ABSTRACT
In multiple regression Y ~ ß0 + ß1X1 + ß2X2 + ß3X1 X2 + É., the interaction term is quantified as the product of X1 and X2. We developed fractional-power interaction regression (FPIR), using ßX1 M X2 N as the interaction term. The rationale of FPIR is that the slopes of Y-X1 regression along the X2 gradient are modeled using the nonlinear function (Slope = ß1 + ß3MX1 M-1 X2 N), instead of the linear function (Slope = ß1 + ß3X2) that regular regressions normally implement. The ranges of M and N are from -56 to 56 with 550 candidate values, respectively. We applied FPIR using a well-studied dataset, nest sites of the crested ibis (Nipponia nippon).We further tested FPIR by other 4692 regression models. FPIRs have lower AIC values (-302 ± 5003.5) than regular regressions (-168.4 ± 4561.6), and the effect size of AIC values between FPIR and regular regression is 0.07 (95% CI: 0.04-0.10). We also compared FPIR with complex models such as polynomial regression, generalized additive model, and random forest. FPIR is flexible and interpretable, using a minimum number of degrees of freedom to maximize variance explained. We have provided a new R package, interactionFPIR, to estimate the values of M and N, and suggest using FPIR whenever the interaction term is likely to be significant. ⢠Introduced fractional-power interaction regression (FPIR) as Y ~ ß0 + ß1X1 + ß2X2 + ß3X1 M X2 N + É to replace the current regression model Y ~ ß0 + ß1X1 + ß2X2 + ß3X1 X2 + É; ⢠Clarified the rationale of FPIR, and compared it with regular regression model, polynomial regression, generalized additive model, and random forest using regression models for 4692 species; ⢠Provided an R package, interactionFPIR, to calculate the values of M and N, and other model parameters.
