The phrase "a linear propensity" is correct and usable in written English.
It can be used in contexts discussing tendencies or inclinations that follow a straight or predictable pattern, often in fields like psychology, economics, or statistics.
Example: "The study revealed a linear propensity for individuals to choose the safer option when faced with risk."
Alternatives: "a straight tendency" or "a direct inclination."
We start with a simple test system that has linear propensity functions (i.e. a j (x)∝ x).
Since the current B-Pred implementation is based on a single parameter (solvent exposure) that is filtered on local model quality, it is reasonable to assume that the method could be further improved by the inclusion of additional structural parameters 12, 34 and/or by combining the solvent exposure, as directly determined from the structure, with classical linear propensity scales.
This analysis was extended by Li [ 36] to non-linear propensity functions by considering SDEs driven by Poisson random measures (see also [ 37]).
In the second step, a vector of 24 features is fed into a linear SVM to calculate propensity of a given input residue to form a MoRF region.
We compared the numbers of decayed, missing, or filled teeth using mixed models adjusted for a linear term of the propensity score.
We expressed the effects of secondhand smoke on the incidence of caries as hazard ratios with 95% confidence intervals, estimated by Cox regression adjusted for a linear term of the propensity score.
Results of uncertainty analyses by applying polynomial chaos expansions to net work and CEA correlate well with those of the nonlinear analysis, hence demonstrating the potential of an uncertainty analysis in improving the prediction of brake squeal propensity using a linear method.
Mean birth weight was significantly lower in wood users (96 g less compared with NG users, p < 0.008) in a linear regression model, unadjusted for propensity scores.
The models included prognostic factors (postoperative breast radiotherapy, pathological tumor size, histologic type, receptor status) modeled using dummy variables, and also propensity score as a linear term to adjust for imbalances in baseline variables influencing the decision to perform axillary dissection.
Moreover, based on propensity-score matching, a linear regression model was used to examine how the different timing of rehabilitation influenced total medical expenses, total rehabilitation expenses, and the number of OPD visits while controlling for gender, age, group, length of stay, CCS, trauma code, and comorbidity.
