dot-slopeshatprimeinference: Standardized Regression Slopes Hypothesis Test and Confidence...

In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description

Standardized Regression Slopes Hypothesis Test and Confidence Intervals

Usage

.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 ≤ft( \boldsymbol{\hat{β}}_{2, 3, \cdots, k} = ≤ft\{ \hat{β}_2, \hat{β}_3, \cdots, \hat{β}_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.

Author(s)

Ivan Jacob Agaloos Pesigan

See Also

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

jeksterslabds/jeksterslabRlinreg documentation built on Jan. 7, 2021, 8:30 a.m.