Fits a model to data, primarily for instances when the explanatory as well as the response variables have significant errors. Minimizes the sum of the squares of the weighted orthogonal distances between each data point and the curve described by the model equation. Can also be used to solve the ordinary least squares problem, where all the errors are attributed to the observations of the dependent variable. Classes : K1a1a3 . Unconstrained linear least squares approximation on univariate data (curve fitting) K1b1a2 . Unconstrained nonlinear least squares approximation by smooth functions, user provides first derivatives L8a4 . Simple linear errors in variables regression L8c5 . Measurement error models L8e1 . Nonlinear least squares regression (i.e., y = F(X,b)) L8e5 . Nonlinear measurement error models Type : Fortran subroutine in ODRPACK package. Access : Some uses prohibited. Portable. Precision: Double. Details : Data-1 Data-2 Data-3 Dependencies-1 Dependencies-2 Example-1 Example-2 Example-3 Source Test Test-output Sites : (1) NETLIB
NETLIB: Public access repository, The University of Tennessee at Knoxville and Bell Laboratories Precision: Double. (Single: SODR) You may access components from NETLIB outside GAMS as follows. Data-2 : echo 'send data2.dat from Example-3 : echo 'send d_drive3 from Source : echo 'send d_odr from Dependencies-: echo 'send d_lpkbls from Dependencies-: echo 'send d_mprec0 from Example-2 : echo 'send d_drive2 from Test-output : echo 'send test.txt from Example-1 : echo 'send d_drive1 from Data-1 : echo 'send data1.dat from Test : echo 'send d_test from Data-3 : echo 'send data3.dat from
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