[R] package lmodel2: p-value RMA fitting?

[katharina]

If anybody might know how to calculate the standard error of the coefficients
(slope, intercept)
of a lmodel2 that would help me a lot...


katharina wrote:
> 
> sorry for spaming, but IU just had an idea not sure if that may be a way
> of
> doing it:
> 
> 1. calculate the standrad error of e.g. intercept as mean of the standards
> errors obtained from the upper/lower
>     confidence intervals of the intercept
>      error_intercept1 = (allo.lmodel2$confidence.intervals[4,2] -
> mean(log(biomass_data$BM_roots)))/1.96
>      error_intercept2 = (allo.lmodel2$confidence.intervals[4,3] -
> mean(log(biomass_data$BM_roots)))/1.96
>      stderr_intercept = round((error_intercept1+error_intercept2)/2,
> digits=8)
> 
> 2. Calculate the t-value as intercept estimate divided by the standard
> error
> from 1. and using
>      the following for calculating a two-tailed p-value
>       p_intercept = 2 * (1 - pt(abs(intercept/stderr_intercept),
> df=length(biomass_data)-1))
> 
> Might this a reasonable approach for a 'rough' estimation of an p-value? I
> glad for every suggestion...
> 
> 
> 
> 2009/7/20 Katharina May <may.katharina at googlemail.com>
> 
>> 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
>>
>>
>>
> 
> 
> -- 
> Time flies like an arrow, fruit flies like bananas.
> 
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