Jackknife-based test for equality of two regressions between distance matrices.
regeqdist(dmx,dmy,grouping,groups=levels(as.factor(grouping))[1:2])# S3 method for regeqdist
print(x,...)
list of class "regeqdist" with components
p-values for intercept and slope.
vector of differences between groups (first minus second) for intercept and slope.
condition numbers of regressions, see kappa.
list. Output objects of lm within the two groups.
list of two lists of two; output object of
jackknife within the two groups for intercept and
slope.
mean of dmx within the two groups used for centering.
t-statistic.
vector of degrees of freedom of t-statistic according to Welch-Sattertwaithe approximation for intercept and slope.
jackknife-estimator of difference between regressions; vector with intercept and slope difference.
vector with jackknife-standard errors for
jackest, intercept and slope.
list of two lists of vectors; jacknife pseudovalues within both groups for intercept and slope estimators.
see above.
dissimilarity matrix or object of class
dist. Explanatory dissimilarities (often these will be proper
distances, but more general dissimilarities that do not
necessarily fulfill the triangle inequality can be used, same for
dmy).
dissimilarity matrix or object of class
dist. Response dissimilarities.
something that can be coerced into a factor,
defining the grouping of
objects represented by the dissimilarities dmx and dmy
(i.e., if grouping has length n, dmx and dmy
must be dissimilarities between n objects).
Vector of two, indicating the two groups defining the
regressions to be compared in the test. These can be
factor levels, integer numbers, or strings, depending on the entries
of grouping.
object of class "regeqdist".
optional arguments for print method.
Christian Hennig christian.hennig@unibo.it https://www.unibo.it/sitoweb/christian.hennig/en
The null hypothesis that the regressions within the two groups are equal is tested using jackknife pseudovalues independently in both groups allowing for potentially different variances of the pseudovalues, and aggregating as in Welch's t-test. Tests are run separately for intercept and slope and aggregated by Bonferroni's rule.
The test cannot be run and many components will be NA in case that
within-group regressions or jackknifed within-group regressions are
ill-conditioned.
This was implemented having in mind an application in which the
explanatory distances represent geographical distances, the response
distances are genetic distances, and groups represent species or
species-candidates. In this application, for testing whether the
regression patterns are compatble with the two groups behaving like a
single species, one would first use regeqdist to test whether a
joint regression for the within-group distances of both groups makes
sense. If this is not rejected, regdistbetween is run to see
whether the between-group distances are compatible with the
within-group distances. On the other hand, if a joint regression on
within-group distances is rejected, regdistbetweenone can be
used to test whether the between-group distances are at least
compatible with the within-group distances of one of the groups, which
can still be the case within a single species, see Hausdorf and Hennig (2019).
Hausdorf, B. and Hennig, C. (2019) Species delimitation and geography. Submitted.
regdistbetween, regdistbetweenone
options(digits=4)
data(veronica)
ver.geo <- coord2dist(coordmatrix=veronica.coord[173:207,],file.format="decimal2")
vei <- prabinit(prabmatrix=veronica[173:207,],distance="jaccard")
loggeo <- log(ver.geo+quantile(as.vector(as.dist(ver.geo)),0.25))
species <-c(rep(1,13),rep(2,22))
rtest <- regeqdist(dmx=loggeo,dmy=vei$distmat,grouping=species,groups=c(1,2))
print(rtest)
Run the code above in your browser using DataLab