The DO Loop
Statistical programming in SAS with an emphasis on SAS/IML programs![Simulate multivariate normal data in SAS by using PROC SIMNORMAL](https://blogs.sas.com/content/iml/files/2017/09/simnormal2-640x336.png)
My article about Fisher's transformation of the Pearson correlation contained a simulation. The simulation uses the RANDNORMAL function in SAS/IML software to simulate multivariate normal data. If you are a SAS programmer who does not have access to SAS/IML software, you can use the SIMNORMAL procedure in SAS/STAT software to
![Fisher's transformation of the correlation coefficient](https://blogs.sas.com/content/iml/files/2017/09/FisherZ2-640x336.png)
Pearson's correlation measures the linear association between two variables. Because the correlation is bounded between [-1, 1], the sampling distribution for highly correlated variables is highly skewed. Even for bivariate normal data, the skewness makes it challenging to estimate confidence intervals for the correlation, to run one-sample hypothesis tests ("Is
![The path of zip codes](https://blogs.sas.com/content/iml/files/2017/09/zipconnect2-640x336.png)
Toe bone connected to the foot bone, Foot bone connected to the leg bone, Leg bone connected to the knee bone,... — American Spiritual, "Dem Bones" Last week I read an interesting article on Robert Kosara's data visualization blog. Kosara connected the geographic centers of the US zip codes in
![Simulate multivariate clusters in SAS Simulate clustered data from a Gaussian mixture distribution](https://blogs.sas.com/content/iml/files/2017/09/mvnmixture-640x336.png)
This article shows how to simulate data from a mixture of multivariate normal distributions, which is also called a Gaussian mixture. You can use this simulation to generate clustered data. The adjacent graph shows three clusters, each simulated from a four-dimensional normal distribution. Each cluster has its own within-cluster covariance,
![Symbolic derivatives in SAS](https://blogs.sas.com/content/iml/files/2017/09/symderiv4.png)
Did you know that you can get SAS to compute symbolic (analytical) derivatives of simple functions, including applying the product rule, quotient rule, and chain rule? SAS can form the symbolic derivatives of single-variable functions and partial derivatives of multivariable functions. Furthermore, the derivatives are output in a form that
![Construct polynomial effects in SAS regression models](https://blogs.sas.com/content/iml/files/2017/09/polyeffect1-410x336.png)
If you use SAS regression procedures, you are probably familiar with the "stars and bars" notation, which enables you to construct interaction effects in regression models. Although you can construct many regression models by using that classical notation, a friend recently reminded me that the EFFECT statement in SAS provides