Biomass Growth Over Time Best Fit Model Help

[lshortna]

Hi everyone. I am having some issues figuring out the best model and code for my data. In a nutshell, I have biomass yield data over time (harvests at various days after planting), for two years, 2019 and 2021. We sampled 12 different plots per harvest, so my individual data points for each harvest day differ by plot number. We have 12 data points each time, but they are not the same plot each harvest time. Harvests based on days after planting differ between years. I know I need to include Year as a random effect. However, when I run a GLM on the data, I don't get a great fit. We believe we need a model that is nonlinear and can better fit to our growth curve, but running an NLIN procedure isn't working for us either. I have been exploring the NLMIXED procedure and think it may be useful. However, I have been using the Tasks functions in SAS Studio (On Demand for Academics) to help with coding, and I can't seem to figure out how to do this for the NLMIXED procedure. Any advice is appreciated because I have no clue what model would be best for us to try! Thanks!  

[lshortna]

To add, here is part of our data, and clearly our current model isn't best representing the data as we need to predict yield from day 0 and on but definitely have biomass yields before 45 DAP.

 

[Ksharp]

I suggest to use percentile regression . and @Rick_SAS wrote many blogs about it .

[SteveDenham]

Two possible directions spring to my mind:

Take a look at the EFFECT statement.  You could fit a spline function, but that really doesn't help you come up with a response function.

Try googling 4 parameter logistic model to see if that makes sense.  There are several examples of fitting this using NLMIXED out there.

SteveDenham

Oh yeah, when you say the NLIN procedure "isn't working", what specifically isn't working.  Are there errors in the log?  Messages in the output?  Inquiring minds want to know.

[Ksharp]

https://blogs.sas.com/content/iml/2018/08/13/quantile-regression-chess-ratings.html

https://blogs.sas.com/content/iml/2018/08/06/score-quantile-regression-sas.html


https://blogs.sas.com/content/iml/2013/04/17/quantile-regression-vs-binning.html

[Ksharp]

As Steve said ,try spline effect.
Or add more effect in your model like:
y=x + x^2 ;
or
y=x + x^2 + x^3;

[t75wez1]

I suggest to explore Local Regression and the Loess Method.

https://www.lexjansen.com/nesug/nesug12/gr/gr07.pdf