1853 search results for "ggplot2"

R code for Chapter 2 of Non-Life Insurance Pricing with GLM

March 13, 2012
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R code for Chapter 2 of Non-Life Insurance Pricing with GLM

We continue working our way through the examples, case studies, and exercises of what is affectionately known here as “the two bears book” (Swedish björn = bear) and more formally as Non-Life Insurance Pricing with Generalized Linear Models by Esbjörn Ohlsson and Börn Johansson (Amazon UK | US

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R code for Chapter 2 of Non-Life Insurance Pricing with GLM

March 13, 2012
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R code for Chapter 2 of Non-Life Insurance Pricing with GLM

We continue working our way through the examples, case studies, and exercises of what is affectionately known here as “the two bears book” (Swedish björn = bear) and more formally as Non-Life Insurance Pricing with Generalized Linear Models by Esbjörn Ohlsson and Börn Johansson (Amazon UK | US). At...

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In case you missed it: February Roundup

March 13, 2012
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In case you missed them, here are some articles from February of particular interest to R users. February 29 marked the 12th anniversary of the release of R 1.0.0, and the release of R 2.14.2. A list of commercial vendors who have integrated R with their products for data, analysis, and presentation. The rmr package (part of the RHadoop...

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R index between two products is somewhat dependent on other products

March 12, 2012
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R index between two products is somewhat dependent on other products

I explained earlier how R-index is used in sensory is used to examine ranking data. The legitimization to use R-index is in the link with d' and with Mann-Whitney statistic. In this post I show there is a dependence on the number of products and p...

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Compiling government positions from the Manifesto Project data with R

March 12, 2012
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Compiling government positions from the Manifesto Project data with R

The Manifesto Project (former Manifesto Research Group, Comparative Manifestos Project) has assembled a database of ‘quantitative content analyses of parties’ election programs from more than 50 countries covering all free, democratic elections since 1945′ and is freely accessible online. The … Continue reading →

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The R-Podcast Episode 3: Basic Interaction with R

March 11, 2012
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In this episode: New versions of R and ggplot2 available, listener feedback, and an interactive session with R. The R code discussed in this episode will be available in our GitHub repository, see the show notes for details. There will be a companion screencast to accompany this episode which will be posted shortly. As always,

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Recovering Marginal Effects and Standard Errors of Interactions Terms Pt. II: Implement and Visualize

March 9, 2012
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Recovering Marginal Effects and Standard Errors of Interactions Terms Pt. II: Implement and Visualize

In the last post I presented a function for recovering marginal effects of interaction terms. Here we implement the function with simulated data and plot the results using ggplot2.       #---Simulate Data and Fit a linear model with an...

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Recovering Marginal Effects and Standard Errors of Interactions Terms Pt. II: Implement and Visualize

March 9, 2012
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Recovering Marginal Effects and Standard Errors of Interactions Terms Pt. II: Implement and Visualize

In the last post I presented a function for recovering marginal effects of interaction terms. Here we implement the function with simulated data and plot the results using ggplot2.       #---Simulate Data and Fit a linear model with an...

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Recovering Marginal Effects and Standard Errors of Interactions Terms Pt. II: Implement and Visualize

March 9, 2012
By
Recovering Marginal Effects and Standard Errors of Interactions Terms Pt. II: Implement and Visualize

In the last post I presented a function for recovering marginal effects of interaction terms. Here we implement the function with simulated data and plot the results using ggplot2.       #---Simulate Data and Fit a linear model with an...

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Mining Twitter for consumer attitudes towards hotels

March 9, 2012
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Mining Twitter for consumer attitudes towards hotels

Couple of months back I read Jeffrey Breen’s presentation on mining Twitter for consumer attitudes towards airlines, so I was just curious how it would look if I estimate the sentiment toward major hotels. So here it is: # load twitter library > library(twitteR) # search for all the hilton tweets > hilton.tweets=searchTwitter('@hilton',n=1500) > length(hilton.tweets)

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