Sentence examples for matrix of predictors from inspiring English sources

A correlation matrix of predictors discovered no significant bivariate correlations greater than r = 0.25.

To ensure that no single predictor swamps the effects of others, the matrix of predictors (X) is centered and scaled, and then λ is chosen by cross-validation.

PLS regression is particularly suited to cases in which the matrix of predictors has more variables than observations, or when there is multi-collinearity among variables [76].

In the model, Y = X * B where Y represents the d' data across all participants in our study, X represents the matrix of predictors corresponding to the variables found to be significant from our analysis, and B the vector of regression coefficients that provides the optimal least squares fit.

Here X i is the matrix of predictors of dataset i and cor ^ denotes Pearson's correlation.

The matrix of predictors X, in equation (9), was generated mimicking the covariance structure of datasets from Table 2.

In the above equation, y is an n-length vector of the response variables; X is an n by p matrix of predictor variables.

The non-parametric coefficient was used because abundance data was strongly negatively skewed in a scatterplot matrix of predictor and response variables.

(b) The correlation matrix of parameters is the minor product moment of the standardized predictor measures divided by N and is given by R = left( {x^{prime} times x} right)/N (2) where, x′ denotes the transpose of the standardized matrix of predictor parameters     2.

Let C i be the matrix of predictor values with X i and Z i for subject i.

For marginal models the general formulation is: (1) logit π j = log π j π 1 = x j T β j where j = 2, …, J categories; π j is the probability of being in category j; π 1 is the probability of being in the reference category; x j T is the transpose of the matrix of predictor variables for each participant; and β j is the vector of coefficients to be estimated for each category j.