC_MAX_POSSIBLE_L1  0.0963140485

 

at first glance I thought that 1 unit of DC_MAX_POSSIBLE_L1  was
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=FALSE , est.initprobs=TRUE

);

 

with hmmDist_1 and hmmDist_2 being hmmCat distributions as specified in
the msm manual.

 

 

Thanks!


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