Analysis of Variance

Source: R/anovatable.R

Analysis of Variance

anovatable(X, y)

Arguments

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.

Value

Returns the ANOVA table.

See also

Other hypothesis testing functions: .anovatable(), ci(), nhst()

Author

Ivan Jacob Agaloos Pesigan

Examples

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

#>         df        SS         MS        F            p
#> Model    1  6636.695 6636.69500 115.9368 6.019852e-26
#> Error 1287 73673.129   57.24408       NA           NA
#> Total 1288 80309.824         NA       NA           NA

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

#>         df       SS         MS        F             p
#> Model    5 25967.28 5193.45611 122.6149 3.453144e-106
#> Error 1283 54342.54   42.35584       NA            NA
#> Total 1288 80309.82         NA       NA            NA