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Root mean PRESS and T values plotted against increasing number of model factors indicated that a two-factor PLS model provided good predictions (overall R value of 72.5%), while simultaneously guarding against overfitting (Supporting Information, Figure 2).
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A two-tailed Brown Forsythe test was performed between the population mean PRESS residuals for this model and the population mean PRESS residuals from other models, and any model with significantly higher mean PRESS residuals (P<0.05) was excluded as a candidate coding scheme for the population activity.
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The final model had the least genetic factors with a root mean PRESS that was insignificantly larger than the model with the minimum root mean PRESS.
As for the single-neuron analysis, the spatial model with the smallest population mean PRESS residuals was the best candidate spatial model describing the population activity.
Spatial models that had significantly higher mean PRESS residuals compared with the best-fit model were excluded as candidate coding schemes for that neuron.
For population analysis, we did a statistical comparison of the mean PRESS residuals across the entire neuronal population for different spatial models.
In both populations, Te was the best model describing population activity as the population mean PRESS residuals were the lowest for this model.
We implemented a 7-fold cross-validation procedure to calculate the root mean predicted residual sum of squares (PRESS) for each number of extracted genetic factors, and the number of extracted genetic factors that had the minimum root mean PRESS was chosen.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com