Posts Tagged ‘ 14.3 ’

Independent streams of random numbers in SAS

May 9, 2018
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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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Independence and overlap in streams of random numbers

May 7, 2018
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Independence and overlap in streams of random numbers

Simulation studies require both randomness and reproducibility, two qualities that are sometimes at odds with each other. A Monte Carlo simulation might need to generate millions of random samples, where each sample contains dozens of continuous variables and many thousands of observations. In simulation studies, the researcher wants each sample

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Use lists to pass parameters to SAS/IML functions

January 24, 2018
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Use lists to pass parameters to SAS/IML functions

A popular way to use lists in the SAS/IML language is to pack together several related matrices into a single data structure that can be passed to a function. Imagine that you have written an algorithm that requires a dozen different parameters. Historically, you would have to pass those parameters

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Create lists by using a natural syntax in SAS/IML

January 22, 2018
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Create lists by using a natural syntax in SAS/IML

SAS/IML 14.3 (SAS 9.4M5) introduced a new syntax for creating lists and for assigning and extracting item in a list. Lists (introduced in SAS/IML 14.2) are data structures that are convenient for holding heterogeneous data. A single list can hold character matrices, numeric matrices, scalar values, and other lists, as

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Visualize patterns of missing values

November 29, 2017
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Visualize patterns of missing values

Missing values present challenges for the statistical analyst and data scientist. Many modeling techniques (such as regression) exclude observations that contain missing values, which can reduce the sample size and reduce the power of a statistical analysis. Before you try to deal with missing values in an analysis (for example,

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