bimodal with a gap

A simple way to program a bimodal distrubiton is with two seperate normal distributions centered differently. This creates two peaks or what wiki calls modes. You can actually use almost any two distributions, but one of the harder statistical opportunities is to find how the data set was formed after combining the two random data distributions. I am attempting to model bimodal continuous coral survival data that includes values of 0 and 1 (0-100% survival). I have attempted to use linear mixed effects models (lmer and glmmTMB) with a few R how best to model continuous bimodal survival data using lmer and ... I wonder if there is any statistical test to "test" the significance of a bimodal distribution. I mean, How much my data meets the bimodal distribution or not? If so, is there any test in the R pro... 3 To think about ways to infer whether your data is bimodal or unimodal you need to hypothesize on whether there is a good fundamental underlying reason that the thing creating your data is bimodal or not.

In the distribution you coded you have two modes: mean1 and mean2 since you are mixing two normal distributions with differing means. This means that your distribution is bimodal because it has two modes. On the other hand a binormal distribution is the two dimensional form of the . Your case is not a binormal distribution. Bimodal residuals in logistic regression — what causes it, is it bad news, and what can be done? Ask Question Asked 2 years ago Modified 1 year, 11 months ago