Sentence examples for matrix of covariates from inspiring English sources

Let also Xij be a design matrix of covariates (1 × p vector, with first element being 1 for the intercept).

We also let X i = (x i 1,..., x iT )' represent the T × P matrix of covariates for subject i.

X is a matrix of covariates (age and an intercept term in this model) and β is a vector of the fixed-effect coefficients.

A logistic model is fitted to model the relationship between the mean E and the design matrix of covariates including the indicator variables for phenotypes.

We utilize the following methods to check covariate models: 1) scatter plot for parameters versus covariates, ETAs versus covariates, WRES versus covariates; 2) scatterplot matrix of covariates.

In addition, we assume an matrix of covariates, including an intercept (in the first column), the phenotype of interest and potential confounders.

These models can be described by the following general model: 1 where y is the vector of trait phenotypes, X is a known incidence matrix that relates the vector of non-genetic, "fixed" effects to y, T= M− E(M), M is a matrix of marker covariates, α is the vector of random, partial-regression coefficients of the marker covariates, and e is a vector of residuals.

Z is the N ×  p matrix of all covariates intended to be observed and the ith row of the matrix is the vector z i.

In addition, let denote the matrix of p covariates and a column of ones for the intercept term, where the values relating to areal unit are denoted by.

For each time trend, PLS regression was performed separately with the ^γsi as the outcomes and the matrix of geographic covariates as the predictors.

Let X t* denote the N* × 1 vector of observed square-root transformed concentrations at monitor locations; R* the N* × p matrix of geographic covariates at monitor locations; X t the N × 1 vector of unknown square-root transformed concentrations at the unobserved subject locations; and R the N × p matrix of geographic covariates at the subject locations.