Delta Method

Calculates delta method sampling variance-covariance matrix for a function of parameters using a numerical Jacobian.

Usage

Delta(
  coef,
  vcov,
  func,
  ...,
  theta = 0,
  alpha = c(0.05, 0.01, 0.001),
  z = TRUE,
  df = NULL
)

Arguments

coef

Numeric vector. Vector of parameters.

vcov

Numeric matrix. Matrix of sampling variance-covariance matrix of parameters.

func

R function.

1. The first argument x is the argument coef.

2. The function algebraically manipulates coef to return a new numeric vector. It is best to have a named vector as an output.

3. The function can take additional named arguments passed using ....

...

Additional arguments to pass to func.

theta

Numeric vector. Parameter values when the null hypothesis is true.

alpha

Numeric vector. Significance level/s.

z

Logical. If z = TRUE, use the standard normal distribution. If z = FALSE, use the t distribution.

df

Numeric. Degrees of freedom if z = FALSE.

Value

Returns an object of class deltamethod which is a list with the following elements:

call

Function call.

args

Function arguments.

coef

Estimates.

vcov

Sampling variance-covariance matrix.

jacobian

Jacobian matrix.

fun

Function used ("Delta").

See also

Other Delta Method Functions: DeltaGeneric()

Author

Ivan Jacob Agaloos Pesigan

Examples

object <- glm(
  formula = vs ~ wt + disp,
  family = "binomial",
  data = mtcars
)
func <- function(x) {
  y <- exp(x)
  names(y) <- paste0("exp", "(", names(x), ")")
  y[-1]
}
Delta(
  coef = coef(object),
  vcov = vcov(object),
  func = func,
  alpha = 0.05
)
#> Call:
#> Delta(coef = coef(object), vcov = vcov(object), func = func, 
#>     alpha = 0.05)
#>              est     se       z      p    2.5%   97.5%
#> exp(wt)   5.0853 7.5805  0.6708 0.5023 -9.7723 19.9429
#> exp(disp) 0.9662 0.0148 65.0838 0.0000  0.9371  0.9952