Monthly Archives: April 2012

Comparing Julia and R’s Vocabularies

April 9, 2012
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While exploring the Julia manual recently, I realized that it might be helpful to put the basic vocabularies of Julia and R side-by-side for easy comparison. So I took Hadley Wickham’s R Vocabulary section from the book he’s putting together on the devtools wiki, put all of the functions Hadley listed into a CSV file,

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Example 9.26: More circular plotting

April 9, 2012
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Example 9.26: More circular plotting

SAS's Rick Wicklin showed a simple loess smoother for the temperature data we showed here. Then he came back with a better approach that does away with edge effects. Rick's smoothing was calculated and plotted on a cartesian plane. In this entry we'll explore another option or two...

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Ichimoku Clouds R Code Trading

April 9, 2012
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Ichimoku Clouds R Code Trading

Quantitative Finance, Technical Trading & Analysis. Fotis Papailias, Dimitrios Thomakos Fotis Quantitative Finance & Technical Trading Ichimoku Clouds R Code Trading Download the full program here Here you can find an R Code for Ichimoku Clouds analysis and trading. Have fun!   # The function for computing the Ichimoku cloud ichimoku <- function(data,pars) { # REMEMBER THAT THE DATA...

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Looking to the difference spectrum

April 9, 2012
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Looking to the difference spectrum

From the previous post, we can make the difference spectrum (once the samples are sorted by moisture) between the sample with the lowest moisture value (position 1), from the sample with the highest moisture value (position 66). This spectra will help ...

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Eclipse + Rcpp + RInside = Magic

April 8, 2012
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Eclipse + Rcpp + RInside = Magic

  I've been doing R/Java development for some time, creating packages both large and small with this tool chain. I have used Eclipse as my package development environment almost exclusively, but I didn't realize how much I relied on the IDE before I had to do some serious R/C++ package development. My first Rcpp package (wordcloud)

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Sampling and the Analysis of Big Data

April 8, 2012
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Sampling and the Analysis of Big Data

After my last post, I came across a few articles supporting the opinion that if you have a good reason to take random samples from a “big” dataset, you’re not committing some kind of sin: Big Data Blasphemy: Why Sample? … Continue reading →

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The lm() function with categorical predictors

April 8, 2012
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The lm() function with categorical predictors

What's with those estimates?By Ben OgorekIn R, categorical variables can be added to a regression using the lm() function without a hint of extra work. But have you ever look at the resulting estimates and wondered exactly what they were?First, let's define a data set.set.seed(12255)n = 30sigma = 2.0AOV.df <- data.frame(category = c(rep("category1", n)     ...

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Small pedigree based mixed model example

April 8, 2012
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Pedigree based mixed models (often called animal models, due to modelling animal performance) are the cornerstone of animal breeding and quantitative genetics. There are many programs that can be used for analyzing your data with these models, e.g., ASREML, BLUPf90, MATVEC, MiXBLUP & MiX99,  SurvivalKit, PEST/VCE, WOMBAT, ...). There are also R packages you...

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Sorting the "Sample Sets" by constituents

April 8, 2012
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Sorting the "Sample Sets" by constituents

I use to see the videos from:http://www.twotorials.com/and the video:How to order and sort stuff in Ris really useful to apply this concept to organize and understand better our sample sets before to proceed to develop a calibration.The idea of this po...

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Predicting apple liking from instrumental data

April 8, 2012
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Predicting apple liking from instrumental data

In this post I will examine if the individual models from previous posts together would make a good predictive model. Obviously, predictive capability is different from descriptive capability, predictions usually have a more simple model. As there was ...

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