Blog Archives

R editor improvements for the next release of Bio7

April 20, 2016
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R editor improvements for the next release of Bio7

20.04.2016 For the upcoming release of Bio7 I worked hard to improve the R editor features. So I added some new features and improvements to assist in the creation of R scripts in Bio7. One of the highlights is the newly integrated dynamic code analysis when writing an R script. Here a short overview of

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Supervised Image Classification With Bio7, R And ImageJ

November 27, 2015
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Supervised Image Classification With Bio7, R And ImageJ

27.11.2015 I created a new Bio7 example which demonstrates how to classify an image with Bio7, ImageJ and R. For the classification I used the “randomForest” R package and an image example of ImageJ so you can reproduce the example quite easily. I made the example script as easy as possible and trained the classifier

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New R plot preferences and improvements for the R editor in Bio7

November 18, 2015
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New R plot preferences and improvements for the R editor in Bio7

18.11.2015 In the current release of Bio7 2.3  two new plot preferences are available to automatically plot data with the size of the visible display (as an image) or with the size of available ImageSizeX and ImageSizeY R workspace variables. The second option is handy if you transfer images from ImageJ to the R workspace

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Competing views on Argentina’s Frontrunner

October 30, 2015
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Competing views on Argentina’s Frontrunner

It might be too early to state anything solid, but the few polls prospecting the runoff election in Argentina seem to be telling two distinct histories. Hugo Haime and IPSOS suggest Daniel Scioli is going up while Elypsis and Polldata say he’s sinking as weeks go by. The last poll published today by Polldata put...

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House Effects in Argentinian polling

October 24, 2015
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House Effects in Argentinian polling

I’ve already posted on “house effects”, the tendency of polling organizations to systematically vary in their results from one another. In this post, I look specifically at these house effects, and show which polling organisations over or under-estimate support for each candidate–compared to the average–in this presidential election in Argentine. The graph above plots the house...

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Argentine’s 2015 Presidential Election Forecasts

October 20, 2015
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Argentine’s 2015 Presidential Election Forecasts

Predictions The model I built to forecast the Argentine’s 2015 presidential election indicates the official candidate have a significant chance of making it right way this Sunday, avoiding a runoff with Mauricio Macri in November. The electoral preference distributions are quite apart from each other, with the distribution of Daniel Scioli about 40% of the positive vote. Preference...</p><p><a href=Read more »

Paranormal Distribution

October 19, 2015
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Paranormal Distribution

In case you want to read an influential paper on this matter, you can find it here. library(ggplot2) library(gridExtra) theme_old <- theme(panel.grid.minor = element_blank(), panel.grid.major = element_blank(), ...

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A Talk About Campaign Finance in Brazil

October 16, 2015
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A Talk About Campaign Finance in Brazil

Last week, I delivered a talk at University of Brasilia about my past research on the topic of campaign finance. I didn’t know in advance about the existence of this seminar. Indeed, it was a big surprise receiving the invitation from Prof. Marcelo Pimentel, who is teaching on Fridays afternoon a class on this topic with more than 50...

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Understanding Margin of Error for Small Populations

October 13, 2015
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Understanding Margin of Error for Small Populations

I got an email today inquiring if the margin of error reported in the newest poll by Datafolha would possibly be misleading. The polling firm often report surveys with regular sizes (1000/2400), so the margin of error calculated is in the range of +/-2% to +/-3%. However, in the latest pool, the pollster sampled 340 congressmen, reporting a...

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The butterfly curve

October 6, 2015
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The butterfly curve

I came across the butterfly curve, which was discovered by Temple Fay. The butterfly curve is produced by a parametric equation where: x = sin(t) * (e^cos(t)-2cos(λt)-sin(t/12)^5)and y = cos(t) * (e^cos(t)-2cos(λt)-sin(t/12)^5). Where t stands for time and λ for a user input variable. library(ggplot2) source("https://raw.githubusercontent.com/danielmarcelino/SciencesPo/master/R/butterfly.R") p4 = butterfly(100, 1000, title="100 x 1000") Date of Analysis: Wed Oct 07 2015 Computation time: 0.01444697 —————————————————

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