Table Of Contents

Fit Intervals (Logarithm » Prediction) (G Dataflow)

Version:
    Last Modified: March 15, 2017

    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 Curve Fitting (Logarithm) 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

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    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%.

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    y

    Dependent values. y must contain at least three points.

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    x

    Independent values. x must be the same size as y.

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    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.

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    amplitude

    Amplitude of the fitted model.

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    error in

    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.

    error in does not contain an error error in contains an error
    If no error occurred before the node runs, the node begins execution normally.

    If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

    If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

    Default: No error

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    scale

    Scale of the fitted model.

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    upper bound

    Upper bound of the prediction interval.

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    lower bound

    Lower bound of the prediction interval.

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    error out

    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.

    error in does not contain an error error in contains an error
    If no error occurred before the node runs, the node begins execution normally.

    If no error occurs while the node runs, it returns no error. If an error does occur while the node runs, it returns that error information as error out.

    If an error occurred before the node runs, the node does not execute. Instead, it returns the error in value as error out.

    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


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