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This article will explain in detail how the basic functions of IML programming are, and the content of the article is of high quality, so the editor will share it with you for reference. I hope you will have some understanding of the relevant knowledge after reading this article.

SAS/IML is a module using matrix language, matrix is very convenient for statistical calculation, and the typical representative is the solution of multivariate linear model. The rows in the matrix represent observations, and the columns can represent variables. Character variables and numeric variables take all. Here are the basic functions of IML, which are only a small part of it.

Matrix operation function

Proc iml; / * declares iml module * / xroom1; y = {1 23}; z = {1 23, 3 56}; o = {'abc'' opq', 'lmn'' rst'}; n_z=nrow (z); / * Matrix dimension, column dimension nrow (), ncol () * / p_o=ncol (o); type_o=type (o) / * type () matrix type, character type C or numeric type N or both include * / nlen=nleng (o); / * nleng matrix length, several strings * / len=length (o); / * length () the character of each string in the matrix, of course, the string length can be changed * / print xjinyjue zjinzo naughz; / * display matrix value * / quit

Using function to create Matrix

Proc iml; Clearj (10recollection 3.14); / * J () function produces a constant matrix with 10 rows and one column 3.14cm / rsquj (1pr 5); / * a row and five columns matrix with the default value of 1cm / mquoj (10L5); / * 10 rows and 5 columns of 0 matrix * / miss=j (3L2); / * 3 rows and 2 columns missing value matrix * / g=repeat ({0 1}, 3J2) / * the REPEAT () function can produce a more general matrix * / seed=j (10); need=uniform (seed) can perform arithmetic calculations and produce a matrix with fixed spacing * / seed=j (10). / * generate pseudorandom matrix, seed is 1 random / / * if you need to generate a large number of pseudorandom numbers, consider RANDGEN program * / call randseed (123); / * seed * / xonomj (10L1); / * x is a column vector * / eQuexx; / * x assign a value to eBay / call randgen (x, "Uniform"); / * generate random vector * / call randgen (e, "Normal"); y=3*x+2+e / * regression model * / print cdepartment r department m department misc iml; iml; s = {12 3, 4 56, 7 8 9, 10 11 12}; transpose=t (s); / * matrix transpose, T () function completes * / s2=shape (sforce 6); / * SHAPE () function is used for matrix reorganization, S2 is a 6-row matrix * / s 3 entries [3L2] / * extract a certain value from the matrix and find the number of rows and rows to complete * / s 4 / s [1 print transpose,s2,s3,s4,s5,s6 2]; s 5 / s [s 4]; / * you can reconstruct the matrix * / s6=vecdiag (s) based on the extraction number; / * vecdiag, you can find the diagonal matrix of the matrix * / print transpose,s2,s3,s4,s5,s6; quit

The function of iml for data management

APPEND adds observations to the end of a SAS data set

CLOSE closes a SAS data set

CREATE creates and opens a new SAS data set for input and output

DELETE marks observations for deletion in a SAS data set

EDIT opens an existing SAS data set for input and output

FIND finds observations

INDEX indexes variables in a SAS data set

LIST lists observations

PURGE purges all deleted observations from a SAS data set

READ reads observations into IML variables

REPLACE writes observations back into a SAS data set

RESET DEFLIB names default libname

SAVE saves changes and reopens a SAS data set

SETIN selects an open SAS data set for input

SETOUT selects an open SAS data set for output

SHOW CONTENTS shows contents of the current input SAS data set

SHOW DATASETS shows SAS data sets currently open

SORT sorts a SAS data set

SUMMARY produces summary statistics for numeric variables

USE opens an existing SAS data set for input

Simple example:

The following is the simple use of the function * / use class; show datasets contents; reset deflib=work; m = {3 69 13}; n = {name sex age weight}; list all point m var n where (sex='F'); use class; read all var {name height}; read all var _ num_ into x; read all var _ num_ into femal where (sex= "F"); print xQuery Edit class; find all where (name= {'HENRY'}) into d; delete point=d; delete all where (age > 12); create class2 from class; setin class2 point 0; sum=0; do data; read next var (weight); sum=sum+weight; end; print sum; create ratio2 var (name htwt); append; show contents; close ratio2; summary var {' height' 'weight'} class {sex} stat {mean std} opt {save}; sort class out=sorted by name Index sex; purge

Simple logic programming

Proc iml; a = {17 22 13 10}; if max (a)

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