This function allows to perform various nonparametric test for homogeneity on two samples of circular data.
circular_test(x, y, test = "dixon", alpha = 0.05, B = NULL, type = "exact", seed = 1982)
x |
first sample |
---|---|
y |
second sample |
test |
considered test ( |
alpha |
significance level (default = 0.05) |
B |
number of bootstrap replications |
seed |
seed used for random number generation |
type |
method to compute pvalues (available methods: |
A list with the following structure:
a list
containing the results of the exact distribution (NULL
if type = "mc"
), see function get_critical_values
for details
a list
containing the results of the approximated distribution obtained by simulation (NULL
if type = "exact"
), see function MC_pvalue
for details
number of bootstrap replications
significance level
the considered test
observed test statistic
a list
containing the observed spacings, see function compute_Sk
for details
expression deparsing of the first dataset
expression deparsing of the second dataset
# Load dataset data(pigeons) # Dixon test (exact pvalue) circular_test(pigeons$experimental, pigeons$control)#> #> Dixon Two Sample Test #> #> Data: pigeons$experimental and pigeons$control #> Test Statistic: 41 #> Exact P-value: 0.02096 #> Bracketing Points and Pair of Signif. Levels: #> c1 = 33 (p1 = 0.0567) #> c2 = 35 (p2 = 0.0460)# Dixon test (approximated pvalue) circular_test( pigeons$experimental, pigeons$control, type = "mc")#> #> Dixon Two Sample Test #> #> Data: pigeons$experimental and pigeons$control #> Test Statistic: 41 #> Approx. P-value: 0.0228 #> P-value stand. error: 0.0009575 #> based on 10000 Monte-Carlo replications