nlme (version 3.1-157)

varConstProp: Constant Plus Proportion Variance Function

Description

This function is a constructor for the varConstProp class, representing a variance function structure corresponding to a two-component error model (additive and proportional error). Letting \(v\) denote the variance covariate and \(\sigma^2(v)\) denote the variance function evaluated at \(v\), the two-component variance function is defined as \(\sigma^2(v) = a^2 + b^2 * v^{2}\), where a is the additive component and b is the relative error component. In order to avoid overparameterisation of the model, it is recommended to use the possibility to fix sigma, preferably to a value of 1 (see examples).

Usage

varConstProp(const, prop, form, fixed)

Arguments

const, prop

optional numeric vectors, or lists of numeric values, with, respectively, the coefficients for the constant and the proportional error terms. Both arguments must have length one, unless a grouping factor is specified in form. If either argument has length greater than one, it must have names which identify its elements to the levels of the grouping factor defined in form. If a grouping factor is present in form and the argument has length one, its value will be assigned to all grouping levels. Only positive values are allowed for const. Default is 0.1 for both const and prop.

form

an optional one-sided formula of the form ~ v, or ~ v | g, specifying a variance covariate v and, optionally, a grouping factor g for the coefficients. The variance covariate must evaluate to a numeric vector and may involve expressions using ".", representing a fitted model object from which fitted values (fitted(.)) and residuals (resid(.)) can be extracted (this allows the variance covariate to be updated during the optimization of an object function). When a grouping factor is present in form, a different coefficient value is used for each of its levels. Several grouping variables may be simultaneously specified, separated by the * operator, as in ~ v | g1 * g2 * g3. In this case, the levels of each grouping variable are pasted together and the resulting factor is used to group the observations. Defaults to ~ fitted(.) representing a variance covariate given by the fitted values of a fitted model object and no grouping factor.

fixed

an optional list with components const and/or power, consisting of numeric vectors, or lists of numeric values, specifying the values at which some or all of the coefficients in the variance function should be fixed. If a grouping factor is specified in form, the components of fixed must have names identifying which coefficients are to be fixed. Coefficients included in fixed are not allowed to vary during the optimization of an objective function. Defaults to NULL, corresponding to no fixed coefficients.

Value

a varConstProp object representing a constant plus proportion variance function structure, also inheriting from class varFunc.

References

Lavielle, M. (2015) Mixed Effects Models for the Population Approach: Models, Tasks, Methods and Tools, Chapman and Hall/CRC. 10.1201/b17203

Pinheiro, J.C., and Bates, D.M. (2000) Mixed-Effects Models in S and S-PLUS, Springer. 10.1007/b98882

Ranke, J., and Meinecke, S. (2019) Error Models for the Kinetic Evaluation of Chemical Degradation Data. Environments 6(12), 124 10.3390/environments6120124

Rocke, David M. and Lorenzato, Stefan (1995) A Two-Component Model for Measurement Error in Analytical Chemistry. Technometrics 37(2), 176--184. 10.1080/00401706.1995.10484302

Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978) Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry 24(11), 1895--1898. 10.1093/clinchem/24.11.1895

Wilson, M.D., Rocke, D.M., Durbin, B. and Kahn, H.D (2004) Detection Limits and Goodness-of-Fit Measures for the Two-Component Model of Chemical Analytical Error. Analytica Chimica Acta 2004, 509, 197--208 10.1016/j.aca.2003.12.047

See Also

varClasses, varWeights.varFunc, coef.varFunc

Examples

# NOT RUN {
# Generate some synthetic data using the two-component error model and use
# different variance functions, also with fixed sigma in order to avoid
# overparameterisation in the case of a constant term in the variance function
times <- c(0, 1, 3, 7, 14, 28, 56, 120)
pred <- 100 * exp(- 0.03 * times)
sd_pred <- sqrt(3^2 + 0.07^2 * pred^2)
n_replicates <- 2

set.seed(123456)
syn_data <- data.frame(
  time = rep(times, each = n_replicates),
  value = rnorm(length(times) * n_replicates,
    rep(pred, each = n_replicates),
    rep(sd_pred, each = n_replicates)))
syn_data$value <- ifelse(syn_data$value < 0, NA, syn_data$value)

f_const <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
  data = syn_data, na.action = na.omit,
  start = list(parent_0 = 100, lrc = -3))
f_varPower <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
  data = syn_data, na.action = na.omit,
  start = list(parent_0 = 100, lrc = -3),
  weights = varPower())
f_varConstPower <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
  data = syn_data, na.action = na.omit,
  start = list(parent_0 = 100, lrc = -3),
  weights = varConstPower())
f_varConstPower_sf <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
  data = syn_data, na.action = na.omit,
  control = list(sigma = 1),
  start = list(parent_0 = 100, lrc = -3),
  weights = varConstPower())
f_varConstProp <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
  data = syn_data, na.action = na.omit,
  start = list(parent_0 = 100, lrc = -3),
  weights = varConstProp())
f_varConstProp_sf <- gnls(value ~ SSasymp(time, 0, parent_0, lrc),
  data = syn_data, na.action = na.omit,
  start = list(parent_0 = 100, lrc = -3),
  control = list(sigma = 1),
  weights = varConstProp())

AIC(f_const, f_varPower, f_varConstPower, f_varConstPower_sf,
  f_varConstProp, f_varConstProp_sf)

# The error model parameters 3 and 0.07 are approximately recovered
intervals(f_varConstProp_sf)
# }