# df Variance year vm(animal, ainv) ide(animal) # Algebraic derivatives for denominator df not available. # Warning in asreml(fixed = laydate ~ age + byear, random = ~vm(animal, ainv) + : Wald.asreml(modelz_ 3, ssType = "conditional", denDF = "numeric") # Model fitted using the sigma parameterization. In addition, using age as continuous variable can help in saving some degree of freedom in the analysis. We could equally have fitted it as a continuous variable, in which case, given potential for a late life decline, we would probably also include a quadratic term. Here age is modeled as a 5-level factor (specified using the function as.factor() at the beginning of the analysis). Wald.asreml(modelw, ssType = "conditional", denDF = "numeric") # Model fitted using the sigma parameterization. 5.1 Univariate model with repeated measures.4.4.3 Adding additional effects and testing significance.4.4.2 Partitioning additive and permanent environment effects.4.2.3 Adding additional effects and testing significance.4.2.2 Partitioning additive and permanent environment effects.3.5.4 Between groups (co)variances and the B-matrix.3.4.5 Between groups (co)variances and the B-matrix.3.4.3 Direct estimate of the correlation instead of the covariance.3.2.7 Between groups (co)variances and the B-matrix.3.2.6 Partitionning (co)variance between groups.3.2.5 Visualisation of the correlation (aka BLUP extraction).3.2.4 Estimate directly the genetic correlation within the model.2.5.7 Covariance between two random effects.2.5.5 Further partitioning of the variance.2.5.4 Testing significance of variance components.2.4.10 Covariance between two random effects.2.4.7 Testing significance of variance components.2.2.8 Covariance between two random effects.2.2.7 Modification of the varaince matrix parameters.2.2.6 Further partitioning the variance.2.2.5 Testing significance of random effects.
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