I just read on the p-value regression ticket a comment from Markus Metz [1]. If I understood correctly, he mentions that the chances of getting small p-values at random is high and we should do a correction. But this would result in non-significant p-values. He concludes that it would be more "appropriate to make prior assumptions about slope, intercept, and effect size, then judge the results according to these prior assumptions".
Does this means that I should not rely on the p-value obtained? Where can I find more information about this? Some colleagues and I are in the process of finishing a paper that uses applies a regression to annual NDVI data and right now, we are discussing if we should (or not) consider the p-values obtained. Thanks and sorry if this is a bit of topic Cheers Daniel [1] https://trac.osgeo.org/grass/ticket/2376#comment:3 On Mon, Oct 16, 2017 at 2:12 PM Daniel Victoria <[email protected]> wrote: > Replying to self and in case helps anyone. > > Solved it by using R and the raster package. Here is a Stackoverflow post > about it > > > https://stackoverflow.com/questions/20262999/how-to-output-regression-summarye-g-p-value-and-coeff-into-a-rasterbrick > > Cheers > Daniel > > On Wed, Oct 11, 2017 at 10:44 AM Daniel Victoria < > [email protected]> wrote: > >> OK, dumb question since I'm a bit (or very) bad at stats. >> >> I'm calculating the slope from a series of rasters using r.series. I see >> that I can also get the t-value and the coefficient of determination. Is >> there a way to get the p-value for the regression? >> >> I've seen that this question has been asked before (in 2012) [1] and it >> ended with the addition of the t-value calculation in r.series. But I >> failed to see how the p-value can be obtained. >> >> I also found this ticket [2], related to the p-value question. >> >> Thanks >> Daniel >> >> [1] - >> http://osgeo-org.1560.x6.nabble.com/Calculate-p-value-for-regression-slope-in-r-series-td5014228.html >> >> [2] https://trac.osgeo.org/grass/ticket/2376 >> >>
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