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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