Computes a linear least-squares regression to fit a set of data.
See Also
| Type | Intent | Optional | Attributes | Name | ||
|---|---|---|---|---|---|---|
| integer(kind=int32), | intent(in) | :: | order |
The order of the equation to fit. This value must be at least one (linear equation), but can be higher as desired, as long as there is sufficient data. |
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| logical, | intent(in) | :: | intercept |
Set to true if the intercept is being computed as part of the regression; else, false. |
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| real(kind=real64), | intent(in) | :: | x(:) |
An N-element array containing the independent variable measurement points. |
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| real(kind=real64), | intent(in) | :: | y(:) |
An N-element array containing the dependent variable measurement points. |
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| real(kind=real64), | intent(out) | :: | coeffs(:) |
An ORDER+1 element array where the coefficients will be written. |
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| real(kind=real64), | intent(out) | :: | ymod(:) |
An N-element array where the modeled data will be written. |
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| real(kind=real64), | intent(out) | :: | resid(:) |
An N-element array where the residual error data will be written (modeled - actual). |
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| type(regression_statistics), | intent(out), | optional | :: | stats(:) |
An M-element array of regression_statistics items where M = ORDER + 1 when intercept is set to true; however, if intercept is set to false, M = ORDER. |
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| real(kind=real64), | intent(in), | optional | :: | alpha |
The significance level at which to evaluate the confidence intervals. The default value is 0.05 such that a 95% confidence interval is calculated. |
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| class(errors), | intent(inout), | optional, | target | :: | err |
A mechanism for communicating errors and warnings to the caller. Possible warning and error codes are as follows. - FS_NO_ERROR: No errors encountered. - FS_ARRAY_SIZE_ERROR: Occurs if any of the arrays are not approriately sized. - FS_INVALID_INPUT_ERROR: Occurs if order is less than 1. - FS_MEMORY_ERROR: Occurs if there is a memory allocation error. |