Sentence examples for explanatory matrices from inspiring English sources

MRM allows for the analysis of two or more matrices [25], where the response matrix (genetic distance) is regressed on multiple explanatory matrices (geographic and temporal distances), while also controlling for the effect of those explanatory matrices.

This dummy matrix was included in the analysis, allowing us to partial out the influence of missing data points and obtain estimates of partial correlation coefficients for our explanatory matrices.

In short, MRQAP calculates partial matrix regression coefficients for a response matrix on several explanatory matrices and then uses a large number of random permutations of rows and columns within matrices to generate a sampling distribution and assign p-values.

We regressed the matrix of standardised time squatted on the explanatory matrices (master network matrix plus matrices of in- minus out-link, order, sex difference, hierarchy difference and recipient betweenness) using UCINET's MRQAP procedure with double Dekker semi-partialling and 10,000 permutations.

We investigated the effects of total out- and in-link strength on standardised effort investment by regressing the matrix of standardised time squatted on explanatory matrices (master matrix plus matrices of in- minus out-link, order, sex difference, hierarchy difference and recipient betweenness) using MRQAP as described in the Materials & Methods.

We built three explanatory matrices: collection site, phenotype (winged or wingless) and environment (habitat type).

These two variables form the explanatory matrix X.

Four combinations of explanatory distance matrices were used: (A) environmental and linear geographic distance, (B) environmental and ln-transformed geographic distance, (C) climatic and linear geographic distance, and (D) climatic and ln-transformed geographic distance.

where X iq is explanatory variable matrix of V iq and β' is the parameter vector associated with the matrix X iq.

Because the explanatory and response matrices X and Y are multiplied in the course of VARPART and RDA analyses, this transformation is equivalent to multiplication by C−1 in standard PGLS approaches.

Assuming that one has the codon usage matrix Y and the matrix of explanatory variables (codon usage indices) X (both have the same rows), then the RDA procedure is to predict the elements (codon usage values) in the matrix Y as (8) Y ^ = X [ X T X ] − 1 X T Y, where the subscript T denotes the transpose of the matrix and −1 denotes the inverse of the matrix.