Case Analysis of blue value and blup value in R language mixed Linear Model

In this article, the editor introduces in detail the case analysis of blue value and blup value in R language mixed linear model, with detailed content, clear steps and proper handling of details. I hope that this article "case analysis of blue value and blup value in R language mixed linear model" can help you solve your doubts.

Blue value VS BLUP value in mixed linear model

The best linear unbiased prediction (best linear unbiased prediction, abbreviated as BLUP), also known as "Brap" [1], is a statistical method used in linear mixed models to predict random effects. The best linear unbiased prediction is proposed by C. R. Henderson. The best linear unbiased prediction (BLUP) of random effect is equivalent to the best linear unbiased estimation (best linear unbiased estimates, BLUE) of fixed effect (see Gauss-Markov theorem). Because the term estimation is used for fixed effects and prediction is used for random effects, the two terms are basically the same. BLUP is widely used in animal breeding. -- wiki

The blup value is equivalent to the prediction of random factors in a mixed linear model.

Blue value, which is equivalent to the estimation of fixed factor in mixed linear model.

Predict means: the predicted mean, fixed factor and random factor can all predict the mean, and its scale is consistent with that of the phenotypic value.

Use processing as a fixed factor

Processing as a fixed factor setwd ("D:\\ 02 ASReml\\ blue VS blup") library (asreml) library (tidyverse) dat