Table Of Contents
Fit Intervals (Logarithm » Prediction) (G Dataflow)
Calculates the prediction interval of the best logarithmic fit for an input data set.
Programming Patterns
If the noise of y is Gaussian-distributed, you must fit the observations with the Logarithm mode of the Curve Fitting node using the Least Square method to obtain the amplitude and scale.
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base
Base of the logarithm.
| Name | Value | Description |
|---|---|---|
| e | 2.71828 | Uses the natural logarithm. |
| 10 | 10 | Uses 10 as the base of the logarithm. |
| 2 | 2 | Uses 2 as the base of the logarithm. |
Default: e
confidence level
Level of certainty for the confidence interval. confidence level must be greater than 0 and less than 1.
Default: 0.95, which means the probability that the best fit falls between lower bound and upper bound is 95%.
y
Dependent values. y must contain at least three points.
x
Independent values. x must be the same size as y.
weight
Weights for the observations.
weight must be the same size as y. weight also must contain non-zero elements. If an element in weight is less than 0, this node uses the absolute value of the element. If you do not wire an input to weight, this node sets all elements of weight to 1.
amplitude
Amplitude of the fitted model.
error in
Error conditions that occur before this node runs. The node responds to this input according to standard error behavior.
Default: No error
scale
Scale of the fitted model.
upper bound
Upper bound of the prediction interval.
lower bound
Lower bound of the prediction interval.
In the following illustration, the region between the upper and lower prediction bounds is the prediction interval.
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported