Daily Archives: 30/09/2014

M1-statistique

PP plot, QQ plot et estimation à noyau

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Reprenons ici le code pour récupérer la base de la taille (et du poids) d’un ensemble de 200 personnes.

Davis=read.table(
"http://socserv.socsci.mcmaster.ca/jfox/Books/Applied-Regression-2E/datasets/Davis.txt")
Davis[12,c(2,3)]=Davis[12,c(3,2)]
X=Davis$height
n=length(X)

La première chose que l’on avait vu la semaine passée est le PP-plot,

https://latex.codecogs.com/gif.latex?\left\{\frac{i}{n},F_\star(X_{i:n})\right\}On va ici ‘tester’ une loi normale (de même moyenne et de même variance que notre échantillon… faisons simple)

PP=data.frame(empirique=(1:n)/n,
theorique=pnorm(sort(X),mean(X),sd(X)))

Continue reading PP plot, QQ plot et estimation à noyau →

Big Data, Big Data, CAS in R, Catastrophes, Computer, Graphics, Maps

Generating Hurricanes with a Markov Spatial Process

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The National Hurricane Center (NHC) collects datasets with all  storms in North Atlantic, the North Atlantic Hurricane Database (HURDAT). For all sorms, we have the location of the storm, every six jours (at midnight, six a.m., noon and six p.m.). Note that we have also the date, the maximal wind speed – on a 6 hour window – and the pressure in the eye of the storm.

It is possible to run the following function

library(XML)
extract.track=function(year=2012,p=TRUE){

Continue reading Generating Hurricanes with a Markov Spatial Process →