Standardized Regression Slopes Hypothesis Test and Confidence Intervals

Standardized Regression Slopes Hypothesis Test and Confidence Intervals

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

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

See also

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

Author

Ivan Jacob Agaloos Pesigan

Examples

# Simple regression------------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
X <- X[, c(1, ncol(X))]
y <- jeksterslabRdatarepo::wages.matrix[["y"]]
slopeshatprimeinference(X = X, y = y)
#>           coef         se        t            p   ci_0.05    ci_0.5    ci_2.5
#> [1,] 0.2874694 0.02669814 10.76739 6.019852e-26 0.1994162 0.2185974 0.2350927
#>       ci_97.5   ci_99.5  ci_99.95
#> [1,] 0.339846 0.3563414 0.3755226

# Multiple regression----------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
# age is removed
X <- X[, -ncol(X)]
slopeshatprimeinference(X = X, y = y)
#>             coef         se         t            p     ci_0.05       ci_0.5
#> [1,] -0.19477502 0.02309626 -8.433184 8.939416e-17 -0.27094940 -0.254355676
#> [2,] -0.07135673 0.02321194 -3.074139 2.155664e-03 -0.14791263 -0.131235792
#> [3,]  0.05077872 0.02344760  2.165626 3.052356e-02 -0.02655441 -0.009708265
#> [4,]  0.48829962 0.02348462 20.792312 5.507605e-83  0.41084437  0.427717121
#> [5,]  0.24607631 0.02370209 10.382050 2.659960e-24  0.16790381  0.184932806
#>           ci_2.5     ci_97.5     ci_99.5     ci_99.95
#> [1,] -0.24008560 -0.14946443 -0.13519436 -0.118600632
#> [2,] -0.11689425 -0.02581921 -0.01147766  0.005199175
#> [3,]  0.00477888  0.09677855  0.11126570  0.128111846
#> [4,]  0.44222714  0.53437211  0.54888213  0.565754879
#> [5,]  0.19957719  0.29257542  0.30721981  0.324248802