f.lik.cat: Calculate likelihood for categorical response
In alfcrisci/bmdModeling: Benchmark dose modeling

Calculate likelihood for categorical response

f.lik.cat(theta, x, y = NA, kk, nn, dtype, fct1 = 1, fct2 = 1, nrp,
  nth = NA, nr.aa = 1, nr.bb = 1, model.ans, model.type, CES = NA,
  ttt = 0, twice = NA, alfa.length = 0, x.full = NA, fct1.full = NA,
  fct2.full = NA, ces.ans = 1, fct3 = 1, fct4 = 1, fct5 = 1,
  CES.cat = 1, decr.zz = TRUE, CES1 = CES1, CES2 = CES2, nn.tot, kk.tot,
  xx.tot = xx.tot, fct3.ref, track = FALSE)

`theta`	numeric vector, the initial regression parameter values
`x`	numeric vector, the dose values
`y`	numeric vector, the response values
`kk`	vector of parameters?
`nn`	vector of total number
`dtype`	integer, determines the type of response
`fct1`	numeric, value for parameter a
`fct2`	numeric, value for parameter b
`nrp`	values?
`nth`	values?
`nr.aa`	values?
`nr.bb`	values?
`model.ans`	integer, type of response model, only 3 available
`model.type`	type of model
`CES`	numeric, value for the CES
`ttt`	numeric, time variable
`twice`	logical, if TRUE two parameters are dependent of the same covariate
`alfa.length`	length of alpha
`x.full`	numeric vector, the dose values
`fct1.full`	numeric, value for parameter a
`fct2.full`	numeric, value for parameter b
`ces.ans`	index type of benchmark response
`fct3`	numeric, value for parameter c
`fct4`	numeric, value for parameter d
`fct5`	numeric, value for parameter e
`CES.cat`	value
`decr.zz`	is z decreasing?
`CES1`	numeric, value for the first CES
`CES2`	numeric, value for the second CES
`nn.tot`	vector of total number
`kk.tot`	vector of parameters?
`xx.tot`	vector of concentrations
`fct3.ref`	reference for parameter 3
`track`	logical, if TRUE (FALSE by default) print the name of the function which is currently being run