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 Examples

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

1	intercepthat(X, y)

`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(), .intercepthat(), .slopeshatprime(), .slopeshat(), betahat(), slopeshatprime(), slopeshat()

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

# Multiple regression----------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
# age is removed
X <- X[, -ncol(X)]
intercepthat(X = X, y = y)