Comments (2)
You have absolutely too many spatial locations in your model for vanilla Gaussian Process (GP) -priored latent factors to work well. Try formulations of HmscRandomLevel(...)
that rely on approximate GP structures. For instance,
rL.spatial = HmscRandomLevel(sData = xycoords, sMethod ="NNGP")
See the help page of HmscRandomLevel(...)
for more details or this paper (https://doi.org/10.1002/ecy.2929) for statistical presentation of implemented methods.
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Thanks, that's really helpful. I'll try the new code and have a look at the paper, hopefully I can get it working.
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Related Issues (20)
- HSMC usage to infer microbial communities? [discussion] HOT 1
- Interpretation of model coefficients in a multivariate poisson GLM with spatial random effect
- incorrect number of dimensions HOT 3
- Can not predict at the same coordinates used to train the model
- Missing help for `importPosteriorFromHPC` function
- Error in cross validation: missing value where TRUE/FALSE needed
- predict with Yc instead of constructGradient to avoid "Error: vector memory exhausted (limit reached?)" ?
- Interpretation of `predictEtaMean` / `predictEtaMeanField` arguments of the predict function
- In cor(lbeta[[i]][k, ], lmu[[i]][k, ]) : the standard deviation is zero HOT 2
- Unexpected trace plots for alpha parameters of a GPP model HOT 4
- Error in `importPosteriorFromHPC` for GPP/Hmsc-hpc models with `alignPost = TRUE` HOT 1
- Spatial Model running extremely slow HOT 6
- Error while converting Hmsc model object to JSON: `Error in rcpp_to_json(x, unbox, digits, numeric_dates, factors_as_string, : negative length vectors are not allowed` HOT 3
- im getting this error in running the Uhlig code
- Question about making predictions when using a hurdle approach
- Inconsistency in spatial model variance partitioning
- Issue with constructGradient() Function in HMSC Package HOT 1
- The use of `setPriors` in `computePredictedValues` function HOT 1
- sampling bias correction in Hmsc (Is it possible to provide a weight to sampling units) HOT 1
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