I wanted to check here to see if a repeated measures ANOVA would be a good option to analyze my data, or if you have any other suggestions? I have read that GLMM is another option for time series data, but my data is not linear. I use R for analysis.
I counted the number of flowers (Total) on 168 plants every other day (Day), so repeated measurements on the same plant subjects (PlantID) evenly spaced through time. I had 4 Treatments: Early, Peak, Late and Control, which differed in the timing that a heatwave was applied. I used 4 growth chambers to grow my plants, with 42 plants in each chamber. There was one chamber for each heat treatment. I used 6 genetic lines of this plant (RIL).
- Research Question:
I am interested in determining if the shapes of the 'flowering schedules' are different across RIL and Treatment combinations. Do control and treatment plants differ in their response to heatwaves? How do the genetic lines differ in their response? For example, do Control/RIL10 plants differ in their flowering response compared to Early/RIL10 plants, and so on for all other combinations.
I included a figure of my data to better illustrate this. Each gam curve represents the average flower counts for one RIL over time. The green curves are Control plants, the red curves are the Early plants. I would basically like to do a post hoc test to see which curves are significantly different from each other, and where they differ, if possible.
- My Data:
Here is an example of my data. Day starts at day 19 because this data is a subset for flower production after the early heatwave ended. This is the flower counts (Total) over time (Day) for plant #85 in the Early heatwave treatment and belonging to RIL 206.
PlantID Date Day Total Treatment RIL 85 June 26 19 2 Early 206 85 June 28 21 5 Early 206 85 June 30 23 15 Early 206 85 July 2 25 15 Early 206 85 July 4 27 29 Early 206 85 July 6 29 67 Early 206
If you would like a larger sample of my data to look at please let me know.
What do you guys think?