Hi,

Does anybody know whether it's possible (and sensible) to calculate R^2 (or something approximating R^2) on the results of a major axis Model II regression done by lmodel2?

The function lmodel2 provides an R^2 for the OLS regression only (example below)

Also, is it possible to extract major axis regression residuals?

Many thanks,
Karen



Example:
> Result <- lmodel2( sqrt(DBH.cm/100) ~ sqrt(height.m), data=data.KR, "relative", "relative", 99)

> Result
Model II regression
Call: lmodel2(formula = sqrt(DBH.cm/100) ~ sqrt(height.m), data = data.KR2004.Call.open, range.y = "relative", range.x = "relative", nperm = 99)
n = 138   r = 0.9901839   r-square = 0.9804641  --> for OLS
Parametric P-values:   2-tailed = 4.123218e-118    1-tailed = 2.061609e-118
Angle between the two OLS regression lines = 0.2637846 degrees
Permutation tests of OLS, MA, RMA slopes: 1-tailed, tail corresponding to sign
A permutation test of r is equivalent to a permutation test of the OLS slope
P-perm for SMA = NA because the SMA slope cannot be tested
Regression results
  Method  Intercept     Slope  Angle (degrees)  P-perm (1-tailed)
1    OLS -0.2460454 0.2452351         13.77901               0.01
2     MA -0.2468372 0.2455126         13.79400               0.01
3    SMA -0.2529825 0.2476662         13.91032                 NA
4    RMA -0.2547898 0.2482996         13.94451               0.01
Confidence intervals
  Method  2.5%-Intercept 97.5%-Intercept  2.5%-Slope 97.5%-Slope
1    OLS      -0.2638713      -0.2282196   0.2393650   0.2511051
2     MA      -0.2636291      -0.2300910   0.2396437   0.2513974
3    SMA      -0.2699308      -0.2364311   0.2418657   0.2536058
4    RMA      -0.2720098      -0.2380816   0.2424441   0.2543344
Eigenvalues: 1.153060 0.001229238
H statistic used for computing C.I. of MA: 3.072071e-05


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