# 4120 search results for "Gis"

## R: An introduction for psychologists

January 13, 2011
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Here are the slides from the Introduction to R session Danny Kaye and I ran at the BPS Mathematics, Statistics & Computing section CPS Workshop (13 December 2010, Nottingham Trent University).

## Big Data Logistic Regression with R and ODBC

December 7, 2010
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Recently I've been doing a lot of work with predictive models using logistic regression.  Logistic regression is great for determing probable outcomes of a independent binary target variable.  R is a great tool for accomplishing this task.&nb...

## Using the "Divide by 4 Rule" to Interpret Logistic Regression Coefficients

December 6, 2010
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I was recently reading a bit about logistic regression in a book on hierarchical/multilevel modeling when I first learned about the "divide by 4 rule" for quickly interpreting coefficients in a logistic regression model in terms of the predicted probabilities of the outcome. The idea is pretty simple. The logistic curve (predicted probabilities) is steepest at the center where...

## Example 8.17: Logistic regression via MCMC

December 6, 2010
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In examples 8.15 and 8.16 we considered Firth logistic regression and exact logistic regression as ways around the problem of separation, often encountered in logistic regression. (Re-cap: Separation happens when all the observations in a category sha...

## Example 8.16: Exact logistic regression

November 30, 2010
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In example 8.15, on Firth logistic regression, we mentioned alternative approaches to separation troubles. Here we demonstrate exact logistic regression. The code for this appears in the book (section 4.1.2) but we don't show an example of it there. ...

## Learn Logistic Regression (and beyond)

November 23, 2010
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One of the current best tools in the machine learning toolbox is the 1930s statistical technique called logistic regression. We explain how to add professional quality logistic regression to your analytic repertoire and describe a bit beyond that. A statistical analyst working on data tends to deliberately start simple move cautiously to more complicated methods. Related posts:

## Example 8.15: Firth logistic regression

November 22, 2010
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In logistic regression, when the outcome has low (or high) prevalence, or when there are several interacted categorical predictors, it can happen that for some combination of the predictors, all the observations have the same event status. A similar e...

## Logistic regression – simulation for a power calculation…

November 18, 2010
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Please note - I’ve spotted a problem with the approach taken in this post – it seems to underestimate power in certain circumstances. I’ll post again with a correction or a more full explanation when I’ve sorted it. So, I posted an answer on cross validation regarding logistic regression.   I thought I’d post it

## Parametric Bootstrap Power Analysis of GISS Temp Data

October 24, 2010
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Previosly, I calculated a bunch of ad-hoc power curves from GISTEMP data. Power is essentially a reframing of the p-value, to see the significance of the trend lines in the global temps. However, power calculations are inherently very noisy, hence, my ad-hoc way of aggregating the data. Another method is to bootstrap through the responses

## Giss Nightlights Replicated

October 18, 2010
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UPDATE: holy open source to the rescue. I posted a question yesterday on a idea peter had. Transaprency for overlaying light maps onto google maps. reminds me of the old Quake days. Well, I know John Carmack, John is an old friend of mine, but I’m no John Carmack. Neither am I Dr Paul Murrell.