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6 Command file: Specifying the terms in the mixed model 86Summary of reserved words, operators and functionsmodel term brief description common usagefixedrandomotherfunctionsand(t[,r])c(f)cos(v,r)ge(f)giv(f,n)gt(f)h(f)ide(f)inv(v[,r])le(f)leg(v,[-]n)lt(f)log(v[,r])ma1(f)ma1out(n)out(n,t)adds r times the design matrix formodel term t to the previous designmatrix; r has a default value of 1. Ift is complex if may be necessary topredefine it by saying -t and(t,r)factor f is fitted with sum to zeroconstraintsforms cosine from v with period rcondition on factor/variable f >= rassociates the nth .giv G-inversewith the factor fcondition on factor/variable f > rfactor f is fitted Helmert constraintsfits pedigree factor f without relationshipmatrixforms reciprocal of v + rcondition on factor/variable f
6 Command file: Specifying the terms in the mixed model 87Summary of reserved words, operators and functionsmodel term brief description common usagefixedrandompol(v,[-]n)forms n+1 orthogonal polynomialsof order 0 (intercept), 1 (linear). . . nfrom the values in v; the interceptpolynomial is omitted if n is precededby the negative sign.pow(x, p[,o]) defines the covariable (x + o) p foruse in the model where x is a variablein the data, p is a power and ois an offset.qtl(f,p)sin(v,r)sqrt(v[,r])uni(f)uni(f,n)xfa(f,k)impute a covariable from markermap information at position pforms sine from v with period rforms square root of v + rforms a factor with a level for eachrecord where factor f is non-zeroforms a factor with a level for eachrecord where factor f has level nis formally a copy of factor f with kextra levels. This is used when fittingextended factor analytic models(XFA, Table 7.3) of order k.√√√√√√√√
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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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Page 64 and 65: 4 Data file preparation 424.1 Intro
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Page 68 and 69: 5 Command file: Reading the data 46
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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
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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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