I was asking to find best fit values for two unknown parameters using the lm() function in r, I have used the lm function before but I am unsure of how to do this for unknown parameters?
I need to use the lm function on this formula
log(C)~ log(A)+ D log(B)
Based off of this model
log(C)~ N(log(A)+ D log(B),σ^2 )
I already have the starting values for C and B in vectors, and I need to estimate A and D? I am not how to do this in r using the lm function.
Thank you!
To minimize the residual sum of squares, just use the lm function. Your output will contain an intercept and a coefficient associated with any predictor variables. Thus:
lm(log(C) ~ log(B), data = my_data)
You will predict log(C) as a linear combination of two parameters: the estimate of the "intercept" and the regression coefficient of log(B). For your purposes, this is log(A) and D respectively.