[This article was first published on theBioBucket*, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here)
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.
This is an example for a permutation test on stratified samples with repeated measurements. Samples are interdependent firstly because they come from several sites and secondly because the sampling was repeated a second time. That is samples of the same sites are dependent and sample t1 and sample t2, taken from the very same places are dependent.
What I want to test is whether there is a difference between timepoint one (t1) and two (t2) or not. A hypothesis could be: the average difference t1-t2 is sign. larger than zero (a one-sided test). Another hypothesis could be: the average difference is sign. different from zero, either larger or smaller (a two-sided test).
If you deal with a distribution of your data that ordinary Linear Mixed Models (LMMs) or Generalized LMMs (GLMMs) can handle you should vote for this option – but sometimes you deal with awkard data and permutation tests may the only thing to bail you out…
What I want to test is whether there is a difference between timepoint one (t1) and two (t2) or not. A hypothesis could be: the average difference t1-t2 is sign. larger than zero (a one-sided test). Another hypothesis could be: the average difference is sign. different from zero, either larger or smaller (a two-sided test).
If you deal with a distribution of your data that ordinary Linear Mixed Models (LMMs) or Generalized LMMs (GLMMs) can handle you should vote for this option – but sometimes you deal with awkard data and permutation tests may the only thing to bail you out…
Read more »
To leave a comment for the author, please follow the link and comment on their blog: theBioBucket*.
R-bloggers.com offers daily e-mail updates about R news and tutorials about learning R and many other topics. Click here if you're looking to post or find an R/data-science job.
Want to share your content on R-bloggers? click here if you have a blog, or here if you don't.