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Category: R

Multiple Hypothesis Testing with R

Published 2019-10-03 by Kevin Feasel

Roland Stevenson shows how we can perform multiple hypothesis tests on data, as well as potential issues:

Both results show that evaluating two tests on the same family of data will lead to a ~10% chance that a researcher will claim a “significant” result if they look for either test to reject the null. Any claim there is a maximum 5% false positive rate would be mistaken. As an exercise, verify that doing the same on \(m=4\) tests will lead to an ~18% chance!

A bad testing platform would be one that claims a maximum 5% false positive rate when any one of multiple tests on the same family of data show significance at the 5% level. Clearly, if a researcher is going to claim that the FWER is no more than \(\alpha\), then they must control for the FWER and carefully consider how individual tests reject the null.

This is worth taking some time to read carefully. H/T R-Bloggers

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Principal Component Analysis in Python

Published 2019-10-02 by Kevin Feasel

Abhinav Choudhary shows us how to implement Principal Component Analysis in Python:

Principal Component Analysis (PCA) is an unsupervised statistical technique used to examine the interrelation among a set of variables in order to identify the underlying structure of those variables. In simple words, suppose you have 30 features column in a data frame so it will help to reduce the number of features making a new feature which is the combined effect of all the feature of the data frame. It is also known as factor analysis.

PCA is quite useful in practice, though it has the unfortunate side effect of making it harder to interpret which factors are driving your solution.

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Using purrr to Eliminate Looped Function Calls

Published 2019-10-01 by Kevin Feasel

Sebastian Sauer demonstrates using the pmap() function in purrr to call a function multiple times with different parameters:

Assume you have to call a function multiple times, but each with (possibly) different argument. Given enough repitioons, you will not want to repeat yourself.

In other words, we would like to loop over function arguments, each round in the loop giving the respective argument’value(s) to the function.

This is one of the benefits of functional-style programming: loops become higher-order functions, which take less time to write and keeps your code from looking like a pyramid of doom.

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Fun with Residual Plots

Published 2019-09-30 by Kevin Feasel

Nina Zumel explains why, when plotting residuals, you always put predictions on the X axis and residuals on the Y axis:

One reason that the proper residual graph (for a well fit model) should smooth out to the line y=0 is known as reversion to mediocrity, or regression to the mean.

Imagine that you have an ideal process that always produces a single value y. You don’t actually observe this “true value”; instead, what you observe is y plus (IID, zero mean) noise. You can build a “model” for this process that predicts the mean of the observations, in this case the value 0.1033149. Then you can calculate the residuals of your “model” in the usual way.

This post went in a direction I wasn’t expecting, and it was all the better for it.

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Topic Modeling

Published 2019-09-30 by Kevin Feasel

Federico Pascual has an article on topic modeling and topic classification:

Topic modeling is an unsupervised machine learning technique that’s capable of scanning a set of documents, detecting word and phrase patterns within them, and automatically clustering word groups and similar expressions that best characterize a set of documents. It’s known as ‘unsupervised’ machine learning because it doesn’t require a predefined list of tags or training data that’s been previously classified by humans.

Since topic modeling doesn’t require training, it’s a quick and easy way to start analyzing your data. However, you can’t guarantee you’ll receive accurate results, which is why many businesses opt to invest time training a topic classification model.

The article is long but worth the read, with examples in Python and additional notes for R.

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Record Transformation with cdata

Published 2019-09-24 by Kevin Feasel

John Mount shows off one of the advantages of using cdata to define data-driven record transformation specifications:

We have a tutorial on how to design such transforms by writing down the shape your incoming data records are arranged in, and also the shape you wish your outgoing data records to be arranged in.

This simple data transform is in fact not a single pivot/un-pivot, as the result records spread data-values over multiple rows and multiple columns at the same time. We call the transform simple, because from a user point of view: it takes records of one form to another form (with the details left to the implementation).

Read the whole thing.

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Linear Regression in Power BI

Published 2019-09-23 by Kevin Feasel

Joseph Yeates shows how to implement linear regression in Power BI:

The goal of a simple linear model is to fit a line onto this plot to summarize the shape of the data using the equation above.

The “a” value is the slope of the fitted line (rise over run) and the “b” value is the intercept on the y-axis (when x is equal to zero).

In the gapminder example, the life expectancy column was assigned as the “y” variable, as it is the outcome that we are interested in predicting or understanding. The year1950 column was assigned as the “x” variable, as it is what we are using to try and measure the change in life expectancy.

This is a little more complicated than adding a regression line to a scatterplot (the “normal” way to do linear regression with Power BI) but this method lets you work with the outputs in a way that the normal method doesn’t.

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WVPlots

Published 2019-09-17 by Kevin Feasel

Nina Zumel announces a new version of WVPlots on CRAN:

WVPlots was originally a catch-all package of ggplot2 visualizations that we at Win-Vector tended to use repeatedly, and wanted to turn into “one-liners.” A consequence of this is that the older visualizations had our preferred color schemes hard-coded in. More recent additions to the package sometimes had palette or color controls, but not in a consistent way. Making color controls more consistent has been a “todo” for a while—one that I’d been putting off. A recent request from user Brice Richard (thanks Brice!) has pushed me to finally make the changes.

Click through to see what’s changed and for an example vignette.

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Icon Maps in R

Published 2019-09-13 by Kevin Feasel

Laura Ellis shows how you can build maps full of little icons:

That was ok, but we should try to make the images more aesthetically pleasing using the magick package. We make each image transparent with the image_transparent() function. We can also make the resulting image a specific color with image_colorize().

I then saved the images using the image_write() function. I manually re-uploaded them to GH.

This was a great example of where laying icons on a map works.

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