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)
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