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How to define a range of values without losing information?
I have a dataset that I want to fit with a monotonically increasing non-linear function. For example, the M-3 to M-2 dataset should be fit with a $a + b(M-3)$
However, M-3-1 is a different dataset. It is important that they will not be fitted together, only the M-3-1 and M-3-2 will be fit.
Therefore, I would like to define this range of values as:
DataId : A collection of values with unique identifiers.
RangeId : A collection of values each with a value between [0,1]
Any ideas how I can fit this with a continuous function?
(Note: these values should be calculated, not defined. I can define a range for the value as [0,1] later, but I can’t do that until after the fitting)
A:
I would agree with the comment that function fitting is probably not the best idea. What you are describing here is a Box-Cox transformation.
The reason I would recommend against it is that the transformation changes the data distribution and, if you’re not careful, may end up with an absurdly disproportionate minimum value, where the interval is split over a few very different values. The docs for the nlminb function in the MASS package have details of Box-Cox transformations.
But, if you insist on fitting, you could use quantile regression. This is the type of problem that classical Quantile Regression (QR) solves. The equations and the R code come from (See equations 3.6, 4.2, and 5.5 in the bqr function)
$q = E(X)$
$D_X = \frac{1}{n-2} \sum_{i=2}^n X_i (X_i – X_1)$
$q_j = q + D_X q_{j-1}$
$\hat{q}
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