gnlmm {repeated} | R Documentation |
gnlmm
fits user-specified nonlinear regression equations to one
or both parameters of the common one and two parameter distributions.
The intercept of the location regression has a normally-distributed
random effect. This normal mixing distribution is computed by
Gauss-Hermite integration.
The scale
of the random effect is the link function to be
applied. For example, if it is log
, the supplied mean function,
mu
, is transformed as exp(log(mu)+sd), where sd is the random
effect parameter.
It is recommended that initial estimates for pmu
and
pshape
be obtained from gnlr
.
Nonlinear regression models can be supplied as formulae where
parameters are unknowns in which case factor variables cannot be used
and parameters must be scalars. (See finterp
.)
The printed output includes the -log likelihood (not the deviance), the corresponding AIC, the maximum likelihood estimates, standard errors, and correlations.
gnlmm(y=NULL, distribution="normal", mu=NULL, shape=NULL, linear=NULL, nest=NULL, pmu=NULL, pshape=NULL, psd=NULL, exact=FALSE, wt=1, delta=1, shfn=FALSE, scale=NULL, points=10, common=FALSE, envir=parent.frame(), print.level=0, typsiz=abs(p), ndigit=10, gradtol=0.00001, stepmax=sqrt(p%*%p)/10, steptol=0.00001, iterlim=100, fscale=1)
y |
A response vector for 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. The beta, simplex, and two-sided power
distributions for proportions do not allow censoring. |
distribution |
Either a character string containing the name of the distribution or a function giving the -log likelihood and calling the location and shape functions. Distributions are 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, and Levy, beta, simplex, and two-sided power. All but the binomial-based distributions and the beta, simplex, and two-sided power may be right and/or left censored. (For definitions of distributions, see the corresponding [dpqr]distribution help.) |
mu |
A user-specified function of pmu , and possibly
linear , giving the regression equation for the location. This
may contain a linear part as the second argument to the function. It
may also be a formula beginning with ~, specifying a either linear
regression function for the location parameter in the Wilkinson and
Rogers notation or a general function with named unknown parameters.
If it contains unknown parameters, the keyword linear may be
used to specify a linear part. If nothing is supplied, the location is
taken to be constant unless the linear argument is given. |
shape |
A user-specified function of pshape , and possibly
linear and/or mu , giving the regression equation for the
dispersion or shape parameter. This may contain a linear part as the
second argument to the function and the location function as last
argument (in which case shfn must be set to TRUE). It may also
be a formula beginning with ~, specifying either a linear regression
function for the shape parameter in the Wilkinson and Rogers notation
or a general function with named unknown parameters. If it contains
unknown parameters, the keyword linear may be used to specify a
linear part and the keyword mu to specify a function of the
location parameter. If nothing is supplied, this parameter is taken to
be constant unless the linear argument is given. 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. |
nest |
The variable classifying observations by the unit upon
which they were observed. Ignored if y or envir has
class, response. |
pmu |
Vector of initial estimates for the location parameters.
If mu is a formula with unknown parameters, their estimates
must be supplied either in their order of appearance in the expression
or in a named list. |
pshape |
Vector of initial estimates for the shape parameters.
If shape is a formula with unknown parameters, their estimates
must be supplied either in their order of appearance in the expression
or in a named list. |
psd |
Initial estimate of the standard deviation of the normal mixing distribution. |
exact |
If TRUE, fits the exact likelihood function for continuous data by integration over intervals of observation, i.e. interval censoring. |
wt |
Weight vector. |
delta |
Scalar or vector giving the unit of measurement (always
one for discrete data) for each response value, set to unity by
default. Ignored if y has class, response. 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.) |
shfn |
If true, the supplied shape function depends on the location (function). The name of this location function must be the last argument of the shape function. |
scale |
The scale on which the random effect is applied:
identity , log , logit , reciprocal , or exp . |
points |
The number of points for Gauss-Hermite integration of the random effect. |
common |
If TRUE, mu and shape must both be either
functions with, as argument, a vector of parameters having some or all
elements in common between them so that indexing is in common
between them or formulae with unknowns. All parameter estimates must
be supplied in pmu . If FALSE, parameters are distinct between
the two functions and indexing starts at one in each function. |
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 gnlm
is returned that contains all of the
relevant information calculated, including error codes.
