# W: Lambert W function, its logarithm and derivative In LambertW: Probabilistic Models to Analyze and Gaussianize Heavy-Tailed, Skewed Data

 W R Documentation

## Lambert W function, its logarithm and derivative

### Description

The Lambert W function W(z) = u is defined as the inverse of (see xexp)

u \exp(u) = z,

i.e., it satisfies W(z) \exp(W(z)) = z.

W evaluates the Lambert W function (W), its first derivative (deriv_W), and its logarithm (log_W). All of them have a principal (branch = 0 (default)) and non-principal branch (branch = -1) solution.

W is a wrapper for lambertW0 and lambertWm1 in the lamW package.

### Usage

W(z, branch = 0)

deriv_W(z, branch = 0, W.z = W(z, branch = branch))

log_deriv_W(z, branch = 0, W.z = W(z, branch = branch))

deriv_log_W(z, branch = 0, W.z = W(z, branch = branch))

log_W(z, branch = 0, W.z = W(z, branch = branch))


### Arguments

 z a numeric vector of real values; note that W(Inf, branch = 0) = Inf. branch either 0 or -1 for the principal or non-principal branch solution. W.z Lambert W function evaluated at z; see Details below for why this is useful.

### Details

Depending on the argument z of W(z) one can distinguish 3 cases:

z ≥q 0

solution is unique W(z) = W(z, branch = 0)

;

-1/e ≤q z < 0

two solutions: the principal (W(z, branch = 0)) and non-principal (W(z, branch = -1)) branch;

z < -1/e

no solution exists in the reals.

log_W computes the natural logarithm of W(z). This can be done efficiently since \log W(z) = \log z - W(z). Similarly, the derivative can be expressed as a function of W(z):

W'(z) = \frac{1}{(1 + W(z)) \exp(W(z))} = \frac{W(z)}{z(1 + W(z))}.

Note that W'(0) = 1 and W'(-1/e) = ∞.

Moreover, by taking logs on both sides we can even simplify further to

\log W'(z) = \log W(z) - \log z - \log (1 + W(z))

which, since \log W(z) = \log z - W(z), simplifies to

\log W'(z) = - W(z) - \log (1 + W(z)).

For this reason it is numerically faster to pass the value of W(z) as an argument to deriv_W since W(z) often has already been evaluated in a previous step.

### Value

numeric; same dimensions/size as z.

W returns numeric, Inf (for z = Inf), or NA if z < -1/e.

Note that W handles NaN differently to lambertW0 / lambertWm1 in the lamW package; it returns NA.

### References

Corless, R. M., G. H. Gonnet, D. E. G. Hare, D. J. Jeffrey and D. E. Knuth (1996). “On the Lambert W function”. Advances in Computational Mathematics, pp. 329-359.

lambertW0 / lambertWm1in the lamW package; xexp.

### Examples


W(-0.25) # "reasonable" input event
W(-0.25, branch = -1) # "extreme" input event

curve(W(x, branch = -1), -1, 2, type = "l", col = 2, lwd = 2)
curve(W(x), -1, 2, type = "l", add = TRUE, lty = 2)
abline(v = - 1 / exp(1))

# For lower values, the principal branch gives the 'wrong' solution;
# the non-principal must be used.
xexp(-10)
W(xexp(-10), branch = 0)
W(xexp(-10), branch = -1)

curve(log(x), 0.1, 5, lty = 2, col = 1, ylab = "")
curve(W(x), 0, 5, add = TRUE, col = "red")
curve(log_W(x), 0.1, 5, add = TRUE, col = "blue")
grid()
legend("bottomright", c("log(x)", "W(x)", "log(W(x))"),
col = c("black", "red", "blue"), lty = c(2, 1, 1))



LambertW documentation built on Sept. 22, 2022, 5:07 p.m.