confidence_interval_fisher function

Confidence Interval around the Mean Direction of Circular Data after Fisher (1993)

For large samples (n >=25) i performs are parametric estimate based on sample_circular_dispersion(). For smaller size samples, it returns a bootstrap estimate.

confidence_interval_fisher(
  x,
  conf.level = 0.95,
  w = NULL,
  axial = TRUE,
  na.rm = TRUE,
  boot = FALSE,
  R = 1000L,
  quiet = FALSE
)

Arguments

• x: numeric vector. Values in degrees.

• conf.level: Level of confidence: $(1 - \alpha \%)/100$. (0.95 by default).

• w: (optional) Weights. A vector of positive numbers and of the same length as x.

• axial: logical. Whether the data are axial, i.e. pi-periodical (TRUE, the default) or directional, i.e. $2 \pi$-periodical (FALSE).

• na.rm: logical value indicating whether NA values in x

  should be stripped before the computation proceeds.

• boot: logical. Force bootstrap estimation

• R: integer. number of bootstrap replicates

• quiet: logical. Prints the used estimation (parametric or bootstrap).

Returns

list

Examples

# Example data from Davis (1986), pp. 316
finland_stria <- c(
  23, 27, 53, 58, 64, 83, 85, 88, 93, 99, 100, 105, 113,
  113, 114, 117, 121, 123, 125, 126, 126, 126, 127, 127, 128, 128, 129, 132,
  132, 132, 134, 135, 137, 144, 145, 145, 146, 153, 155, 155, 155, 157, 163,
  165, 171, 172, 179, 181, 186, 190, 212
)
confidence_interval_fisher(finland_stria, axial = FALSE)
confidence_interval_fisher(finland_stria, axial = FALSE, boot = TRUE)

data(san_andreas)
data("nuvel1")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
sa.por <- PoR_shmax(san_andreas, PoR, "right")
confidence_interval_fisher(sa.por$azi.PoR, w = 1 / san_andreas$unc)
confidence_interval_fisher(sa.por$azi.PoR, w = 1 / san_andreas$unc, boot = TRUE)

References

N.I. Fisher (1993) Statistical Analysis of Circular Data, Cambridge University Press.