4102 search results for "Gis"

How to perform a Logistic Regression in R

September 13, 2015
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Logistic regression is a method for fitting a regression curve, y = f(x), when y is a categorical variable. The typical use of this model is predicting y given a set of predictors x. The predictors can be continuous, categorical or a mix of both. The categorical variable y, in general, can assume different values.

Logistic Regression in R – Part Two

September 2, 2015
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$Logistic Regression in R – Part Two$

My previous post covered the basics of logistic regression. We must now examine the model to understand how well it fits the data and generalizes to other observations. The evaluation process involves the assessment of three distinct areas – goodness of fit, tests of individual predictors, and validation of predicted values – in order to

Predicting creditability using logistic regression in R (part 1)

September 2, 2015
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As I said in the previous post, this summer I’ve been learning some of the most popular machine learning algorithms and trying to apply what I’ve learned to real world scenarios. The German Credit dataset provided by the UCI Machine Learning Repository is another great example of application.The German Credit dataset contains 1000 samples of applicants asking for...

Logistic Regression in R – Part One

September 1, 2015
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$Logistic Regression in R – Part One$

Please note that an earlier version of this post had to be retracted because it contained some content which was generated at work. I have since chosen to rewrite the document in a series of posts. Please recognize that this may take some time. Apologies for any inconvenience.   Logistic regression is used to analyze the

Searching Twitter with ArcGIS Pro Using R

August 18, 2015
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I committed to testing this a long time ago, however, a number of other projects intervened, so I have only just got around to writing up this short tutorial. One of the exciting things from the ESRI Developers Conference this year was the launch of the R-ArcGIS bridge. In simple terms, this enables you to run R...

Evaluating Logistic Regression Models

August 17, 2015
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Logistic regression is a technique that is well suited for examining the relationship between a categorical response variable and one or more categorical or continuous predictor variables. The model is generally presented in the following format, where β refers to the parameters and x represents the independent variables. log(odds)=β0+β1∗x1+...+βn∗xn The log(odds), or log-odds ratio, is defined

Bootstrap with logistic regression

August 1, 2015
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A quick example of bootstraping a logistic regression. Nothing special here, example could be extended to any other type of model that has a coef() method. library(boot) logit_test <- function(d,indices) { d <- d fit <- glm(your ~ formula, data = d, family = "binomial"

Empirical bias analysis of random effects predictions in linear and logistic mixed model regression

July 30, 2015
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In the first technical post in this series, I conducted a numerical investigation of the biasedness of random effect predictions in generalized linear mixed models (GLMM), such as the ones used in the Surgeon Scorecard, I decided to undertake two explorations: firstly, the behavior of these estimates as more and more data are gathered for each

July 25, 2015
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As I was putting together the coord_proj ggplot2 extension I had posted a (https://gist.github.com/hrbrmstr/363e33f74e2972c93ca7) that I shared on Twitter. Said gist received a comment (several, in fact) and a bunch of us were painfully reminded of the fact that there is no built-in way to receive notifications from said comment activity. @jennybryan posited that it

Logistic Growth, S Curves, Bifurcations, and Lyapunov Exponents in R

July 24, 2015
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If you’ve ever wondered how logistic population growth (the Verhulst model), S curves, the logistic map, bifurcation diagrams, sensitive dependence on initial conditions, “orbits”, deterministic chaos, and Lyapunov exponents are related to