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Calibration plot for a misspecified logistic model

Rick WicklinMay 14, 2018 16

Calibration plots in SAS

A logistic regression model is a way to predict the probability of a binary response based on values of explanatory variables. It is important to be able to assess the accuracy of a predictive model. This article shows how to construct a calibration plot in SAS. A calibration plot is

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Analytics | Programming Tips

Rick WicklinMay 9, 2018 0

Independent streams of random numbers in SAS

In a previous blog post, I discussed ways to produce statistically independent samples from a random number generator (RNG). The best way is to generate all samples from one stream. However, if your program uses two or more SAS DATA steps to simulate the data, you cannot use the same

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Analytics | Data Visualization

Rick WicklinMay 2, 2018 4

Order variables in a heat map or scatter plot matrix

Order matters. When you create a graph that has a categorical axis (such as a bar chart), it is important to consider the order in which the categories appear. Most software defaults to alphabetical order, which typically gives no insight into how the categories relate to each other. Alphabetical order

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Analytics | Data Visualization

Rick WicklinApril 30, 2018 4

Assign colors in heat maps: A study of married couples and college majors

Some say that opposites attract. Others say that birds of a feather flock together. Which is it? Phillip N. Cohen, a professor of sociology at the University of Maryland, recently posted an interesting visualization that indicates that married couples who are college graduates tend to be birds of a feather.

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Analytics | Programming Tips

Rick WicklinApril 25, 2018 0

An easier way to run thousands of regressions

SAS programmers on SAS discussion forums sometimes ask how to run thousands of regressions of the form Y = B0 + B1*X_i, where i=1,2,.... A similar question asks how to solve thousands of regressions of the form Y_i = B0 + B1*X for thousands of response variables. I have previously

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Analytics | Data Visualization | Programming Tips

Euclidean and L1 distances between observations and a target value for standardized data

Rick WicklinMarch 28, 2018 3

Find the distances between observations and a target value

Suppose you want to find observations in multivariate data that are closest to a numerical target value. For example, for the students in the Sashelp.Class data set, you might want to find the students whose (Age, Height, Weight) values are closest to the triplet (13, 62, 100). The way to

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Analytics | Data Visualization

Rick WicklinMarch 26, 2018 3

A zipper plot for visualizing coverage probability in simulation studies

Simulation studies are used for many purposes, one of which is to examine how distributional assumptions affect the coverage probability of a confidence interval. This article describes the "zipper plot," which enables you to compare the coverage probability of a confidence interval when the data do or do not follow

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Analytics | Learn SAS

Rick WicklinMarch 21, 2018 2

The conjugate gradient method

I often claim that the "natural syntax" of the SAS/IML language makes it easy to implement an algorithm or statistical formula as it appears in a textbook or journal. The other day I had an opportunity to test the truth of that statement. A SAS programmer wanted to implement the

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Analytics

Rick WicklinMarch 12, 2018 1

Pi, special functions, and distributions

Welcome to my annual Pi Day post. Every year on March 14th (written 3/14 in the US), geeky mathematicians and their friends celebrate "all things pi-related" because 3.14 is the three-decimal approximation to pi. Pi is a mathematical constant that never changes. Pi is the same value today as it

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Analytics | Programming Tips

Rick WicklinMarch 7, 2018 11

Fit a distribution from quantiles

Data analysts often fit a probability distribution to data. When you have access to the data, a common technique is to use maximum likelihood estimation (MLE) to compute the parameters of a distribution that are "most likely" to have produced the observed data. However, how can you fit a distribution

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