Today is the birthday of Bernhard Riemann, a German mathematician who made fundamental contributions to the fields of geometry, analysis, and number theory. Riemann is definitely on my list of the greatest mathematicians of all time, and his conjecture about the distribution of prime numbers is one of the great
Missing values are a fact of life. Many statistical analyses, such as regression, exclude observations that contain missing values prior to forming matrix equations that are used in the analysis. This post shows how to find rows of a data matrix that contain missing values and how to remove those
A frequently performed task in data analysis is identifying all the observations in a data set that satisfy certain conditions. For example, you might want to identify all of the female patients in your study or to identify all patients whose systolic blood pressure is greater than 140 mm Hg.
The R You Ready blog posed an interesting problem. Essentially, you have a vector that contains n(n+1)/2 elements, and you want to pack those elements into the upper left triangular portion of a matrix. For example, if your data are proc iml; /** vector v is given: ncol(v) = n(n+1)/2 for
When programmers begin learning a new computer language, the first program they write is often one that prints the text “Hello, World!” Successfully writing a Hello World program assures the programmer that the software is successfully installed and that all necessary features are working: parsers, compilers, linkers, and so on.
