By Jamis J. Perrett

ISBN-10: 1441955569

ISBN-13: 9781441955562

ISBN-10: 1441955577

ISBN-13: 9781441955579

Linear versions classes are usually provided as both theoretical or utilized. as a result, scholars could locate themselves both proving theorems or utilizing high-level techniques like PROC GLM to investigate information. There exists a spot among the derivation of formulation and analyses that disguise those formulation in the back of appealing consumer interfaces. This ebook bridges that hole, demonstrating concept placed into perform.

Concepts offered in a theoretical linear versions path are usually trivialized in utilized linear versions classes by way of the power of high-level SAS tactics like PROC combined and PROC REG that require the consumer to supply a couple of ideas and statements and in go back produce large quantities of output. This e-book makes use of PROC IML to teach how analytic linear types formulation might be typed at once into PROC IML, as they have been awarded within the linear types path, and solved utilizing facts. This is helping scholars see the hyperlink among idea and alertness. This additionally assists researchers in constructing new methodologies within the region of linear versions.

The publication comprises whole examples of SAS code for plenty of of the computations suitable to a linear types direction. in spite of the fact that, the SAS code in those examples automates the analytic formulation. The code for high-level strategies like PROC combined can be incorporated for side-by-side comparability. The e-book computes simple descriptive facts, matrix algebra, matrix decomposition, probability maximization, non-linear optimization, and so on. in a layout conducive to a linear types or a distinct themes direction.

Also incorporated within the e-book is an instance of a simple research of a linear combined version utilizing constrained greatest chance estimation (REML). the instance demonstrates assessments for mounted results, estimates of linear services, and contrasts. the instance starts off by means of exhibiting the stairs for interpreting the knowledge utilizing PROC IML after which presents the research utilizing PROC combined. this enables scholars to stick to the method that bring about the output.

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Extra resources for A SAS/IML companion for linear models

Example text

Another type of DO-END section, called DO-iterate, can also be used in programming. The DO-iterate statement is of the form DO i = 1 TO 10 BY 2; Where I is a variable name that is assigned by the user, 1 is a starting value assigned by the user, 10 is an ending value assigned by the user, and BY 2 is an optional specification of increment that is assigned by the user. 1 Statements 43 A DO-iterate statement starts a DO-END section that can execute the statements contained within that section multiple times prior to continuing with the execution of the statements that follow the DO-END section.

The DO loop iterates five times. That is due to the fact that it is known that there are only five total lines of data values to read from the text file. Having skipped four lines with the previous INPUT statement (three skipped by the three back slashes and the fourth skipped because the INPUT statement read the fourth line but created no variables because no variable names were included in that first INPUT statement), the INPUT statement within the DO loop gives a name to the variables STUDENT and SCORE.

The statement col_ssq=a[##,]; computes the sum of the square of elements of all rows for each column. The statement mean_col_max=a[<>,][,:]; includes two subscript reduction operators. The first operator produces a row vector containing the column maximums. The second operator computes the row means. Together, the two operators produce the mean of the column maximums. 2 Element-wise Operators Element-wise operators perform mathematical operations on individual elements of a matrix. The following is a list of IML element-wise operators.

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

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