Sentence examples for matrix for random from inspiring English sources

Inspection of the variance-covariance matrix for random participant-specific effects suggests that the underlying structure of men's perceptions of women's nonverbal, dating-relevant cues may be two-dimensional, rather than four-dimensional, as would be expected if participants relied completely independently on the four stimulus dimensions.

Z denotes the design matrix for random effects with a ~ N (0, Gσa) being the vector of polygenic effects, σa2 the additive genetic variance and G the genetic covariance matrix and e ~ N (0, Iσe), a vector of residual effects.

Mixed animal models are as follows: where the vector y represents the phenotypic values, X is the design matrix for fixed effect, and Z is the design matrix for random effects.

While (10) comprises an additional mN equations, the part of the coefficient matrix for random effects is much sparser than for the MUV model, as each element of A-1 contributes only m + q non-zero elements, compared to q2 in (8).

The three mixed models have a general expression as follows: y = β X + Z u + e where X is the design matrix for fixed effects β, Z is the design matrix for random effects, and u and e are the residual matrix that follows the distribution e ~ N 0,σeI).

The same linear regression model, in which in both, step 1 and step 2, G can be viewed as a design matrix for random effects of the genotypes, was applied to estimate the variance components, including genotypic variance, genotype × environment interaction variance and variance of effective error across both segregating populations (Table  1).

Z i is a design matrix for the random effects that allows random subject deviations from the population average response.

Here we report on the possible application of starch as a dye-doped biopolymeric matrix designed for random lasing generation.

60 The generalised linear mixed model incorporates a vector of random effects and a design matrix for the random effects.

Longitudinal cognitive measures were modeled using linear mixed-effects regression with a random intercept and slope and an unstructured covariance matrix for the random effects.

The statistical model can be expressed as: y = μ + Zβ Z + Gβ+ e, where y = column vector of phenotypic values, μ = population mean of the phenotypic values, Z = incidence matrix for a random additive effects, βZ = column vector of the random additive effects, G = the genotype value of the candidate SNP, β = the regression coefficient of the candidate SNP, and e = random residual.