A loess regression is fit to the surface measurements and residuals are
calculated.
The most extreme 0.25% of residuals are filtered from further consideration.
The result is called the raw signature of the bullet land.
Adapted from fit_loess
in bulletr
Value
a list of a data frame of the original bullet measurements extended by loess fit, residuals, and standard errors and two plots: a plot of the fit, and a plot of the bullet's land signature.
Examples
library(dplyr)
ccdata <- data_frame(
x = seq(0, 6, .001),
value = 10 - (3 - x)^2 + rnorm(length(x), sd = .25)
)
cc_fit_loess(ccdata = ccdata)
#> # A tibble: 6,001 × 7
#> x value fitted raw_sig se abs_resid chop
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <lgl>
#> 1 0 0.549 1.01 -0.464 0.0123 0.464 FALSE
#> 2 0.001 1.29 1.02 0.268 0.0123 0.268 FALSE
#> 3 0.002 0.746 1.02 -0.279 0.0123 0.279 FALSE
#> 4 0.003 0.806 1.03 -0.224 0.0123 0.224 FALSE
#> 5 0.004 0.824 1.04 -0.213 0.0122 0.213 FALSE
#> 6 0.005 1.04 1.04 -0.00465 0.0122 0.00465 FALSE
#> 7 0.006 0.715 1.05 -0.333 0.0122 0.333 FALSE
#> 8 0.007 0.460 1.05 -0.594 0.0122 0.594 FALSE
#> 9 0.008 1.07 1.06 0.0106 0.0122 0.0106 FALSE
#> 10 0.009 1.28 1.07 0.210 0.0122 0.210 FALSE
#> # ℹ 5,991 more rows