similar to: Fitting Theoretical Distributions to Daily Rainfall Data

I have a time series of rainfall in a dataframe. I would like to be able to aggregate this using a sliding window approach- i.e. a new 24 hourly total is calculated for each hours rainfall. I'm struggling to understand how this might be achieved - currently I've tried looping a sum function to re-calculate a new total at every stage of the loop. for (inp[[9]] in

Hello, I have some daily rainfall data from rain gages. E.g.: date p1 p2 p3 20/04/2002 10.2 8.6 6.3 21/04/2002 0.4 1.6 1.4 22/04/2002 0.2 0.0 0.4 23/04/2002 5.2 1.0 0.2 I'd like to plot them in a column plot by day, to be able to compare them. I made an example in exell, that I attached, but can't find out how to do it in R. The data vector is a POSIXct. Thanks, Wouter

I am a student doing my MSc Research Methods, i am working on my thesis research on analysing and modelling of crop failure risks due to drought in selected districts in Malawi. The analysis and modelling will focus on two crop stages of development: just after planting and flowering stages. I have rainfall, temperature, Relative humidity, wind speed, coordinate location data for the past 30 years

Hi, I'm a newbie in using R and I would like to have a few clues as to how I could compute and plot a distribution of daily rainfall intensity in different categories. I have daily values (mm/day) for several years and I need to show the frequency of 0-1, 1-2.5, 2.5-5, 5-10, 10-20, 20+ mm/day. Can this be done easily? Thanks, Etienne

Hi @all, I am biting my nails with the following problem: I have a data set of daily rainfall measurements for the last 20 years. What I want to do is calculate the daily cumulative sum of rainfall but only for every year which means that the cumulative sum has to be reset each year. After the calculations I want to plot each year of cumulative rainfall as a separate line in one graph preferably

Hi, You might get more help from the R-sig-geo list, which is devoted to spatial topics. However. The *.asc file is an ArcGIS raster export format. You should use whatever the appropriate import commands are for your own gridded rainfall data. If you have a different format, you might or might not be able to import it directly with raster. ?raster will tell you more about the kinds of formats

Dear R users, I need to produce rainfall maps using R. I know that this is possible, I looked though the web, I found the example below reported (the author is Andrew Tredennick). I would ask you if this is the most performing way to make rainfall maps; if yes would someone be able to give me an example of how file.asc and pointfile.csv should be? If no would somebody please show me another way

Thank you Sarah and Mike for your explanations. My final objective is to produce maps (png image or any kind of extension I can import in LaTeX) where rainfall data are interpolated, using the Inverse Distance method or Kriging. My input file (pointfile.csv in the reported example) reports the station code, lat and long of the meteorological station and the rainfall value (which might be the

Fwiw the engine behind geom_raster needs explicit observation-per-row form for input (with no structural normalization), so conversion to points is perfectly proper here, albeit confusing in context. (It's closer to what graphics devices actually use ultimately, but the expansion is laid out very early in ggplot2 because there's no standard for intermediate forms.) Cheers, Mike On Wed,

Hi all, How does this look as a potential FFI for our theoretical Ruby++ ? I''m not sure how to do mixins, though. Some sort of special directive? Or just add it to inheritance chain? Anyway, this is just a rough prototype. No attempt at an actual implementation yet. Regards, Dan // foo.h - Function prototypes #define _FOO_H_ using namespace ruby; // Subclass Object class Foo :

Dear R helpers   I am working on the credit risk default data and am referring to "An introduction to Credit Risk Modelling" by Christian Bluhm. The literature affirms that 'the default frequencies grow exponentially with decreasing credit worthiness'.   I have a data of rating wise default frequencies. The idea is to fit exponentail of the type Y = a * exp( b*X ) where Y is

vhost_zerocopy_callback accesses VQ right after it drops the last ubuf reference. In theory, this could race with device removal which waits on the ubuf kref, and crash on use after free. Do all accesses within rcu read side critical section, and all synchronize on release. Since callbacks are always invoked from bh, synchronize_rcu_bh seems enough and will help release complete a bit faster.

Dear Experts, Can anybody please direct me to proper MDCT theory reference which will help me understand the code better? I'm trying to optimize MDCT for a embedded platform. So need good understanding about theory behind & implementation. Thanks in advance for your time. Warm Regards, Mahantesh -------------- next part -------------- An HTML attachment was scrubbed... URL:

http://en.wikipedia.org/wiki/Modified_discrete_cosine_transform is an OK place to start. On Mon, Jul 8, 2013 at 7:33 AM, Mahantesh Belakhindi <girishec28 at gmail.com>wrote: > Dear Experts, > > Can anybody please direct me to proper MDCT theory reference which will > help me understand the code better? > > I'm trying to optimize MDCT for a embedded platform. So need

Dear all I am a PhD student of Computer Scince at Simon Fraser University ( http://www.cs.sfu.ca) interested in applying to GSoC. My PhD is focused on theoretical computer science, but since Sep. 2008 I have started working on Software projects again. Currently I am working in COSTAR lab on a high performance regular expression engine based on Parallel bit streams technology. A considerable part

On Tue, Jul 30, 2019 at 11:51:40AM +0100, Richard W.M. Jones wrote: > Previously discussed here: > https://www.redhat.com/archives/libguestfs/2019-July/msg00213.html > > It turns out that deferring callbacks is a PITA. (It would be a bit > easier if C has closures.) However by rewriting the example we can > avoid the need to use the cookie at all and make it run a bit more

I had an academic question for the list. What is the theoretical limit of the number of simultaneous sources in Icecast? What limits the number of sources that can run? Is it purely a function of the resources on the server or is there any inherit limitations? And given enough resources would extreme number of sources like 10,000 - 100,000 be expected to be supported? Cheers

I feel like a dope for asking this, but after reading various articles I'm still not quite sure I get it. What is the relationship or similarity between a "probability" prediction from a classfication random forest and a Bayesian posterior probability? Can they be considered similar, or would I need to take that probability and project it through a likelihood (I'm thinking,

Hello, I have a doubt about the plotting position of the theoretical quantile for the qqnorm command in R. Let F be the theoretical distribution of Y, we observed a sample of size n, y1,y2, ..., yn. We then sort it and comspare these empirical quantiles to the expected ones from F. For the plotting poition, there are several options: 1. i/(n+1) 2. (i-.375)/(n+.25) 3. (i- .3175)/ (n + .365) etc.

Hi Folks, This is not (well, not yet) an R question as such, though it is preparatory to embarking on an R-based study. Scenario: X has a lognormal distribution. Jointly distributed with X is a "tag" Y: Y = 0 or 1, with Prob(Y=1|X) = exp(L)/(1+exp(L)), L = a + b*X I am interested in the distribution of X conditional on Y, which is proportional to f(x,Y) =