PCMFit(X, tree, model, SE = matrix(0, PCMNumTraits(model),
PCMTreeNumTips(tree)), metaI = PCMInfo(X, tree, model, SE),
positiveValueGuard = Inf, argsPCMParamLowerLimit = NULL,
argsPCMParamUpperLimit = NULL, matParInit = NULL,
numRunifInitVecParams = if (is.null(matParInit)) 1000L else 0L,
numGuessInitVecParams = if (is.null(matParInit)) 100L else 0L,
numCallsOptim = 10L, control = NULL, doParallel = FALSE,
verbose = FALSE)
Arguments
X |
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 ). |
tree |
a phylo object with N tips. |
model |
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). |
SE |
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:
- if
getOption("PCMBase.Transpose.Sigma_x", FALSE) == FALSE (default): VE[, , i] <- SE[, , i] %*% t(SE[, , i])
- if
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)) . |
metaI |
a list returned from a call to PCMInfo(X, tree, model, SE) ,
containing meta-data such as N, M and k. Alternatively, this can be a
character string naming a function or a function object that returns such
a list, e.g. the functionPCMInfo or the function PCMInfoCpp
from the PCMBaseCpp package. |
positiveValueGuard |
a real number (not necessarily positive) used during
the fit as a threshold for highly positive but likely incorrect log-likelihood
values. This argument is set to Inf by default and should be used only
as a last escape. The recommended way to of preventing such numerical errors
is by setting more stringent values for the runtime options
PCMBase.Threshold.EV and PCMBase.Threshold.SV (see
PCMOptions ). |
argsPCMParamLowerLimit, argsPCMParamUpperLimit |
named lists with
arguments passed to the functions PCMParamLowerLimit and
PCMParamUpperLimit , respectively. Default: NULL . |
matParInit |
a matrix of any number of rows and p columns where, p is
the number of variable numerical parameters in the model
(equal to PCMParamCount(model) ). Each row of this matrix specifies a
suggested starting location for the optim L-BFGS-B run. Default: NULL,
meaning that the initial parameters are to be chosen at random and/or using
calls to GuessInitVecParams function. |
numRunifInitVecParams, numGuessInitVecParams |
integers specifying how
many parameter vectors should be drawn from a uniform distribution between
PCMParamLowerLimit(model) and PCMParamUpplerLimit(model) , and
how many parameter vectors should be generated by jittering the resulting
vector from a call to GuessInitVecParams . Before starting the
optimization the model likelihood is evaluated at each of these vectors and
the top numCallsOptim vectors are chosen for starting locations for
optimization. The default settings are
numRunifInitVecParams = if( is.null(matParInit) ) 1000L else 0L and
numGuessInitVecParams = if( is.null(matParInit) ) 100L else 0L . |
numCallsOptim |
integer specifying the maximum number of calls to
optim function. Default: 10. Note that this parameter would be
overwritten by a smaller nrow(matParInit) (if matParInit is
specified) or by a smaller number of generated initial parameter vectors that
satisfy the parameter limits (see also the arguments
numRunifInitVecParams,numGuessInitVecParams and
argsPCMParamLowerLimit , argsPCMParamUpperLimit ). |
control |
a list passed as control argument to optim .
Default: NULL. |
doParallel |
logical indicating if optim calls should be executed in
parallel. Default: FALSE. |
verbose |
logical indicating if information messages should be printed
to the console while running. Default: FALSE. |
Value
an object of class PCMFit
References
Mitov, V., Bartoszek, K., & Stadler, T. (2019). Automatic generation of
evolutionary hypotheses using mixed Gaussian phylogenetic models.
Proceedings of the National Academy of Sciences of the United States of
America, 35, 201813823. http://doi.org/10.1073/pnas.1813823116
Mitov, V., Bartoszek, K., Asimomitis, G., & Stadler, T. (2019). Fast
likelihood calculation for multivariate Gaussian phylogenetic models with
shifts. Theoretical Population Biology.
http://doi.org/10.1016/j.tpb.2019.11.005
See also