dot-intercepthat: Estimated Regression Intercept \hat{beta}_{1}
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description Usage Arguments Details Value Author(s) See Also

Estimated Regression Intercept \hat{β}_{1}

1	.intercepthat(slopeshat = NULL, muyhat = NULL, muXhat = NULL, X, y)

`slopeshat`	Numeric vector of length `p` or `p` by `1` matrix. p \times 1 column vector of estimated regression slopes ≤ft( \boldsymbol{\hat{β}}_{2, 3, \cdots, k} = ≤ft\{ \hat{β}_2, \hat{β}_3, \cdots, \hat{β}_k \right\}^{T} \right) .
`muyhat`	Numeirc. Estimated mean of the regressand variable y ≤ft( \hat{μ}_y \right).
`muXhat`	Vector of length `p` or `p` by `1` matrix. p \times 1 column vector of the estimated means of the regressor variables X_2, X_3, \cdots, X_k ≤ft( \boldsymbol{μ}_{\mathbf{X}} = ≤ft\{ μ_{X_2}, μ_{X_3}, \cdots, μ_{X_k} \right\} \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 intercept β_1 is given by

\hat{β}_1 = \hat{μ}_y - \boldsymbol{\hat{μ}}_{\mathbf{X}} \boldsymbol{\hat{β}}_{2, \cdots, k}^{T} .

Returns the estimated intercept \hat{β}_1 of a linear regression model derived from the estimated means and the slopes ≤ft( \boldsymbol{\hat{β}}_{2, \cdots, k} \right) .

Ivan Jacob Agaloos Pesigan

Other beta-hat functions: .betahatnorm(), .betahatqr(), .betahatsvd(), .slopeshatprime(), .slopeshat(), betahat(), intercepthat(), slopeshatprime(), slopeshat()