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Fit Intervals (Gaussian Peak » Prediction) (G Dataflow)

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    Last Modified: January 9, 2017

    Calculates the prediction interval of the best Gaussian fit for an input data set.

    Programming Patterns

    If the noise of y is Gaussian-distributed, you must fit the observations with the Gaussian Peak mode of the Curve Fitting node using the Least Square method to obtain the amplitude, center, and standard deviation.

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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 four 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.

    Default: No error

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    center

    Center of the fitted model.

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    standard deviation

    Standard deviation 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.

    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


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