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8 Command file: Multivariate analysis 1448.3 Variance structuresUsing the notation of Chapter 7, consider a multivariate analysis with t traits andn units in which the data are ordered traits within units. An algebraic expressionfor the variance matrix in this case isI n ⊗ Σwhere Σ (t×t) is an unstructured variance matrix.Specifying multivariate variance structures in ASRemlA more sophisticated error structure is requiredfor multivariate analysis. For a standardmultivariate analysis• the error structure for the residual must bespecified as two-dimensional with independentrecords and an unstructured variancematrix across traits; records may have observationsmissing in different patterns andthese are handled internally during analysis,• the R structure must be ordered traitswithin units, that is, the R structure definitionline for units must be specified beforethe line for Trait,• variance parameters are variances not varianceratios,• the R structure definition line for units,that is, 1485 0 ID, could be replaced by0 or 0 0 ID; this tells ASReml to fill in theOrange Wether Trial 1984-8SheepID !ITRIALBloodLine !ITEAM *YEAR *GFW YLD FDIAMwether.dat !skip 1GFW FDIAM ∼ Trait Trait.YEAR,!r Trait.TEAM Trait.SheepID1 2 21485 0 IDTrait 0 US3*0Trait.TEAM 2Trait 0 US3*0TEAM 0 IDTrait.SheepID 2Trait 0 US3*0SheepID 0 IDpredict YEAR Traitnumber of units and is a useful option when the exact number of units in thedata is not known to the user,• the error variance matrix is specified by the model Trait 0 US– the initial values are for the lower triangle of the (symmetric) matrix specifiedrow-wise,– finding reasonable initial values can be a problem. If initial values are writtenon the next line in the form q * 0 where q is t(t + 1)/2 and t is the numberof traits, ASReml will take half of the phenotypic variance matrix of the dataas an initial value, see .as file in code box for example,
8 Command file: Multivariate analysis 145• the special qualifiers relating to multivariate analysis are !ASUV and !ASMV t,see Table 5.4 for detail– to use an error structure other than US for the residual stratum you mustalso specify !ASUV (see Table 5.4) and include mv in the model if there aremissing values,– to perform a multivariate analysis when the data have already been expandeduse !ASMV t (see Table 5.4)– t is the number of traits that ASReml should expect,– the data file must have t records for each multivariate record althoughsome may be coded missing.8.4 The output for a multivariate analysisBelow is the output returned in the .asr file for this analysis.ASReml 1.63o [01 Jun 2005] Orange Wether Trial 1984-88Build: j [01 Jul 2005] 32 bit13 Jul 2005 09:38:00.928 32.00 Mbyte Windows wetherLicensed to: Arthur Gilmour************************************************************ SYNTAX change: A/B now means A A.B ** ** Contact support@asreml.co.uk for licensing and support ****************************************************** ARG *Folder: C:\data\asr\UG2\manexTAG !IBloodLine !IQUALIFIERS: !SKIP 1Reading wether.dat FREE FORMAT skipping 1 linestraitsBivariate analysis of GFW and FDIAMUsing 1485 records of 1485 readModel term Size #miss #zero MinNon0 Mean MaxNon01 TAG 521 0 0 1 261.0956 5212 TRIAL 0 0 3.000 3.000 3.0003 BloodLine 27 0 0 1 13.4323 274 TEAM 35 0 0 1 18.0067 355 YEAR 3 0 0 1 2.0391 3
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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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Page 178 and 179: 10 Tabulation of the data andpredic
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Page 192 and 193: 11 Functions of variancecomponentsI
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Page 210 and 211: 13 Description of output filesIntro
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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
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15 Examples 248The input file conta
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15 Examples 250Source Model terms G
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15 Examples 252ltage example 5−3
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15 Examples 254The focus is modelli
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15 Examples 256y1 y3 y5 y7 y10 ~ Tr
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15 Examples 258power model shown in
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15 Examples 260Residual US=UnStr 2
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15 Examples 262the current model).
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15 Examples 264Slate Hall example f
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15 Examples 266Predicted values of
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15 Examples 268wheat.asd !skip 1 !D
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Tullibigeal trial i2 1Variogram of
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15 Examples 272column evaluated at
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15 Examples 274this is for the pair
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15 Examples 276Table 15.8: Estimate
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15 Examples 278(and in fact necessa
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15 Examples 280Tr.run 22 0 US .79 .
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15 Examples 282Interpretation of re
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15 Examples 284The independence of
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15 Examples 286Note that the data f
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15 Examples 288Table 15.11 Orange d
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15 Examples 290217Predicted values
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15 Examples 292105Residual0-5-10200
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15 Examples 294Table 15.13: REML es
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15 Examples 29615*0. #Asreml will e
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15 Examples 298Covariance/Variance/
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15 Examples 300Eigen values 4.382 0
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15 Examples 302Animal modelIn this
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15 Examples 304additive genetic var
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BibliographyAbramowitz, M. and Steg
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Bibliography 308Gilmour, A. R., Cul
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Bibliography 310Schall, R. (1991).
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IndexABORTASR.NOW, 65FINALASR.NOW,
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Index 314free format, 42functions o
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Index 316!==, 53!>, 53!>=, 53!*, 53
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Index 318!STEP, 71!SUBSET, 71!SUB,
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Index 320parameter, 7variance compo
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