PMC

Variable importance of random forests in the simulated uninformative data set after inducing misclassification. Each point indicates the median importance value of the random-forest variable across 10,000 simulations. The bars indicate the 95% simulation interval (2.5th and 97.5th percentiles of the 10,000 iterations describing where 95% of the simulated estimates lie). A) Variable importance in the original simulated uninformative data set without misclassification; B) variable importance under the scenario of nondifferential misclassification of X5 in which the sensitivity of predictor X5 was 0.45 and specificity was 0.99; C) variable importance under the scenario of differential misclassification of X5 in which the sensitivity of predictor X5 among persons with the outcome was 0.45, specificity of predictor X5 among those with the outcome was 0.99, the sensitivity of predictor X5 among those without the outcome was 0.40, and specificity of predictor X5 among those without the outcome was 0.95; D) variable importance under the scenario of nondifferential outcome misclassification in which the sensitivity of the outcome was 0.85 and specificity was 0.99; E) variable importance under the scenario of differential outcome misclassification in which the sensitivity of the outcome among persons with predictor X5 was 0.9, specificity among those with predictor X5 was 0.99, the sensitivity among those without predictor X5 was 0.85, and specificity among those without predictor X5 was 0.95.

Variable importance of random forests in simulated informative data set 2 after inducing misclassification. Each point indicates the median importance value of the random-forest variable across 10,000 simulations. The bars indicate the 95% simulation interval (2.5th and 97.5th percentiles of the 10,000 iterations describing where 95% of the simulated estimates lie). A) Variable importance in the original simulated informative data set 2 without misclassification; B) variable importance under the scenario of nondifferential misclassification of X1 in which the sensitivity of predictor X1 was 0.45 and specificity was 0.99; C) variable importance under the scenario of differential misclassification of X1 in which the sensitivity of predictor X1 among persons with the outcome was 0.50, specificity of predictor X1 among those with the outcome was 0.95, the sensitivity of predictor X1 among those without the outcome was 0.45, and specificity of predictor X1 among those without the outcome was 0.90; D) variable importance under the scenario of nondifferential outcome misclassification in which the sensitivity of the outcome was 0.90 and specificity was 0.99; E) variable importance under the scenario of differential outcome misclassification in which the sensitivity of the outcome among persons with predictor X1 was 0.9, specificity among those with predictor X1 was 0.99, the sensitivity among those without predictor X1 was 0.85, and specificity among those without predictor X1 was 0.95.

Graphical representation of the terms that constitute the MARS model for the tool flank wear (VB): (a) first order term of the predictor variable Depth of cut (DOC); (b) first order term of the predictor variable Feed; (c) first order term of the predictor variable AC spindle motor current (smcAC); (d) first order term of the predictor variable Material; (e) second order term of the predictor variables Time and Depth of cut; (f) second order term of the predictor variables Time and AC spindle motor current (smcAC); (g) second order term of the predictor variables Time and Material; (h) second order term of the predictor variables Depth of cut (DOC) and Feed; (i) second order term of the predictor variables Feed and AC spindle motor current (smcAC); (j) second order term of the predictor variables Feed and Spindle vibration (vib_spindle); (k) second order term of the predictor variables AC spindle motor current (smcAC) and Table vibration (vib_table); (l) second order term of the predictor variables AC spindle motor current (smcAC) and Spindle vibration (vib_spindle); and (m) second order term of the predictor variables Acoustic emission at spindle (AE_spindle) and Material.

Multiple mediator model. X: predictor variable; Y: outcome variable; M_i: mediator variable; a_i: association between predictor (X) and potential mediator (M_i); b_i: association between potential mediator (M_i) and outcome variable (Y); c: overall association between predictor variable (X) and outcome variable (Y); c’: direct effect (unmediated) of predictor variable (X) on outcome variable (Y).