Uses two-sample Kolmogorov-Smirnov test or the one-sample t-test to compare the geometric characteristics of the original track with the characteristics of the simulated track.
test.verification.3d(track1, track2, alpha = 0.05, plot = FALSE, test = "ks")data.frame or list of data.frames with x,y,z coordinates of the original track
data.frame or list of data.frames with x,y,z coordinates of the simulated track
scalar: significance level, default alpha = 0.05
logical: plot the densities or differences of turn angle, lift angle and step length of the two tracks?
character: either "ks" or "ttest" to choose the kind of test procedure.
Test objects of the 6 two-sample Kolmogorov-Smirnov test conducted.
By choosing test = "ttest" a random sample, without replacement is taken from the longer track,
to shorten it to the length of the longer track. The order of the shorter track is also sampled randomly.
Then the two randomly ordered vectors of turn angles, lift angles and step lengths are substracted from each other.
If the both tracks stem from the same distributions the the mean deviatio should tend to towards zero, therefore the
difference is tested two-sided against mu = 0 with a one-sample t-test.
By setting test = "ks" a two-sample Kolmogorov-Smirnov test is carried out on the distributions of turn angles,
lift angles and step lengths of the two tracks.
test.verification.3d(niclas, niclas)
#> |*** Two-sample Kolmogorov-Smirnov test ***
#> |H0: Probability distributions do not differ significantly
#> |H1: Probability distributions differ significantly
#> |Turn angle - p-value: 1 > 0.05, *H0*, autodifferences - p-value: 1 > 0.05, *H0*
#> |Lift angle - p-value: 1 > 0.05, *H0*, autodifferences - p-value: 1 > 0.05, *H0*
#> |Step length - p-value: 1 > 0.05, *H0*, autodifferences - p-value: 1 > 0.05, *H0*
#> [[1]]
#>
#> Two-sample Kolmogorov-Smirnov test
#>
#> data: t1 and t2
#> D = 0, p-value = 1
#> alternative hypothesis: two-sided
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#>
#> [[2]]
#>
#> Two-sample Kolmogorov-Smirnov test
#>
#> data: l1 and l2
#> D = 0, p-value = 1
#> alternative hypothesis: two-sided
#>
#>
#> [[3]]
#>
#> Two-sample Kolmogorov-Smirnov test
#>
#> data: d1 and d2
#> D = 0, p-value = 1
#> alternative hypothesis: two-sided
#>
#>
#> [[4]]
#>
#> Two-sample Kolmogorov-Smirnov test
#>
#> data: diffT1 and diffT2
#> D = 0, p-value = 1
#> alternative hypothesis: two-sided
#>
#>
#> [[5]]
#>
#> Two-sample Kolmogorov-Smirnov test
#>
#> data: diffL1 and diffL2
#> D = 0, p-value = 1
#> alternative hypothesis: two-sided
#>
#>
#> [[6]]
#>
#> Two-sample Kolmogorov-Smirnov test
#>
#> data: diffD1 and diffD2
#> D = 0, p-value = 1
#> alternative hypothesis: two-sided
#>
#>