Internal vegan functions that are not intended to be called directly, but only within other functions.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
ordiParseFormula(formula, data, xlev = NULL, na.action = na.fail,
subset = NULL, X)
ordiTerminfo(d, data)
ordiNAexclude(x, excluded)
ordiNApredict(omit, x)
ordiArgAbsorber(..., shrink, origin, scaling, triangular,
display, choices, const, truemean, FUN)
centroids.cca(x, mf, wt)
getPermuteMatrix(perm, N, strata = NULL)
howHead(x, ...)
pasteCall(call, prefix = "Call:")
veganCovEllipse(cov, center = c(0, 0), scale = 1, npoints = 100)
veganMahatrans(x, s2, tol = sqrt(.Machine$double.eps))
hierParseFormula(formula, data)
GowerDblcen(x, na.rm = TRUE)
addLingoes(d)
addCailliez(d)
``` |

The description here is only intended for vegan
developers: these functions are not intended for users, but they
only should be used within functions. In general, these functions
are not exported to the namespace, but you must use
`get`

or `:::`

to directly call these
functions.

`ordiParseFormula`

returns a list of three matrices (dependent
variables, and `model.matrix`

of constraints and
conditions, possibly `NULL`

) needed in constrained
ordination. Argument `xlev`

is passed to
`model.frame`

. If the left-hand-side was already
evaluated in calling code, it can be given as argument `X`

and
will not be re-evaluated. `ordiTermInfo`

finds the term
information for constrained ordination as described in
`cca.object`

. `ordiNAexclude`

implements
`na.action = na.exclude`

for constrained ordination finding WA
scores of CCA components and site scores of unconstrained component
from `excluded`

rows of observations. Function
`ordiNApredict`

pads the result object with these or with WA
scores similarly as `napredict`

.

`ordiArgAbsorber`

absorbs arguments of `scores`

function of vegan so that these do not cause superfluous
warnings in graphical function `FUN`

. If you implement
`scores`

functions with new arguments, you should update
`ordiArgAbsorber`

.

`centroids.cca`

finds the weighted centroids of variables.

`getPermuteMatrix`

interprets user input and returns a
permutation matrix where each row gives indices of observations for
a permutation. The input `perm`

can be a single number for the
number of simple permutations, a result of
`how`

defining a permutation scheme or a
permutation matrix. Function `permuted.index`

was used earlier
to generate permutations, but it is now deprecated.

`howHead`

formats the permutation scheme of
`how`

for display. The formatting is more
compact than the one used in `print`

in the permute
package, and shows only non-default choices. This output is normally
used when printing the results of vegan permutations.

`pasteCall`

prints the function call so that it is nicely wrapped
in `Sweave`

output.

`veganCovEllipse`

finds the coordinates for drawing a
covariance ellipse.

`veganMahatrans`

transforms data matrix so that its Euclidean
distances are Mahalanobis distances. The input data `x`

must be
a matrix centred by columns, and `s2`

its covariance matrix. If
`s2`

is not given, covariance matrix is found from `x`

within the function.

`hierParseFormula`

returns a list of one matrix (left hand side)
and a model frame with factors representing hierarchy levels
(right hand side) to be used in `adipart`

,
`multipart`

and `hiersimu`

.

`GowerDblcen`

performs the Gower double centring of a matrix of
dissimilarities. Similar function was earlier available as a compiled
code in stats, but it is not a part of official API, and
therefore we have this poorer replacement.

`addLingoes`

and `addCailliez`

find the constant added to
non-diagonal (squared) dissimilarities to make all eigenvalues
non-negative in Principal Co-ordinates Analysis
(`wcmdscale`

, `capscale`

). Function
`cmdscale`

implements the Cailliez method. The argument
is a matrix of dissimilarities.

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