3826 search results for "gis"

Flotsam 12: early June linkathon

June 1, 2013
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A list of interesting R/Stats quickies to keep the mind distracted: A long draft Advanced Data Analysis from an Elementary Point of View by Cosma Shalizi, in which he uses R to drive home the message. Not your average elementary point of view. Good notes by Frank Davenport on starting using R with data from

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Visualizing a One-Way ANOVA using D3.js

May 31, 2013
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A while ago I was playing around with the JavaScript package D3.js, and I began with this visualization—that I never really finished—of how a one-way ANOVA is calculated. I wanted to make the visualization interactive, and I did integrate some interactive elements. For instance, if you hover over a data point it will show the residual, and its value will be highlighted in...

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Scenario analysis for option strategies.

May 30, 2013
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Scenario analysis for option strategies.

Introduction to the project I am working on at the moment. It is more a playground for option strategies than "project" at this stage. The idea is to develop accurate scenario analysis on portfolio, which is based on a single underlying. In the past I ...

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Parallel Processing: When does it worth?

May 29, 2013
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Parallel Processing: When does it worth?

Most computers nowadays have few cores that incredibly help us with our daily computing duties. However, when statistical softwares do use parallelization for analyzing data faster? R, my preferred analytical package, does not take too much advantage of multicore processing by default. In fact, R has been inherently a “single-processor” package until nowadays. Stata, another

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Parallel Processing: When does it worth?

May 28, 2013
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Parallel Processing: When does it worth?

Most computers nowadays have few cores that incredibly help us with our daily computing duties. However, when statistical softwares do use parallelization for analyzing data faster? R, my preferred analytical package, does not take too much advantage of multicore processing by default. In fact, R has been inherently a “single-processor” package until nowadays. Stata, another decent...

Read more »

Parallel Processing: When does it worth?

May 28, 2013
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Parallel Processing: When does it worth?

Parallel computing is incredibly useful, but not every thing worths distribute across as many cores as possible. Most computers nowadays have few cores that incredibly help us with our daily computing duties. However, when statistical softwares do use parallelization for analyzing data faster? R, my preferred analytical package, does not take too much advantage of multicore processing...

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Intended or Unintended Consequences

May 28, 2013
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Intended or Unintended Consequences

(This article was first published on Timely Portfolio, and kindly contributed to R-bloggers) A quick glimpse at the US 10y Treasury Bond rate since 2000 seems benign with low volatility and a general downward trend. require(latticeExtra)require(quantmod)US10y <- getSymbols("^TNX", from = "2000-01-01", auto.assign = FALSE)asTheEconomist(xyplot(US10y, scales = list(y = list(rot = 1)), main = "US 10y Yield Since 2000"))...

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Using R to communicate via a socket connection

May 28, 2013
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Occasionally, the need arises to communicate with R via another process. There are packages available to facilitate this communication, but for simple problems, a socket connection may be the answer. Nearly all software languages have a socket communic...

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Writing a Minimal Working Example (MWE) in R

May 27, 2013
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Writing a Minimal Working Example (MWE) in R

How to Ask for Help using R How to Ask for Help using R The key to getting good help with an R problem is to provide a minimally working reproducible example (MWRE). Making an MWRE is really easy with R, and it will help ensure that...

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Bayesian model II regression

May 27, 2013
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Bayesian model II regression

Regression is a mainstay of ecological and evolutionary data analysis. For example, a disease ecologist may use body size (e.g. a weight from a scale with measurement error) to predict infection. Classical linear regression assumes no error in covariates; they are known exactly. This is rarely the case in ecology, and ignoring error in covariates can bias regression coefficient...

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