ZIPLNfit {PLNmodels}

An R6 Class to represent a ZIPLNfit

Description

The function ZIPLN() fits a model which is an instance of an object with class ZIPLNfit.

This class comes with a set of R6 methods, some of which are useful for the end-user and exported as S3 methods. See the documentation for coef(), sigma(), predict().

Fields are accessed via active binding and cannot be changed by the user.

Details

Covariates for the Zero-Inflation parameter (using a logistic regression model) can be specified in the formula RHS using the pipe (⁠~ PLN effect | ZI effect⁠) to separate covariates for the PLN part of the model from those for the Zero-Inflation part. Note that different covariates can be used for each part.

Active bindings

n

number of samples/sites

q

number of dimensions of the latent space

p

number of variables/species

d

number of covariates in the PLN part

d0

number of covariates in the ZI part

nb_param_zi

number of parameters in the ZI part of the model

nb_param_pln

number of parameters in the PLN part of the model

nb_param

number of parameters in the ZIPLN model

model_par

a list with the matrices of parameters found in the model (B, Sigma, plus some others depending on the variant)

var_par

a list with two matrices, M and S2, which are the estimated parameters in the variational approximation

optim_par

a list with parameters useful for monitoring the optimization

latent

a matrix: values of the latent vector (Z in the model)

latent_pos

a matrix: values of the latent position vector (Z) without covariates effects or offset

fitted

a matrix: fitted values of the observations (A in the model)

vcov_model

character: the model used for the covariance (either "spherical", "diagonal", "full" or "sparse")

zi_model

character: the model used for the zero inflation (either "single", "row", "col" or "covar")

loglik

(weighted) variational lower bound of the loglikelihood

loglik_vec

element-wise variational lower bound of the loglikelihood

BIC

variational lower bound of the BIC

entropy

Entropy of the variational distribution

entropy_ZI

Entropy of the variational distribution

entropy_PLN

Entropy of the Gaussian variational distribution in the PLN component

ICL

variational lower bound of the ICL

criteria

a vector with loglik, BIC, ICL and number of parameters

Methods

Public methods

• ZIPLNfit$update()

• ZIPLNfit$new()

• ZIPLNfit$optimize()

• ZIPLNfit$optimize_vestep()

• ZIPLNfit$predict()

• ZIPLNfit$show()

• ZIPLNfit$print()

• ZIPLNfit$clone()

Method update()

Update a ZIPLNfit object

Usage

ZIPLNfit$update(
  B = NA,
  B0 = NA,
  Pi = NA,
  Omega = NA,
  Sigma = NA,
  M = NA,
  S = NA,
  R = NA,
  Ji = NA,
  Z = NA,
  A = NA,
  monitoring = NA
)

Arguments

B

matrix of regression parameters in the Poisson lognormal component

B0

matrix of regression parameters in the zero inflated component

Pi

Zero inflated probability parameter (either scalar, row-vector, col-vector or matrix)

Omega

precision matrix of the latent variables

Sigma

covariance matrix of the latent variables

M

matrix of mean vectors for the variational approximation

S

matrix of standard deviation parameters for the variational approximation

R

matrix of probabilities for the variational approximation

Ji

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

Z

matrix of latent vectors (includes covariates and offset effects)

A

matrix of fitted values

monitoring

a list with optimization monitoring quantities

Returns

Update the current ZIPLNfit object

Method new()

Initialize a ZIPLNfit model

Usage

ZIPLNfit$new(data, control)

Arguments

data

a named list used internally to carry the data matrices

control

a list for controlling the optimization. See details.

Method optimize()

Call to the Cpp optimizer and update of the relevant fields

Usage

ZIPLNfit$optimize(data, control)

Arguments

data

a named list used internally to carry the data matrices

control

a list for controlling the optimization. See details.

Method optimize_vestep()

Result of one call to the VE step of the optimization procedure: optimal variational parameters (M, S, R) and corresponding log likelihood values for fixed model parameters (Sigma, B, B0). Intended to position new data in the latent space.

Usage

ZIPLNfit$optimize_vestep(
  data,
  B = self$model_par$B,
  B0 = self$model_par$B0,
  Omega = self$model_par$Omega,
  control = ZIPLN_param(backend = "nlopt")$config_optim
)

Arguments

data

a named list used internally to carry the data matrices

B

Optional fixed value of the regression parameters in the PLN component

B0

Optional fixed value of the regression parameters in the ZI component

Omega

inverse variance-covariance matrix of the latent variables

control

a list for controlling the optimization. See details.

Returns

A list with three components:

• the matrix M of variational means,

• the matrix S of variational standard deviations

• the matrix R of variational ZI probabilities

• the vector Ji of (variational) log-likelihood of each new observation

• a list monitoring with information about convergence status

Method predict()

Predict position, scores or observations of new data. See predict.ZIPLNfit() for the S3 method and additional details

Usage

ZIPLNfit$predict(
  newdata,
  responses = NULL,
  type = c("link", "response", "deflated"),
  level = 1,
  envir = parent.frame()
)

Arguments

newdata

A data frame in which to look for variables with which to predict. If omitted, the fitted values are used.

responses

Optional data frame containing the count of the observed variables (matching the names of the provided as data in the PLN function), assuming the interest in in testing the model.

type

Scale used for the prediction. Either "link" (default, predicted positions in the latent space), "response" (predicted average counts, accounting for zero-inflation) or "deflated" (predicted average counts, not accounting for zero-inflation and using only the PLN part of the model).

level

Optional integer value the level to be used in obtaining the predictions. Level zero corresponds to the population predictions (default if responses is not provided) while level one (default) corresponds to predictions after evaluating the variational parameters for the new data.

envir

Environment in which the prediction is evaluated

Returns

A matrix with predictions scores or counts.

Method show()

User friendly print method

Usage

ZIPLNfit$show(
  model = paste("A multivariate Zero Inflated Poisson Lognormal fit with",
    private$covariance, "covariance model.\n")
)

Arguments

model

First line of the print output

Method print()

User friendly print method

Usage

ZIPLNfit$print()

Method clone()

The objects of this class are cloneable with this method.

Usage

ZIPLNfit$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

## Not run: 
# See other examples in function ZIPLN
data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
myPLN <- ZIPLN(Abundance ~ 1, data = trichoptera)
class(myPLN)
print(myPLN)

## End(Not run)