Zero Inflated Poisson lognormal model

Source: R/ZIPLN.R

Fit the multivariate Zero Inflated Poisson lognormal model with a variational algorithm. Use the (g)lm syntax for model specification (covariates, offsets, subset).

ZIPLN(
  formula,
  data,
  subset,
  zi = c("single", "row", "col"),
  control = ZIPLN_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 PLN is called.

subset

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

zi

a character describing the model used for zero inflation, either of

  • "single" (default, one parameter shared by all counts)

  • "col" (one parameter per variable / feature)

  • "row" (one parameter per sample / individual). If covariates are specified in the formula RHS (see details) this parameter is ignored.

control

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

Value

an R6 object with class ZIPLNfit

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.

See also

The class ZIPLNfit

Examples

data(trichoptera)
trichoptera <- prepare_data(trichoptera$Abundance, trichoptera$Covariate)
## Use different models for zero-inflation...
myZIPLN_single <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "single")
#> 
#>  Initialization...
#>  Adjusting a ZI-PLN model with full covariance model and single specific parameter(s) in Zero inflation component.
#>  DONE!
if (FALSE) {
myZIPLN_row    <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "row")
myZIPLN_col    <- ZIPLN(Abundance ~ 1, data = trichoptera, zi = "col")
## ...including logistic regression on covariates
myZIPLN_covar  <- ZIPLN(Abundance ~ 1 | 1 + Wind, data = trichoptera)
}