Hi Akos,
Thank you so much for this suggestion! Indeed, I have 25 data points in each
column, and yes, the data are normally distributed. Using the pnorm function is
actually quite useful, as well. To that end, could the values of exceedance
probability from pnorm be somehow plotted against their associated
precipitation thresholds on an xy plot, for example?
Another idea that came to mind is the use of Probability Density Functions, but
can these really be used to graphically show exceedance probabilities?
Thanks, again!
-----Original Message-----
From: Bede-Fazekas Ákos <[email protected]>
To: [email protected]
Sent: Wed, Feb 15, 2023 1:59 am
Subject: Re: [R-sig-Geo] Plotting probability exceedance
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!
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>
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