Posts Tagged ‘ data analysis ’

A quantile regression analysis of chess ratings by age

August 13, 2018
By
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

The post Read more »

Tags: ,
Posted in SAS | Comments Off on A quantile regression analysis of chess ratings by age

How to score and graph a quantile regression model in SAS

August 6, 2018
By
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

The post Read more »

Tags: , , ,
Posted in SAS | Comments Off on How to score and graph a quantile regression model in SAS

Which variables are in the final selected model?

August 1, 2018
By

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

The post Read more »

Tags: , ,
Posted in SAS | Comments Off on Which variables are in the final selected model?

Meaningful names for columns of a design matrix

July 30, 2018
By
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

The post Read more »

Tags: , ,
Posted in SAS | Comments Off on Meaningful names for columns of a design matrix

Color cells in a mosaic plot by deviation from independence

July 25, 2018
By
Color cells in a mosaic plot by deviation from independence

Back in SAS 9.3M2 (SAS/STAT 12.1), PROC FREQ introduced mosaic plots to visualize the joint frequencies in a contingency table. By default, the cells in a mosaic plot are colored according to levels of one of the categorical variables in the analysis. However, in 2013 I showed how you can

The post Read more »

Tags: ,
Posted in SAS | Comments Off on Color cells in a mosaic plot by deviation from independence

Compute derivatives for nonparametric regression models

July 5, 2018
By
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.

The post Read more »

Tags: , ,
Posted in SAS | Comments Off on Compute derivatives for nonparametric regression models

Ranking US presidents

July 2, 2018
By
Ranking US presidents

Which president of the United States is ranked the greatest by presidential historians? This article visualizes the results of the 2018 Presidential Greatness Survey, which was created and administered by B. Rottinghaus and J. Vaughn. They analyzed 166 responses from experts in political science who ranked the 44 US presidents

The post Read more »

Tags: , ,
Posted in SAS | Comments Off on Ranking US presidents

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

June 27, 2018
By
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

The post Read more »

Tags: , ,
Posted in SAS | Comments Off on Reduced models: A way to choose initial parameters for a mixed model

Welcome!

SAS-X.com offers news and tutorials about the various SAS® software packages, contributed by bloggers. You are welcome to subscribe to e-mail updates, or add your SAS-blog to the site.

Sponsors







Dear readers, proc-x is looking for sponsors who would be willing to support the site in exchange for banner ads in the right sidebar of the site. If you are interested, please e-mail me at: tal.galili@gmail.com
SAS and all other SAS Institute Inc. product or service names are registered trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates USA registration.