August 2018

Mapping the stock market using self-organizing maps

August 6, 2018 | Data-based investor

Self-organizing maps are an unsupervised learning approach for visualizing multi-dimensional data in a two-dimensional plane. They are great for clustering and finding out correlations in the data. In this post we apply self-organizing maps on historical US stock market data to find out interesting correlations and clusters. We'll use data ...
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StatsModels: the first nail in R’s coffin

August 5, 2018 | Derek Jones

In 2012, when I decided to write a book on evidence-based software engineering, R was the obvious system to use for data analysis. At the time, lots of new books had “using R” or “with R” added at the end of their titles; I chose “using R”. When developers tell me ... [Read more...]

Collecting Expressions in R

August 5, 2018 | John Mount

Not a full R article, but a quick note demonstrating by example the advantage of being able to collect many expressions and pack them into a single extend_se() node. This example may seem extreme or unnatural. However we have seen once you expose a system to enough users you ...
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How Do We Draw a Line?

August 5, 2018 | @aschinchon

She dreams in colour, she dreams in red, can’t find a better man (Better Man, Pearl Jam) Today I bring another experiment based on The Quick Draw! Data from Google, one of my most fortunate discoveries of the last times. The Quick Draw! is a web game developed by ...
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Estimating treatment effects and ICCs from (G)LMMs on the observed scale using Bayes, Part 1: lognormal models

August 5, 2018 | Kristoffer Magnusson

When a multilevel model includes either a non-linear transformation (such as the log-transformation) of the response variable, or of the expectations via a GLM link-function, then the interpretation of the results will be different compared to a standard Gaussian multilevel model; specifically, the estimates will be on a transformed scale ... [Read more...]

fitur 0.6.0 Release

August 4, 2018 | R on Thomas Roh

Adding continuous distribution testing functions Kolmogorov-Smirnov, Anderson-Darling, and Cramer-Von Mises S3 methods have now been added for distfit objects Code reformatting and cleanup fitur on CRAN Continuous Distribution GOF Tests
library(fitur)
x <- rgamma(100, 1, 1)
fit <- fit_univariate(x, 'gamma')
ks_test(fit, x)
## 
##  One-sample Kolmogorov-Smirnov test
## 
## data:  x
## D = 0.082165, p-value = 0.5093
## alternative hypothesis: two-sided
ad_test(fit, x)
## 
##  Anderson-Darling test of goodness-of-fit
##  Null hypothesis: distribution 'distfun[[2]]'
## 
## data:  x
## An = 0.49058, p-value = 0.756
cvm_test(fit, x)
## 
##  Cramer-von Mises test of goodness-of-fit
##  Null hypothesis: distribution 'distfun[[2]]'
## 
## data:  x
## omega2 = 0.079325, p-value = 0.6968
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Longitudinal heat plots

August 4, 2018 | Matthieu Rolland

During our research on the effect of prednisone consumption during pregency on health outcomes of the baby (Palmsten K, Rolland M, Hebert MF, et al., Patterns of prednisone use during pregnancy in women with rheumatoid arthritis: Daily and cumulative dose. Pharmacoepidemiol Drug Saf. 2018 Apr;27(4):430-438. https://www.ncbi.nlm.nih....
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Introducing the HCmodelSets Package

August 4, 2018 | insightr

By Henrique Helfer Hoeltgebaum Introduction I am happy to introduce the package HCmodelSets, which is now available on CRAN. This package implements the methods proposed by Cox, D.R. and Battey, H.S. (2017). In particular it performs the reduction, exploratory and … Continue reading →
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