3740 search results for "gis"

Introduction to Logistic Regression with R

October 6, 2015
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Introduction to Logistic Regression with R

In my previous blog I have explained about linear regression. In today’s post I will explain about logistic regression.         Consider a scenario where we need to predict a medical condition of a patient (HBP) ,HAVE HIGH BP or NO HIGH BP, based on some observed symptoms – Age, weight, Issmoking, Systolic...

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Predicting creditability using logistic regression in R: cross validating the classifier (part 2)

September 15, 2015
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Predicting creditability using logistic regression in R: cross validating the classifier (part 2)

Now that we fitted the classifier and run some preliminary tests, in order to get a grasp at how our model is doing when predicting creditability we need to run some cross validation methods.Cross validation is a model evaluation method that does not use conventional fitting measures (such as R^2 of linear regression) when trying to evaluate the model....

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How to perform a Logistic Regression in R

September 13, 2015
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How to perform a Logistic Regression in R

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.

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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

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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...

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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

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Searching Twitter with ArcGIS Pro Using R

August 18, 2015
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Searching Twitter with ArcGIS Pro Using R

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...

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Searching Twitter with ArcGIS Pro Using R

August 18, 2015
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Searching Twitter with ArcGIS Pro Using R

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...

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Evaluating Logistic Regression Models

August 17, 2015
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Evaluating Logistic Regression Models

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

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

July 30, 2015
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Empirical bias analysis of random effects predictions in linear and logistic mixed model regression

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

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