3/7/2024 0 Comments Asreml r identify outliers![]() ![]() Experiments of this type frequently occur in performance evaluation analyses of diagnostic tests or analyzers (devices) quantifying various types of measurement (im)precision (see e.g. VCs can be predicted in random models ( random effects or variance component models) and LMMs ( linear mixed -effects- models) by application of either analysis of variance (ANOVA)-type estimation or Restricted Maximum Likelihood (REML). Also included, but usually of less importance in the field of VCA: Estimation of fixed effects and least square means (LS means) as well as testing linear hypotheses of fixed effects/LS means of linear mixed models (LMMs). Moreover, there are methods provided for estimating confidence intervals (CI) of VCs along with different graphical tools to better understand the data and for detecting outliers. Thus, VCA is the procedure of estimating the amount of the VCs’ contribution to the total variability in the dependent variable. Proportions of the total variability found to be attributed to these random effects variables are called variance components (VC). VCA are a way to assess how the variability of a dependent variable is structured taking into account its association with one or multiple random-effects variables. The main objective of R-package VCA is to perform variance component analyses (VCA). ![]()
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