# Monthly Archives: October 2013

## That’s Smooth

October 10, 2013
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I had someone ask me the other day how to take a scatterplot and draw something other than a straight line through the graph using Excel.  Yes, it can be done in Excel and it’s really quite simple, but there are some limitations when using the stock Excel dialog screens. So it is probably in

## RegEx: Named Capture in R (Round 2)

October 9, 2013
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Previously, I came up with a solution to R's less than ideal handling of named capture in regular expressions with my re.capture() function. A little more than a year later, the problem is rearing its ugly - albeit subtly different - head again. I now...

## Please, never use my codes without checking twice (at least)!

October 9, 2013
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I wanted to get back on some interesting experience, following a discussion I had with Carlos after my class, this morning. Let me simplify the problem, and change also the dataset. Consider the following dataset __ db = read.table("http://freakonometrics.free.fr/db2.txt",header=TRUE,sep=";") Let me change also one little thing (in the course, we use the age of people as explanatory variables, so...

## Classification using neural net in r

October 9, 2013
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This is mostly for my students and myself for future reference.Classification is a supervised task , where we need preclassified data and then on new data , I can predict.Generally we holdout a % from the data available for testing and we call them tra...

## More Polarized

October 9, 2013
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I have immersed myself in polar coordinates.  Here is another little reconstruction that I did.

## Tomorrow: Webinar on Time-to-Event Models

October 9, 2013
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We're thrilled to have John Wallace and Tess Nesbitt from DataSong join our Fall webinar series tomorrow, with a great presentation on time to event models. If you're trying to predict when an event will occur (for example, a consumer buying a product) or trying to infer why events occur (what were the factors that led to a component...

## Fast Bayesian Inference with INLA

October 9, 2013
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I am currently a research fellow and 4th year PhD candidate within the INLA group.  If you deal with Bayesian models and have never heard about INLA, I sincerely think you should spend a small portion of your time to at least know what it is. If you have heard about it before, you know how nice

## Some heuristics about spline smoothing

October 8, 2013
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$\mathbb{E}(Y\vert X=x)=h(x)$

Let us continue our discussion on smoothing techniques in regression. Assume that . where is some unkown function, but assumed to be sufficently smooth. For instance, assume that  is continuous, that exists, and is continuous, that  exists and is also continuous, etc. If  is smooth enough, Taylor’s expansion can be used. Hence, for which can also be writen as for...

## R in Raw

October 8, 2013
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This nice little tool Raw from DensityDesign transforms text from your clipboard to d3.  For those yearning to access Raw from R, here is an easy way to do it. Use this function read.excel from StatisticallySignificant’s post Copying Data from Excel to R and Back.  Once you run the function, your data will be copied tab-delimited to the clipboard. ...

## Some heuristics about local regression and kernel smoothing

October 8, 2013
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$\mathbb{E}(Y\vert X=x)=\beta_0+\beta_1 x$

In a standard linear model, we assume that . Alternatives can be considered, when the linear assumption is too strong. Polynomial regression A natural extension might be to assume some polynomial function, Again, in the standard linear model approach (with a conditional normal distribution using the GLM terminology), parameters can be obtained using least squares, where a regression of...