confidence | R Documentation |
Probabilistic limit on the location of the true or population mean direction, assuming that the estimation errors are normally distributed.
confidence_angle(x, conf.level = 0.95, w = NULL, axial = TRUE, na.rm = TRUE)
confidence_interval(x, conf.level = 0.95, w = NULL, axial = TRUE, na.rm = TRUE)
x |
numeric vector. Values in degrees. |
conf.level |
Level of confidence: |
w |
(optional) Weights. A vector of positive numbers and of the same
length as |
axial |
logical. Whether the data are axial, i.e. pi-periodical
( |
na.rm |
logical value indicating whether |
The confidence angle gives the interval, i.e. plus and minus the confidence angle, around the mean direction of a particular sample, that contains the true mean direction under a given level of confidence.
Angle in degrees
Davis (1986) Statistics and data analysis in geology. 2nd ed., John Wiley & Sons.
Jammalamadaka, S. Rao and Sengupta, A. (2001). Topics in Circular Statistics, Sections 3.3.3 and 3.4.1, World Scientific Press, Singapore.
mean_resultant_length()
, circular_sd_error()
# 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_angle(finland_stria, axial = FALSE)
confidence_interval(finland_stria, axial = FALSE)
data(san_andreas)
data("nuvel1")
PoR <- subset(nuvel1, nuvel1$plate.rot == "na")
sa.por <- PoR_shmax(san_andreas, PoR, "right")
confidence_angle(sa.por$azi.PoR, w = 1 / san_andreas$unc)
confidence_interval(sa.por$azi.PoR, w = 1 / san_andreas$unc)
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