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For every time step in the TCE dataset and for each of the different feature sets, a 5×5-fold cross-validation scheme was applied to train and to evaluate linear models for predicting the change in VL and CD4+ T-cell count, respectively.
The simulated data in this study was used previously to evaluate linear models (LM), threshold models (TM) and survival analysis (SA) [ 33].
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Seventy-two studies (80%) evaluated linear models assuming Gaussian residuals (e.g. OLS), of which 35 (39%) studies evaluated only linear models, and 37 (41%) evaluated these alongside other specifications.
where y echo(k) is the echo signal component of the microphone signal y(k) and (hat y k)) is the echo estimate obtained using the model to be evaluated (linear model, PB-HGM, PBSA-HGM, ESA-HM).
The statistical significance for the comparison of body weight curves obtained in the presence of vehicle or rimonabant treatment for NC or HFD subgroups was evaluated with linear models with repeated measures using SPSS program.
Significant predictors were evaluated by linear models controlling for potential confounding factors.
Overall symptom load was quantified by counting the symptoms or signs, and differences between strains evaluated using linear models.
Overall symptom load was quantified by counting the symptoms or signs, and differences in overall symptom load between strains evaluated using linear models.
Differences in age, BMI, marine fatty acids, use of medication and smoking between the sufficient and deficient groups were evaluated using linear models and χ tests.
For each ecotype, the statistical model evaluated linear, quadratic, and cubic effects of MAP1961 on percent cover.
Affymetrix gene expression profiles were evaluated using linear modeling and rank product analyses.
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