Standardized Regression Slopes Hypothesis Test and Confidence Intervals

Source: R/betahatinference.R

Standardized Regression Slopes Hypothesis Test and Confidence Intervals

.slopeshatprimeinference(
  slopeshatprime = NULL,
  sehatslopeshatprime = NULL,
  sehatslopeshatprimetype = "textbook",
  adjust = FALSE,
  n,
  X,
  y
)

Arguments

slopeshatprime

Numeric vector of length p or p by 1 matrix. \(p \times 1\) column vector of estimated standardized regression slopes \(\left( \boldsymbol{\hat{\beta}}_{2, 3, \cdots, k} = \left\{ \hat{\beta}_2, \hat{\beta}_3, \cdots, \hat{\beta}_k \right\}^{T} \right)\) .
sehatslopeshatprime

Numeric vector of length p or p by 1 matrix. Standard errors of estimates of standardized regression slopes.
sehatslopeshatprimetype

Character string. Standard errors for standardized regression slopes hypothesis test. Options are sehatslopeshatprimetype = "textbook" and sehatslopeshatprimetype = "delta".
adjust

Logical. If sehatslopeshatprimetype = "delta" and adjust = TRUE, uses n - 3 to adjust sehatslopeshatprime for bias. This adjustment is recommended for small sample sizes.
n

Integer. Sample size.
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.

See also

Other inference functions: .betahatinference(), betahatinference(), slopeshatprimeinference()

Author

Ivan Jacob Agaloos Pesigan