Finds the k-dimension linear curve values and the set of k-dimension linear fit coefficients, which describe the k-dimension linear curve that best represents the input data set using the Least Square, Least Absolute Residual, or Bisquare method.
Whether the node computes the covariance matrix.
|do not compute Covariance||Does not compute the covariance matrix.|
|compute Covariance||Computes the covariance matrix.|
Default: do not compute Covariance
Observed data set. The number of elements in y must equal the number of rows in H.
Matrix that represents the formula you use to fit the data set.
Example of Obtaining the Matrix H
Assume you have a set of observations (x, y). For example, you can obtain a set of observations through data acquisition. Also, assume you think the relationship between x and y is of the following form:
The following equation describes matrix H.
In this example, the number of columns in H equals the number of elements in x. the number of rows in H equals 5.
Array of weights for the observations.
weight must be the same size as y. If you do not wire an input to weight, this node sets all elements of weight to 1. If an element in weight is less than 0, this node uses the absolute value of the element.
Value that determines when to stop the iterative adjustment of coefficients when you use the Least Absolute Residual or Bisquare methods.
If tolerance is less than or equal to 0, this node sets tolerance to 0.0001.
How tolerance Affects the Outputs with Different Fitting Methods
For the Least Absolute Residual method, if the relative difference of the weighted mean error of the polynomial fit in two successive iterations is less than tolerance, this node returns resulting polynomial coefficients. For the Bisquare method, if any relative difference between polynomial coefficients in two successive iterations is less than tolerance, this node returns the resulting polynomial coefficients.
Error conditions that occur before this node runs. The node responds to this input according to standard error behavior.
Default: No error
Algorithm this node uses to compute the best fit.
Use the SVD for Rank Deficient H algorithm only if H is rank deficient or does not have a full rank and if all other algorithms are unsuccessful.
|SVD||0||Uses the SVD algorithm.|
|Givens||1||Uses the Givens algorithm.|
|Givens2||2||Uses the Givens2 algorithm.|
|Householder||3||Uses the Householder algorithm.|
|LU Decomposition||4||Uses the LU Decomposition algorithm.|
|Cholesky||5||Uses the Cholesky algorithm.|
|SVD for Rank Deficient H||6||Uses the SVD for Rank Deficient H algorithm.|
|Least Square||0||Uses the least square method.|
|Least Absolute Residual||1||Uses the least absolute residual method.|
|Bisquare||2||Uses the bisquare method.|
Default: Least Square
Set of coefficients that minimize chi squared.
Matrix of covariance C with k-by-k elements. cjk is the covariance between ai and ak. cjj is the variance of aj. This node uses the following equation to compute the covariance matrix C:
Actual weight of general linear fitting if the fitting method is Bisquare.
If the fitting method is Least Square or Least Absolute Residual, this output returns the value you enter for weight.
Error information. The node produces this output according to standard error behavior.
Weighted mean error of the fitted model.
If method is Least Absolute Residual, residue is the weighted mean absolute error. Otherwise, residue is the weighted mean square error.
The following linear data model demonstrates how to configure the input parameters and how to use this node to obtain the fitted values and the set of least square coefficients that best represents the relationship of observations (x, y):
Where This Node Can Run:
Desktop OS: Windows
FPGA: Not supported