slopeshatprimeinference: Standardized Regression Slopes Hypothesis Test and Confidence...
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description Usage Arguments Author(s) See Also Examples

Standardized Regression Slopes Hypothesis Test and Confidence Intervals

1	slopeshatprimeinference(X, y, sehatslopeshatprimetype = "textbook")

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

Ivan Jacob Agaloos Pesigan

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

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

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