Calculates the prediction interval of the best logarithmic fit for an input data set.
If the noise of y is Gaussian-distributed, you must fit the observations with the Curve Fitting (Logarithm) node using the least square method to obtain the amplitude and scale.
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
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%.
Dependent values. y must contain at least three points.
Independent values. x must be the same size as y.
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 of the fitted model.
Error conditions that occur before this node runs.
The node responds to this input according to standard error behavior.
Standard Error Behavior
Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.
Default: No error
Scale of the fitted model.
Upper bound of the prediction interval.
Lower bound of the prediction interval.
Error information.
The node produces this output according to standard error behavior.
Standard Error Behavior
Many nodes provide an error in input and an error out output so that the node can respond to and communicate errors that occur while code is running. The value of error in specifies whether an error occurred before the node runs. Most nodes respond to values of error in in a standard, predictable way.
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: This product does not support FPGA devices