permustats function extracts permutation results of
vegan functions. Its support functions can find quantiles and
standardized effect sizes, plot densities and Q-Q plots.
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permustats(x, ...) ## S3 method for class 'permustats' summary(object, interval = 0.95, ...) ## S3 method for class 'permustats' densityplot(x, data, xlab = "Permutations", ...) ## S3 method for class 'permustats' density(x, observed = TRUE, ...) ## S3 method for class 'permustats' qqnorm(y, observed = TRUE, ...) ## S3 method for class 'permustats' qqmath(x, data, observed = TRUE, ylab = "Permutations", ...)
The object to be handled.
numeric; the coverage interval reported.
Add observed statistic among permutations.
Other arguments passed to the function. In
permustats function extracts permutation results and
observed statistics from several vegan functions that perform
permutations or simulations.
summary method of
permustats estimates the
standardized effect sizes (SES) as the difference of observed
statistic and mean of permutations divided by the standard deviation
of permutations (also known as z-values). It also prints the
the mean, median, and limits which contain
of permuted values. With the default (
interval = 0.95), for
two-sided test these are (2.5%, 97.5%) and for one-sided tests
either 5% or 95% quantile depending on the test direction. The
mean, quantiles and z values are evaluated from permuted
values without observed statistic.
densityplot methods display the
kernel density estimates of permuted values. When observed value of
the statistic is included in the permuted values, the
densityplot method marks the observed statistic as a vertical
line. However the
density method uses its standard
method and cannot mark the obseved value.
qqmath display Q-Q plots of
permutations, optionally together with the observed value (default)
which is shown as horizontal line in plots.
permutation values against standard Normal variate.
defaults to the standard Normal as well, but can accept other
alternatives (see standard
qqnorm are based on
standard R methods and accept their arguments. They only handle one
statistic, and cannot be used when several test statistic were
qqmath are lattice graphics, and can be
used both for one and several statistics. All these functions pass
arguments to their underlying functions; see their documentation.
permustats can extract permutation statistics from the
permustats function returns an object of class
"permustats". This is a list of items
permutations which contains permuted
alternative which contains text defining the
character of the test (
density methods return their standard result objects.
Jari Oksanen with contributions from Gavin L. Simpson
permustats.permutest.betadisper method and related
summary.permustats and the
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data(dune) data(dune.env) mod <- adonis(dune ~ Management + A1, data = dune.env) ## use permustats perm <- permustats(mod) summary(perm) densityplot(perm) qqmath(perm) ## example of multiple types of statistic mod <- with(dune.env, betadisper(vegdist(dune), Management)) pmod <- permutest(mod, nperm = 99, pairwise = TRUE) perm <- permustats(pmod) summary(perm, interval = 0.90)
Loading required package: permute Loading required package: lattice This is vegan 2.4-3 statistic SES mean lower median upper Pr(perm) Management 3.0730 4.9489 1.0309 0.9443 1.7981 0.002 ** A1 2.7676 2.8427 1.0327 0.9160 2.1656 0.021 * --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Interval (Upper - Lower) = 0.95) statistic SES mean lower median upper Pr(perm) Overall (F) 1.9506 0.6252 1.1798 0.8134 2.5912 0.160 BF-HF (t) -0.5634 -0.4703 0.0221 -1.9582 0.0326 2.0450 0.567 BF-NM (t) -2.2387 -1.9505 0.0390 -1.8576 0.0248 1.9234 0.041 * BF-SF (t) -1.1675 -0.9549 -0.0052 -2.0393 0.0093 1.8597 0.289 HF-NM (t) -2.1017 -1.8494 -0.0042 -1.7651 -0.0489 2.0033 0.061 . HF-SF (t) -0.8789 -0.7492 -0.0166 -1.8624 -0.0453 1.8806 0.397 NM-SF (t) 0.9485 0.8862 -0.0097 -1.7603 -0.0003 1.6414 0.355 --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 (Interval (Upper - Lower) = 0.9)
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