Extending R by Chambers, John M PDF

By Chambers, John M

ISBN-10: 1498775721

ISBN-13: 9781498775724

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Here is a linear regression from the BMDP manual [14], simplified and omitting a few lines: / PROBLEM / INPUT TITLE IS ’WERNER BLOOD CHEMISTRY DATA’. VARIABLES ARE 5. 1)’. / VARIABLE NAMES ARE ID, AGE, WEIGHT, URICACID, CHOLSTRL. / REGRESS DEPENDENT IS CHOLSTRL. INDEPENDENT ARE AGE, WEIGHT, URICACID. / END Along with suitable input data, this would provide a batch “job” to run. The result of the linear regression in this example would be some printed output summarizing the input problem and the computation performed.

Human efficiency would benefit, even at the cost of extra machine computation. S was designed as An Interactive Environment for Data Analysis and Graphics, as the first book [3] was titled. 2 The first version of S to be distributed, beginning in the early 1980s, expressed a least squares regression as: 2 Iverson’s APL system [24] had pioneered this mode of computing and might itself have been used, except that interfacing to the existing Fortran library was essential and not part of the APL paradigm then.

On balance, the problems can largely be avoided and overall the pluses of the evolution exceed the minuses by a considerable margin. 3 (one of my treasured possessions) is evidence of the event. For understanding R, it needs to be repeated that creating an “open-source S” was not a routine exercise or simply an implementation according to specification. The blue book and white book established the user’s view, if sometimes vaguely. Supporting it all, the original subroutine library used by S incorporated many of the available state-of-the-art algorithms (for numerical linear algebra, for sorting and searching and for random number generation, among others).

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Extending R by Chambers, John M

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