michaeldumelle
commented
9 months ago Thanks @damondamondamon for the issue and for the kind words about the software! A few thoughts below:
- Yes, it should read "readjusting."
- You are correct that the message has to do with the unbalanced distribution in your categorical variables. If one of the partitions does not have both soil types, you will receive the message you did. You can supply your own partitions via
indextolocal, and this is what I recommend you do to avoid the message. By default,spmodeluseskmeans()to select the indexes, so you could do this on your own, and if you have a partition without both soil types, either select a new set of partitions or rearrange a few observations. Also see my answer to "Related question 2" below. - Related question 1: This paper outlines our big data methods for spatial models. Equation 13 is the default variance formula we use (
var_adjust = "theoretical"tolocal) to compute the variances of the explanatory variable slopes estimates. Notice that there are $V_{i,i}^{-1}$ terms, which end up being singular when both soil types are not observed in the corresponding $X_i$ matrix. Whenvar_adjust = "none"tolocal, only $T{xx}^{-1}$ from Equation 13 is used to compute the variances of the explanatory variable slope estimates. The important takeaway is that we use a "shortcut" to fit the model to large spatial data and then must use Equation 13 to get the theoretically correct variances of the explanatory variable slope estimates. If we don't apply this adjustment (or we can't because one of the $V{i,i}$ terms are singular), the explanatory variable slope estimates tend to be a little too small, leading to narrower confidence intervals. Regardless of thevar_adjusttype, the spatial covariance parameter estimates and explanatory variable estimates are the same (all that changes is the variance of the explanatory variable slope estimates). - Related question 2: This is a great suggestion, but we are unlikely to implement this directly in
spmodelin the near future, as there are already some pieces of software in the tidymodels ecosystem (rsample and spatialsample) designed for this. You can use these pieces of software to partition your data and use the partitions to create the vector that is theindexargument tolocal. - Minor side question: The team is aware of this and plans to fix it. When there are no random effects specified via the
randomargument tosplm(), the model should be equivalent tolm()and fit much faster. However, this fix may take a bit of time to implement as we need to create a custom routine for it that works with everything else. We did not do this originally because we believed people would primarily uselm()to fit non-spatial models, but we now see the utility in making model comparisons between spatial and non-spatial models directly usingspmodel's existing architecture, which necessitates fitting a model withsplm(..., spcov_type = "none").
Please let us know if you have any additional questions!
Dear spmodel-Team,
thanks a lot for this helpful package!
I was using your package within a inference context to estimate the effect of auxiliary variables on my target variable within a spatial setting. These auxiliary variables are to some extent categorical. Something like:
"Yield ~ Soil_Type (Categorical) + Elevation (Continuous) + etc"
When working with large datasets (n > 10.000 with partition size > 500), I sometimes get the warning message:
At least one partition's inverse covariance matrix is singular. Redjusting using var_adjust = "none".(I guess it should be readjusting?)While this could be due to some real singularity in the covariance matrix, I rather assume that it occurs due to the rather unbalanced distribution in my categorical variables (e.g. two soil types with imbalanced 90%/10% distribution).
Related questions / suggestions:
Minor side question: When using spmodel for inference (in my case n = 6.000) and defining the spcov_type as "none" (just as a non-spatial reference) and define local explicitly to "FALSE", I would still assume a routine that is equivalent to lm. Still, the computational time is exceptionally high (lm ~1sec, splm > 500sec). Will try to add replicable example.