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An R6 Class to represent a PLNfit in a LDA framework

Source: R/PLNLDAfit-class.R
PLNLDAfit.Rd

The function PLNLDA() produces an instance of an object with class PLNLDAfit.

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(), the plot() method for LDA visualization and predict() method for prediction

See also

The function PLNLDA.

Super class

PLNfit -> PLNLDAfit

Active bindings

rank

the dimension of the current model

nb_param

number of parameters in the current PLN model

model_par

a list with the matrices associated with the estimated parameters of the PLN model: B (covariates), Sigma (latent covariance), C (latent loadings), P (latent position) and Mu (group means)

percent_var

the percent of variance explained by each axis

corr_map

a matrix of correlations to plot the correlation circles

scores

a matrix of scores to plot the individual factor maps

group_means

a matrix of group mean vectors in the latent space.

Methods

Public methods

Inherited methods

PLNLDAfit$new()

Initialize a PLNLDAfit object

Usage

PLNLDAfit$new(
  grouping,
  responses,
  covariates,
  offsets,
  weights,
  formula,
  control
)

Arguments

grouping

a factor specifying the class of each observation used for discriminant analysis.

responses

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

covariates

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

offsets

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

weights

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

formula

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

control

list controlling the optimization and the model

PLNLDAfit$optimize()

Compute group means and axis of the LDA (noted B in the model) in the latent space, update corresponding fields

Usage

PLNLDAfit$optimize(grouping, responses, covariates, offsets, weights, config)

Arguments

grouping

a factor specifying the class of each observation used for discriminant analysis.

responses

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

covariates

design matrix. Automatically built from the covariates and the formula from the call

offsets

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

weights

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

config

list controlling the optimization

X

Abundance matrix.

PLNLDAfit$postTreatment()

Update R2, fisher and std_err fields and visualization

Usage

PLNLDAfit$postTreatment(
  grouping,
  responses,
  covariates,
  offsets,
  weights = rep(1, nrow(responses)),
  config_post,
  config_optim
)

Arguments

grouping

a factor with group memberships

responses

the matrix of responses (counts)

covariates

the matrix of covariates

offsets

the matrix of offsets

weights

an optional vector of observation weights. Default is uniform weights.

config_post

a list for controlling the post-treatments (optional bootstrap, jackknife, R2, etc.).

config_optim

list controlling the optimization parameters

PLNLDAfit$setVisualization()

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

Usage

PLNLDAfit$setVisualization(scale.unit = FALSE)

Arguments

scale.unit

Logical. Should LDA scores be rescaled to have unit variance

PLNLDAfit$plot_individual_map()

Plot the factorial map of the LDA

Usage

PLNLDAfit$plot_individual_map(
  axes = 1:min(2, self$rank),
  main = "Individual Factor Map",
  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.

plot

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

Returns

a ggplot2::ggplot graphic

PLNLDAfit$plot_correlation_map()

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

Usage

PLNLDAfit$plot_correlation_map(
  axes = 1:min(2, self$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 ggplot2::ggplot graphic

PLNLDAfit$plot_LDA()

Plot a summary of the PLNLDAfit object

Usage

PLNLDAfit$plot_LDA(
  nb_axes = min(3, self$rank),
  var_cols = "default",
  plot = TRUE
)

Arguments

nb_axes

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

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

PLNLDAfit$predict()

Predict group of new samples

Usage

PLNLDAfit$predict(
  newdata,
  type = c("posterior", "response", "scores"),
  scale = c("log", "prob"),
  prior = NULL,
  control = PLN_param(backend = "nlopt"),
  envir = parent.frame()
)

Arguments

newdata

A data frame in which to look for variables, offsets and counts with which to predict.

type

The type of prediction required. The default are posterior probabilities for each group (in either unnormalized log-scale or natural probabilities, see "scale" for details), "response" is the group with maximal posterior probability and "scores" is the average score along each separation axis in the latent space, with weights equal to the posterior probabilities.

scale

The scale used for the posterior probability. Either log-scale ("log", default) or natural probabilities summing up to 1 ("prob").

prior

User-specified prior group probabilities in the new data. If NULL (default), prior probabilities are computed from the learning set.

control

a list for controlling the optimization. See PLN() for details.

envir

Environment in which the prediction is evaluated

PLNLDAfit$show()

User friendly print method

Usage

PLNLDAfit$show()

PLNLDAfit$clone()

The objects of this class are cloneable with this method.

Usage

PLNLDAfit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

if (FALSE) { # \dontrun{
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLNLDA <- PLNLDA(Abundance ~ 1, grouping = Group, data = trichoptera)
class(myPLNLDA)
print(myPLNLDA)
} # }