479 search results for "boxplot"

Modeling gene expression with broom: a case study in tidy analysis

November 25, 2015
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Modeling gene expression with broom: a case study in tidy analysis

Previously in this series Cleaning and visualizing genomic data: a case study in tidy analysis In the last post, we examined an available genomic dataset from Brauer et al 2008 about yeast gene expression under nutrient starvation. We learned to tidy it with the dplyr and tidyr packages, and saw how useful this tidied form is for visualizing...

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How to Search for Census Data from R

November 16, 2015
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How to Search for Census Data from R

In my course Learn to Map Census Data in R I provide people with a handful of interesting demographics to analyze. This is convenient for teaching, but people often want to search for other demographic statistics. To address that, today I will work through an example of starting with a simple demographic question and using R The post

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Free Webinar: Learn to Map Unemployment Data in R

November 10, 2015
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Free Webinar: Learn to Map Unemployment Data in R

Last month I ran my first webinar (“Make a Census Explorer with Shiny”). About 100 people showed up, and feedback from the participants was great. I also had a lot of fun myself. Because of this, I’ve decided to do one more webinar before my free trial with the webinar service ends. Here are the The post

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Factor codings for models in R

Factor codings for models in R

I am holding an exercise on generalised models these days. Preparing a task on factor coding in generalised linear models, I realised that the help on the internet on that is not so easy to understand. At least what I found. So in order to help people ...

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The 5th Tribe, Support Vector Machines and caret

October 15, 2015
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The 5th Tribe, Support Vector Machines and caret

by Joseph Rickert In his new book, The Master Algorithm, Pedro Domingos takes on the heroic task of explaining machine learning to a wide audience and classifies machine learning practitioners into 5 tribes*, each with its own fundamental approach to learning problems. To the 5th tribe, the analogizers, Pedro ascribes the Support Vector Machine (SVM) as it's master algorithm....

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Hypothesis-Driven Development Part V: Stop-Loss, Deflating Sharpes, and Out-of-Sample

September 24, 2015
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Hypothesis-Driven Development Part V: Stop-Loss, Deflating Sharpes, and Out-of-Sample

This post will demonstrate a stop-loss rule inspired by Andrew Lo’s paper “when do stop-loss rules stop losses”? Furthermore, it … Continue reading →

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Hypothesis-Driven Development Part V: Stop-Loss, Deflating Sharpes, and Out-of-Sample

September 24, 2015
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Hypothesis-Driven Development Part V: Stop-Loss, Deflating Sharpes, and Out-of-Sample

This post will demonstrate a stop-loss rule inspired by Andrew Lo’s paper “when do stop-loss rules stop losses”? Furthermore, it … Continue reading →

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Convergence and Asymptotic Results

September 24, 2015
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Convergence and Asymptotic Results

Last week, in our mathematical statistics course, we’ve seen the law of large numbers (that was proven in the probability course), claiming that given a collection  of i.i.d. random variables, with To visualize that convergence, we can use > m=100 > mean_samples=function(n=10){ + X=matrix(rnorm(n*m),nrow=m,ncol=n) + return(apply(X,1,mean)) + } > B=matrix(NA,100,20) > for(i in 1:20){ + B=mean_samples(i*10) + } > colnames(B)=as.character(seq(10,200,by=10)) > boxplot(B) It is...

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Fitting a neural network in R; neuralnet package

September 23, 2015
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Fitting a neural network in R; neuralnet package

Neural networks have always been one of the most fascinating machine learning model in my opinion, not only because of the fancy backpropagation algorithm, but also because of their complexity (think of deep learning with many hidden layers) and structure inspired by the brain. Neural networks have not always been popular, partly because they were,

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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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