1091 search results for "how to import image file to R"

Measuring Randomness in Capital Markets

September 29, 2013
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Measuring Randomness in Capital Markets

What is Random? As previously discussed, there’s no universal measure of randomness. Randomness implies the lack of pattern and the inability to predict future outcomes. However, The lack of an obvious model doesn’t imply randomness anymore than a curve fit one implies order. So what actually constitutes randomness, how can we quantify it, and why do we care? Randomness $\neq$ Volatility, and Predictability $\neq$ Profit First...

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Measuring Randomness in Capital Markets

September 29, 2013
By
Measuring Randomness in Capital Markets

What is Random? As previously discussed, there’s no universal measure of randomness. Randomness implies the lack of pattern and the inability to predict future outcomes. However, The lack of an obvious model doesn’t imply randomness anymore than a curve fit one implies order. So what actually constitutes randomness, how can we quantify it, and why do we care? Randomness $neq$ Volatility, and Predictability $neq$ Profit First...

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Classification with O-PLS-DA

September 29, 2013
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Classification with O-PLS-DA

Partial least squares (PLS) is a versatile algorithm which can be used to predict either continuous or discrete/categorical variables. Classification with PLS is termed PLS-DA, where the DA stands for discriminant analysis.  The PLS-DA algorithm has many favorable properties for dealing with multivariate data; one of the most important of which is how variable collinearity is

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Making of elliplot package

September 28, 2013
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Making of elliplot package

The elliplot package is my first package for R.  So I want to write down details of making that, both for myself and for people following. original source The ellipseplot … Continue reading →

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Detecting Weak Instruments in R

September 23, 2013
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Detecting Weak Instruments in R

Any instrumental variables (IV) estimator relies on two key assumptions in order to identify causal effects: That the excluded instrument or instruments only effect the dependent variable through their effect on the endogenous explanatory variable or variables (the exclusion restriction), That the correlation between the excluded instruments and the endogenous explanatory variables is strong enough

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informative hypotheses (book review)

September 18, 2013
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informative hypotheses (book review)

The title of this book Informative Hypotheses somehow put me off from the start: the author, Hebert Hoijtink, seems to distinguish between informative and uninformative (deformative? disinformative?) hypotheses. Namely, something like H0: μ1=μ2=μ3=μ4 is “very informative” and the alternative Ha is completely uninformative, while the “alternative null” H1: μ1<μ2=μ3<μ4 is informative. (Hence the < signs on

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Where’s the Magic? (EMD and SSA in R)

September 17, 2013
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Where’s the Magic? (EMD and SSA in R)

When I first heard of SSA (Singular Spectrum Analysis) and the EMD (Empirical Mode Decomposition) I though surely I’ve found a couple of magical methods for decomposing a time series into component parts (trend, various seasonalities, various cycles, noise). And … Continue reading →

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A nifty area plot (or a bootleg of a ggplot geom)

September 17, 2013
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A nifty area plot (or a bootleg of a ggplot geom)

The ideas for most of my blogs usually come from half-baked attempts to create some neat or useful feature that hasn’t been implemented in R. These ideas might come from some analysis I’ve used in my own research or from some other creation meant to save time. More often than not, my blogs are motivated

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New robust statistical functions in WRS package – Guest post by Rand Wilcox

September 16, 2013
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New robust statistical functions in WRS package – Guest post by Rand Wilcox

Today a new version (0.23.1) of the WRS package (Wilcox’ Robust Statistics) has been released. This package is the companion to his rather exhaustive book on robust statistics, “Introduction to Robust Estimation and Hypothesis Testing” (Amazon Link de/us). For a fail-safe installation of the package, follow this instruction. As a guest post, Rand Wilcox describes

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MCMSki IV, Jan. 6-8, 2014, Chamonix (news #6)

September 13, 2013
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MCMSki IV, Jan. 6-8, 2014, Chamonix (news #6)

A reposted item of news about MCMSki IV: as posted by Brad Carlin this afternoon to the Biometrics Section and Bayesian Statistical Science Section of the ASA, The fifth joint international meeting of the IMS (Institute of Mathematical Statistics) and ISBA (International Society for Bayesian Analysis), nicknamed “MCMSki IV”, will be held in Chamonix Mont-Blanc,

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