2328 search results for "time series"

Twelve Graphs & Dashboards You Should See On Climate Change, Science, & Public Opinion

April 28, 2015
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Twelve Graphs & Dashboards You Should See On Climate Change, Science, & Public Opinion

Plotly has teamed up with The White House on President Obama’s Climate Data Initiative to explore and explain climate trends. This post is our first contribution. You’ll see interactive graphs about: temperature and CO2 (4), climate change & environmental impact (4), attitudes about global warming (3), and a population graph. If you like this post, please share...

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Situational Baseball: Analyzing Runs Potential Statistics

April 28, 2015
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Situational Baseball: Analyzing Runs Potential Statistics

by Mark Malter After reading the book, Analyzing Baseball with R, by Max Marchi and Jim Albert, I decided to expand on some of their ideas relating to runs created and put them into an R shiny app . The Server and UI code are linked at the bottom of the Introduction tab. I downloaded the Retrosheet play-by-play data...

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Using R and lme/lmer to fit different two- and three-level longitudinal models

April 21, 2015
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Using R and lme/lmer to fit different two- and three-level longitudinal models

I often get asked how to fit different multilevel models (or individual growth models, hierarchical linear models or linear mixed-models, etc.) in R. In this guide I have compiled some of the more common and/or useful models (at least common in clinical psychology), and how to fit them using nlme::lme() and lme4::lmer(). I will cover the common two-level random...

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AusDM 2015 submission deadline extended to Thursday 30 April

April 20, 2015
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AusDM 2015 submission deadline extended to Thursday 30 April

The 13th Australasian Data Mining Conference (AusDM 2015) Sydney, Australia, 8–9 August 2015 (co-located with SIGKDD’15) URL: http://ausdm15.ausdm.org/ The Australasian Data Mining Conference has established itself as the premier Australasian meeting for both practitioners and researchers in data mining. It … Continue reading →

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The Non-parametric Bootstrap as a Bayesian Model

April 17, 2015
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The Non-parametric Bootstrap as a Bayesian Model

The non-parametric bootstrap was my first love. I was lost in a muddy swamp of zs, ts and ps when I first saw her. Conceptually beautiful, simple to implement, easy to understand (I thought back then, at least). And when she whispered in my ear, “I make no assumptions regarding the underlying distribution”, I was in love. This love...

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Recommending Recommender Systems When Preferences Are Not Driven By Simple Features

April 15, 2015
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Recommending Recommender Systems When Preferences Are Not Driven By Simple Features

Why does lifting out a slice make the pizza appear more appealing?We can begin our discussion with the ultimate feature bundle - pizza toppings. Technically, a menu would only need to list all the toppings and allow the customers to build their own piz...

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Animated paths in R: toy example

April 14, 2015
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Animated paths in R: toy example

Recently I have shared examples of animations made using R which feature traversal of pathways, specifically grid cell borders and great circle arcs. Here I provide the basic code required to generate a path sequence from a path. The fundamental ideas are as follows: Pathways are represented by lines. Lines are represented by a series

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The Logical Invest Enhanced Bond Rotation Strategy (And the Importance of Dividends)

April 7, 2015
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The Logical Invest Enhanced Bond Rotation Strategy (And the Importance of Dividends)

This post will display my implementation of the Logical Invest Enhanced Bond Rotation strategy. This is a strategy that indeed … Continue reading →

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Reader Morghulis

April 7, 2015
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Reader Morghulis

TL;DR: Memento mori. After reading too much Seneca, I’m meditating on death like a statistician, by counting how many of GRRM’s readers did not even survive to see the HBO show (much less the end of the book series). Rough … Continue reading →

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White Matter Segmentation in R

April 7, 2015
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White Matter Segmentation in R

Goals and Overall Approach We will use multiple packages and pieces of software for white matter (and gray matter/cerebro spinal fluid (CSF)) segmentation. The overall approach will be, with the required packages in parentheses: N4 Inhomogeneity Bias-Field Correction (extrantsr and ANTsR) Brain extraction using BET and additional tools (extrantsr and fslr) FAST for tissue-class segmentation.

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