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), but not for major axis regression.
Also, is it possible to extract major axis regression residuals? (Resid did not
work on the lmodel2 results.)
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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