The parameters and models that are being used need to be consistent with the biology that is being studied. If it's appropriate, scaling the upper and lower bounds of your data can improve the consistency of ED50 estimates as this removes the requirement for the model to estimate those parameters. Something like this can be used for scaling;
(VALUE - topdrugmean)/(nodrugmean-topdrugmean)
The Akaike Information criterion can be used to measure the goodness-of-fit for different models (see the preceding links).
The parameters and models that are being used need to be consistent with the biology that is being studied. If it's appropriate, scaling the upper and lower bounds of your data can improve the consistency of ED50 estimates as this removes the requirement for the model to estimate those parameters. Something like this can be used for scaling;
(VALUE - topdrugmean)/(nodrugmean-topdrugmean)
The Akaike Information criterion can be used to measure the goodness-of-fit for different models (see the preceding links).
The different models that can be used to fit the curves do differ. The linked paper and this website outline the differences; the discussion revolves around an
Rpackage calledDRCbut much of the information is generally applicable. Modelling can also be used to estimate ED50.The parameters and models that are being used need to be consistent with the biology that is being studied. If it's appropriate, scaling the upper and lower bounds of your data can improve the consistency of ED50 estimates as this removes the requirement for the model to estimate those parameters. Something like this can be used for scaling;
The Akaike Information criterion can be used to measure the goodness-of-fit for different models (see the preceding links).
The different models that can be used to fit the curves do differ. The linked paper and this website outline the differences; the discussion revolves around an
Rpackage calledDRCbut much of the information is generally applicable. Modelling can also be used to estimate ED50.The parameters and models that are being used need to be consistent with the biology that is being studied. If it's appropriate, scaling the upper and lower bounds of your data can improve the consistency of ED50 estimates as this removes the requirement for the model to estimate those parameters. Something like this can be used for scaling;
The Akaike Information criterion can be used to measure the goodness-of-fit for different models (see the preceding links).
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