The linear model is specified in a fairly intuitive manner. Coding of parameter file for univariate genetic analysis. 4871 # covariate !mO fat !mO # Dependent variables # Pedigree file # Data file wwt - mu age sex sex.age con&r) !r tag dam i(dam) litter !f grp 0 # No special variance structures Figure 1. 49 # coded I=Male, 2=Female # coded 1.4 for SS TS MS MM #coded l. analysis of coopworth data tag -1 sire dam -1 49 grp sex 2 !A brr 4 litter 4871 age wwt !mO ywt !mO gfw !mO fdm wwt/coop.ped wwtJcoop.bin # Coded as in Pedigree File # coded l. or NA, the Genstat, SAS and S-PLUS conventions.
#Asreml with random variable only free
In free format files, missing values may be indicated by *. bin extension on the data file indicates it is a binary file but ASREML also reads free format data files. The identities must match those in the data file. The pedigree file has three fields - animal, sire, dam - and is free format.
After naming the data fields, the names of the pedigree tile and the data file are given. Note the use of # for comments and blank lines which are ignored. The !mO qualifier on each of the dependent variables sets any zeros in these fields to missing values.
#Asreml with random variable only code
A levels value of -1 is a special code which indicates that the number of levels is to be determined from the pedigree file. If the levels need coding, qualifiers !I and !A for integer and alphabetic codes respectively, allow this. For design factors, specify the number of levels in the factor here. After each name is any information required to interpret that field. The next section lists the names of the 14 fields in the data file. The control file has a fmed structure rather than using keywords to identify sections. UNIVARIATE EXAMPLE Figure 1 displays the control file for a univariate analysis of weaning weight fitting direct genetic, maternal genetic, maternal environment and litter as random effects. Vol12 perform the iterations, strategic use of starting values and limiting the number of iterations will help. For bigger analyses where it takes time to 386 Proc. ASREML caters for linear dependencies in the model (usually from overparameterization) by setting singular effects to zero. Note the syntax for fitting interactions (sex.age) and for constraining a factor with sum to zero constraints (con(brr)). With ASREML, it is easy to reorder, add or delete terms in the model and refit to test hypotheses of interest. (The sum of commonly called a Wald statistic.) However, except of variance could be performed, it is not obvious what and the residual variance produces an F-ratio squares divided by the residual variance is for simple cases where a traditional analysis denominator degrees of freedom to use. Dividing the sum of squares by its degrees of freedom with which to test the model term. ASREML calculates incremental sums of squares for these terms adjusted for `preceding' terms in this set and all terms after !r. By default, significance testing is performed for the fixed effects listed before !r. The model line from the univariate example is wwt - mu age sex sex.age con(brr) !r tag dam i(dam) litter !f grp This line identifies the dependent variable, the fixed terms and the random terms (between !r and !t). TESTING FIXED EFFECTS Testing of fixed effects is facilitated by the convenient syntax for specifying the linear model. This paper describes the use of ASREML for quantitative genetic applications using examples to highlight some of its features. The core routines are the engine for the new EEML directive in Genstat 5 release 4.1 and there is an interface for S-PLUS. It is flexible with respect to both fixed and random terms in the model and solves larger problems efficiently while having a convenient user interface. 1996) is a comprehensive mixed model analysis program. It data and multivariate achieved by using the of the mixed model specifying the linear INTRODUCTION ASREML (Gihnour et al. Keywords: RBML, genetic parameters, mixed model, BLUP, animal model. Testing of fixed effects is facilitated by a convenient syntax for model.
Computing efficiency is Average Information REML algorithm and taking advantage of the equations. Gilmour NSW Agriculture, Agricultural Research and Veterinary Centre, Orange, NSW, 2800 SUMMARY ASREML was developed during 1996 as a comprehensive mixed models caters for spatial analysis of field experiments, analysis of repeated measures analysis of data using additive genetic relationships. Vol I2 ASREML FOR TESTING FIXED EFFECTS AND ESTIMATING MULTIPLE TRAIT VARIANCE COMPONENTS A.R.
0 kommentar(er)
