Download pdf guide - VSN International

More documents

Recommendations

Info

15 Examples 288Table 15.11 Orange data: AOV decompositionstratum decomposition type df or neconstant 1 F 1ageage F 1spl(age,7) R 5fac(age) R 7treetree RC 5age.treex.tree RC 5spl(age,7).tree R 25errorRAn extract of the ASReml input file iscirc ~ mu age !r Tree 4.6 Tree.age .000094 spl(age,7) .1,spl(age,7).Tree 2.3 fac(age) 13.90 0 1Tree 22 0 US 4.6 .00001 .0000945 0 0predict age Tree !IGNORE fac(age)We stress the importance of model building in these settings, where we generallycommence with relatively simple variance models and update to more complexvariance models if appropriate. Table 15.12 presents the sequence of fitted modelswe have used. Note that the REML log-likelihoods for models 1 and 2 arecomparable and likewise for models 3 to 6. The REML log-likelihoods are notcomparable between these groups due to the inclusion of the fixed season termin the second set of models.We begin by modelling the variance matrix for the intercept and slope for eachtree, Σ, as a diagonal matrix as there is no point including a covariance componentbetween the intercept and slope if the variance component(s) for one (orboth) is zero. Model 1 also does not include a non-smooth component at theoverall level (that is, fac(age)). Abbreviated output is shown below.
15 Examples 289Table 15.12 Sequence of models fitted to the Orange datamodelterm 1 2 3 4 5 6tree y y y y y yage.tree y y y y y y(covariance) n n n n n yspl(age,7) y y y y n ytree.spl(age,7) y y y n y yfac(age) n y y n n nseason n n y y y yREML log-likelihood -97.78 -94.07 -87.95 -91.22 -90.18 -87.4312 LogL=-97.7788 S2= 6.3550 33 dfSource Model terms Gamma Component Comp/SE % CTree 5 5 4.79025 30.4420 1.24 0 PTree.age 5 5 0.939436E-04 0.597011E-03 1.41 0 Pspl(age,7) 5 5 100.513 638.759 1.55 0 Pspl(age,7).Tree 25 25 1.11728 7.10033 1.44 0 PVariance 35 33 1.00000 6.35500 1.74 0 PAnalysis of Variance NumDF DenDF F_inc Prob7 mu 1 4.0 47.04 0.0023 age 1 4.0 95.00
Page 1 and 2:
ASRemlUser GuideRelease 2.0A R Gilm
Page 3 and 4:
Published by:E-mail:Website:VSN Int
Page 5 and 6:
PrefaceiiASReml is one of several u
Page 7 and 8:
ContentsPrefaceiList of TablesxviiL
Page 9 and 10:
Contentsvi3.3 The ASReml data file
Page 11 and 12:
ContentsviiiCombining rows from sep
Page 13 and 14:
Contentsx7.6 Variance structure qua
Page 15 and 16:
Contentsxii12.2 The command line .
Page 17 and 18:
Contentsxiv15 Examples 24115.1 Intr
Page 19:
List of Tablesxvi7.3 Details of the
Page 22 and 23:
List of Figuresxix15.8 Rice bloodwo
Page 24 and 25:
1 Introduction 21.1 What ASReml can
Page 26 and 27:
1 Introduction 41.4 How to use this
Page 28 and 29:
2 Some theoryThe linear mixed model
Page 30 and 31:
2 Some theory 8laid out in a rectan
Page 32 and 33:
2 Some theory 10product of matrices
Page 34 and 35:
2 Some theory 12where H = R + ZGZ
Page 36 and 37:
2 Some theory 14where y i is the
Page 38 and 39:
2 Some theory 162.4 Combining varia
Page 40 and 41:
2 Some theory 18balanced one-way cl
Page 42 and 43:
2 Some theory 20L, r × p, and l, r
Page 44 and 45:
2 Some theory 22the terms A, B, C,
Page 46 and 47:
2 Some theory 24Kenward and Roger A
Page 48 and 49:
3 A guided tourIntroductionNebraska
Page 50 and 51:
3 A guided tour 28column rectangula
Page 52 and 53:
3 A guided tour 30(see Chapter 2) t
Page 54 and 55:
