This function pre-processes the given tree and data in order to create meta-information used during likelihood calculation.
PCMInfo(
X,
tree,
model,
SE = matrix(0, PCMNumTraits(model), PCMTreeNumTips(tree)),
verbose = FALSE,
preorder = NULL,
...
)
a named list with the following elements:
k x N matrix denoting the trait data;
k x k x N array denoting the measurement error variance covariance
matrix for each for each tip i = 1,...,N. See the parameter SE
in
PCMLik.
total number of nodes in the tree;
number of tips;
number of traits;
number of parts on the tree (distinct elements of tree$edge.part);
number of regimes in the model (elements of attr(model, regimes));
number of free parameters describing the model;
an integer vector corresponding to tree$edge with the regime for each branch in tree;
an integer vector of 0's and 1's corresponding to the rows in tree$edge indicating the presence of a jump at the corresponding branch;
a logical matrix of dimension k x M denoting the present coordinates
for each node; in special cases this matrix can be edited by hand after calling
PCMInfo and before passing the returned list to PCMLik. Otherwise, this matrix
can be calculated in a custom way by specifying the option PCMBase.PCMPresentCoordinatesFun.
See also PCMPresentCoordinates
and PCMOptions
.
This list is passed to PCMLik
.
a k x N
numerical matrix with possible NA
and
NaN
entries. For i=1,..., N
, the column i
of X contains
the measured trait values for species i
(the tip with integer
identifier equal to i
in tree
). Missing values can be either
not-available (NA
) or not existing (NaN
). These two values are
treated differently when calculating likelihoods (see
PCMPresentCoordinates
).
a phylo object with N tips.
an S3 object specifying both, the model type (class, e.g. "OU") as well as the concrete model parameter values at which the likelihood is to be calculated (see also Details).
a k x N matrix specifying the standard error for each measurement in
X. Alternatively, a k x k x N cube specifying an upper triangular k x k
factor of the variance covariance matrix for the measurement error
for each tip i=1, ..., N
. When SE
is a matrix, the k x k
measurement error variance matrix for a tip i
is calculated as
VE[, , i] <- diag(SE[, i] * SE[, i], nrow = k)
. When SE
is a
cube, the way how the measurement variance matrix for a tip i
is
calculated depends on the runtime option PCMBase.Transpose.Sigma_x
as follows:
getOption("PCMBase.Transpose.Sigma_x", FALSE) == FALSE
(default): VE[, , i] <- SE[, , i] %*% t(SE[, , i])
getOption("PCMBase.Transpose.Sigma_x", FALSE) == TRUE
: VE[, , i] <- t(SE[, , i]) %*% SE[, , i]
Note that the above behavior is consistent with the treatment of the model
parameters Sigma_x
, Sigmae_x
and Sigmaj_x
, which are
also specified as upper triangular factors.
Default: matrix(0.0, PCMNumTraits(model), PCMTreeNumTips(tree))
.
logical indicating if some debug-messages should printed.
an integer vector of row-indices in tree$edge matrix as returned
by PCMTreePreorder. This can be given for performance speed-up when several
operations needing preorder are executed on the tree. Default : NULL
.
additional arguments used by implementing methods.