# 3614 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...

## Supervised Classification, beyond the logistic

March 5, 2015
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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...

## Supervised Classification, Logistic and Multinomial

March 2, 2015
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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...

## 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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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

## R in Insurance 2015: Registration Opened

February 3, 2015
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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 ...

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

February 1, 2015
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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

## Turning R into a GIS – Mapping the weather in Germany

January 29, 2015
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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,...

## 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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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

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

January 18, 2015
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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

## 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...