PLNmodels (version 0.11.7)

PLNPCA: Poisson lognormal model towards Principal Component Analysis

Description

Fit the PCA variants of the Poisson lognormal with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets).

Usage

PLNPCA(
  formula,
  data,
  subset,
  weights,
  ranks = 1:5,
  control_init = list(),
  control_main = list()
)

Value

an R6 object with class PLNPCAfamily, which contains a collection of models with class PLNPCAfit

Arguments

formula

an object of class "formula": a symbolic description of the model to be fitted.

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.

subset

an optional vector specifying a subset of observations to be used in the fitting process.

weights

an optional vector of observation weights to be used in the fitting process.

ranks

a vector of integer containing the successive ranks (or number of axes to be considered)

control_init

a list for controlling the optimization at initialization. See details of function PLN().

control_main

a list for controlling the main optimization process. See details.

Details

The list of parameters control_main controls the optimization of the main process, with the following entries:

• "ftol_rel" stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 1e-8

• "ftol_abs" stop when an optimization step changes the objective function by less than ftol multiplied by the absolute value of the parameter. Default is 0

• "xtol_rel" stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 1e-4

• "xtol_abs" stop when an optimization step changes every parameters by less than xtol multiplied by the absolute value of the parameter. Default is 0

• "maxeval" stop when the number of iteration exceeds maxeval. Default is 10000

• "maxtime" stop when the optimization time (in seconds) exceeds maxtime. Default is -1 (no restriction)

• "algorithm" the optimization method used by NLOPT among LD type, i.e. "CCSAQ", "MMA", "LBFGS", "VAR1", "VAR2". See NLOPT documentation for further details. Default is "CCSAQ".

• "trace" integer for verbosity.

See Also

The classes PLNPCAfamily and PLNPCAfit

Examples

#' ## Use future to dispatch the computations on 2 workers
if (FALSE) {
future::plan("multisession", workers = 2)
}

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPCA <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5)

# Shut down parallel workers
if (FALSE) {
future::plan("sequential")
}