3273 search results for "Map"

Introduction to parallel computing in R

January 31, 2014
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For R beginners, for loop is an elementary flow-control device that simplifies repeatedly calling functions with different parameters. A possible block of code is like this: run <- function(i) { return((i+1)/(i^2+1)) } for(i in 1:100) { run(i) } In this code, we first define a function that calculates something, and then run the function from i = 1 to i = 100....

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Introduction to parallel computing in R

January 31, 2014
By

For R beginners, for loop is an elementary flow-control device that simplifies repeatedly calling functions with different parameters. A possible block of code is like this: run <- function(i) { return((i+1)/(i^2+1)) } for(i in 1:100) { run(i) } In this code, we first define a function that calculates something, and then run the function from i = 1 to i = 100....

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ShareLaTeX now supports knitr

January 31, 2014
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ShareLaTeX (click here to register a free account) is a wonderful and reliable on-line editor for writing and compiling LaTeX documents “in the cloud” as well as working together in real-time (imagine Google Docs supporting LaTeX => you get ShareLaTeX).…Read more ›

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Introducing the ecoengine package

January 30, 2014
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Introducing the ecoengine package

Natural history museums have long been valuable repositories of data on species diversity. These data have been critical for fostering and shaping the development of fields such as biogeography and systematics. The importance of these data repositories is becoming increasingly important, especially in the context of climate change, where a strong understanding of how species responded to past...

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R for spatial analysis tutorial + video

January 30, 2014
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On 24th January 2014 I ran a one day practical course on an "Introduction to Spatial Data Visualisation in R" at the University of Leeds, with the help of demonstrators Rachel Oldroyd and Alistair Leak, who came up from London for the event. The course is designed for people completely new to R, who are especially interested in its spatial...

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GMT topography colours (II)

January 30, 2014
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GMT topography colours (II)

This follows an item about GMT colours. In the meantime I have found a website illustrating the colours, and also the definition files for those palettes. The palette in question is named GMT_relief, and it is defined in a file that is as follows. # $Id: GMT_relief.cpt,v 1.1 2001/09/23 23:11:20 pwessel Exp $ # # Colortable for whole earth...

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R for spatial analysis tutorial + video

January 30, 2014
By

On 24th January 2014 I ran a one day practical course on an "Introduction to Spatial Data Visualisation in R" at the University of Leeds, with the help of demonstrators Rachel Oldroyd and Alistair Leak, who came up from London for the event. The course is designed for people completely new to R, who are especially interested in its spatial...

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Quantitative Finance Applications in R – 3: Plotting xts Time Series

January 28, 2014
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Quantitative Finance Applications in R – 3: Plotting xts Time Series

by Daniel Hanson, QA Data Scientist, Revolution Analytics Introduction and Data Setup Last time, we included a couple of examples of plotting a single xts time series using the plot(.) function (ie, said function included in the xts package). Today, we’ll look at some quick and easy methods for plotting overlays of multiple xts time series in a single...

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Expected overestimation of Cohen’s d under publication bias

January 27, 2014
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Expected overestimation of Cohen’s d under publication bias

Earlier this week I read this article about “Why Publishing Everything Is More Effective than Selective Publishing of Statistically Significant Results” by Mercal et al (2014). The authors simulated different meta-analytic scenarios and came to the conclusion that publishing everything is more effective for the scientific collective. This got me thinking about...

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Expected overestimation of Cohen’s d under publication bias

January 27, 2014
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Expected overestimation of Cohen’s d under publication bias

In this post I will use the theoretical and empirical sampling distribution of Cohen's d to show the expected overestimation due to selective publishing. I will look at the overestimation for various sample sizes when the population effect is 0, 0.2, 0.5 and 0.8. The conclusion is that you should be weary of effect sizes from small samples, and...

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