3181 search results for "gis"

R / Finance 2015 Open for Registration

March 31, 2015
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The annoucement below just went to the R-SIG-Finance list. More information is as usual at the R / Finance page. Registration for R/Finance 2015 is now open! The conference will take place on May 29 and 30, at UIC in Chicago. Building on the success of the previous conferences in 2009-2014, we expect more than 250 attendees from around...

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Supervised Classification, beyond the logistic

March 5, 2015
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Supervised Classification, beyond the logistic

In our data-science class, after discussing limitations of the logistic regression, e.g. the fact that the decision boundary line was a straight line, we’ve mentioned possible natural extensions. Let us consider our (now) standard dataset clr1 <- c(rgb(1,0,0,1),rgb(0,0,1,1)) clr2 <- c(rgb(1,0,0,.2),rgb(0,0,1,.2)) x <- c(.4,.55,.65,.9,.1,.35,.5,.15,.2,.85) y <- c(.85,.95,.8,.87,.5,.55,.5,.2,.1,.3) z <- c(1,1,1,1,1,0,0,1,0,0) df <- data.frame(x,y,z) plot(x,y,pch=19,cex=2,col=clr1) One can consider a quadratic...

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Supervised Classification, Logistic and Multinomial

March 2, 2015
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Supervised Classification, Logistic and Multinomial

We will start, in our Data Science course,  to discuss classification techniques (in the context of supervised models). Consider the following case, with 10 points, and two classes (red and blue) > clr1 <- c(rgb(1,0,0,1),rgb(0,0,1,1)) > clr2 <- c(rgb(1,0,0,.2),rgb(0,0,1,.2)) > x <- c(.4,.55,.65,.9,.1,.35,.5,.15,.2,.85) > y <- c(.85,.95,.8,.87,.5,.55,.5,.2,.1,.3) > z <- c(1,1,1,1,1,0,0,1,0,0) > df <- data.frame(x,y,z) > plot(x,y,pch=19,cex=2,col=clr1) To get...

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More 3D Graphics (rgl) for Classification with Local Logistic Regression and Kernel Density Estimates (from The Elements of Statistical Learning)

February 7, 2015
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More 3D Graphics (rgl) for Classification with Local Logistic Regression and Kernel Density Estimates (from The Elements of Statistical Learning)

This post builds on a previous post, but can be read and understood independently. As part of my course on statistical learning, we created 3D graphics to foster a more intuitive understanding of the various methods that are used to relax the assumption of linearity (in the predictors) in regression and classification methods. The authors

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R in Insurance 2015: Registration Opened

February 3, 2015
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R in Insurance 2015: Registration Opened

The registration for the third conference on R in Insurance on Monday 29 June 2015 at the University of Amsterdam has opened. This one-day conference will focus again on applications in insurance and actuarial science that use R, the lingua franca for ...

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Some 3D Graphics (rgl) for Classification with Splines and Logistic Regression (from The Elements of Statistical Learning)

February 1, 2015
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Some 3D Graphics (rgl) for Classification with Splines and Logistic Regression (from The Elements of Statistical Learning)

This semester I'm teaching from Hastie, Tibshirani, and Friedman's book, The Elements of Statistical Learning, 2nd Edition. The authors provide a Mixture Simulation data set that has two continuous predictors and a binary outcome. This data is used to demonstrate classification procedures by plotting classification boundaries in the two predictors. For example, the figure below

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Turning R into a GIS – Mapping the weather in Germany

January 29, 2015
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Turning R into a GIS – Mapping the weather in Germany

Nothing has gotten more attention in the visualization world like the map-based insights, or in other words, just plotting on a map different KPIs to allow for a playful discovery experience. I must admit,...

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Register now for RStudio Shiny Workshops in D.C., New York, Boston, L.A., San Francisco and Seattle

January 28, 2015
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Register now for RStudio Shiny Workshops in D.C., New York, Boston, L.A., San Francisco and Seattle

Great news for Shiny and R Markdown enthusiasts! An Interactive Reporting Workshop with Shiny and R Markdown is coming to a city near you. Act fast as only 20 seats are available for each workshop. You can find out more / register by clicking on the link for your city! East Coast West Coast March

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NASA GISS’s Annual Global Temperature Anomaly Trends (dplyr/ggplot version)

January 18, 2015
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NASA GISS’s Annual Global Temperature Anomaly Trends (dplyr/ggplot version)

D Kelly O’Day did a great post on charting NASA’s Goddard Institute for Space Studies (GISS) temperature anomaly data, but it sticks with base R for data munging & plotting. While there’s absolutely nothing wrong with base R operations, I thought a modern take on the chart using dplyr, magrittr & tidyr for data manipulation

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SAS PROC MCMC example in R: Logistic Regression Random-Effects Model

January 18, 2015
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In this post I will run SAS example Logistic Regression Random-Effects Model in four R based solutions; Jags, STAN, MCMCpack and LaplacesDemon. To quote the SAS manual: 'The data are taken from Crowder (1978). The Seeds data set is a 2 x 2 fa...

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