repeated (version 1.1.9)

gnlmix: Generalized Nonlinear Regression with a Random Parameter

Description

gnlmix fits user-specified nonlinear regression equations to one or both parameters of the common one and two parameter distributions. One parameter of the location regression is random with some specified mixing distribution.

Usage

gnlmix(
  y = NULL,
  distribution = "normal",
  mixture = "normal",
  random = NULL,
  nest = NULL,
  mu = NULL,
  shape = NULL,
  linear = NULL,
  pmu = NULL,
  pshape = NULL,
  pmix = NULL,
  delta = 1,
  common = FALSE,
  envir = parent.frame(),
  print.level = 0,
  typsize = abs(p),
  ndigit = 10,
  gradtol = 1e-05,
  stepmax = 10 * sqrt(p %*% p),
  steptol = 1e-05,
  iterlim = 100,
  fscale = 1,
  eps = 1e-04,
  points = 5,
  steps = 10
)

Value

A list of class gnlm is returned that contains all of the relevant information calculated, including error codes.

Arguments

y

A response vector of uncensored data, a two column matrix for binomial data, or an object of class, response (created by restovec) or repeated (created by rmna or lvna). If the repeated data object contains more than one response variable, give that object in envir and give the name of the response variable to be used here.

distribution

The distribution for the response: binomial, beta binomial, double binomial, mult(iplicative) binomial, Poisson, negative binomial, double Poisson, mult(iplicative) Poisson, gamma count, Consul generalized Poisson, logarithmic series, geometric, normal, inverse Gauss, logistic, exponential, gamma, Weibull, extreme value, Cauchy, Pareto, Laplace, Levy, beta, simplex, or two-sided power. (For definitions of distributions, see the corresponding [dpqr]distribution help.)

mixture

The mixing distribution for the random parameter: normal, Cauchy, logistic, Laplace, inverse Gauss, gamma, inverse gamma, Weibull, beta, simplex, or two-sided power. The first four have zero location parameter, the next three have unit location parameter, and the last two have location parameter set to 0.5.

random

The name of the random parameter in the mu formula.

nest

The variable classifying observations by the unit upon which they were observed. Ignored if y or envir has class, response or repeated.

mu

A user-specified formula containing named unknown parameters, giving the regression equation for the location parameter. This may contain the keyword, linear referring to a linear part.

shape

A user-specified formula containing named unknown parameters, giving the regression equation for the shape parameter. This may contain the keyword, linear referring to a linear part. If nothing is supplied, this parameter is taken to be constant. This parameter is the logarithm of the usual one.

linear

A formula beginning with ~ in W&R notation, specifying the linear part of the regression function for the location parameter or list of two such expressions for the location and/or shape parameters.

pmu

Vector of initial estimates for the location parameters. These must be supplied either in their order of appearance in the formula or in a named list.

pshape

Vector of initial estimates for the shape parameters. These must be supplied either in their order of appearance in the expression or in a named list.

pmix

Initial estimate for the logarithm of the dispersion parameter of the mixing distribution.

delta

Scalar or vector giving the unit of measurement (always one for discrete data) for each response value, set to unity by default. For example, if a response is measured to two decimals, delta=0.01. If the response is transformed, this must be multiplied by the Jacobian. The transformation cannot contain unknown parameters. For example, with a log transformation, delta=1/y. (The delta values for the censored response are ignored.)

common

If TRUE, the formulae with unknowns for the location and shape have names in common. All parameter estimates must be supplied in pmu.

envir

Environment in which model formulae are to be interpreted or a data object of class, repeated, tccov, or tvcov; the name of the response variable should be given in y. If y has class repeated, it is used as the environment.

print.level

Arguments for nlm.

typsize

Arguments for nlm.

ndigit

Arguments for nlm.

gradtol

Arguments for nlm.

stepmax

Arguments for nlm.

steptol

Arguments for nlm.

iterlim

Arguments for nlm.

fscale

Arguments for nlm.

eps

Precision of the Romberg integration.

points

For the Romberg integration, the number of extrapolation points so that 2*points is the order of integration, by default set to 5; points=2 is Simpson's rule.

steps

For the Romberg integration, the maximum number of steps, by default set to 10.

Author

J.K. Lindsey

Details

It is recommended that initial estimates for pmu and pshape be obtained from gnlr.

These nonlinear regression models must be supplied as formulae where parameters are unknowns. (See finterp.)

Examples

dose <- c(9,12,4,9,11,10,2,11,12,9,9,9,4,9,11,9,14,7,9,8)
#y <- rgamma(20,shape=2+0.3*dose,scale=2)+rep(rnorm(4,0,4),rep(5,4))
y <- c(8.674419, 11.506066, 11.386742, 27.414532, 12.135699,  4.359469,
       1.900681, 17.425948,  4.503345,  2.691792,  5.731100, 10.534971,
      11.220260,  6.968932,  4.094357, 16.393806, 14.656584,  8.786133,
      20.972267, 17.178012)
resp <- restovec(matrix(y, nrow=4, byrow=TRUE), name="y")
reps <- rmna(resp, tvcov=tvctomat(matrix(dose, nrow=4, byrow=TRUE), name="dose"))

# same linear normal model with random normal intercept fitted four ways
# compare with growth::elliptic(reps, model=~dose, preg=c(0,0.6), pre=4)
glmm(y~dose, nest=individuals, data=reps)
gnlmm(reps, mu=~dose, pmu=c(8.7,0.25), psh=3.5, psd=3)
gnlmix(reps, mu=~a+b*dose+rand, random="rand", pmu=c(8.7,0.25),
	pshape=3.44, pmix=2.3)
if (FALSE) {
# gamma model with log link and random normal intercept fitted three ways
glmm(y~dose, family=Gamma(link=log), nest=individuals, data=reps, points=8)
gnlmm(reps, distribution="gamma", mu=~exp(a+b*dose), pmu=c(2,0.03),
	psh=1, psd=0.3)
gnlmix(reps, distribution="gamma", mu=~exp(a+b*dose+rand), random="rand",
	pmu=c(2,0.04), pshape=1, pmix=-2)

# gamma model with log link and random gamma mixtures
gnlmix(reps, distribution="gamma", mixture="gamma",
	mu=~exp(a*rand+b*dose), random="rand", pmu=c(2,0.04),
	pshape=1.24, pmix=3.5)
gnlmix(reps, distribution="gamma", mixture="gamma",
	mu=~exp(a+b*dose)*rand, random="rand", pmu=c(2,0.04),
	pshape=1.24, pmix=2.5)
}