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15 Examples 298Covariance/Variance/Correlation Matrix CORRelation2.573 1.025 0.65683.024 3.382 0.78300.1526 0.2086 0.2098E-01Note the correlation between wwt and ywt is estimated at 1.025.The results from this analysis can be automatically used by ASReml for the nextpart, if the .rsv is copied prior to running the next part. That is, we add the!PATH 2 coding to the job, copy mt1.rsv to mt2.rsv so that when we run !PATH2 it starts from where !PATH 1 finished, and run the job usingasreml -cnrw64 mt 2The Loglikelihood from this run is −20000 − 1427.37.Finally, we use the !PATH 3 coding to obtain the final analysis, copy mt2.rsv tomt3.rsv and run the final stage starting from the stage 2 results. Note that weare using the automatic updating associated with !CONTINUE. A portion of thefinal output file isNotice: LogL values are reported relative to a base of -20000.00NOTICE: 76 singularities detected in design matrix.1 LogL=-1427.37 S2= 1.0000 35006 df : 2 components constrained2 LogL=-1424.58 S2= 1.0000 35006 df3 LogL=-1421.07 S2= 1.0000 35006 df : 1 components constrained4 LogL=-1420.11 S2= 1.0000 35006 df5 LogL=-1419.93 S2= 1.0000 35006 df6 LogL=-1419.92 S2= 1.0000 35006 df7 LogL=-1419.92 S2= 1.0000 35006 df8 LogL=-1419.92 S2= 1.0000 35006 df9 LogL=-1419.92 S2= 1.0000 35006 df10 LogL=-1419.92 S2= 1.0000 35006 df11 LogL=-1419.92 S2= 1.0000 35006 dfSource Model terms Gamma Component Comp/SE % Cat(Trait,1).age.grp 49 49 0.135360E-02 0.135360E-02 2.03 0 Pat(Trait,2).age.grp 49 49 0.101561E-02 0.101561E-02 1.24 0 Pat(Trait,4).age.grp 49 49 0.176505E-02 0.176505E-02 1.13 0 Pat(Trait,5).age.grp 49 49 0.209279E-03 0.209279E-03 1.68 0 Pat(Trait,1).sex.grp 49 49 0.919610 0.919610 2.89 0 Pat(Trait,2).sex.grp 49 49 15.3912 15.3912 3.50 0 Pat(Trait,3).sex.grp 49 49 0.279496 0.279496 3.71 0 Pat(Trait,5).sex.grp 49 49 1.44032 1.44032 1.80 0 PResidual UnStru 1 1 9.46220 9.46220 33.30 0 U: : : : :
15 Examples 299Covariance/Variance/Correlation Matrix UnStructured Residual9.462 0.5691 0.2356 0.1640 0.21837.332 17.54 0.4241 0.2494 0.46390.2728 0.6686 0.1417 0.3994 0.16790.9625 1.994 0.2870 3.642 0.4875E-010.8336 2.412 0.7846E-01 0.1155 1.541Covariance/Variance/Correlation Matrix UnStructured Tr.sire0.5941 0.7044 0.2966 0.2032 0.27030.6745 1.544 0.1364E-01-0.1224 0.57260.2800E-01 0.2076E-02 0.1500E-01 0.1121 -0.4818E-020.6238E-01-0.6056E-01 0.5469E-02 0.1586 -0.63310.3789E-01 0.1294 -0.1073E-03-0.4586E-01 0.3308E-01Covariance/Variance/Correlation Matrix UnStructured at(Tr,1).dam2.161 1.010 0.76632.196 2.186 0.83010.1577 0.1718 0.1959E-01Covariance/Variance/Correlation Matrix UnStructured at(Tr,1).lit3.547 0.5065 -0.1099 -0.4096E-011.555 2.657 0.1740 -0.5150-0.2787E-01 0.3821E-01 0.1815E-01-0.3282-0.7312E-01-0.7957 -0.4191E-01 0.8984Analysis of Variance NumDF F_inc15 Tr.age 5 98.9516 Tr.brr 15 116.7217 Tr.sex 5 59.7819 Tr.age.sex 4 4.90In the .res file is reported an eigen analysis of these four variance structures.Eigen Analysis of UnStructured matrix for ResidualEigen values 22.458 5.210 3.395 1.160 0.103Percentage 69.474 16.118 10.502 3.588 0.3181 0.4970 -0.8663 0.0141 0.0470 0.00272 0.8509 0.4765 -0.1316 -0.1746 -0.03273 0.0335 0.0230 0.0585 -0.0048 0.99744 0.1168 0.0871 0.9843 0.0769 -0.06335 0.1187 0.1196 -0.1010 0.9805 0.0039Eigen Analysis of UnStructured matrix for Tr.sireEigen values 1.904 0.304 0.114 0.013 0.010Percentage 81.199 12.963 4.859 0.535 0.4441 0.4578 0.7476 0.4695 -0.1052 0.00872 0.8860 -0.3646 -0.2766 0.0248 -0.07003 0.0077 0.0798 0.0826 0.9438 -0.30984 -0.0163 0.5260 -0.8015 0.1116 0.26125 0.0710 -0.1587 0.2320 0.2918 0.9115Eigen Analysis of UnStructured matrix for at(Tr,1).dam
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ASRemlUser GuideRelease 2.0A R Gilm
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Published by:E-mail:Website:VSN Int
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PrefaceiiASReml is one of several u
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ContentsPrefaceiList of TablesxviiL
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Contentsvi3.3 The ASReml data file
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ContentsviiiCombining rows from sep
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Contentsx7.6 Variance structure qua
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Contentsxii12.2 The command line .
