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.kathar...@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. > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > > -- View this message in context: http://www.nabble.com/package-lmodel2%3A-p-value-RMA-fitting--tp24568041p24569200.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
