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We plot the goodness of fit as a function of the number of predictors used in the model.
The proposed stepwise and lasso regressions reduce the number of predictors to a few.
Limitations include a limited number of predictors and possibility of generalizability of the results.
Forecasting Using Principal Components from a Large Number of Predictors, Journal of the American Statistical Association, 2002.
The developed models are reduced to have a minimum number of predictors and interactions.
Our method and theory allow the number of predictors to be larger than the number of observations.
A number of predictors identified in prior research (e.g. hopelessness) were unrelated to subsequent suicidal ideation in the current study.
We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect.
We compute the relative importance of the predictors, and compare performance in matching the human expert rankings as we vary the number of predictors used in training.
Accounting for the different number of predictors included in each model by using adjusted R2 values for the variance partitioning analyses produced very similar results (see Supplementary Table 3b).
As R2 values could be affected by the different number of predictors included in each model66, we also conducted variance partitioning using the adjusted R2 (see Supplementary Table 3b), which gave very similar results.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com