Hi *,
is there a way to obtain some kind of p-value for a model fitted with RMA
using the lmodel2 package?
I know that p-values are discussed and criticized a lot and as you can image
from my question I'm not
very much of a statistican (only writing my bachelor thesis).
As fare as I understood the confidence interval statistic correctly, a
coefficient is regarded as statistically
significant if the corresponding CI does not include 0 (null hypothesis).
But can I obtain some kind of a
p-value to say that it is highly significant (< 0.01), significant
(0.05),... like in the output of lm?
Sorry for bothering everybody with this, well, probably rather idiotic
question, but I don't know where to
continue from this point...
Thanks,
Katharina
Here the output of my lmodel2 regression:
Model II regression
Call: lmodel2(formula = log(AGB) ~ log(BM_roots), data = biomass_data,
range.y = "interval", range.x = "interval", nperm = 99)
n = 1969 r = 0.9752432 r-square = 0.9510993
Parametric P-values: 2-tailed = 0 1-tailed = 0
Angle between the two OLS regression lines = 1.433308 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 0.6122146 1.038792 46.09002 0.01
2 MA 0.5787299 1.066868 46.85300 0.01
3 SMA 0.5807645 1.065162 46.80725 NA
4 RMA 0.5792123 1.066463 46.84216 0.01
Confidence intervals
Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
1 OLS 0.5779465 0.6464828 1.028376 1.049207
2 MA 0.5659033 0.5914203 1.056227 1.077622
3 SMA 0.5682815 0.5931260 1.054797 1.075628
4 RMA 0.5663916 0.5918989 1.055826 1.077213
Eigenvalues: 19.83213 0.2475542
H statistic used for computing C.I. of MA: 2.502866e-05
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