Hello, You should know or make assumptions on the distribution of the precipitation. Let's say it is normally distributed (i.e. bell-shaped). Then you can calculate the probability of exceeding the quantile /q/ by pnorm(q, mean, sd, lower.tail = FALSE). If you have several spatial points and a lot of measurments (stored in columns of the sf/data.frame) for each of the points, then use apply(X, MARGIN = 1, FUN = function(measurements) {return(pnorm(q, mean, sd, lower.tail = FALSE))}) and you can display the probabilities in a map.
HTH, Ákos __________ Ákos Bede-Fazekas Centre for Ecological Research, Hungary 2023.02.15. 1:28 keltezéssel, rain1290--- via R-sig-Geo írta: > Hi there, > I have climate data pertaining to extreme precipitation, as well as carbon > emissions associated with those precipitation values in a dataframe. > The goal of my analysis would be to determine the probability of exceeding > specific thresholds of precipitation extremes, as well as showing this > graphically (I am imagining this by placing extreme precipitation on the the > x-axis and exceedance probabilities on the y-axis). > My question is if anyone has an idea how to approach this, or a good starting > place? I have looked online, but there is nothing specific to really draw on. > Thank you for your time, and I look forward to your response! > [[alternative HTML version deleted]] > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-geo [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo