weighted_rayleigh | R Documentation |
Weighted version of the Rayleigh test (or V0-test) for uniformity against a distribution with a priori expected von Mises concentration.
weighted_rayleigh(x, mu = NULL, w = NULL, axial = TRUE, quiet = FALSE)
x |
numeric vector. Values in degrees |
mu |
The a priori expected direction (in degrees) for the alternative hypothesis. |
w |
numeric vector weights of length |
axial |
logical. Whether the data are axial, i.e. |
quiet |
logical. Prints the test's decision. |
The Null hypothesis is uniformity (randomness). The alternative is a
distribution with a (specified) mean direction (mu
).
If statistic >= p.value
, the null hypothesis of randomness is rejected and
angles derive from a distribution with a (or the specified) mean direction.
a list with the components:
R
or C
mean resultant length or the dispersion (if mu
is
specified). Small values of R
(large values of C
) will reject
uniformity. Negative values of C
indicate that vectors point in opposite
directions (also lead to rejection).
statistic
Test statistic
p.value
significance level of the test statistic
rayleigh_test()
# Load data
data("cpm_models")
data(san_andreas)
PoR <- equivalent_rotation(subset(cpm_models, model == "NNR-MORVEL56"), "na", "pa")
sa.por <- PoR_shmax(san_andreas, PoR, "right")
data("iceland")
PoR.ice <- equivalent_rotation(subset(cpm_models, model == "NNR-MORVEL56"), "eu", "na")
ice.por <- PoR_shmax(iceland, PoR.ice, "out")
data("tibet")
PoR.tib <- equivalent_rotation(subset(cpm_models, model == "NNR-MORVEL56"), "eu", "in")
tibet.por <- PoR_shmax(tibet, PoR.tib, "in")
# GOF test:
weighted_rayleigh(tibet.por$azi.PoR, mu = 90, w = 1 / tibet$unc)
weighted_rayleigh(ice.por$azi.PoR, mu = 0, w = 1 / iceland$unc)
weighted_rayleigh(sa.por$azi.PoR, mu = 135, w = 1 / san_andreas$unc)
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