I have a panel dataset which contains diameter measurement data for 88 trees obtained from a mapped stand in Duke Forest. The description of variables is:
Each tree is indexed by a unique ID (ID), and the diameter of a given year (yr) is the variable cm(cm). For each year, three variables of weather information are also available: annual precipitation (annualprec), average summer Palmer Drought Severity Index (summerpdsi), and average winter temperature (wintertemp).
Now I want to study both stand (population) level growth and individual level growth. I currently use fixed effect and random effects model. These two models allow me to investigate the effects of the 3 weather variables. But my main focus should be stand level growth and individual level growth. I don't know what method I should use to attain this goal.
The dataset can be seen here: http://www2.stat.duke.edu/~lm186/data/diamdata.txt
Each tree is indexed by a unique ID (ID), and the diameter of a given year (yr) is the variable cm(cm). For each year, three variables of weather information are also available: annual precipitation (annualprec), average summer Palmer Drought Severity Index (summerpdsi), and average winter temperature (wintertemp).
Now I want to study both stand (population) level growth and individual level growth. I currently use fixed effect and random effects model. These two models allow me to investigate the effects of the 3 weather variables. But my main focus should be stand level growth and individual level growth. I don't know what method I should use to attain this goal.
The dataset can be seen here: http://www2.stat.duke.edu/~lm186/data/diamdata.txt
