Matvec Users’ Guide

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32 CHAPTER 3. MATRIX OBJECT vech A.vech(), where A is a matrix, returns a vector converting from lower triangular part of matrix A columnby-column. For example, > A=[1,2;3,4] Col 1 Col 2 Row 1 1 2 Row 2 3 4 > A.vech() zeros i = 1 i = 2 i = 3 1 3 4 zeros(m,n) creates a matrix object of m by n, with each element being zero. A.zeros() resets each element of matrix A to be zero. 3.5 Special Matrices In <strong>Matvec</strong>, there are 11 special matrices: ones, zeros, identity, sample, hilb, invhilb, hankel, vander, hadamard, pascal, toeplitz. The first four are builtin functions, while the rest are defined in macro package “special matrix”. 3.5.1 ones ones(m,n) creates a matrix object of order m by n, with each element being unit. For example, > A = ones(2,3) 3.5.2 zeros Col 1 Col 2 Col 3 Row 1 1 1 1 Row 2 1 1 1 zeros(m,n) creates a matrix object of order m by n, with each element being zero. For example, > A = zeros(2,3) Col 1 Col 2 Col 3 Row 1 0 0 0 Row 2 0 0 0 3.5.3 identity identity(m,n) creates an identity matrix of order m by n. For example, > A = identity(3,3) Col 1 Col 2 Col 3 Row 1 1 0 0 Row 2 0 1 0
3.5. SPECIAL MATRICES 33 Row 3 0 0 1 > A = identity(2,3) Col 1 Col 2 Col 3 Row 1 1 0 0 Row 2 0 1 0 3.5.4 sample sample(m,n) returns a random sample from UniformDist(0.0,1.0), stored in a matrix of m by n. For example, > A = sample(2,3) Col 1 Col 2 Col 3 Row 1 0.792847 0.619559 0.927987 Row 2 0.89682 0.990276 0.266462 3.5.5 other special matrices Other special matrices in <strong>Matvec</strong> are hilb, invhilb, hankel, vander, hadamard, pascal, toeplitz. They are privided in a macro package special matrix. See details in Chapter 9, section 9.3.
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Page 5 and 6: iv CONTENTS 3.3.1 Accessing element
Page 7 and 8: vi CONTENTS 10.7 Variance Component
Page 9 and 10: Chapter 1 A Tour of Matvec 1.1 Runn
Page 11 and 12: 1.5. GETTING ONLINE HELP 3 Example
Page 13 and 14: 1.8. VARIABLES, EXPRESSIONS, AND ST
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Page 17 and 18: Chapter 2 Working on Objects Matvec
Page 19 and 20: 2.4. MATRIX OBJECT 11 Another metho
Page 21 and 22: 2.7. DATA OBJECT 13 2.6.3 inbcoef P
Page 23 and 24: Chapter 3 Matrix Object Matrix comp
Page 25 and 26: 3.3. MANIPULATION 17 3.2.2 Relation
Page 27 and 28: 3.4. COMPUTATION 19 3.3.3 Selecting
Page 29 and 30: 3.4. COMPUTATION 21 This member fun
Page 31 and 32: 3.4. COMPUTATION 23 > A.fliplr() fl
Page 33 and 34: 3.4. COMPUTATION 25 max A.max() ret
Page 35 and 36: 3.4. COMPUTATION 27 reshape A.resha
Page 37 and 38: 3.4. COMPUTATION 29 sort If A is a
Page 39: 3.4. COMPUTATION 31 Col 1 Col 2 Col
Page 43 and 44: Chapter 4 Program Flow Control The
Page 45 and 46: 4.3. WHILE STATEMENT 37 that all th
Page 47 and 48: Chapter 5 File Stream Control Matve
Page 49 and 50: Chapter 6 Time Control Though I do
Page 51 and 52: 6.2. WESTERN CALENDAR 43 6.2 Wester
Page 53 and 54: Chapter 7 User-Defined Function 7.1
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Page 57 and 58: 8.3. EXAMPLES 49 Figure 8.1: The cu
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Page 77 and 78: Chapter 11 Generalized Linear Mixed
Page 79 and 80: 11.1. GENERALIZED LINEAR MODEL 71 T
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11.2. PENALIZED QUASI-LIKELIHOOD 83
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11.2. PENALIZED QUASI-LIKELIHOOD 85
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Chapter 12 Segregation and Linkage
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Chapter 13 Statistical Distribution
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13.2. CONTINUOUS DISTRIBUTION 91 4.
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13.2. CONTINUOUS DISTRIBUTION 93 Ex
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13.2. CONTINUOUS DISTRIBUTION 95 13
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13.2. CONTINUOUS DISTRIBUTION 97 Pr
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13.2. CONTINUOUS DISTRIBUTION 99 Ex
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13.2. CONTINUOUS DISTRIBUTION 101 M
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13.3. DISCRETE DISTRIBUTION 103 Fig
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13.3. DISCRETE DISTRIBUTION 105 Mat
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13.3. DISCRETE DISTRIBUTION 107 13.
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Chapter 14 Exception Exceptions in
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Chapter 15 Matvec C++ API 15.1 An O
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Index *, 16 *=, 16 +, 16 ++, 16 +=,
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INDEX 115 leapyear, 43 least-square
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INDEX 117 t distribution, 95 cdf, 9
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