gendat_linreg: Generate Random Data From a Linear Regression Model.
In jeksterslabds/jeksterslabRds: Jekster's Lab Data Science and Statistics

Description Usage Arguments Details Value Author(s) See Also Examples

Generates data from a k-variable linear regression model.

1	gendat_linreg(n, beta, rFUN_X = rnorm, rFUN_y = rnorm, X_args, y_args)

`n`	Sample size.
`beta`	k \times 1 vector of k regression parameters.
`rFUN_X`	The distribution function used to generate values of \mathbf{X}. The default value is `rnorm` for the Gaussian probability density function.
`rFUN_y`	The distribution function used to generate values of the residuals ε. The default value is `rnorm` for the Gaussian probability density function.
`X_args`	List of arguments to pass to `rFUN_X`.
`y_args`	List of arguments to pass to `rFUN_y`.

Randomly generates the data matrix \mathbf{X} and the regressand data \mathbf{y} using specified population parameters defined by \mathbf{y}_{n \times 1} = \mathbf{X}_{n \times k} \boldsymbol{β}_{k \times 1} + \boldsymbol{ε}_{n \times 1}. Refer to gendat_linreg_X on how \mathbf{X} is generated and gendat_linreg_y on how \mathbf{y} is generated.

Returns a list with two elements X and y.

X is the data matrix, that is an n \times k matrix of n observations of k regressors, which includes a regressor whose value is 1 for each observation.
y n \times 1 vector of observations on the regressand.

Ivan Jacob Agaloos Pesigan

Other data generating functions: gendat_linreg_X(), gendat_linreg_y(), gendat_mvn_a(), gendat_mvn_fe(), gendat_mvn(), gendat_vm(), gendat()

data <- gendat_linreg(
  n = 100,
  beta = c(.5, .5, .5),
  rFUN_X = rnorm,
  rFUN_y = rnorm,
  X_args = list(mean = 0, sd = 1),
  y_args = list(mean = 0, sd = 1)
)