PLNPCAfit {PLNmodels}R Documentation

An R6 Class to represent a PLNfit in a PCA framework

Description

The function PLNPCA() produces a collection of models which are instances of object with class PLNPCAfit. This class comes with a set of methods, some of them being useful for the user: See the documentation for the methods inherited by PLNfit and the plot() methods for PCA visualization

Super class

PLNmodels::PLNfit -> PLNPCAfit

Active bindings

rank

the dimension of the current model

nb_param

number of parameters in the current PLN model

entropy

entropy of the variational distribution

model_par

a list with the matrices associated with the estimated parameters of the pPCA model: Theta (covariates), Sigma (latent covariance) and B (latent loadings)

percent_var

the percent of variance explained by each axis

corr_circle

a matrix of correlations to plot the correlation circles

scores

a matrix of scores to plot the individual factor maps (a.k.a. principal components)

rotation

a matrix of rotation of the latent space

Methods

Public methods

Inherited methods

Method new()

Initialize a PLNPCAfit object

Usage
PLNPCAfit$new(
  rank,
  responses,
  covariates,
  offsets,
  weights,
  model,
  xlevels,
  control
)
Arguments
rank

rank of the PCA (or equivalently, dimension of the latent space)

responses

the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily

covariates

design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily

offsets

offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily

weights

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

model

model used for fitting, extracted from the formula in the upper-level call

xlevels

named listed of factor levels included in the models, extracted from the formula in the upper-level call and used for predictions.

control

a list for controlling the optimization. See details.

Method update()

Update a PLNPCAfit object

Usage
PLNPCAfit$update(
  Theta = NA,
  Sigma = NA,
  B = NA,
  M = NA,
  S2 = NA,
  Z = NA,
  A = NA,
  Ji = NA,
  R2 = NA,
  monitoring = NA
)
Arguments
Theta

matrix of regression matrix

Sigma

variance-covariance matrix of the latent variables

B

matrix of PCA loadings (in the latent space)

M

matrix of mean vectors for the variational approximation

S2

matrix of variance vectors for the variational approximation

Z

matrix of latent vectors (includes covariates and offset effects)

A

matrix of fitted values

Ji

vector of variational lower bounds of the log-likelihoods (one value per sample)

R2

approximate R^2 goodness-of-fit criterion

monitoring

a list with optimization monitoring quantities

Returns

Update the current PLNPCAfit object

Method optimize()

Call to the C++ optimizer and update of the relevant fields

Usage
PLNPCAfit$optimize(responses, covariates, offsets, weights, control)
Arguments
responses

the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily

covariates

design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily

offsets

offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily

weights

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

control

a list for controlling the optimization. See details.

Method setVisualization()

Compute PCA scores in the latent space and update corresponding fields.

Usage
PLNPCAfit$setVisualization(scale.unit = FALSE)
Arguments
scale.unit

Logical. Should PCA scores be rescaled to have unit variance

Method postTreatment()

Update R2, fisher, std_err fields and set up visualization after optimization

Usage
PLNPCAfit$postTreatment(responses, covariates, offsets, weights, nullModel)
Arguments
responses

the matrix of responses (called Y in the model). Will usually be extracted from the corresponding field in PLNfamily

covariates

design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily

offsets

offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily

weights

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

nullModel

null model used for approximate R2 computations. Defaults to a GLM model with same design matrix but not latent variable.

Method compute_fisher()

Safely compute the fisher information matrix (FIM)

Usage
PLNPCAfit$compute_fisher(type = c("wald", "louis"), X = NULL)
Arguments
type

approximation scheme to compute the fisher information matrix. Either wald (default) or louis. type = "louis" results in smaller confidence intervals.

X

design matrix used to compute the FIM

Returns

a sparse matrix with sensible dimension names

Method latent_pos()

Compute matrix of latent positions, noted as Z in the model. Useful to compute the likelihood or for data visualization

Usage
PLNPCAfit$latent_pos(covariates, offsets)
Arguments
covariates

design matrix (called X in the model). Will usually be extracted from the corresponding field in PLNfamily

offsets

offset matrix (called O in the model). Will usually be extracted from the corresponding field in PLNfamily

Returns

a n x q matrix of latent positions.

Method plot_individual_map()

Plot the factorial map of the PCA

Usage
PLNPCAfit$plot_individual_map(
  axes = 1:min(2, self$rank),
  main = "Individual Factor Map",
  plot = TRUE,
  cols = "default"
)
Arguments
axes

numeric, the axes to use for the plot when map = "individual" or "variable". Default it c(1,min(rank))

main

character. A title for the single plot (individual or variable factor map). If NULL (the default), an hopefully appropriate title will be used.

plot

logical. Should the plot be displayed or sent back as ggplot object

cols

a character, factor or numeric to define the color associated with the individuals. By default, all individuals receive the default color of the current palette.

Returns

a ggplot graphic

Method plot_correlation_circle()

Plot the correlation circle of a specified axis for a PLNLDAfit object

Usage
PLNPCAfit$plot_correlation_circle(
  axes = 1:min(2, self$rank),
  main = "Variable Factor Map",
  cols = "default",
  plot = TRUE
)
Arguments
axes

numeric, the axes to use for the plot when map = "individual" or "variable". Default it c(1,min(rank))

main

character. A title for the single plot (individual or variable factor map). If NULL (the default), an hopefully appropriate title will be used.

cols

a character, factor or numeric to define the color associated with the variables. By default, all variables receive the default color of the current palette.

plot

logical. Should the plot be displayed or sent back as ggplot object

Returns

a ggplot graphic

Method plot_PCA()

Plot a summary of the PLNPCAfit object

Usage
PLNPCAfit$plot_PCA(
  nb_axes = min(3, self$rank),
  ind_cols = "ind_cols",
  var_cols = "var_cols",
  plot = TRUE
)
Arguments
nb_axes

scalar: the number of axes to be considered when map = "both". The default is min(3,rank).

ind_cols

a character, factor or numeric to define the color associated with the individuals. By default, all variables receive the default color of the current palette.

var_cols

a character, factor or numeric to define the color associated with the variables. By default, all variables receive the default color of the current palette.

plot

logical. Should the plot be displayed or sent back as ggplot object

Returns

a grob object

Method show()

User friendly print method

Usage
PLNPCAfit$show()

Method clone()

The objects of this class are cloneable with this method.

Usage
PLNPCAfit$clone(deep = FALSE)
Arguments
deep

Whether to make a deep clone.

See Also

The function PLNPCA, the class PLNPCAfamily

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPCAs <- PLNPCA(Abundance ~ 1 + offset(log(Offset)), data = trichoptera, ranks = 1:5)
myPCA <- getBestModel(myPCAs)
class(myPCA)
print(myPCA)
[Package PLNmodels version 0.10.6 Index]