permustats: Extract, Analyse and Display Permutation Results
In vegan: Community Ecology Package
Description
Usage
Arguments
Details
Value
Author(s)
See Also
Examples
Description
The permustats function extracts permutation results of
vegan functions. Its support functions can find quantiles and
standardized effect sizes, plot densities and Q-Q plots.
Usage
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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", ...)
Arguments
object, x, y
The object to be handled.
interval
numeric; the coverage interval reported.
xlab, ylab
Arguments of
densityplot and
qqmath functions.
observed
Add observed statistic among permutations.
data
Ignored.
...
Other arguments passed to the function. In
density these are passed to density.default.
Details
The permustats function extracts permutation results and
observed statistics from several vegan functions that perform
permutations or simulations.
The 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 interval percent
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.
The density and 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 plot
method and cannot mark the obseved value.
The qqnorm and qqmath display Q-Q plots of
permutations, optionally together with the observed value (default)
which is shown as horizontal line in plots. qqnorm plots
permutation values against standard Normal variate. qqmath
defaults to the standard Normal as well, but can accept other
alternatives (see standard qqmath).
Functions density and 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
evaluated. The densityplot and
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.
The permustats can extract permutation statistics from the
results of adonis, anosim,
anova.cca, mantel, mantel.partial,
mrpp, oecosimu, ordiareatest,
permutest.cca, protest, and
permutest.betadisper.
Value
The permustats function returns an object of class
"permustats". This is a list of items "statistic" for
observed statistics, permutations which contains permuted
values, and alternative which contains text defining the
character of the test ("two.sided", "less" or
"greater"). The qqnorm and
density methods return their standard result objects.
Author(s)
Jari Oksanen with contributions from Gavin L. Simpson
(permustats.permutest.betadisper method and related
modifications to summary.permustats and the print
method) and Eduard Szöcs (permustats.anova.cca).
See Also
density, densityplot,
qqnorm, qqmath.
Examples
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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)
Example output
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)
vegan documentation built on May 2, 2019, 5:51 p.m.
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Description Usage Arguments Details Value Author(s) See Also Examples
Description
The permustats function extracts permutation results of
vegan functions. Its support functions can find quantiles and
standardized effect sizes, plot densities and Q-Q plots.
Usage
1 2 3 4 5 6 7 8 9 10 11 | 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", ...)
|
Arguments
object, x, y |
The object to be handled. |
interval |
numeric; the coverage interval reported. |
xlab, ylab |
Arguments of
|
observed |
Add observed statistic among permutations. |
data |
Ignored. |
... |
Other arguments passed to the function. In
|
Details
The permustats function extracts permutation results and
observed statistics from several vegan functions that perform
permutations or simulations.
The 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 interval percent
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.
The density and 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 plot
method and cannot mark the obseved value.
The qqnorm and qqmath display Q-Q plots of
permutations, optionally together with the observed value (default)
which is shown as horizontal line in plots. qqnorm plots
permutation values against standard Normal variate. qqmath
defaults to the standard Normal as well, but can accept other
alternatives (see standard qqmath).
Functions density and 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
evaluated. The densityplot and
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.
The permustats can extract permutation statistics from the
results of adonis, anosim,
anova.cca, mantel, mantel.partial,
mrpp, oecosimu, ordiareatest,
permutest.cca, protest, and
permutest.betadisper.
Value
The permustats function returns an object of class
"permustats". This is a list of items "statistic" for
observed statistics, permutations which contains permuted
values, and alternative which contains text defining the
character of the test ("two.sided", "less" or
"greater"). The qqnorm and
density methods return their standard result objects.
Author(s)
Jari Oksanen with contributions from Gavin L. Simpson
(permustats.permutest.betadisper method and related
modifications to summary.permustats and the print
method) and Eduard Szöcs (permustats.anova.cca).
See Also
density, densityplot,
qqnorm, qqmath.
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | 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)
|
Example output
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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