fit_multiple_models: Fit Multiple Models

In molic: Multivariate Outlier Detection in Contingency Tables

Description

Conduct multiple novelty tests for a new observation

Usage

fit_multiple_models(
  A,
  z,
  response,
  alpha = 0.05,
  type = "fwd",
  q = 0.5,
  comp = NULL,
  nsim = 10000,
  ncores = 1,
  trace = TRUE,
  validate = TRUE
)

Arguments

A	A character matrix or data.frame
z	Named vector. Same names as colnames(A) but without the class variable
response	A character with the name of the class variable of interest
alpha	The significance level
type	Character ("fwd", "bwd", "tree" or "tfwd") - the type of interaction graph to be used
q	Penalty term in the stopping criterion when fitting the interaction graph (0 = AIC and 1 = BIC)
comp	A list with character vectors. Each element in the list is a component in the graph (using expert knowledge)
nsim	Number of simulations
ncores	Number of cores to use in parallelization
trace	Logical indicating whether or not to trace the procedure
validate	Logical. If true, it checks if A has only single character values and converts it if not.

Value

An object of type multiple_models; a list of of novely objects from which one can query pvalues etc. for outlierdetection.

See Also

fit_outlier, fit_mixed_outlier

Examples

library(dplyr)
set.seed(1)

# A patient with psoriasis
z <- unlist(derma[2, 1:10])

d <- derma[, c(names(z), "ES")] %>%
     filter(ES %in% c("chronic dermatitis", "psoriasis"))

m <- fit_multiple_models(d, z, "ES", nsim = 1000, trace = FALSE, validate = FALSE)

plot(m)
print(m)

molic documentation built on June 2, 2021, 5:07 p.m.