Bootstrap uncertainty for a WISSI result.

Yields n_iter stress tensors from resampled datasets. The dispersion Theta-bar on \(S^5\) approximates the data noise level (Eq. 37: Theta ~ d-bar).

Usage

slip_inversion_wissi_boot(x, n_iter = 500L, seed = NULL, ...)

Arguments

x

object of class "Pair" or "Fault" with at least 4 rows.

n_iter

Number of bootstrap replicates. Default 500.

seed

Optional RNG seed.

...

Additional arguments passed to slip_inversion_wissi().

Value

A named list with:

optimal

slip_inversion_wissi() result for the full dataset

thetas

length-n_iter vector of angular stress distances from optimal

dispersion

mean Theta (approximates noise level p of data)

sd

standard deviation of Theta values

D_bar

mean Orife-Lisle distance from optimal

DM_bar

mean Michael distance from optimal

See also

Other wissi: slip_inversion_wissi(), slip_inversion_wissi_polyphase()

Examples

res <- slip_inversion_wissi_boot(angelier1990$AVB, n_iter = 4)

stereoplot()
angelier(angelier1990$KAM, col = 'grey')
lines(res$optimal$principal_axes, res$sd, col = 2:4)
points(res$optimal$principal_axes, pch = 16:18, cex = 2, col= 2:4)
text(res$optimal$principal_axes, 
label = rownames(res$optimal$principal_axes), col= 2:4, adj = -.25)