"the programmer should make a number of definite assertions which can be checked individually, and from which the correctness of the whole program follows" - Alan Turin
In my last post I illustrated the performance boost generated by using matrix operations to conduct least squares regression calculations. Matrices by their nature require numerical data. So what about handling a categorical predictor variable? To do this it’s necessary to create dummy variables – separate variables for each unique level of the predictor variable.
I’m working on some predictive modelling projects and I need to iteratively compute R2 statistics over 100’s of variables. Each time I do the calculations I need to go and have an extended coffee break – and I’m starting to buzz with too much caffeine so I thought I would look to see whether I could make my code more efficient!
A column of a JMP table acts as the primary source of data for most types of analysis within JMP. That is evident by the fact that the first task you perform when you launch a platform is to assign columns to roles:
A string variable is created by enclosing a text sequence within double quotes:
str = "this is a string";
If the string needs to contain special characters, including the double quote character itself, the special characters are prefixed with an escape sequence consisting of a backslash (\) following by an exclamation mark (!).
With a little effort you can create some stunning interfaces to front-end your JSL scripts. The individual elements of the user interface are known as display boxes. They can be used to add content to a window and to control alignment.