PLNPCA: Poisson lognormal model towards Principal Component Analysis

In PLNmodels: Poisson Lognormal Models

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 = PLNPCA_param())

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	a list-like structure for controlling the optimization, with default generated by PLNPCA_param(). See the associated documentation. for details.

Value

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

See Also

The classes PLNPCAfamily and PLNPCAfit, and the configuration function PLNPCA_param().

Examples

#' ## Use future to dispatch the computations on 2 workers
## Not run: 
future::plan("multisession", workers = 2)

## End(Not run)

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
## Not run: 
future::plan("sequential")

## End(Not run)

PLNmodels documentation built on Aug. 24, 2023, 5:11 p.m.