3 A guided tour 32els. Similarly fo
Page 56 and 57:
3 A guided tour 34mand to run nin89
Page 58 and 59:
3 A guided tour 36* SYNTAX change:
Page 60 and 61:
3 A guided tour 38repl 3 -0.8713 1.
Page 62 and 63:
3 A guided tour 40title linenin all
Page 64 and 65:
4 Data file preparation 424.1 Intro
Page 66 and 67:
4 Data file preparation 44Fixed for
Page 68 and 69:
5 Command file: Reading the data 46
Page 70 and 71:
Page 72 and 73:
Page 74 and 75:
Page 76 and 77:
Page 78 and 79:
Page 80 and 81:
Page 82 and 83:
Page 84 and 85:
Page 86 and 87:
Page 88 and 89:
Page 90 and 91:
Page 92 and 93:
Page 94 and 95:
Page 96 and 97:
Page 98 and 99:
Page 100 and 101:
Page 102 and 103:
Page 104 and 105:
6 Command file: Specifying theterms
Page 106 and 107:
6 Command file: Specifying the term
Page 108 and 109:
Page 110 and 111:
Page 112 and 113:
Page 114 and 115:
Page 116 and 117:
Page 118 and 119:
Page 120 and 121:
Page 122 and 123:
Page 124 and 125:
Page 126 and 127:
Page 128 and 129:
7 Command file: Specifying variance
Page 130 and 131:
Page 132 and 133:
Page 134 and 135:
Page 136 and 137:
Page 138 and 139:
Page 140 and 141:
Page 142 and 143:
Page 144 and 145:
Page 146 and 147:
Page 148 and 149:
Page 150 and 151:
Page 152 and 153:
Page 154 and 155:
Page 156 and 157:
Page 158 and 159:
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
8 Command file: Multivariate analys
Page 166 and 167:
Page 168 and 169:
Page 170 and 171:
9 Command file: Genetic analysisInt
Page 172 and 173:
9 Command file: Genetic analysis 15
Page 174 and 175:
Page 176 and 177:
Page 178 and 179:
10 Tabulation of the data andpredic
Page 180 and 181:
10 Tabulation of the data and predi
Page 182 and 183:
Page 184 and 185:
Page 186 and 187:
Page 188 and 189:
Page 190 and 191:
Page 192 and 193:
11 Functions of variancecomponentsI
Page 194 and 195:
11 Functions of variance components
Page 196 and 197:
11 Functions of variance components
Page 198 and 199:
12 Command file: Running the jobInt
Page 200 and 201:
12 Command file: Running the job 17
Page 202 and 203:
Page 204 and 205:
Page 206 and 207:
Page 208 and 209:
Page 210 and 211:
13 Description of output filesIntro
Page 212 and 213:
13 Description of output files 190A
Page 214 and 215:
13 Description of output files 192i
Page 216 and 217:
13 Description of output files 194f
Page 218 and 219:
13 Description of output files 196.
Page 220 and 221:
13 Description of output files 198o
Page 222 and 223:
13 Description of output files 200E
Page 224 and 225:
13 Description of output files 202t
Page 226 and 227:
13 Description of output files 204|
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
Page 248 and 249:
14 Error messages 226:11 column 11
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 256 and 257:
Page 258 and 259:
Page 260 and 261: 14 Error messages 238Alphabetical l
Page 262 and 263: 14 Error messages 240Alphabetical l
Page 264 and 265: 15 Examples 24215.1 IntroductionIn
Page 266 and 267: 15 Examples 244For simple variance
Page 268 and 269: 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 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
Page 318 and 319: 15 Examples 29615*0. #Asreml will e
Page 320 and 321: 15 Examples 298Covariance/Variance/
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
show all

Download pdf guide - VSN International

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?