Model I and Model II Regressions
MATLAB® shell-scripts for linear regression analysis
- Model I regressions
- lsqfity - Standard Model I linear regression
- [Y-on-X regression]
- Fit line by minimizing y-residuals only
- All data points are given equal weight
- Use whenever X is controlled and Y is measured
- lsqfitx - Alternate or Reversed Model I regression
- [X-on-Y regression]
- Fit line by minimizing x-residuals only
- All data points are given equal weight
- lsqfityw - Weighted Model I regression
- [wY-on-X regression]
- Fit line by minimizing y-residuals only
- Data points are given varying weight
- Use whenever X is controlled and Y is measured with varying uncertainty
- Please note that:
- This m-file gives the correct results for all parameters (Bevington and Robinson,1992); but,
- the uncertainties in the slope and intercept are much smaller than the same uncertainties calculated by lsqfity.m or lsqfitxi.m (see the results table).
- lsqfityz - Weighted Model I regression - revised Jan 2000
- [zY-on-X regression]
- This is the same regression as lsqfityw.m, except that:
- York's (1966) equations for the uncertainties in weighted slope and intercept are used.
- The revised uncertainties are more are more in line with those from lsqfity.m.
- Model II regressions
- lsqfitma - Pearson's Major Axis
- aka: first principal component
- [Correlation of X & Y]
- Line is fit by minimizing BOTH x- and y-residuals simultaneously
- All data are given equal weight
- Use when units and range of X and Y are the same
- lsqfitgm - Geometric Mean regression
- aka: reduced major axis or standard major axis
- [Correlation of X & Y]
- Slope of line is the geometric mean of the two slopes determined by regressing Y-on-X and X-on-Y
- All data are given equal weight
- Use when units or range of X and Y are different
- Please note that:
- This algorithm uses both lsqfity.m and lsqfitx.m for determining the slope.
- The uncertainties in the slope and intercept are estimated by analogy with the symmetrical uncertainty limits for a model I regression following the treatment by Ricker (1973).
- lsqbisec - Least Squares Bisector
- [Regression/correlation of X & Y]
- Slope of line is determined by bisecting the minor angle between the two model I regressions:
Y-on-X and X-on-Y - All data are given equal weight
- Use when units or range of X and Y are different
- Please note that:
- This algorithm uses both lsqfity.m and lsqfitx.m for determining the slope.
- The uncertainties in the slope and intercept are estimated by analogy with the symmetrical uncertainty limits for a model I regression following the treatment by Ricker (1973).
- lsqcubic - Least-Squares-Cubic
- [Regression/correlation of wX & wY.]
- Line is fit by minimizing both x- and y-residuals simultaneously for WEIGHTED data points.
- Each data point can be given its own weight either as the inverse-square of the actual measurement precision or as the inverse-square of the product of the relative measurement precision for the method times concentration.
- Use when the measurement error of X and/or Y varies.
- Please note that:
- This algorithm uses lsqfitma.m for the first estimate of the slope.
- Iteration proceeds until the change in the slope is less than the user defined limit.
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