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

Description Usage Arguments Value Author(s) Examples

Descriptive Statistics

descriptives(
  X,
  y,
  varnamesX = NULL,
  varnamey = NULL,
  plot = TRUE,
  moments = TRUE,
  cor = TRUE,
  mardia = TRUE
)

`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.
`varnamesX`	Optional. Character vector of length `k`. Variable names for matrix `X`.
`varnamey`	Optional. Character string. Variable name for vector `y`.
`plot`	Logical. Display scatter plot matrix.
`moments`	Logical. Print central moments (means, standard deviations, skewness, and kurtosis).
`cor`	Logical. Print correlations.
`mardia`	Logical. Estimate Mardia's multivariate skewness and kurtosis.

Returns a list with the following elements:

X: 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: n \times 1 matrix of observations on the regressand variable.
data: n \times k matrix with the following columns y, X_2, X_3, \cdots, X_k.
n: Sample size.
k: Number of regressors which includes a regressor whose value is 1 for each observation on the first column.
p: Number of partial regression coefficients are slopes.
df1: Degrees of freedom 1.
df2: Degrees of freedom 2.
muhatX: Vector of length p of estimated means of X_2, X_3, \cdots, X_k ≤ft( \boldsymbol{\hat{μ}}_{\mathbf{X}} = ≤ft\{ \hat{μ}_{X_2}, \hat{μ}_{X_3}, \cdots, \hat{μ}_{X_k} \right\} \right).
muhaty: Estimated mean of the regressand variable ≤ft( \hat{μ}_y \right)
muhat: Vector of length p of estimated means of the regressand variable y and X_2, X_3, \cdots, X_k ≤ft( \boldsymbol{\hat{μ}} = ≤ft\{ \hat{μ}_{y}, \hat{μ}_{X_2}, \hat{μ}_{X_3}, \cdots, \hat{μ}_{X_k} \right\} \right).
Rhat: k \times k matrix of estimated correlations ≤ft( \boldsymbol{\hat{R}}_{y, X_{2, 3, \cdots, k}} \right).
Rhat.p: k \times k p-values associated with the estimated correlation matrix.
RXhat: p \times p matrix of estimated correlations between regressor variables ≤ft( \boldsymbol{\hat{R}}_{X_{2, 3, \cdots, k}} \right).
ryXhat: Vector of length p of estimated correlations between the regressand variables and the regressor variables ≤ft( \boldsymbol{\hat{r}}_{y, X_{2, 3, \cdots, k}} = ≤ft\{ \hat{r}_{y, X_2}, \hat{r}_{y, X_3}, \cdots, \hat{r}_{y, X_k} \right\} \right).
Sigmahat: k \times k matrix of estimated covariances ≤ft( \boldsymbol{\hat{Σ}}_{y, X_{2, 3, \cdots, k}} \right).
SigmaXhat: p \times p matrix of estimated covariances between regressor variables ≤ft( \boldsymbol{\hat{Σ}}_{X_{2, 3, \cdots, k}} \right).
sigmayXhat: Vector of length p of estimated covariances between the regressand variables and the regressor variables ≤ft( \boldsymbol{\hat{σ}}_{y, X_{2, 3, \cdots, k}} = ≤ft\{ \hat{σ}_{y, X_2}, \hat{σ}_{y, X_3}, \cdots, \hat{σ}_{y, X_k} \right\} \right).
sigma2Xhat: Vector of length p of estimated variances of X_2, X_3, \cdots, X_k ≤ft( \boldsymbol{\hat{σ}}_{X_{2, 3, \cdots, k}}^{2} = ≤ft\{ \hat{σ}_{X_2}^{2}, \hat{σ}_{X_2}^{2}, \cdots \hat{σ}_{X_k}^{2} \right\} \right).
sigma2yhat: Estimated variance of y ≤ft( \hat{σ}_{y}^{2} \right).
sigmaXhat: Vector of length p of estimated standard deviation of X_2, X_3, \cdots, X_k ≤ft( \boldsymbol{\hat{σ}}_{X_{2, 3, \cdots, k}} = ≤ft\{ \hat{σ}_{X_2}, \hat{σ}_{X_2}, \cdots \hat{σ}_{X_k} \right\} \right).
sigmayhat: Estimated standard deviation of y ≤ft( \hat{σ}_{y} \right).
sigma2hat: Vector of length k of estimated variances of the regressand variable y and X_2, X_3, \cdots, X_k ≤ft( \boldsymbol{\hat{σ}}_{y, X_{2, 3, \cdots, k}}^{2} = ≤ft\{ \hat{σ}_{y}^{2}, \hat{σ}_{X_2}^{2}, \hat{σ}_{X_2}^{2}, \cdots \hat{σ}_{X_k}^{2} \right\} \right).
sigmahat: Vector of length k of estimated standard deviations of the regressand variable y and X_2, X_3, \cdots, X_k ≤ft( \boldsymbol{\hat{σ}}_{y, X_{2, 3, \cdots, k}} = ≤ft\{ \hat{σ}_{y}, \hat{σ}_{X_2}, \hat{σ}_{X_2}, \cdots \hat{σ}_{X_k} \right\} \right).
skewhat: Vector of length k of estimated skewness of the regressand variable y and X_2, X_3, \cdots, X_k ≤ft( \boldsymbol{\hat{γ}}_{1} = ≤ft\{ \hat{γ}_{1y}, \hat{γ}_{1X_{2}}, \hat{γ}_{1X_{3}}, \cdots, \hat{γ}_{1X_{k}} \right\} \right) .
kurthat: Vector of length k of estimated excess kurtosis of the regressand variable y and X_2, X_3, \cdots, X_k ≤ft( \boldsymbol{\hat{γ}}_{2} = ≤ft\{ \hat{γ}_{2y}, \hat{γ}_{2X_{2}}, \hat{γ}_{2X_{3}}, \cdots, \hat{γ}_{2X_{k}} \right\} \right) .
mardiahat: Vector is estimates of Mardia's multivariate skewness and kurtosis and their associated test statistics and p-values.

Ivan Jacob Agaloos Pesigan

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

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