elliptic(response, model="linear", distribution="elliptic", times=NULL, dose=NULL, ccov=NULL, tvcov=NULL, nest=NULL, torder=0, interaction=NULL, transform="identity", link="identity", autocorr="exponential", pell=NULL, preg=rep(1,4), pvar=var(y), varfn=NULL, pre=NULL, par=NULL, delta=NULL, shfn=F, common=F, envir=sys.frame(sys.parent()), print.level=0, gradtol=0.00001, typsiz=abs(theta), stepmax=10*sqrt(theta%*%theta), steptol=0.00001, iterlim=100, ndigit=10, fscale=1)
response
|
A list of two or three column matrices with response
values, times, and possibly nesting categories, for each individual,
one matrix or dataframe of response values, or an object of class,
response (created by restovec ) or repeated (created by
rmna ).
|
model
|
The model to be fitted for the location. Builtin choices
are (1) linear for linear models with time-varying covariate; if
torder > 0, a polynomial in time is automatically fitted; (2)
logistic for a four-parameter logistic growth curve; (3)
pkpd for a first-order one-compartment pharmacokinetic model.
Otherwise, set this to a function of the parameters or a formula
beginning with ~, specifying either a linear regression function for
the location parameter in the Wilkinson and Rogers notation or a
general function with named unknown parameters that describes the
location, returning a vector the same length as the number of
observations, in which case ccov and tvcov cannot be used.
|
distribution
|
If elliptic , a multivariate
elliptically-contoured distribution is fitted unless pell is
NULL, in which case a multivariate normal distribution is fitted. If
Student t , a multivariate Student t distribution is fitted and
a value must be given for pell .
|
times
|
When response is a matrix, a vector of possibly
unequally spaced times when they are the same for all individuals or a
matrix of times. Not necessary if equally spaced. Ignored if response
has class, response or repeated.
|
dose
|
A vector of dose levels for the pkpd model , one per
individual.
|
ccov
|
A vector or matrix containing time-constant baseline
covariates with one line per individual, a model formula using
vectors of the same size, or an object of class, tccov (created by
tcctomat ). If response has class, repeated, with a
linear , logistic , or pkpd model, the covariates
must be supplied as a Wilkinson and Rogers formula unless none are
to be used. For the pkpd and logistic models, all
variables must be binary (or factor variables) as different values of
all parameters are calculated for all combinations of these variables
(except for the logistic model when a time-varying covariate is
present). It cannot be used when model is a function.
|
tvcov
|
A list of vectors or matrices with time-varying
covariates for each individual (one column per variable), a matrix or
dataframe of such covariate values (if only one covariate), or an
object of class, tvcov (created by tvctomat ). If times
are not the same as for responses, the list can be created with
gettvc . If response has class, repeated, with a
linear , logistic , or pkpd model, the covariates
must be supplied as a Wilkinson and Rogers formula unless none are
to be used. Only one time-varying covariate is allowed except for the
linear model ; if more are required, set model equal to
the appropriate mean function. This argument cannot be used when model
is a function.
|
nest
|
When response is a matrix, a vector of length equal
to the number of responses per individual indicating which responses
belong to which nesting category. Categoriess must be consecutive
increasing integers. This option should always be specified if nesting
is present. Ignored if response has class, repeated.
|
torder
|
When the linear model is chosen, order of the
polynomial in time to be fitted.
|
interaction
|
Vector of length equal to the number of
time-constant covariates, giving the levels of interactions between
them and the polynomial in time in the linear model .
|
transform
|
Transformation of the response variable: identity ,
exp , square , sqrt , or log .
|
link
|
Link function for the location: identity , exp ,
square , sqrt , or log . For the linear model ,
if not the identity , initial estimates of the regression
parameters must be supplied (intercept, polynomial in time,
time-constant covariates, time-varying covariates, in that order).
|
autocorr
|
The form of the autocorrelation function:
exponential is the usual rho^|t_i-t_j|; gaussian is
rho^((t_i-t_j)^2); cauchy is 1/(1+rho(t_i-t_j)^2);
spherical is ((|t_i-t_j|rho)^3-3|t_i-t_j|rho+2)/2 for
|t_i-t_j|<=1/rho and zero otherwise; IOU is the integrated
Ornstein-Uhlenbeck process, (2rho min(t_i,t_j)+exp(-rho t_i)
+exp(-rho t_j)-1 -exp(rho|ti-t_j|))/2rho^3.
|
pell
|
Initial estimate of the power parameter of the
multivariate elliptically-contoured distribution or of the degrees of
freedom parameter of the multivariate Student t distribution. If
missing and distribution is elliptic , the
multivariate normal distribution is used.
|
preg
|
Initial parameter estimates for the regression model.
