![]() ![]() You can read more about this issue here: Crossed vs nested random effects: how do they differ and how are they specified correctly in lme4?Ī model of the type VO2 ~ genotype + temperature + temperature:genotype + (1|miceID) should work, then check the assumption of normality and decide whether or not you need to find an alternative to the ANOVA. Using the Cost of Flight data generated, we will be seeking an explanation for variances among each group, trying to answer the question: why do people's cost of flight differ from the grand mean. If you have tested some mice for a particular temperature, and then other mice for another temperature, then you may want to do that if you have some repeated measurements. In JMP, we can perform One-Factor (one way) ANOVA using two different methods: Fit Y by X, and Fit Model. If this is the case, then I do not think nesting individuals within temperature is the right thing to do. It seems to me that you have tested each mice in every temperature condition. Have a look at your distribution, if your sample size is low and the distribution doesn't seem to tend towards a normal distribution, you will have to find another statistical test that does not require the data to be normally distributed. 1 I am trying to run a two-way ANOVA in JMP where I have the following variables- Fixed effects: 1. ![]() You can find more about this debate here: Normality assumption and sample size ![]() The assumption of normality is sometimes hard to verify because some tests are too powerful for datasets with a large sample size. For the violation of normality, I am assuming that you may have a large sample size since you are working with mice. ![]()
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