Take the 2-minute tour ×
Cross Validated is a question and answer site for people interested in statistics, machine learning, data analysis, data mining, and data visualization. It's 100% free, no registration required.

I have kinetic data measured under several treatments (one experiment per treatment) which can be fitted by several nonlinear two-parameter models, how can I compare whether there there are differences in response that vary by treatment?

Comparing model fits. My initial thought was to use an F-test to compare relative residual sum-of-squares (RSS) across different data sets for each model, but if I understand correctly, this measures RSS relative to differences in degrees of freedom, so is more suitable for comparing a subset of a model relative to the full model for the same data set. Is there a similar test that would be appropriate in this case (to compare the same model applied to different data sets rather than different models with varying number of parameters to the same data set)?

Comparing regression coefficients. I found this reference which suggests to use a t-test where the test statistic is calculated by aggregating the standard errors of the nonlinear regression coefficients ($\hat{\beta}$ is the coefficient estimate and $\hat{s}_e$ is its standard error; subscripts 1 and 2 denote estimates for treatments 1 and 2):

$t = \frac{\hat{\beta}_1 - \hat{\beta}_2}{\sqrt{\hat{s}_{e,1}^2+\hat{s}_{e,2}^2}}$

with the combined degree of freedom being the sum of the original two. Is this a standard approach and is there a good literature/textbook reference?

Thanks in advance.

share|improve this question
    
see here for an approach –  Glen_b Aug 20 '13 at 0:37

1 Answer 1

You can check the following link http://www.graphpad.com/guides/prism/6/curve-fitting/index.htm?reg_interpreting_comparison_of_mod.htm

share|improve this answer

We're looking for long answers that provide some explanation and context. Don't just give a one-line answer; explain why your answer is right, ideally with citations. Answers that don't include explanations may be removed.

    
Welcome to the site, @Curie. This isn't quite an answer by CV standards. We are trying to build a permanent repository of statistical information in the form of high-quality questions & answers. As such, 1 thing we worry about is linkrot. It's fine to have a link as a reference, but not as a standalone answer. Would you mind expanding this to provide an explanation? –  gung Sep 10 '13 at 16:07

Your Answer

 
discard

By posting your answer, you agree to the privacy policy and terms of service.

Not the answer you're looking for? Browse other questions tagged or ask your own question.