Monthly Archives: May 2017

Feature Selection : Select Important Variables with Boruta Package

May 30, 2017
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Feature Selection : Select Important Variables with Boruta Package

This article explains how to select important variables using boruta package in R. Variable Selection is an important step in a predictive modeling project. It is also called 'Feature Selection'. Every private and public agency has started tracking data and collecting information of various attributes. It results to access to too many predictors for a predictive model. But not...

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Hospital Infection Scores – R Shiny App

May 30, 2017
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Hospital Infection Scores – R Shiny App

Medicare Data – R Shiny App About two weeks ago I created an extremely rough version of an R Shiny Application surrounding Medicare data. Right after publishing the blog post, I received a lot of input for improvement and help from others. Here’s a look a look at the latest version of the Medicare Shiny

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Summarizing big data in R

May 30, 2017
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Our next "R and big data tip" is: summarizing big data. We always say "if you are not looking at the data, you are not doing science"- and for big data you are very dependent on summaries (as you can’t actually look at everything). Simple question: is there an easy way to summarize big data … Continue reading Summarizing...

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plotly 4.7.0 now on CRAN

May 30, 2017
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plotly 4.7.0 now on CRAN

I’m super excited about plotly 4.7.0 – it includes numerous improvements and new features related to performance, mapping, and API requests. It also includes support for something folks have wanted for a very long time – fixed coordinates via ggplotly()! In other words, if you use coord_equal(), coord_fixed(), etc to specify a ratio between the

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New package polypoly (helper functions for orthogonal polynomials)

May 29, 2017
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New package polypoly (helper functions for orthogonal polynomials)

Last week, I released a new package called polypoly to CRAN. It wraps up some common tasks for dealing with orthogonal polynomials into a single package. The README shows off the main functionality, as well as the neat “logo” I made for the packag...

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Generating Dockerfiles for reproducible research with R

May 29, 2017
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This post is the draft of the vignette for a new R package by o2r team members Matthias and Daniel. Find the original file in the package repository on GitHub. 1. Introduction 2. Creating a Dockerfile 3. Including resources 4. Image metadata ...

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April New Package Picks

May 29, 2017
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April New Package Picks

Here are my picks for the “Top 40” new packages submitted to CRAN in April 2017. These selections, which were culled from 208 submissions, are organized into four categories: Data, Finance, Statistics and Utilities. The number of entries in the Data and Utilities categories reflect the initiatives of R developers to connect to external resources. Data comtradr v0.0.1: Provides functions to...

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Exploring LSTMs

May 29, 2017
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Exploring LSTMs

The first time I learned about LSTMs, my eyes glazed over. Not in a good, jelly donut kind of way. It turns out LSTMs are a fairly simple extension to neural networks, and they're behind a lot of the amazing achievements deep learning has made in the past few years. So I'll try to present them as intuitively as possible –...

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Mixed models for ANOVA designs with one observation per unit of observation and cell of the design

May 29, 2017
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Mixed models for ANOVA designs with one observation per unit of observation and cell of the design

Together with David Kellen I am currently working on an introductory chapter to mixed models for a book edited by Dan Spieler and Eric Schumacher (the current version can be found here). The goal is to provide a theoretical and practical introduction that is targeted mainly at experimental psychologists, neuroscientists, and others working with experimental

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Instrumental Variables in R exercises (Part-3)

May 29, 2017
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Instrumental Variables in R exercises (Part-3)

This is the third part of the series on Instrumental Variables. For other parts of the series follow the tag instrumental variables. In this exercise set we will use Generalized Method of Moments (GMM) estimation technique using the examples from part-1 and part-2. Recall that GMM estimation relies on the relevant moment conditions. For OLS Related exercise sets:Instrumental Variables...

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