R for Beginners: Some Simple Code to Produce Informative Graphs, Part One
A Tutorial by D. M. Wiig The R programming language has a multitude of packages that can be used to display various types of graph. For a new user looking...continue reading.
A Tutorial by D. M. Wiig The R programming language has a multitude of packages that can be used to display various types of graph. For a new user looking...continue reading.
There is a plethora of classification algorithms available to people who have a bit of coding experience and a set of data. A common machine learning method is the random...continue reading.
by Angelika Stefan & Felix Schönbrodt When reading about Bayesian statistics, you regularly come across terms like “objective priors“, “prior odds”, “prior distribution”, and “normal prior”. However, it may not...continue reading.
You’ve just made an amazing plot in R, and the only thing remaining is finding the right colours to use. Arghhh this part is never fun… You’re probably familiar with...continue reading.
The power of a DNN does not only come from its depth but also come from its flexibility of accommodating complex network structures. For instance, the DNN shown below consists...continue reading.
Stoltzmaniac is going local in today’s blog post! I dug into the City of Fort Collins open data and published my findings below. The data was surprisingly clean and laid...continue reading.
The Situation You are a consultant who has been hired by a business that sells one commodity product. On December 31st the price is $100 per unit. The business owner...continue reading.
The Type-I Pareto distribution has a probability function shown as below f(y; a, k) = k * (a ^ k) / (y ^ (k + 1)) In the formulation, the...continue reading.
When estimating generalized linear models for binary outcomes, we often choose the logit link function by default and seldom consider other alternatives such as probit or cloglog. The Pregibon test...continue reading.
I recently published a new explorable visualization of equivalence, and non-inferiority testing. The visualization lets you explore the decision rules associated with these different hypotheses. It also shows how the...continue reading.
After rewatching the thanksgiving classic, Bill and Ted’s Excellent Adventure, it reminded me of the history of #Rstats and its current status as the defacto software for general data programming....continue reading.
Modeling the frequency is one of the most important aspects in operational risk models. In the previous post (https://statcompute.wordpress.com/2016/05/13/more-flexible-approaches-to-model-frequency), the importance of flexible modeling approaches for both under-dispersion and over-dispersion...continue reading.
On my trip to Japan, I took this photo of the stairs leading to the “Rucker Park of Tokyo.” I crossed up some Tokyo cats, they were garbage. That one girl...continue reading.
The severity measure in operational loss models has an empirical distribution with positive values and a long tail to the far right. To estimate regression models for severity measures with...continue reading.
I’d like to share some tips and recommendations on building htmlwidgets, based on my own learning experience while creating timevis. These tips are mostly concerned with making your htmlwidget more...continue reading.
After having carefully followed the online official Shiny tutorial, I decided to make a quick try at making my very first Shiny App. I should say that I found myself...continue reading.
Hadley Wickham in a recent blog post mentioned that “Factors have a bad rap in R because they often turn up when you don’t want them.” I believe Factors are an...continue reading.
This document contains a collection of various Shiny tricks that I commonly use or that I know many people ask about. Each link contains a complete functional Shiny app that...continue reading.
From Safari Books Online (https://www.safaribooksonline.com/blog/2016/02/10/data-science-qa/) —Recently, we were able to ask five questions of Murtaza Haider, about the new book from IBM Press called “Getting Started with Data Science: Making Se…continue reading.
Overfitting is a concern for overly complex models. When a model suffers from the overfitting, it will tend to over-explain the model training data and can’t generalize well in the...continue reading.