August 2019

Calculating Always-Valid p-values in R

August 21, 2019 | R Views

In this post, we will develop a framework for always-valid inference based on the paper Always Valid Inference: Continuous Monitoring of A/B Tests (2019 Johari, Pekelis, Walsh). Using an always-valid p-value allows us to continuously monitor A/B tests, and potentially stop the test early in a valid way1. In ...
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Detecting outlier samples in PCA

August 21, 2019 | Florian Privé

In this post, I present something I am currently investigating (feedback welcome!) and that I am implementing in my new package {bigutilsr}. This package can be used to detect outlier samples in Principal Component Analysis (PCA). remotes::install_github("privefl/bigutilsr") library(bigutilsr) I present three different statistics of outlierness ...
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Geographic projections and transformations

August 20, 2019 | the Geocomputation with R website

Introduction This workbook outlines key concepts and functions related to map projections — also referred to as coordinate reference systems (CRSs) — and transformation of geographic data from one projection to another. It is based on the open source book Geocomputation with R, and Chapter 6 in particular. It was developed for the ‘...
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Notes on Becoming an RStudio Certified Trainer

August 20, 2019 | Ari Lamstein

I recently became an RStudio Certified Trainer, and thought that it might interest the broader R Community to learn about this new program. For those who don’t know, RStudio has recently put together a process to independently verify that R trainers (a) are proficient with the Tidyverse and (b) ...
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Modern reporting for R with Dash

August 19, 2019 | modern.data

Creating an effective, informative, and aesthetically appealing report to showcase your data can be tedious: it’s often difficult to display your data and your plots together in an uncluttered manner, and even harder to implement interactivity between the individual elements. Dash for R facilitates this task, providing an intuitive ...
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How to get an AUC confidence interval

August 19, 2019 | Andrew Treadway

Background AUC is an important metric in machine learning for classification. It is often used as a measure of a model’s performance. In effect, AUC is a measure between 0 and 1 of a model’s performance that rank-orders predictions from a model. For a detailed explanation of AUC, see this ...
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RcppQuantuccia 0.0.3

August 19, 2019 | Thinking inside the box

A maintenance release of RcppQuantuccia arrived on CRAN earlier today. RcppQuantuccia brings the Quantuccia header-only subset / variant of QuantLib to R. At the current stage, it mostly offers date and calendaring functions. This release was trigger... [Read more...]

Prediction intervals for Generalized Additive Models (GAMs)

August 19, 2019 | R-bloggers on Mikkel Meyer Andersen

Finding prediction intervals (for future observations) is something different than finding confidence intervals (for unknown population parameters). Here, I demonstrate one approach to doing so. First we load the library and simulate some data:
library(mgcv)
set.seed(1)
dat <- gamSim(eg = 1, n = 400, dist = "normal", scale = 2)
## Gu & Wahba 4 term additive model
The simulated in dat contains the “truth” in the f variables:
str(dat)
## 'data.frame':    400 obs. of  10 variables:
##  $ y : num  3.3407 -0.0758 10.6832 8.7291 14.9911 ...
##  $ x0: num  0.266 0.372 0.573 0.908 0.202 ...
##  $ x1: num  0.659 0.185 0.954 0.898 0.944 ...
##  $ x2: num  0.8587 0.0344 0.971 0.7451 0.2733 ...
##  $ x3: num  0.367 0.741 0.934 0.673 0.701 ...
##  $ f : num  5.51 3.58 8.69 8.75 16.19 ...
##  $ f0: num  1.481 1.841 1.948 0.569 1.184 ...
##  $ f1: num  3.74 1.45 6.74 6.02 6.6 ...
##  $ f2: num  2.98e-01 2.88e-01 8.61e-05 2.16 8.40 ...
##  $ f3: num  0 0 0 0 0 0 0 0 0 0 ...
fit_lm <- lm(f ~ f0 + f1 + f2 + f3, data = dat)
plot(dat$f, predict(fit_lm))
abline(0, 1)
And what can ...
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