Standard Errors of Standardized Estimates of Regression Coefficients (Textbook)

Standard Errors of Standardized Estimates of Regression Coefficients (Textbook)

sehatslopeshatprimetb(X, y)

Arguments

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.

References

Yuan, K., Chan, W. (2011). Biases and Standard Errors of Standardized Regression Coefficients. Psychometrika 76, 670-690. doi:10.1007/s11336-011-9224-6.

See also

Other standard errors of estimates of regression coefficients functions: .sehatbetahatbiased(), .sehatbetahat(), .sehatslopeshatprimedelta(), .sehatslopeshatprimetb(), sehatbetahatbiased(), sehatbetahat(), sehatslopeshatprimedelta()

Author

Ivan Jacob Agaloos Pesigan

Examples

# Simple regression------------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
X <- X[, c(1, ncol(X))]
y <- jeksterslabRdatarepo::wages.matrix[["y"]]
sehatslopeshatprimetb(X = X, y = y)
#>      sehatslopeshatprime
#> [1,]          0.02669814

# Multiple regression----------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
# age is removed
X <- X[, -ncol(X)]
sehatslopeshatprimetb(X = X, y = y)
#>      sehatslopeshatprime
#> [1,]          0.02309626
#> [2,]          0.02321194
#> [3,]          0.02344760
#> [4,]          0.02348462
#> [5,]          0.02370209