LEADER 05458nam 2200709 450 001 9910132156303321 005 20200520144314.0 010 $a1-118-86176-0 010 $a1-118-86181-7 035 $a(CKB)3710000000218291 035 $a(EBL)1765090 035 $a(SSID)ssj0001289141 035 $a(PQKBManifestationID)11793377 035 $a(PQKBTitleCode)TC0001289141 035 $a(PQKBWorkID)11307234 035 $a(PQKB)11531924 035 $a(OCoLC)887507303 035 $a(MiAaPQ)EBC1765090 035 $a(Au-PeEL)EBL1765090 035 $a(CaPaEBR)ebr10907567 035 $a(CaONFJC)MIL637319 035 $a(PPN)183866584 035 $a(EXLCZ)993710000000218291 100 $a20140822h20142014 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 10$aIntroduction to mixed modelling $ebeyond regression and analysis of variance /$fN. W. Galwey 205 $aSecond edition. 210 1$aChichester, [England] :$cWiley,$d2014. 210 4$dİ2014 215 $a1 online resource (504 p.) 300 $aDescription based upon print version of record. 311 $a1-322-06068-1 311 $a1-119-94549-6 320 $aIncludes bibliographical references at the end of each chapters and index. 327 $aCover; Title Page; Copyright; Contents; Preface; Chapter 1 The need for more than one random-effect term when fitting a regression line; 1.1 A data set with several observations of variable Y at each value of variable X; 1.2 Simple regression analysis: Use of the software GenStat to perform the analysis; 1.3 Regression analysis on the group means; 1.4 A regression model with a term for the groups; 1.5 Construction of the appropriate F test for the significance of the explanatory variable when groups are present; 1.6 The decision to specify a model term as random: A mixed model 327 $a1.7 Comparison of the tests in a mixed model with a test of lack of fit 1.8 The use of Residual Maximum Likelihood (REML) to fit the mixed model; 1.9 Equivalence of the different analyses when the number of observations per group is constant; 1.10 Testing the assumptions of the analyses: Inspection of the residual values; 1.11 Use of the software R to perform the analyses; 1.12 Use of the software SAS to perform the analyses; 1.13 Fitting a mixed model using GenStat's Graphical User Interface (GUI); 1.14 Summary; 1.15 Exercises; References 327 $aChapter 2 The need for more than one random-effect term in a designed experiment 2.1 The split plot design: A design with more than one random-effect term; 2.2 The analysis of variance of the split plot design: A random-effect term for the main plots; 2.3 Consequences of failure to recognize the main plots when analysing the split plot design; 2.4 The use of mixed modelling to analyse the split plot design; 2.5 A more conservative alternative to the F and Wald statistics; 2.6 Justification for regarding block effects as random 327 $a2.7 Testing the assumptions of the analyses: Inspection of the residual values 2.8 Use of R to perform the analyses; 2.9 Use of SAS to perform the analyses; 2.10 Summary; 2.11 Exercises; References; Chapter 3 Estimation of the variances of random-effect terms; 3.1 The need to estimate variance components; 3.2 A hierarchical random-effects model for a three-stage assay process; 3.3 The relationship between variance components and stratum mean squares; 3.4 Estimation of the variance components in the hierarchical random-effects model; 3.5 Design of an optimum strategy for future sampling 327 $a3.6 Use of R to analyse the hierarchical three-stage assay process 3.7 Use of SAS to analyse the hierarchical three-stage assay process; 3.8 Genetic variation: A crop field trial with an unbalanced design; 3.9 Production of a balanced experimental design by `padding'' with missing values; 3.10 Specification of a treatment term as a random-effect term: The use of mixed-model analysis to analyse an unbalanced data set; 3.11 Comparison of a variance component estimate with its standard error; 3.12 An alternative significance test for variance components 327 $a3.13 Comparison among significance tests for variance components 330 $aThis book first introduces the criterion of Restricted Maximum Likelihood (REML) for the fitting of a mixed model to data before illustrating how to apply mixed model analysis to a wide range of situations, how to estimate the variance due to each random-effect term in the model, and how to obtain and interpret Best Linear Unbiased Predictors (BLUPs) estimates of individual effects that take account of their random nature. It is intended to be an introductory guide to a relatively advanced specialised topic, and to convince the reader that mixed modelling is neither so specialised nor so d 606 $aMultilevel models (Statistics) 606 $aExperimental design 606 $aRegression analysis 606 $aAnalysis of variance 615 0$aMultilevel models (Statistics) 615 0$aExperimental design. 615 0$aRegression analysis. 615 0$aAnalysis of variance. 676 $a519.5 700 $aGalwey$b Nick$0102196 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910132156303321 996 $aIntroduction to mixed modelling$9731956 997 $aUNINA