Table Of Contents

Fit Intervals (Polynomial » Prediction) (G Dataflow)

Version:
    Last Modified: March 15, 2017

    Calculates the prediction interval of the best polynomial 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 (Polynomial) node using the least square method to obtain polynomial coefficients.

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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. The number of sample points in y greater than the number of elements in polynomial coefficients.

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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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    polynomial coefficients

    Coefficients of the fitted model in ascending order of power. If the total number of elements in polynomial coefficients is m, the polynomial order is m - 1.

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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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    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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