I want to test the Mistcherlich Model to data I get from papers.
I have data of agronomic yield and an environmental variable (naturally I have the standard deviation and the number of repetition) and I want to test whether there is a Mistcherlich relation.
Mitscherlich equation is Y=Ymax * (1-e(-b*x))
Where Y is the yield, x my environmental variable and Ymax and b are the two paramaters of my model: the maximum yield and the curve parameter.
To model it without considering the sd, I use the nls function but I would like to know wether is possible to use the sd, or use a weight for the data.
Which R function can do that?
Thanks
Thanks Wendel.
The applied regression in the article you sent is polynomial, wich doesn't work for nonlinear and nonpolynomial equation
Finally I find a solution for the fitting step, using the R nls function.
Primary I had to determine a weight for every point, based on variance and number of experimental repetition: Wi=Ni / vi.
Finally I used the nls function, giving the weights input into the function:
start2
Note that if you do a meta-analysis you should perhaps add the experiment/ original publication as a random effect. This can be done as an intercept or on one or several of the parameters.
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