dot-slopesprime: Regression Standardized Slopes \boldsymbol{beta}_{2, \cdots,...
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Derives the standardized slopes \boldsymbol{β}_{2, \cdots, k}^{\prime} of a linear regression model as a function of correlations.

1	.slopesprime(RX = NULL, ryX = NULL, X, y)

`RX`	`p` by `p` numeric matrix. p \times p matrix of correlations between the regressor variables X_2, X_3, \cdots, X_k ≤ft( \mathbf{R}_{\mathbf{X}} \right).
`ryX`	Numeric vector of length `p` or `p` by `1` matrix. p \times 1 vector of correlations between the regressand variable y and the regressor variables X_2, X_3, \cdots, X_k ≤ft( \mathbf{r}_{\mathbf{y}, \mathbf{X}} = ≤ft\{ r_{y, X_2}, r_{y, X_3}, \cdots, r_{y, X_k} \right\}^{T} \right).
`X`	`n` by `k` numeric matrix. The data matrix \mathbf{X} (also known as design matrix, model matrix or regressor matrix) is an n \times k matrix of n observations of k regressors, which includes a regressor whose value is 1 for each observation on the first column.
`y`	Numeric vector of length `n` or `n` by `1` matrix. The vector \mathbf{y} is an n \times 1 vector of observations on the regressand variable.

The linear regression standardized slopes are calculated using

\boldsymbol{β}_{2, \cdots, k}^{\prime} = \mathbf{R}_{\mathbf{X}}^{T} \mathbf{r}_{\mathbf{y}, \mathbf{X}}

where

\mathbf{R}_{\mathbf{X}} is the p \times p correlation matrix of the regressor variables X_2, X_3, \cdots, X_k and
\mathbf{r}_{\mathbf{y}, \mathbf{X}} is the p \times 1 column vector of the correlations between the regressand y variable and regressor variables X_2, X_3, \cdots, X_k