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

By Jamis J. Perrett

Linear types classes are frequently provided as both theoretical or utilized. therefore, scholars could locate themselves both proving theorems or utilizing high-level approaches like PROC GLM to investigate info. There exists a niche among the derivation of formulation and analyses that conceal those formulation in the back of appealing person interfaces. This booklet bridges that hole, demonstrating idea placed into perform.

Concepts provided in a theoretical linear types path are frequently trivialized in utilized linear versions classes by way of the power of high-level SAS strategies like PROC combined and PROC REG that require the consumer to supply a number of techniques and statements and in go back produce colossal quantities of output. This booklet makes use of PROC IML to teach how analytic linear versions formulation could be typed at once into PROC IML, as they have been offered within the linear types path, and solved utilizing facts. This is helping scholars see the hyperlink among thought and alertness. This additionally assists researchers in constructing new methodologies within the sector of linear types.

The publication comprises entire examples of SAS code for lots of of the computations correct to a linear types path. although, the SAS code in those examples automates the analytic formulation. The code for high-level strategies like PROC combined can also be incorporated for side-by-side comparability. The ebook computes uncomplicated descriptive records, matrix algebra, matrix decomposition, probability maximization, non-linear optimization, and so forth. in a structure conducive to a linear versions or a unique issues direction.

Also integrated within the publication is an instance of a easy research of a linear combined version utilizing limited greatest chance estimation (REML). the instance demonstrates checks for fastened results, estimates of linear capabilities, and contrasts. the instance begins through exhibiting the stairs for reading the information utilizing PROC IML after which presents the research utilizing PROC combined. this permits scholars to persist with the method that result in the output.

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J. 1 – The NROW and NCOL functions: The NROW and NCOL functions compute the number of rows and the number of columns in a matrix, respectively. 2 – The LOC function: From its main description, “finds the nonzero elements of a matrix,” one might question the LOC function’s usefulness. How often is it useful to identify the nonzero elements of a matrix? The LOC function is useful for that, but is also has other seemingly unrelated uses. One such use is the avoidance of unnecessary looping. The following code uses two DO loops to find and print the odd numbers in each column of the matrix M.

The following code sets the default library to TEMP and then changes the name of the data set OLD to NEW. 3 Commands Commands are used to perform specific system actions, such as storing and loading matrices and modules, or performing special data processing requests. The following is a sample list of some IML commands and the actions they perform. 38 2 IML Language Structure Command Action APPEND CLOSE CREATE DELETE EDIT FIND INDEX LIST PURGE READ REPLACE RESET DEFLIB SAVE SETIN SETOUT SHOW CONTENTS SHOW DATASETS SORT SUMMARY USE adds observations to the end of a SAS data set closes a SAS data set creates and opens a new SAS data set for input and output marks observations for deletion in a SAS data set opens an existing SAS data set for input and output finds observations indexes variables in a SAS data set lists observations purges all deleted observations from a SAS data set reads observations into IML variables writes observations back into a SAS data set names default libname saves changes and reopens a SAS data set elects an open SAS data set for input elects an open SAS data set for output shows contents of the current input SAS data set shows SAS data sets currently open sorts a SAS data set produces summary statistics for numeric variables opens an existing SAS data set for input These IML commands can be used to control displayed output, get system information, manage SAS data sets, etc.

0010133 2 8 . 1 Statements 39 0010134 4 2 . 0010135 4 1 . 0010136 3 4 . RUN; PROC PRINT DATA=info; RUN; PROC IML; dep_table = {15 17 20 25 30 STORE dep_table; SHOW STORAGE; QUIT; 13 14 17 20 25 10 8 6 11 7 5 13 11 10 15 12 8 20 15 10 3 3 6 5 5 1 1 4 3 1 1 1 1 1 1 1, 1, 1, 1, 1}; PROC IML; LOAD dep_table; USE info; READ ALL VAR{type,age} INTO info2; EDIT info; DO i=1 TO NROW(info2); value = dep_table[info2[i],info2[i,2]]; REPLACE POINT i; END; CLOSE info; QUIT; PROC PRINT DATA = info; RUN; The DATA step creates the SAS data set INFO with values for the variables ID, TYPE, and AGE.

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