Introduction An example 6-state model Estimation Data Fitting Simulation mstate hesim Comparison Probabilistic sensitivity analysis mstate hesim Comparison ...

Here we go again, using a Twitter trend as blog fodder! Colin Fay launched an inspiring movement by sharing his R goals of 2019. My #RStats goals for 2019: 1️⃣ Becoming entirely fluent with {data.table}2️⃣ Getting at ease with {Rcpp} What are yours?#rdatatable #rcpp— Colin Fay 🤘 (@_ColinFay) December 29, 2018 It’s been ...

RcppDynProg is a new Rcpp based R package that implements simple, but powerful, table-based dynamic programming. This package can be used to optimally solve the minimum cost partition into intervals problem (described below) and is useful in building piecewise estimates of functions (shown in this note). The abstract problem The ...

Well, 2018 has flown by and today seems like an appropriate time to take a look at the landscape of R bloggerdom as seen through the eyes of readers of R-bloggers and R Weekly. We’ll do this via a new package designed to make it easier to treat Feedly as ...

CategoriesAdvanced Modeling Tags Linear Regression Principal Component Analysis R Programming In this post, I am going to build a statistical learning model as based upon plant leaf datasets introduced in part one of this tutorial. We have available three datasets, each one providing sixteen samples each of one-hundred plant species. ...

Giddy up, giddy it up Wanna move into a fool's gold room With my pulse on the animal jewels Of the rules that you choose to use to get loose With the luminous moves Bored of these limits, let me get, let me get it like Wow! When it comes ...

CategoriesGetting Data Tags Data Management Data Visualisation Exploratory Analysis R Programming In this post, I am going to run an exploratory analysis of the plant leaf dataset as made available by UCI Machine Learning repository at this link. The dataset is expected to comprise sixteen samples each of one-hundred plant ...

The truth is out there R readers, but often it is not what we have been led to believe. The previous post examined the strong positive results bias in optimism corrected bootstrapping (a method of assessing a machine learning model’s predictive power) with increasing p (completely random features). There ...

Introduction Packages CRAN availability of tidymodels packages: Unified Modelling Syntax Statistical Tests and Model Selection Resampling, Feature Engineering and Performance Metrics Modeling Data Response Variable lstat Correlations lstat vs categorical variables Preprocessing with recipe Summary Recipe Resampling with rsample Modelling with caret Wrapper Apply Wrapper Assess Performance with yardstick Parameters ...

In the previous parts of the series we demonstrated a positive results bias in optimism corrected bootstrapping by simply adding random features to our labels. This problem is due to an ‘information leak’ in the algorithm, meaning the training and test datasets are not kept seperate when estimating the optimism. ...

Recently I wanted to learn how to use emojis as points in a scatterplot points. It seems like the emojifont package is a popular way to do it. However, I couldn’t seem to get it to work on my machine … Continue reading →

As 2018 nears its end, it’s time for me to look back on my R/Twitter year with the same simple method as last year: let me identify and webshoot my 9 best tweets of 2018! Downloading and opening my Twitter data Like in 2017 I tweeted too much and therefore was unable ...

Welcome to part III of debunking the optimism corrected bootstrap in high dimensions (quite high number of features) in the Christmas holidays. Previously we saw with a reproducible code implementation that this method is very bias when we have many features (50-100 or more). I suggest avoiding this method until ...