basicdrm (version 0.3.2)

findBestHillModel: Selects a best-fitting Hill model given defaults

Description

Using the function fitHillModel(), this function fits four Hill models with minimal and maximal effects either varying or fixed at the given default values; it then selects the best fitting model based on the Bayesian information criterio or Akaike information criterion, and returns a Hill fit object with information from all fits included.

Usage

findBestHillModel(
  formula,
  data,
  defaults,
  weights = NULL,
  start = NULL,
  direction = 0,
  lower = NULL,
  upper = NULL,
  useBIC = TRUE
)

Value

An object of class hillrm. Contains all of the values found in any hillrm object (see fitHillModel()), as well as allfits, a named list of lists containing the coefficients and parvectors for each of the individual fits, as well as the Bayesian information criterion (bic) and Akaike informtion criterion (aic) values for each fit.

Arguments

formula

Either an object of class formula such as would be provided to a modeling function like lm(), or a numeric vector of concentration values (including 0 or Inf)

data

If forumula is a symbolic formula, a data frame containing the specified values. If formula is a numeric vector of concentrations, a numeric vector of response values

defaults

A two value numeric vector containing the default minimal effect and the default maximal effect, in that order

weights

A vector of weights (between 0 and 1) the same length as conc and act which determines the weight with which each measurement will impact the the sum of squared errors. Weights will be multiplied by errors before squaring. If NULL (the default) all weights will be set to 1. Can be a numeric vector, or the name of a column in data if formula is a symbolic formula

start

A vector of four starting values for the Hill model to be fit. Any values not being fit will be fixed at these starting values. If left as NULL, a starting vector will be estimated from the data.

direction

Determines the possible directionality of the dose response model. If 0 (the default) no additional constraints are placed on the parameters. If greater than 0, the fitting will require that the maximal effect is greater than the minimal effect. If less than 0, the fitting wll require tha the maximal effect is less than the minimal effect.

lower

A length-four vector of lower bounds on the Hill parameter values. Any parameters for which you do not wish to specify a bound can be set to NA.

upper

A vector of upper bounds on the Hill parameter values. Works the same as parameter lower.

useBIC

Determines the information criterion to be used. If TRUE (the default), uses the Bayesian information criterion. If FALSE, uses the Akaike information criterion

Examples

conc <- c(0,2^(-6:3),Inf)
hpar <- c(1,3,0,75)
response <- evalHillModel(conc, hpar) + rnorm(length(conc),sd=7.5)

hfit <- findBestHillModel(conc,response,defaults=c(0,100))