deltamethod: The delta method

In msm: Multi-State Markov and Hidden Markov Models in Continuous Time

The delta method

Description

Delta method for approximating the standard error of a transformation g(X) of a random variable X = (x_1, x_2, \ldots), given estimates of the mean and covariance matrix of X.

Usage

deltamethod(g, mean, cov, ses = TRUE)

Arguments

g	A formula representing the transformation. The variables must be labelled x1, x2,...{} For example, ~ 1 / (x1 + x2) If the transformation returns a vector, then a list of formulae representing (g_1, g_2, \ldots) can be provided, for example list( ~ x1 + x2, ~ x1 / (x1 + x2) )
mean	The estimated mean of X
cov	The estimated covariance matrix of X
ses	If TRUE, then the standard errors of g_1(X), g_2(X),\ldots are returned. Otherwise the covariance matrix of g(X) is returned.

Details

The delta method expands a differentiable function of a random variable about its mean, usually with a first-order Taylor approximation, and then takes the variance. For example, an approximation to the covariance matrix of g(X) is given by

Cov(g(X)) = g'(\mu) Cov(X) [g'(\mu)]^T

where \mu is an estimate of the mean of X. This function uses symbolic differentiation via deriv.

A limitation of this function is that variables created by the user are not visible within the formula g. To work around this, it is necessary to build the formula as a string, using functions such as sprintf, then to convert the string to a formula using as.formula. See the example below.

If you can spare the computational time, bootstrapping is a more accurate method of calculating confidence intervals or standard errors for transformations of parameters. See boot.msm. Simulation from the asymptotic distribution of the MLEs (see e.g. Mandel 2013) is also a convenient alternative.

Value

A vector containing the standard errors of g_1(X), g_2(X), \ldots or a matrix containing the covariance of g(X).

Author(s)

C. H. Jackson [e-mail]

References

Oehlert, G. W. (1992) A note on the delta method. American Statistician 46(1).

Mandel, M. (2013) Simulation based confidence intervals for functions with complicated derivatives. The American Statistician 67(2):76-81.

Examples

## Simple linear regression, E(y) = alpha + beta x 
x <- 1:100
y <- rnorm(100, 4*x, 5)
toy.lm <- lm(y ~ x)
estmean <- coef(toy.lm)
estvar <- summary(toy.lm)$cov.unscaled * summary(toy.lm)$sigma^2

## Estimate of (1 / (alphahat + betahat))
1 / (estmean[1] + estmean[2])
## Approximate standard error
deltamethod (~ 1 / (x1 + x2), estmean, estvar) 

## We have a variable z we would like to use within the formula.
z <- 1
## deltamethod (~ z / (x1 + x2), estmean, estvar) will not work.
## Instead, build up the formula as a string, and convert to a formula.
form <- sprintf("~ %f / (x1 + x2)", z)
form
deltamethod(as.formula(form), estmean, estvar)

msm documentation built on Oct. 5, 2024, 1:07 a.m.