Background:Familiarity with a simulation platform can seduce modellers into accepting untested assumptions for convenience of implementation.These assumptions may have consequences greater than commonly suspected,and ...Background:Familiarity with a simulation platform can seduce modellers into accepting untested assumptions for convenience of implementation.These assumptions may have consequences greater than commonly suspected,and it is important that modellers remain mindful of assumptions and remain diligent with sensitivity testing.Methods:Familiarity with a technique can lead to complacency,and alternative approaches and software can reveal untested assumptions.Visual modelling environments based on system dynamics may help to make critical assumptions more evident by offering an accessible visual overview and empowering a focus on representational rather than computational efficiency.This capacity is illustrated using a cohort-based forest growth model developed for mixed species forest.Results:The alternative model implementation revealed that untested assumptions in the original model could have substantial influence on simulated outcomes.Conclusions:An important implication is that modellers should remain conscious of all assumptions,consider alternative implementations that reveal assumptions more clearly,and conduct sensitivity tests to inform decisions.展开更多
SUMMARY Linear regression is widely used in biomedical and psychosocial research.A critical assumption that is often overlooked is homoscedasticity.Unlike normality,the other assumption on data distribution,homoscedas...SUMMARY Linear regression is widely used in biomedical and psychosocial research.A critical assumption that is often overlooked is homoscedasticity.Unlike normality,the other assumption on data distribution,homoscedasticity is often taken for granted when fitting linear regression models.However,contrary to popular belief,this assumption actually has a bigger impact on validity of linear regression results than normality.In this report,we use Monte Carlo simulation studies to investigate and compare their effects on validity of inference.展开更多
文摘Background:Familiarity with a simulation platform can seduce modellers into accepting untested assumptions for convenience of implementation.These assumptions may have consequences greater than commonly suspected,and it is important that modellers remain mindful of assumptions and remain diligent with sensitivity testing.Methods:Familiarity with a technique can lead to complacency,and alternative approaches and software can reveal untested assumptions.Visual modelling environments based on system dynamics may help to make critical assumptions more evident by offering an accessible visual overview and empowering a focus on representational rather than computational efficiency.This capacity is illustrated using a cohort-based forest growth model developed for mixed species forest.Results:The alternative model implementation revealed that untested assumptions in the original model could have substantial influence on simulated outcomes.Conclusions:An important implication is that modellers should remain conscious of all assumptions,consider alternative implementations that reveal assumptions more clearly,and conduct sensitivity tests to inform decisions.
文摘SUMMARY Linear regression is widely used in biomedical and psychosocial research.A critical assumption that is often overlooked is homoscedasticity.Unlike normality,the other assumption on data distribution,homoscedasticity is often taken for granted when fitting linear regression models.However,contrary to popular belief,this assumption actually has a bigger impact on validity of linear regression results than normality.In this report,we use Monte Carlo simulation studies to investigate and compare their effects on validity of inference.
