Sentence examples for matrix of phenotypic from inspiring English sources
The matrix of phenotypic correlation coefficients among 23 traits was determined.
The variance covariance matrix of phenotypic observations is, In addition to additive SNP effects, dominant SNP effects are modeled for SNP having three genotypes and its heterozygosity > 10%.
The variance covariance matrix of phenotypic observations for this model is, Variance components and effects were again estimated by REML [ 24, 25].
The more easily estimated matrix of phenotypic variances and covariances P can be used as a surrogate for G, especially in the case of high heritability morphological characters [ 1- 4].
The phenotypic values are assumed to follow a normal distribution with mean Xg and variance-covariance matrix V, which can also be expressed as Var y = V = ⊕ i = 1 m V i, where Vi = variance-covariance matrix of phenotypic values of sibs in family i, and m = number of families.
To estimate within-sex phenotypic covariances among traits, we ran multivariate equivalents of the models represented by Equation 1, where y now represents a matrix of phenotypic observations of all traits measured within each sex and μ is a vector of means for each phenotypic trait.
The matrices of phenotypic variance (P) were computed as VCV matrices based on the 14 FCs per mandible.
This method relies on comparison of matrices of phenotypic and genotypic similarity between unrelated individuals (Yang et al. 2010).
Finally, a correlation between pairwise Euclidean distance matrices of phenotypic and genotypic characteristics was estimated with Mantel´s non-parametric test [ 28] after 10,000 permutations.
The variance covariance matrix (V) of phenotypic observations for the model can be written as, where A is the numerator relationship matrix based on pedigree information only, and I is an identity matrix.
In the second step, the genetic relationship matrix is compared to a matrix of pairwise phenotypic similarity using a restricted maximum likelihood random-effects mixed linear model.