J.K. Lindsey
finterp
, fmr
, glm
,
gnlmix
, glmm
,
gnlr
, gnlr3
,
hnlmix
, lm
,
nlr
, nls
.
library(gnlm) # data objects sex <- c(0,1,1) sx <- tcctomat(sex) dose <- matrix(rpois(30,10),nrow=3) dd <- tvctomat(dose) # vectors for functions dose <- as.vector(t(dose)) sex <- c(rep(0,10),rep(1,20)) nest <- rbind(rep(1,10),rep(2,10),rep(3,10)) #y <- rgamma(30,2,scale=exp(0.2+0.1*dose+0.1*sex+rep(rnorm(3),rep(10,3)))/2) y <- c(0.6490851,0.9313931,0.4765569,0.4188045,2.8339637,2.8158090, 2.6059975,2.9958184,2.7351583,3.2884980,1.1180961,0.9443986,1.7915571, 9.0013379,2.3969570,3.4227356,0.5045518,0.7452521,1.8712467,3.6814198, 0.1489849,1.0327552,0.6102406,1.1536620,2.9145237,9.2847798,5.6454605, 1.9759672,1.5798008,5.1024496) y <- restovec(matrix(y, nrow=3), nest=nest, name="y") reps <- rmna(y, ccov=sx, tvcov=dd) # # log linear regression with gamma distribution mu <- function(p) exp(p[1]+p[2]*sex+p[3]*dose) print(z <- gnlr(y, dist="gamma", mu=mu, pmu=c(1,0,0), pshape=1)) gnlmm(y, dist="gamma", mu=mu, nest=nest, pmu=z$coef[1:3], pshape=z$coef[4], psd=0.1, points=3) # or equivalently gnlmm(y, dist="gamma", mu=~exp(b0+b1*sex+b2*dose), nest=nest, pmu=z$coef[1:3], pshape=z$coef[4], psd=0.1, points=3, envir=reps) # or with identity link print(z <- gnlr(y, dist="gamma", mu=~sex+dose, pmu=c(0.1,0,0), pshape=1)) gnlmm(y, dist="gamma", mu=~sex+dose, nest=nest, pmu=z$coef[1:3], pshape=z$coef[4], psd=0.1, points=3) # or gnlmm(y, dist="gamma", mu=~b0+b1*sex+b2*dose, nest=nest, pmu=z$coef[1:3], pshape=z$coef[4], psd=0.1, points=3, envir=reps) # # nonlinear regression with gamma distribution mu <- function(p) p[1]+exp(p[2]+p[3]*sex+p[4]*dose) print(z <- gnlr(y, dist="gamma", mu=mu, pmu=c(1,1,0,0), pshape=1)) gnlmm(y, dist="gamma", mu=mu, nest=nest, pmu=z$coef[1:4], pshape=z$coef[5], psd=0.1, points=3) # or mu2 <- function(p, linear) p[1]+exp(linear) gnlmm(y, dist="gamma", mu=mu2, linear=~sex+dose, nest=nest, pmu=z$coef[1:4], pshape=1, psd=0.1, points=3) # or gnlmm(y, dist="gamma", mu=~a+exp(linear), linear=~sex+dose, nest=nest, pmu=z$coef[1:4], pshape=1, psd=0.1, points=3) # or gnlmm(y, dist="gamma", mu=~b4+exp(b0+b1*sex+b2*dose), nest=nest, pmu=z$coef[1:4], pshape=z$coef[5], psd=0.1, points=3, envir=reps) # # include regression for the shape parameter with same mu function shape <- function(p) p[1]+p[2]*sex print(z <- gnlr(y, dist="gamma", mu=mu, shape=shape, pmu=z$coef[1:4], pshape=rep(1,2))) gnlmm(y, dist="gamma", mu=mu, shape=shape, nest=nest, pmu=z$coef[1:4], pshape=z$coef[5:6], psd=0.1, points=3) # or gnlmm(y, dist="gamma", mu=mu, shape=shape, nest=nest, pmu=z$coef[1:4], pshape=z$coef[5:6], psd=0.1, points=3, envir=reps) # or gnlmm(y, dist="gamma", mu=~b4+exp(b0+b1*sex+b2*dose), shape=~a1+a2*sex, nest=nest, pmu=z$coef[1:4], pshape=z$coef[5:6], psd=0.1, points=3, envir=reps)