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Contentsxiv15 Examples 24115.1 Intr
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List of Tablesxvi7.3 Details of the
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List of Figuresxix15.8 Rice bloodwo
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1 Introduction 21.1 What ASReml can
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1 Introduction 41.4 How to use this
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2 Some theoryThe linear mixed model
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2 Some theory 8laid out in a rectan
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2 Some theory 10product of matrices
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2 Some theory 12where H = R + ZGZ
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2 Some theory 14where y i is the
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2 Some theory 162.4 Combining varia
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2 Some theory 18balanced one-way cl
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2 Some theory 20L, r × p, and l, r
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2 Some theory 22the terms A, B, C,
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2 Some theory 24Kenward and Roger A
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3 A guided tourIntroductionNebraska
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3 A guided tour 28column rectangula
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3 A guided tour 30(see Chapter 2) t
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3 A guided tour 32els. Similarly fo
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3 A guided tour 34mand to run nin89
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3 A guided tour 36* SYNTAX change:
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3 A guided tour 38repl 3 -0.8713 1.
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3 A guided tour 40title linenin all
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4 Data file preparation 424.1 Intro
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4 Data file preparation 44Fixed for
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5 Command file: Reading the data 46
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6 Command file: Specifying theterms
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6 Command file: Specifying the term
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7 Command file: Specifying variance
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8 Command file: Multivariate analys
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9 Command file: Genetic analysisInt
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9 Command file: Genetic analysis 15
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10 Tabulation of the data andpredic
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10 Tabulation of the data and predi
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11 Functions of variancecomponentsI
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11 Functions of variance components
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11 Functions of variance components
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12 Command file: Running the jobInt
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12 Command file: Running the job 17
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13 Description of output filesIntro
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13 Description of output files 190A
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13 Description of output files 192i
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13 Description of output files 194f
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13 Description of output files 196.
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13 Description of output files 198o
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13 Description of output files 200E
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13 Description of output files 202t
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13 Description of output files 204|
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NIN alliance trial 1989 9a 1Residua
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13 Description of output files 208w
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13 Description of output files 210T
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14 Error messagesIntroductionCommon
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14 Error messages 214filename!last
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14 Error messages 216variety !AQUAL
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14 Error messages 2188. misspelt va
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14 Error messages 220Missing/faulty
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14 Error messages 222Last line read
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14 Error messages 224Model specific
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14 Error messages 226:11 column 11
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14 Error messages 22814.5 Informati
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14 Error messages 230List of warnin
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14 Error messages 232Alphabetical l
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15 Examples 24215.1 IntroductionIn
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15 Examples 244For simple variance
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15 Examples 2460.2_cwt Marvellous 1
Page 270 and 271: 15 Examples 248The input file conta
Page 272 and 273: 15 Examples 250Source Model terms G
Page 274 and 275: 15 Examples 252ltage example 5−3
Page 276 and 277: 15 Examples 254The focus is modelli
Page 278 and 279: 15 Examples 256y1 y3 y5 y7 y10 ~ Tr
Page 280 and 281: 15 Examples 258power model shown in
Page 282 and 283: 15 Examples 260Residual US=UnStr 2
Page 284 and 285: 15 Examples 262the current model).
Page 286 and 287: 15 Examples 264Slate Hall example f
Page 288 and 289: 15 Examples 266Predicted values of
Page 290 and 291: 15 Examples 268wheat.asd !skip 1 !D
Page 292 and 293: Tullibigeal trial i2 1Variogram of
Page 294 and 295: 15 Examples 272column evaluated at
Page 296 and 297: 15 Examples 274this is for the pair
Page 298 and 299: 15 Examples 276Table 15.8: Estimate
Page 300 and 301: 15 Examples 278(and in fact necessa
Page 302 and 303: 15 Examples 280Tr.run 22 0 US .79 .
Page 304 and 305: 15 Examples 282Interpretation of re
Page 306 and 307: 15 Examples 284The independence of
Page 308 and 309: 15 Examples 286Note that the data f
Page 310 and 311: 15 Examples 288Table 15.11 Orange d
Page 312 and 313: 15 Examples 290217Predicted values
Page 314 and 315: 15 Examples 292105Residual0-5-10200
Page 316 and 317: 15 Examples 294Table 15.13: REML es
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Page 322 and 323: 15 Examples 300Eigen values 4.382 0
Page 324 and 325: 15 Examples 302Animal modelIn this
Page 326 and 327: 15 Examples 304additive genetic var
Page 328 and 329: BibliographyAbramowitz, M. and Steg
Page 330 and 331: Bibliography 308Gilmour, A. R., Cul
Page 332 and 333: Bibliography 310Schall, R. (1991).
Page 334 and 335: IndexABORTASR.NOW, 65FINALASR.NOW,
Page 336 and 337: Index 314free format, 42functions o
Page 338 and 339: Index 316!==, 53!>, 53!>=, 53!*, 53
Page 340 and 341: Index 318!STEP, 71!SUBSET, 71!SUB,
Page 342: Index 320parameter, 7variance compo
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