Comments (4)
The hidden_paths
function computes the most probable states also for the missing values, which is how unequal sequence lengths are handled internally. But indeed, it makes often sense to set the tails to voids for visualization etc. I'll add an an option for this.
from seqhmm.
In my case, and I suspect in many cases, they are not true "missing" observations. The sequences simply have different length. I will add a check in my code to reset these positions to void until you are able to add this option.
from seqhmm.
Yes this makes sense. I just pushed an update, the hidden_paths
function now has an argument respect_void
(default is TRUE) which replaces the hidden states with voids according to the observed sequences. Let me know if you encounter problems, I didn't fully test this yet.
from seqhmm.
Seems to be working fine. Thanks for the quick fix
from seqhmm.
Related Issues (20)
- [Help request] How to test the model? HOT 1
- can this package support Multivariate Discrete HMM?? HOT 3
- will it work for multivariate time series prediction : different continues or/and discrete/category observation HOT 4
- HMM cluster assignments HOT 2
- seqdata should be a state sequence object HOT 1
- Apply to financial time series? HOT 1
- Parsing HMMER3 files HOT 2
- 'System seems singular' and 'EM algorithm failed' for Mixture Markov Model HOT 2
- Building a model with different sequence lengths HOT 1
- Parallel computation HOT 2
- Forward probability of MHMM vs HMM HOT 1
- standard errors for HMM parameters HOT 3
- Maximum number of colours in cpal/colorpalette HOT 4
- Extracting combinations of emitted states HOT 4
- Error in if (em.con$reltol < resEM$change) { : argument is of length zero HOT 5
- Absorbing state broken in `build_mm()` (seqhmm 1.2.1-1) HOT 1
- Runtime Estimation HOT 1
- EM algorithm failed HOT 1
- Error: number of labels must equal number of states in the alphabet HOT 4
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from seqhmm.