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15 Examples 2460.2_cwt Marvellous 108.5000 9.1070 E0.2_cwt Victory 89.6667 9.1070 E0.2_cwt Golden_rain 98.5000 9.1070 E0_cwt Marvellous 86.6667 9.1070 E0_cwt Victory 71.5000 9.1070 E0_cwt Golden_rain 80.0000 9.1070 EPredicted values with SED(PV)126.833118.500 9.71503124.833 9.71503 9.71503117.167 7.68295 9.71503 9.71503110.833 9.71503 7.68295 9.71503 9.71503114.667 9.71503 9.71503 7.68295 9.715039.71503108.500 7.68295 9.71503 9.71503 7.682959.71503 9.7150389.6667 9.71503 7.68295 9.71503 9.715037.68295 9.71503 9.7150398.5000 9.71503 9.71503 7.68295 9.715039.71503 7.68295 9.71503 9.7150386.6667 7.68295 9.71503 9.71503 7.682959.71503 9.71503 7.68295 9.71503 9.7150371.5000 9.71503 7.68295 9.71503 9.715037.68295 9.71503 9.71503 7.68295 9.715039.7150380.0000 9.71503 9.71503 7.68295 9.715039.71503 7.68295 9.71503 9.71503 7.682959.71503 9.71503SED: Standard Error of Difference: Min 7.6830 Mean 9.1608 Max 9.715015.3 Unbalanced nested design - RatsThe second example we consider is a data set which illustrates some furtheraspects of testing fixed effects in linear mixed models. This example differs fromthe split plot example, as it is unbalanced and so more care is required in assessingthe significance of fixed effects.The experiment was reported by Dempster et al. (1984) and was designed tocompare the effect of three doses of an experimental compound (control, lowand high) on the maternal performance of rats. Thirty female rats (dams) wererandomly split into three groups of 10 and each group randomly assigned to thethree different doses. All pups in each litter were weighed. The litters differed intotal size and in the numbers of males and females. Thus the additional covariate,littersize was included in the analysis. The differential effect of the compound
15 Examples 247on male and female pups was also of interest. Three litters had to be droppedfrom experiment, which meant that one dose had only 7 dams. The analysismust account for the presence of between dam variation, but must also recognisethe stratification of the experimental units (pups within litters) and that dosesand littersize belong to the dam stratum. Table 15.2 presents an indicative AOVdecomposition for this experiment.Table 15.2 Rat data: AOV decompositionstratum decomposition type df or neconstant 1 F 1damsdose F 2littersize F 1dam R 27dams.pupssex F 1dose.sex F 2errorRThe dose and littersize effects are tested against the residual dam variation, whilethe remaining effects are tested against the residual within litter variation. TheASReml input to achieve this analysis is presented below.Rats exampledose 3 !Asex 2 !Alittersizedam 27pup 18weightrats.asd !DOPATH 1 # Change DOPATH argument to select each PATH!PATH 1weight ~ mu littersize dose sex dose.sex !r dam!PATH 2weight ~ mu out(66) littersize dose sex dose.sex !r dam!PATH 3weight ~ mu littersize dose sex !r dam!PATH 4weight ~ mu littersize dose sex
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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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Page 228 and 229: NIN alliance trial 1989 9a 1Residua
Page 230 and 231: 13 Description of output files 208w
Page 232 and 233: 13 Description of output files 210T
Page 234 and 235: 14 Error messagesIntroductionCommon
Page 236 and 237: 14 Error messages 214filename!last
Page 238 and 239: 14 Error messages 216variety !AQUAL
Page 240 and 241: 14 Error messages 2188. misspelt va
Page 242 and 243: 14 Error messages 220Missing/faulty
Page 244 and 245: 14 Error messages 222Last line read
Page 246 and 247: 14 Error messages 224Model specific
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Page 250 and 251: 14 Error messages 22814.5 Informati
Page 252 and 253: 14 Error messages 230List of warnin
Page 254 and 255: 14 Error messages 232Alphabetical l
Page 264 and 265: 15 Examples 24215.1 IntroductionIn
Page 266 and 267: 15 Examples 244For simple variance
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
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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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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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