In our article What is a large enough random sample? we pointed out that if you wanted to measure a proportion to an accuracy “a” with chance of being wrong of “d” then a idea was to guarantee you had a sample size of at least: This is the central ... [Read more...]

Using correlation to track model performance is “a mistake that nobody would ever make” combined with a vague “what would be wrong if I did do that” feeling. I hope after reading this feel a least a small urge to double check your work and presentations to make sure you ... [Read more...]

I know “officially” data scientists all always work in “big data” environments with data in a remote database, streaming store or key-value system. But in day to day work Excel files and Excel export files get used a lot and cause a disproportionate amount of pain. I would like to ... [Read more...]

Model level fit summaries can be tricky in R. A quick read of model fit summary data for factor levels can be misleading. We describe the issue and demonstrate techniques for dealing with them.When modeling you often encounter what are commonly called categorical variables, which are called factors in ... [Read more...]

In our article How robust is logistic regression? we pointed out some basic yet deep limitations of the traditional full-step Newton-Raphson or Iteratively Reweighted Least Squares methods of solving logistic regression problems (such as in R‘s standard glm() implementation). In fact in the comments we exhibit a well posed ... [Read more...]

Logistic Regression is a popular and effective technique for modeling categorical outcomes as a function of both continuous and categorical variables. The question is: how robust is it? Or: how robust are the common implementations? (note: we are using robust in a more standard English sense of performs well for ... [Read more...]

What does a generalized linear model do? R supplies a modeling function called glm() that fits generalized linear models (abbreviated as GLMs). A natural question is what does it do and what problem is it solving for you? We work some examples and place generalized linear models in context with ... [Read more...]

A primary problem data scientists face again and again is: how to properly adapt or treat variables so they are best possible components of a regression. Some analysts at this point delegate control to a shape choosing system like neural nets. I feel such a choice gives up far too ... [Read more...]

Hollywood movies are obsessed with outrunning explosions and outrunning crashing alien spaceships. For explosions the movies give the optimal (but unusable) solution: run straight away. For crashing alien spaceships they give the same advice, but in this case it is wrong. We demonstrate the correct angle to flee. Running from ... [Read more...]

The design of the statistical programming language R sits in a slightly uncomfortable place between the functional programming and object oriented paradigms. The upside is you get a lot of the expressive power of both programming paradigms. A downside of this is: the not always useful variability of the language’... [Read more...]

I suspect I am not unique in not being able to remember how to control the point shapes in R. Part of this is a documentation problem: no package ever seems to write the shapes down. All packages just use the “usual set” that derives from S-Plus and was carried ... [Read more...]

Much of the data that the analyst uses exhibits extraordinary range. For example: incomes, company sizes, popularity of books and any “winner takes all process”; (see: Living in A Lognormal World). Tukey recommended the logarithm as an important “stabilizing transform” (a transform that brings data into a more usable form ... [Read more...]

A lot of people consider the static typing found in languages such as C, C++, ML, Java and Scala as needless hairshirtism. They consider the dynamic typing of languages like Lisp, Scheme, Perl, Ruby and Python as a critical advantage (ignoring other features of these languages and other efforts at ... [Read more...]

We have been consistently impressed by and enjoyed the wealth of R wisdom available on the R-bloggers aggregation site. Therefore Win-Vector LLC is granting the right to reformat and redistribute (with attribution and link) our blog‘s R content in the R-bloggers site and feeds. We hope to see our ... [Read more...]

Programmers should definitely know how to use R. I don’t mean they should switch from their current language to R, but they should think of R as a handy tool during development.Again and again I find myself working with Java code like the following. td.linenos { background-color: #f0... [Read more...]

One of the recurring frustrations in data analytics is that your data is never in the right shape. Worst case: you are not aware of this and every step you attempt is more expensive, less reliable and less informative than you would want. Best case: you notice this and have ... [Read more...]

This is a tutorial on how to try out a new package in R. The summary is: expect errors, search out errors and don’t start with the built in examples or real data. Suppose you want to try out a novel statistical technique? A good fraction of the time ... [Read more...]

One of the current best tools in the machine learning toolbox is the 1930s statistical technique called logistic regression. We explain how to add professional quality logistic regression to your analytic repertoire and describe a bit beyond that. A statistical analyst working on data tends to deliberately start simple move ... [Read more...]

Having worked with Unix (BSD, HPUX, IRIX, Linux and OSX), Windows (NT4, 2000, XP, Vista and 7) for quite a while I have seen a lot of different software tools. I would like to quickly exhibit my “must have” list. These are the packages that I find to be the single “must ... [Read more...]

Readers returning to our blog will know that Win-Vector LLC is fairly “pro-R.” You can take that to mean “in favor or R” or “professionally using R” (both statements are true). Some days we really don’t feel that way. Consider the following snippet of R code where we create ... [Read more...]