2047 search results for "Regression"

Ensemble Packages in R

April 8, 2014
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Ensemble Packages in R

by Mike Bowles Mike Bowles is a machine learning expert and serial entrepreneur. This is the second post in what is envisioned as a four part series that began with Mike's Thumbnail History of Ensemble Models. One of the main reasons for using R is the vast array of high-quality statistical algorithms available in R. Ensemble methods provide a...

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In case you missed it: March 2014 roundup

April 7, 2014
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In case you missed them, here are some articles from March of particular interest to R users: Francis Smart offers five excellent reasons to use R, and notes that R is the top Google Search for statistical software. Revolution Analytics is offering R training for SAS users in Singapore and online. The number of R user groups worldwide continues...

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Author inflation in academic literature

April 6, 2014
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Author inflation in academic literature

There seems to be a general consensus that author lists in academic articles are growing. Wikipedia says so, and I’ve also come across a published letter and short Nature article which accept this is the case and discuss ways of … Continue reading →

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Flip the script, or, the joys of coord_flip()

April 4, 2014
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Flip the script, or, the joys of coord_flip()

Has this ever happened to you?I hate it when the labels on the x-axis overlap, but this can be hard to avoid. I can stretch the figure out, but then the data become farther apart and the space where I want to put the figure (either in a talk or a paper...

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Some R Resources for GLMs

April 3, 2014
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Some R Resources for GLMs

by Joseph Rickert Generalized Linear Models have become part of the fabric of modern statistics, and logistic regression, at least, is a “go to” tool for data scientists building classification applications. The ready availability of good GLM software and the interpretability of the results logistic regression makes it a good baseline classifier. Moreover, Paul Komarek argues that, with a...

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Inference for ARCH processes

April 2, 2014
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Inference for ARCH processes

Consider some ARCH() process, say ARCH(), where with a Gaussian (strong) white noise . > n=500 > a1=0.8 > a2=0.0 > w= 0.2 > set.seed(1) > eta=rnorm(n) > epsilon=rnorm(n) > sigma2=rep(w,n) > for(t in 3:n){ + sigma2=w+a1*epsilon^2+a2*epsilon^2 + epsilon=eta*sqrt(sigma2) + } > par(mfrow=c(1,1)) > plot(epsilon,type="l",ylim=c(min(epsilon)-.5,max(epsilon))) > lines(min(epsilon)-1+sqrt(sigma2),col="red") (the red line is the conditional variance process). > par(mfrow=c(1,2)) > acf(epsilon,lag=50,lwd=2)...

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IV Estimates via GMM with Clustering in R

April 1, 2014
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IV Estimates via GMM with Clustering in R

In econometrics, generalized method of moments (GMM) is one estimation methodology that can be used to calculate instrumental variable (IV) estimates. Performing this calculation in R, for a linear IV model, is trivial. One simply uses the gmm() function in the excellent gmm package like an lm() or ivreg() function. The gmm() function will estimate

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Process and observation uncertainty explained with R

March 31, 2014
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Process and observation uncertainty explained with R

Once up on a time I had grand ambitions of writing blog posts outlining all of the examples in the Ecological Detective.1 A few years ago I participated in a graduate seminar series where we went through many of the examples in this book. I am not a population biologist by trade but many of

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Bayesian Data Analysis [BDA3 – part #2]

March 30, 2014
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Bayesian Data Analysis [BDA3 – part #2]

Here is the second part of my review of Gelman et al.’ Bayesian Data Analysis (third edition): “When an iterative simulation algorithm is “tuned” (…) the iterations will not in general converge to the target distribution.” (p.297) Part III covers advanced computation, obviously including MCMC but also model approximations like variational Bayes and expectation propagation

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Theil’s Blus Residuals and R Tools for Testing and Removing Autocorrelation and Heteroscedasticity

March 28, 2014
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Guest post by Hrishikesh (Rick) D. Vinod, Professor of Economics, Fordham University. Theil (1968) proposed a transformation of regression residuals so that they are best, unbiased, linear, scalar (BLUS). No R code is available to implement them. I am providing the detailed description of the properties of BLUS residuals to the uninitiated and code. The matrix algebra itself is...

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