2966 search results for "GIS"

Veterinary Epidemiologic Research: GLM (part 4) – Exact and Conditional Logistic Regressions

March 22, 2013
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Veterinary Epidemiologic Research: GLM (part 4) – Exact and Conditional Logistic Regressions

Next topic on logistic regression: the exact and the conditional logistic regressions. Exact logistic regression When the dataset is very small or severely unbalanced, maximum likelihood estimates of coefficients may be biased. An alternative is to use exact logistic regression, available in R with the elrm package. Its syntax is based on an events/trials formulation.

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Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)

March 19, 2013
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Veterinary Epidemiologic Research: GLM – Evaluating Logistic Regression Models (part 3)

Third part on logistic regression (first here, second here). Two steps in assessing the fit of the model: first is to determine if the model fits using summary measures of goodness of fit or by assessing the predictive ability of the model; second is to deterime if there’s any observations that do not fit the

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The evolution of EU legislation (graphed with ggplot2 and R)

March 19, 2013
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The evolution of EU legislation (graphed with ggplot2 and R)

During the last half century the European Union has adopted more than 100 000 pieces of legislation. In this presentation I look into the patterns of legislative adoption over time. I tried to create clear and engaging graphs that provide … Continue reading →

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Veterinary Epidemiologic Research: GLM – Logistic Regression (part 2)

March 17, 2013
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Veterinary Epidemiologic Research: GLM – Logistic Regression (part 2)

Second part on logistic regression (first one here). We used in the previous post a likelihood ratio test to compare a full and null model. The same can be done to compare a full and nested model to test the contribution of any subset of parameters: Interpretation of coefficients Note: Dohoo do not report the

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Veterinary Epidemiologic Research: GLM – Logistic Regression

March 14, 2013
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Veterinary Epidemiologic Research: GLM – Logistic Regression

We continue to explore the book Veterinary Epidemiologic Research and today we’ll have a look at generalized linear models (GLM), specifically the logistic regression (chapter 16). In veterinary epidemiology, often the outcome is dichotomous (yes/no), representing the presence or absence of disease or mortality. We code 1 for the presence of the outcome and 0

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Stop Sign Project Post1: Some GIS stuff done in R

February 26, 2013
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(This article was first published on bRogramming, and kindly contributed to R-bloggers) To leave a comment for the author, please follow the link and comment on his blog: bRogramming. R-bloggers.com offers daily e-mail updates about R news and tutorials on topics such as: visualization (ggplot2, Boxplots, maps, animation), programming (RStudio, Sweave, LaTeX, SQL, Eclipse, git, hadoop, Web Scraping) statistics...

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Registration for ‘R in Insurance’ conference has opened

February 19, 2013
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Registration for ‘R in Insurance’ conference has opened

The registration for the first conference on R in Insurance on Monday 15 July 2013 at Cass Business School in London has opened. The intended audience of the conference includes both academics and practitioners who are active or interested in the applications of R in insurance. The 2013 R in Insurance conference builds...

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Gist for previous posts

February 17, 2013
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The more I use it, the more I understand the benefits and value of Github as a code-sharing resource. The gist found here is the R code for my posts on run scoring trends by league (found here, here, and here).  I will continue to use Github for t...

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Evolution of a logistic regression

January 28, 2013
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Evolution of a logistic regression

In my last post I showed how one can easily summarize the outcome of a logistic regression. Here I want to show how this really depends on the data-points that are used to estimate the model. Taking a cue from the evolution of a correlation I have plotted the estimated Odds Ratios (ORs) depending on

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How much can we learn from an empirical result? A Bayesian approach to power analysis and the implications for pre-registration.

January 18, 2013
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How much can we learn from an empirical result? A Bayesian approach to power analysis and the implications for pre-registration.

Just like a lot of political science departments, here at Rice a group of faculty and students meet each week to discuss new research in political methodology. This week, we read a new symposium in Political Analysis about the pre-registration of studies in political science. To briefly summarize, several researchers argued that political scientists should

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