fitModels {benchdamic}R Documentation

fitModels

Description

A wrapper function that fits the specified models for each taxon of the count data and computes the mean difference (MD) and zero probability difference (ZPD) between estimated and observed values.

Usage

fitModels(
  counts,
  models = c("NB", "ZINB", "DM", "ZIG", "HURDLE"),
  scale_ZIG = c("default", "median"),
  scale_HURDLE = c("default", "median"),
  verbose = TRUE
)

Arguments

counts

a phyloseq object or a matrix of counts with features (OTUs, ASVs, genes) by row and samples by column.

models

character vector which assumes the values NB, ZINB, DM, ZIG, and HURDLE.

scale_ZIG

character vector, either median or default to choose between the median of the library size or one thousand to scale normalization factors for the zero-inflated gaussian model.

scale_HURDLE

character vector, either median or default to choose between the median of the library size or one million to scale raw counts for the truncated gaussian hurdle model.

verbose

an optional logical value. If TRUE information on the steps of the algorithm is printed. Default verbose = TRUE.

Value

list of data.frame objects for each model. The first two columns contain the properly transformed observed values for mean and zero proportion, while the third and the fourth columns contain the estimated values for the mean and the zero rate respectively.

See Also

fitNB, fitZINB, fitDM, fitZIG, and fitHURDLE for the model estimations, prepareObserved for raw counts preparation, and meanDifferences for the Mean Difference (MD) and Zero Probability Difference (ZPD) computations.

Examples

# Generate some random counts
counts <- matrix(rnbinom(n = 60, size = 3, prob = 0.5), nrow = 10, ncol = 6)
# Estimate the counts assuming several distributions
GOF <- fitModels(
    counts = counts, models = c(
        "NB", "ZINB",
        "DM", "ZIG", "HURDLE"
    ), scale_ZIG = c("median", "default"), scale_HURDLE =
        c("median", "default")
)

head(GOF)

[Package benchdamic version 1.0.0 Index]