Returns the standard error, calculated by the delta method, of the sum of the elements of `theta`

. This is a convenience function that calls into the `deltamethod`

function in the `msm`

package to do the actual calculation.

`delta.sum <- function(theta, v)`

`se.sum <- function(fit, m=NULL)`

`theta`

: Vector of coefficients.`v`

: Covariance matrix corresponding to the elements of`theta`

.`fit`

:`lm`

output.`m`

: Vector holding the indexes of the coefficients to sum. If`NULL`

, it identifies all coefficients.

`fit`

is the output of a regression with three coefficients. You need the standard error of the sum of all three coefficients.

`delta.sum(coefficients(fit), vcov(fit))`

`fit2`

is the output of a regression with five coefficients. You need the standard error of the sum of the second and fourth coefficients.

```
# Grab the relevant coefficients
b <- coefficients(fit)[c(2,4)]
# This will grab the right submatrix
# Keeps the elements in the right place
v <- vcov(fit)[c(2,4), c(2,4)]
delta.sum(b, v)
```

The second example is pretty verbose. You have to call the `coefficients`

function and pull out the elements you want. You have to pull the appropriate elements out of `vcov`

, and you might not even be aware of how to do it. Therefore a further convenience function `se.sum`

is included for cases where youâ€™re working with linear regression output. Note that `delta.sum`

works with any estimation output, as long as you have the covariance matrix.

This achieves the same thing as the previous example:

`se.sum(fit, c(2,4))`

If you want the standard error of the sum of all coefficients in the regression, you can omit the second argument:

`se.sum(fit)`

Please keep in mind that, while this is the most convenient case, it only works with `lm`

output.