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Analytics | Data Visualization | Learn SAS

Rick WicklinAugust 15, 2018 1

Optimization with nonlinear constraints in SAS

This article shows how to perform an optimization in SAS when the parameters are restricted by nonlinear constraints. In particular, it solves an optimization problem where the parameters are constrained to lie in the annular region between two circles. The end of the article shows the path of partial solutions

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Analytics | Data Visualization

Rick WicklinAugust 13, 2018 6

A quantile regression analysis of chess ratings by age

My colleague, Robert Allison, recently published an interesting visualization of the relationship between chess ratings and age. His post was inspired by the article "Age vs Elo — Your battle against time," which was published on the chess.com website. ("Elo" is one of the rating systems in chess.) Robert Allison's

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Analytics | Programming Tips

Rick WicklinAugust 6, 2018 0

How to score and graph a quantile regression model in SAS

This article shows how to score (evaluate) a quantile regression model on new data. SAS supports several procedures for quantile regression, including the QUANTREG, QUANTSELECT, and HPQUANTSELECT procedures. The first two procedures do not support any of the modern methods for scoring regression models, so you must use the "missing

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Analytics | Learn SAS

Rick WicklinAugust 1, 2018 2

Which variables are in the final selected model?

When you use a regression procedure in SAS that supports variable selection (GLMSELECT or QUANTSELECT), did you know that the procedures automatically produce a macro variable that contains the names of the selected variables? This article provides examples and details. A previous article provides an overview of the 'SELECT' procedures

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Analytics | Learn SAS | Programming Tips

Rick WicklinJuly 30, 2018 1

Meaningful names for columns of a design matrix

A programmer recently asked a question on a SAS discussion forum about design matrices for categorical variables. He had generated a design matrix by using PROC GLMMOD and wanted to use the design columns in a subsequent procedure. However, the columns were named COL1, COL2, COL3,..., so he couldn't tell

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Analytics | Learn SAS | Programming Tips

Rick WicklinJuly 23, 2018 6

How to use the %BOOT and %BOOTCI macros in SAS

Since the late 1990s, SAS has supplied macros for basic bootstrap and jackknife analyses. This article provides an example that shows how to use the %BOOT and %BOOTCI macros. The %BOOT macro generates a bootstrap distribution and computes basic statistics about the bootstrap distribution, including estimates of bias, standard error,

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Analytics | Programming Tips

Rick WicklinJuly 9, 2018 3

The intersection of two line segments

Back in high school, you probably learned to find the intersection of two lines in the plane. The intersection requires solving a system of two linear equations. There are three cases: (1) the lines intersect in a unique point, (2) the lines are parallel and do not intersect, or (3)

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Analytics | Programming Tips

Rick WicklinJuly 5, 2018 7

Compute derivatives for nonparametric regression models

SAS enables you to evaluate a regression model at any location within the range of the data. However, sometimes you might be interested in how the predicted response is increasing or decreasing at specified locations. You can use finite differences to compute the slope (first derivative) of a regression model.

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Analytics | Programming Tips

Rick WicklinJune 27, 2018 2

Reduced models: A way to choose initial parameters for a mixed model

This article describes how to obtain an initial guess for nonlinear regression models, especially nonlinear mixed models. The technique is to first fit a simpler fixed-effects model by replacing the random effects with their expected values. The parameter estimates for the fixed-effects model are often good initial guesses for the

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Analytics

Rick WicklinJune 25, 2018 3

Use a grid search to find initial parameter values for regression models in SAS

When you fit nonlinear fixed-effect or mixed models, it is difficult to guess the model parameters that fit the data. Yet, most nonlinear regression procedures (such as PROC NLIN and PROC NLMIXED in SAS) require that you provide a good guess! If your guess is not good, the fitting algorithm,

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