Fit the model parameters by optimizing a histogram metric

Usage

fit_distributions(
  x,
  metric = c("mle", "jaccard", "intersection", "ks", "mse", "chisq"),
  truncated = FALSE,
  distributions = c("norm", "gamma", "gamma_flip", "unif")
)

# S3 method for numeric
fit_distributions(
  x,
  metric = c("mle", "jaccard", "intersection", "ks", "mse", "chisq"),
  truncated = FALSE,
  distributions = c("norm", "gamma", "gamma_flip", "unif")
)

# S3 method for table
fit_distributions(
  x,
  metric = c("mle", "jaccard", "intersection", "ks", "mse", "chisq"),
  truncated = FALSE,
  distributions = c("norm", "gamma", "gamma_flip", "unif")
)

# S3 method for GenomicHistogram
fit_distributions(
  x,
  metric = c("mle", "jaccard", "intersection", "ks", "mse", "chisq"),
  truncated = FALSE,
  distributions = c("mle", "norm", "gamma", "gamma_flip", "unif")
)

# S3 method for Histogram
fit_distributions(
  x,
  metric = c("mle", "jaccard", "intersection", "ks", "mse", "chisq"),
  truncated = FALSE,
  distributions = c("norm", "gamma", "gamma_flip", "unif")
)

Arguments

x: numeric vector, representing data to be fit
metric: a subset of mle, jaccard, intersection, ks, mse, chisq indicating metrics to use for fit optimization
truncated: logical, whether to fit truncated distributions
distributions: character vector indicating distributions, subset of norm, gamma, gamma_flip and unif.

Value

a nested list where each sublist represents a model with the following data

par: a character string denoting the region_id of the Histogram
dist: the distribution name
metric: the metric used to fit the distribution
value: the fitted value of the metric function
dens: a function that returns the density of the fitted distribution