composite_r_matrix | R Documentation |

This function computes the weighted (or unweighted, by default) composite correlation between a set of X variables and a set of Y variables.

composite_r_matrix( r_mat, x_col, y_col, wt_vec_x = rep(1, length(x_col)), wt_vec_y = rep(1, length(y_col)) )

`r_mat` |
Correlation matrix from which composite correlations are to be computed. |

`x_col` |
Column indices of variables from 'r_mat' in the X composite (specify a single variable if Y is an observed variable rather than a composite). |

`y_col` |
Column indices of variables from 'r_mat' in the Y composite (specify a single variable if Y is an observed variable rather than a composite). |

`wt_vec_x` |
Weights to be used in forming the X composite (by default, all variables receive equal weight). |

`wt_vec_y` |
Weights to be used in forming the Y composite (by default, all variables receive equal weight). |

This is computed as:

*r_composite = (t(wt_x) Rxy wt_y) / (sqrt(t(wt_x) Rxx wt_x) * sqrt(t(wt_y) Ryy wt_y))*

where *r_composite* is the composite correlation, *wt* is a vector of weights, and *R* is a correlation matrix. The subscripts of *wt* and *R* indicate the variables indexed within the vector or matrix.

A composite correlation

Mulaik, S. A. (2010). *Foundations of factor analysis*.
Boca Raton, FL: CRC Press. pp. 83–84.

composite_r_scalar(mean_rxy = .3, k_vars_x = 4, mean_intercor_x = .4) R <- reshape_vec2mat(.4, order = 5) R[-1,1] <- R[1,-1] <- .3 composite_r_matrix(r_mat = R, x_col = 2:5, y_col = 1)

psychmeta documentation built on Aug. 26, 2022, 5:14 p.m.

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