Comments (3)
Can you give a reproducible example of this? The line 329 is related to variance computation based on the importance sampling, there is likely some issue in computing the weights in the previous stages, meaning that importance sampling likely does not work in your case for some reason (poor model specification, approximation does not work, numerical issues, ...), leading for example infinite weights.
For these cases there probably should be some checks in place after the importance sampling and more informative error message.
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Yes, sorry for the delay - I have been away. I've attached a snippet of the problem (I don't have permission to share fully).
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Thanks. There are couple of issues in your example:
First, the argument for the new data is called newdata
, whereas in your script you use argument testData
which is ignored as there is not such argument for the predict method.
Second, as you have NA values in the Z array, it is impossible to predict the corresponding values of y (as y_t = link_function(Z_t*alpha_t)). So that causes the issue where there are missing values in the importance samples, which then cause issues in the later computations.
While you cannot get predictions for those y values, you can still get predictions for others, if you just set NA values in Z for example to zero. That shouldn't affect the predictions of other observations as corresponding values of y are still NA.
I now added the NAOK argument anyway so things should work now as expected.
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Related Issues (20)
- Handle intercepts in the model HOT 6
- Model non-gaussian multivariate outcome HOT 5
- Positive log likelihood for Poisson and Binomial distribution HOT 5
- How to predict more time series HOT 1
- Wrong convergence without warnings or errors HOT 15
- Requesting State Offset Term or Control Input specification HOT 2
- KFS function HOT 4
- Change .f95 suffix to .f90 HOT 1
- Exact diffuse intialisation HOT 3
- Exogenous variable
- Documentation: "more complex model" example HOT 2
- Returning Importance Samples in predict.SSModel HOT 3
- How to extract time varying coefficients and CIs corresponding to each variable in SSMregression HOT 2
- How to extract the overdispersion parameters in regression models assuming negative binomial distribution HOT 7
- Poor time variability of time-varying regression coefficients and Inconsistency of param estimates among methods in fitSSM HOT 9
- Standardized residuals HOT 6
- Difficulty with setting a1 and P1 when using SSMseasonal and reduced number of harmonics HOT 1
- Question building an Ar(2) model HOT 6
- same Log Likelihood of different SSModel HOT 6
- Kalman filter HOT 1
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