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For the unequal sampling intensity issue, we tested three modeling strategies: fitting models using all the data, down-sampling the intensified stratum; and building separate models for each stratum.
Domain and item level models using all three modelling approaches reached an acceptable degree of predictive performance with domain models performing well in out-of-sample validation.
We train models using all available examples from ShakeFive2.
The PAAS system is implemented in MATLAB/Simulink models using all digital approach.
For each of the three global models, we used the dredge function in the R MuMIn package v1.10.5 (Barton 2014), which constructs models using all possible combinations of the predictor variables supplied in each global model.
Specifically, we examined the validity of a self-reported question concerning changes in menstrual cycle length and two full prediction models using all available data concerning menstrual cycles as potential indicators of perimenopause.
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Cox proportional hazards models used all univariate covariates.
Linear mixed models use all available longitudinal data while adjusting for within-patient correlation.
These models use all available PWV data and account for correlation between repeated measures within individuals.
These models use all available data and take into account the interrelationship between within-individual data points.
Linear mixed models use all data available at each time point; thus missing data imputation was not undertaken.
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