2765 search results for "gis"

Predict User’s Return Visit within a day part-2

October 22, 2012
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Predict User’s Return Visit within a day part-2

Welcome to the second part of the series on predicting user’s revisit to the website. In my earlier blog Logistic Regression with R, I discussed what is logistic regression. In the first part of the series, we applied logistic regression to available data set. The problem statement there was whether a user will return in

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Predict User’s Return Visit within a day part-1

October 22, 2012
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Predict User’s Return Visit within a day part-1

In my earlier blog, I have discussed about what is logistic regression? And how logistic model is generated in R? Now we will apply that learning on a specific problem of prediction. In this post, I will create a basic model to predict whether a user will return on website in next 24 hours. This

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Carl Morris Symposium on Large-Scale Data Inference (2/3)

October 20, 2012
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Carl Morris Symposium on Large-Scale Data Inference (2/3)

Continuing the summary of last week’s symposium on statistics and data visualization (see part 1 and part 3)… Here I describe Dianne Cook’s discussion of visual inference, and Rob Kass’ talk on statistics in cognitive neuroscience. [Edit: I've added a few … Continue reading →

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Introduction to Bayesian lecture: Accompanying handouts and demos

October 19, 2012
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Introduction to Bayesian lecture: Accompanying handouts and demos

I recently posted the slides from a guest lecture that I gave on Bayesian methods for biologists/ecologist. In an effort to promote active learning, the class was not a straight forward lecture, but rather a combination of informational input from me and opportunities for students to engage with the concepts via activities and discussion of

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Stella Copeland’s Intro to Mixed Models in R

October 19, 2012
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In D-RUG today, Stella Copeland gave a quick introduction to mixed models in R. Here’s the script that she presented: Get the data file for this script here Stella also recommends this paper by Ben Bolker as a quick introduction to the topic.

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Up and Coming R User Group meetings

October 19, 2012
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Mango Solutions are pleased to announce the forthcoming R user Group meetings that they will be hosting or participating in.  To attend, please see the registration information on the relevant websites:   1.       GreatBoston useR Group      (http://www.meetup.com/Boston-useR/ ) Date:                     Tuesday 23rd October Venue:                 IBM Cambridge, 1 Rogers Street, Cambridge, MA Time:                     6.15pm Presentation:    Creating and Designing Applications...

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Adding a background to your ggplot

October 19, 2012
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Adding a background to your ggplot

I really enjoy using the DW-NOMINATE data for examples, as I do here. Sometimes it’s useful to indicate regions in the background of a plot — perhaps two-dimensional regions of interest, perhaps one-dimensional periods in time. It’s...

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The rapidly increasing ideology of the US Republican Party

October 18, 2012
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The rapidly increasing ideology of the US Republican Party

The chart below comes by way of the is.R blog and shows the average ideology of the members of the United State House of Representatives within the Republican (red) and Democratic (blue) parties. (Other parties are shown in green.) The chart is shown as a time series, from the first US congress in 1789, to the most recent full...

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Soccer is all about money (?) – Part 2: Simple analyses

October 18, 2012
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Soccer is all about money (?) – Part 2: Simple analyses

Alright, now we have all the data we need in one dataframe. To make this code work, I assume you ran the code from Part 1. We need the dataframe big.tab.All the data presented here is based on the data from 18/10/2012. You can run an analysis with the actual data or I can do it at...

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Introduction to Bayesian Methods guest lecture

October 18, 2012
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Introduction to Bayesian Methods guest lecture

This is a talk I gave this week in Advanced Biostatistics at McGill. The goal was to provide an gentle introduction to Bayesian methodology and to demonstrate how it is used for inference and prediction. There is a link to an accompanying R script in the slides

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