A SAS/IML companion for linear models by Jamis J. Perrett

By Jamis J. Perrett

Linear versions classes are usually offered as both theoretical or utilized. hence, scholars may perhaps locate themselves both proving theorems or utilizing high-level tactics like PROC GLM to research info. There exists a spot among the derivation of formulation and analyses that cover those formulation in the back of beautiful consumer interfaces. This ebook bridges that hole, demonstrating concept placed into perform.

Concepts offered in a theoretical linear versions direction are frequently trivialized in utilized linear types classes via the power of high-level SAS tactics like PROC combined and PROC REG that require the consumer to supply a couple of thoughts and statements and in go back produce huge quantities of output. This ebook makes use of PROC IML to teach how analytic linear versions formulation may be typed at once into PROC IML, as they have been offered within the linear types direction, and solved utilizing facts. This is helping scholars see the hyperlink among concept and alertness. This additionally assists researchers in constructing new methodologies within the zone of linear versions.

The booklet includes whole examples of SAS code for lots of of the computations correct to a linear versions path. even if, the SAS code in those examples automates the analytic formulation. The code for high-level methods like PROC combined can also be incorporated for side-by-side comparability. The publication computes simple descriptive information, matrix algebra, matrix decomposition, probability maximization, non-linear optimization, and so on. in a layout conducive to a linear versions or a different subject matters direction.

Also incorporated within the publication is an instance of a easy research of a linear combined version utilizing limited greatest probability estimation (REML). the instance demonstrates checks for fastened results, estimates of linear features, and contrasts. the instance starts off via exhibiting the stairs for interpreting the knowledge utilizing PROC IML after which presents the research utilizing PROC combined. this permits scholars to stick to the method that result in the output.

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2 Explain the difference between subscript reduction operators and element-wise operators as they apply to SAS/IML. 3 Explain the difference between comparison operators and logical operators. 4 A researcher is interested in printing out the status, “healthy” or “unhealthy” for each of nine subjects based on their BMI score. 5 and 25. 5<= bmi <= 25 THEN status="healthy"; ELSE status="unhealthy"; PRINT status; QUIT; There are several coding issues that prevent the above code from printing the status for each of the nine subjects.

It makes use of the IML commands STORE, SHOW STORAGE, LOAD, USE, READ, EDIT, and REPLACE. 5 – Updating values in a data set: Often tables must be used in calculations to comply with regulations. In this example a table of depreciation rates is used to calculate the current value of a product based on the type and age of product. The SAS data set INFO contains the ID, type, and age of the various products. The data set also includes the variable VALUE with missing values currently recorded for each observation.

10 – Creating a matrix from a data set (created from text file using list input): In this example a SAS data set, CLASS_B, is first created from the text file and then the two column vectors, STUDENTS and SCORES, are created from a CLASS_B data set using PROC IML. 9. There are more options available in the DATA step for reading in data from different types of text files than there are in PROC IML. As well, certain types of data manipulation may be more easily performed in a DATA step than in PROC IML.

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