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Hello! I've been exploring KFAS for a few days now, and it's simply amazing! Thank you for creating it! One thing I have yet to figure out how to do is estimate the dispersion parameter for the negative binomial. u(t) appears to be set to 1 when I fit the model with fitSSM. For now, I have:
When I extract fit_silly2$model$u, it's just a time-series of 1s.
Is there a way to have the model estimate the dispersion parameter as well? My understanding is that I need this in order to estimate a function (e.g., sum of the observations from the most recent 4 time points) of the prediction values (e.g., apply the negative binomial observation model to the signals produced from the importanceSSM())
Thank you for your help!
The text was updated successfully, but these errors were encountered:
By default, the fitSSM function estimates only unknown parameters in Q and H (in Gaussian case), and in general you need to provide your own function which updates the model given the parameters. So something like this:
Thank you so much! I think the difference between a "state" (e.g., level, slope, 11 seasonal dummy variables) and a "parameter" was confusing me. Thanks again!
Hello! I've been exploring KFAS for a few days now, and it's simply amazing! Thank you for creating it! One thing I have yet to figure out how to do is estimate the dispersion parameter for the negative binomial. u(t) appears to be set to 1 when I fit the model with fitSSM. For now, I have:
model2 <- SSModel(TERMS_ts ~ SSMseasonal(period=12,Q=NA,sea.type = c("dummy")) + SSMtrend(degree=2,Q = list(matrix(NA), matrix(NA))),distribution="negative binomial")
fit_silly2 <- fitSSM(model2,inits=c(rep(0,13)))
When I extract fit_silly2$model$u, it's just a time-series of 1s.
Is there a way to have the model estimate the dispersion parameter as well? My understanding is that I need this in order to estimate a function (e.g., sum of the observations from the most recent 4 time points) of the prediction values (e.g., apply the negative binomial observation model to the signals produced from the importanceSSM())
Thank you for your help!
The text was updated successfully, but these errors were encountered: