October 2014

R Class for Wildfire Scientists

October 29, 2014 | Jonathan Callahan

This entry is part 14 of 14 in the series Using RMazama Science has just finished creating class materials on using R for the AirFire team at the USFS Pacific Wildland Fire Sciences Lab in Seattle, Washington. This team of …   read more ... [Read more...]

Jobs at Amazon

October 28, 2014 | Rob J Hyndman

I do not normally post job adverts, but this was very specifically targeted to “applied time series candidates” so I thought it might be of sufficient interest to readers of this blog. Here is an excerpt from an email I received from someone at Amazon: Amazon is aggressively recruiting in ... [Read more...]

reliable ABC model choice via random forests

October 28, 2014 | xi'an

After a somewhat prolonged labour (!), we have at last completed our paper on ABC model choice with random forests and submitted it to PNAS for possible publication. While the paper is entirely methodological, the primary domain of application of ABC model choice methods remains population genetics and the diffusion of ...
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Type III tests and R

October 28, 2014 | Joseph Rickert

by Terry M. Therneau Ph.D. Faculty, Mayo Clinic About a year ago there was a query about how to do "type 3" tests for a Cox model on the R help list, which someone wanted because SAS does it. The SAS addition looked suspicious to me, but as the author ... [Read more...]

Flexible Beta Modeling

October 27, 2014 | statcompute

[This article was first published on Yet Another Blog in Statistical Computing » S+/R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. library(betareg) library(sas7bdat) df1 <- read.sas7bdat('lgd.sas7bdat') df2 <- df1[df1$y < 1, ] fml <- as.formula('y ~ x2 + x3 + x4 + x5 + x6 | x3 + x4 | x1 + x2') ### LATENT-CLASS BETA REGRESSION: AIC = -565 ### mdl1 <- betamix(fml, data = df2, k = 2, FLXcontrol = list(iter.max = 500, minprior = 0.1)) print(mdl1) #betamix(formula = fml, data = df2, k = 2, FLXcontrol = list(iter.max = 500, # minprior = 0.1)) # #Cluster sizes: # 1 2 #157 959 summary(mdl1, which = 'concomitant') # Estimate Std. Error z value Pr(>|z|) #(Intercept) -1.35153 0.41988 -3.2188 0.001287 ** #x1 2.92537 1.13046 2.5878 0.009660 ** #x2 2.82809 1.42139 1.9897 0.046628 * summary(mdl1) #$Comp.1$mean # Estimate Std. Error z value Pr(>|z|) #(Intercept) -0.8963228 1.0385545 -0.8630 0.3881108 #x2 3.1769062 0.6582108 4.8266 1.389e-06 *** #x3 -0.0520060 0.0743714 -0.6993 0.4843805 #x4 4.9642998 1.4204071 3.4950 0.0004741 *** #x5 0.0021647 0.0022659 0.9554 0.3393987 #x6 0.0248573 [...] [Read more...]

Visualizing the History of Epidemics

October 27, 2014 | PirateGrunt

I really like National Geographic. Their magazine is great, their television documentaries are done well and they helped give me a lifelong love of maps. They generate very good information and help shed light on the world we all share. So why is this graphic so awful? Let's have a ... [Read more...]

Create Fashion Fingerprints with R

October 27, 2014 | David Smith

How do you summarize fashion? For New York Fashion Week, the New York Times used the idea of "Fashion Fingerprints", distilling a designer's collections into small fragments highlighting the palette. Here's what Marc Jacobs' current collection looks like: Click through for an interactive version where you can explore each design, ... [Read more...]

Size Doesn’t Matter

October 26, 2014 | aschinchon

An invisible red thread connects those destined to meet, regardless of time, place or circumstances. The thread may stretch or tangle, but never break (Ancient Chinese Legend) I use to play once a year with my friends to Secret Santa (in Spain we call it Amigo Invisible). As you can ...
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Probable Points and Credible Intervals, Part 1

October 26, 2014 | Rasmus Bååth

After having broken the Bayesian eggs and prepared your model in your statistical kitchen the main dish is the posterior. The posterior is the posterior is the posterior, given the model and the data it contains all the information you need and anything else will be a little bit less ... [Read more...]

Syrian Refugee Informal Settlement Animation

October 26, 2014 | jlebeau

This is an animation of the growth in Syrian refugee informal camp settlements in Lebanon over the past several years.  You can see from the .gif that the growth was most dramatic in terms of the number of settlements in the Summer of 2013.  ... [Read more...]

Quarterback Completion Heatmap Using dplyr

October 26, 2014 | Mark T Patterson

Several months ago, I found Bryan Povlinkski's (really nicely cleaned) dataset with 2013 NFL play-by-play information, based on data released by Brian Burke at Advanced Football Analytics. I decided to browse QB completion rates based on Pass Location (Left, Middle, Right), Pass Distance (Short or Deep), and Down. I ended up ... [Read more...]

ALUES: Agricultural Land Use Evaluation System, R package

October 26, 2014 | Al-Ahmadgaid Asaad

Authors:Arnold R. Salvacion                                                                       [email protected] Analysis and Visualization using R (blog)                                          Al-Ahmadgaid B. Asaad (maintainer)[email protected] Land Use Evaluation System (ALUES) is an R package that evaluates land suitability for different crop production. The package is based on the Food and Agriculture Organization (FAO) and the ... [Read more...]

Tuning Laplaces Demon III

October 26, 2014 | Wingfeet

This is the third post with LaplacesDemon tuning. same problem, different algorithms. For introduction and other code see this post. The current post takes algorithms Independence Metropolis to Reflective Slice Sampler.Independence MetropolisIndependen... [Read more...]

Model Segmentation with Recursive Partitioning

October 26, 2014 | statcompute

[This article was first published on Yet Another Blog in Statistical Computing » S+/R, and kindly contributed to R-bloggers]. (You can report issue about the content on this page here) Want to share your content on R-bloggers? click here if you have a blog, or here if you don't. library(party) df1 <- read.csv("credit_count.csv") df2 <- df1[df1$CARDHLDR == 1, ] mdl <- mob(DEFAULT ~ MAJORDRG + MINORDRG + INCOME + OWNRENT | AGE + SELFEMPL, data = df2, family = binomial(), control = mob_control(minsplit = 1000), model = glinearModel) print(mdl) #1) AGE <= 22.91667; criterion = 1, statistic = 48.255 # 2)* weights = 1116 #Terminal node model #Binomial GLM with coefficients: #(Intercept) MAJORDRG MINORDRG INCOME OWNRENT # -0.6651905 0.0633978 0.5182472 -0.0006038 0.3071785 # #1) AGE > 22.91667 # 3)* weights = 9383 #Terminal node model #Binomial GLM with coefficients: #(Intercept) MAJORDRG MINORDRG INCOME OWNRENT # -1.4117010 0.2262091 0.2067880 -0.0003822 -0.2127193 ### TEST FOR STRUCTURAL CHANGE ### sctest(mdl, node = 1) # AGE SELFEMPL #statistic 4.825458e+01 20.88612025 #p.value 1.527484e-07 0.04273836 summary(mdl, node = 2) #Coefficients: # Estimate Std. Error z value Pr(>|z|) #(Intercept) -0.6651905 0.2817480 -2.361 0.018229 * #MAJORDRG 0.0633978 0.3487305 0.182 0.855743 #MINORDRG 0.5182472 0.2347656 2.208 0.027278 * #INCOME -0.0006038 [...] [Read more...]
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