hnlmix {repeated} | R Documentation |

`hnlmix`

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.

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`

.)

hnlmix(y=NULL, distribution="normal", mixture="normal", random=NULL, nest=NULL, mu=NULL, shape=NULL, linear=NULL, pmu=NULL, pshape=NULL, pmix=NULL, prandom=NULL, delta=1, common=FALSE, envir=parent.frame(), print.level=0, typsiz=abs(p), ndigit=10, gradtol=0.00001, stepmax=10*sqrt(p%*%p), steptol=0.00001, iterlim=100, fscale=1, eps=1.0e-4)

`y` |
A response vector of uncensored data, a two column matrix
for binomial data or censored data, with the second column being the
censoring indicator (1: uncensored, 0: right censored, -1: left
censored), 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 (whose
initial values are supplied in `prandom` ):
normal, logistic, inverse Gauss, gamma, inverse gamma, Weibull, or beta. The
first two have zero location parameter, the next three have unit location
parameter, and the last one has location parameter set to 0.5. |

`random` |
The name of the random parameter in the `mu` formula. |

`nest` |
The cluster 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` |
If NULL, this parameter is estimated from the variances. If a value is given, it is taken as fixed. |

`prandom` |
Either one estimate of the random effects or one for
each cluster (see `nest` ), in which case the last value is not
used. If the location parameter of the mixing distribution is zero,
the last value is recalculated so that their sum is zero; if it
is unity, they must all be positive and the last value is recalculated
so that the sum of their logarithms is zero; if it is 0.5, they must
all lie in (0,1) and the last value is recalculated so that the sum of
their logits is zero. |

`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. |

`others` |
Arguments controlling `nlm` . |

A list of class `hnlmix`

is returned that contains all of the
relevant information calculated, including error codes.

The two variances and shrinkage estimates of the random effects are provided.

J.K. Lindsey

`carma`

, `finterp`

,
`elliptic`

, `glmm`

,
`gnlmix`

, `gnlmm`

,
`gnlr`

, `kalseries`

,
`nlr`

, `nls`

.

library(growth) dose <- c(9,12,4,9,11,10,2,11,12,9,9,9,4,9,11,9,14,7,9,8) #y <- rgamma(20,2+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 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) hnlmix(reps, mu=~a+b*dose+rand, random="rand", pmu=c(8.7,0.25), pshape=3.44, prandom=0) # 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) hnlmix(reps, distribution="gamma", mu=~exp(a+b*dose+rand), random="rand", pmu=c(2,0.04), pshape=1, prandom=0) # gamma model with log link and random gamma mixtures hnlmix(reps, distribution="gamma", mixture="gamma", mu=~exp(a*rand+b*dose), random="rand", pmu=c(2,0.04), pshape=1.24, prandom=1) hnlmix(reps, distribution="gamma", mixture="gamma", mu=~exp(a+b*dose)*rand, random="rand", pmu=c(2,0.04), pshape=1.24, prandom=1)

[Package *repeated* version 1.0 Index]