Only required for linear model if the link is not the
identity or a variance function is fitted.
|
pvar
|
Initial parameter estimate for the variance. If more than
one value is provided, the log variance depends on a polynomial in
time. With the pkpd model , if four values are supplied, a
nonlinear regression for the variance is fitted.
|
varfn
|
The builtin variance function has the variance
proportional to a function of the location: pvar*v(mu) = identity
or square . If pvar contains two initial values, an additive
constant is included: pvar(1)+pvar(2)*v(mu). Otherwise, either a
function or a formula beginning with ~, specifying either a linear
regression function in the Wilkinson and Rogers notation or a general
function with named unknown parameters for the log variance can be
supplied, yielding a vector the same length as the number of
observations.
|
pre
| Zero, one or two parameter estimates for the variance components, depending on the number of levels of nesting. |
par
| If supplied, an initial estimate for the autocorrelation parameter. |
delta
| Scalar or vector giving the unit of measurement for each response value, set to unity by default. For example, if a response is measured to two decimals, delta=0.01. Ignored if response has class, response or repeated. |
shfn
| If TRUE, the supplied variance function depends on the mean function. The name of this mean function must be the last argument of the variance function. |
common
|
If TRUE, mu and varfn must both be
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; all parameter estimates must be supplied in preg .
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.
If response has class repeated , it is used as the
environment.
|
others
|
Arguments controlling nlm .
|
elliptic
fits a special case of the multivariate
elliptically-contoured distribution, called the multivariate power
exponential distribution. It includes the multivariate normal
(power=1), the multivariate Laplace (power=0.5), and the multivariate
uniform (power -> infinity) distributions as special cases.
With two levels of nesting, the first is the individual and the second will consist of clusters within individuals.
For clustered (non-longitudinal) data, where only random effects will be fitted, the `times' may be any strictly increasing sequence distinguishing the responses on an individual.
It is designed to fit linear and nonlinear models with time-varying covariates observed at arbitrary time points. A continuous-time AR(1) and zero, one, or two levels of nesting can be handled.
Nonlinear regression models can be supplied as formulae where
parameters are unknowns. Factor variables cannot be used and
parameters must be scalars. (See finterp
.)
When an AR(1) of exponential
form and/or a single random
intercept is estimated for the multivariate normal distribution,
marginal and individual profiles can be plotted using
profile
and iprofile
and residuals with
plot.residuals
.
elliptic
is returned.carma
, finterp
, gar
,
gettvc
, glmm
, gnlmm
,
gnlr
, iprofile
, kalseries
,
potthoff
, profile
,
read.list
, restovec
, rmna
,
tcctomat
, tvctomat
.# linear models y <- matrix(rnorm(40),ncol=5) x1 <- gl(2,4) x2 <- gl(2,1,8) # independence with time trend elliptic(y, ccov=~x1, torder=2) # AR(1) elliptic(y, ccov=~x1, torder=2, par=0.1) elliptic(y, ccov=~x1, torder=3, interact=3, par=0.1) # random intercept elliptic(y, ccov=~x1+x2, interact=c(2,0), torder=3, pre=2) # # nonlinear models times <- rep(1:20,2) dose <- c(rep(2,20),rep(5,20)) mu <- function(p) exp(p[1]-p[3])*(dose/(exp(p[1])-exp(p[2]))* (exp(-exp(p[2])*times)-exp(-exp(p[1])*times))) shape <- function(p) exp(p[1]-p[2])*times*dose*exp(-exp(p[1])*times) conc <- matrix(rnorm(40,mu(log(c(1,0.3,0.2))),sqrt(shape(log(c(0.1,0.4))))), ncol=20,byrow=T) conc[,2:20] <- conc[,2:20]+0.5*(conc[,1:19]-matrix(mu(log(c(1,0.3,0.2))), ncol=20,byrow=T)[,1:19]) conc <- ifelse(conc>0,conc,0.01) # with builtin function # independence elliptic(conc, model="pkpd", preg=log(c(0.5,0.4,0.1)), dose=c(2,5)) # AR(1) elliptic(conc, model="pkpd", preg=log(c(0.5,0.4,0.1)), dose=c(2,5), par=0.1) # add variance function elliptic(conc, model="pkpd", preg=log(c(0.5,0.4,0.1)), dose=c(2,5), par=0.1, varfn=shape, pvar=log(c(0.5,0.2))) # multivariate elliptical distribution elliptic(conc, model="pkpd", preg=log(c(0.5,0.4,0.1)), dose=c(2,5), par=0.1, varfn=shape, pvar=log(c(0.5,0.2)), pell=1) # multivariate Student t distribution elliptic(conc, model="pkpd", preg=log(c(0.5,0.4,0.1)), dose=c(2,5), par=0.1, varfn=shape, pvar=log(c(0.5,0.2)), pell=5, distribution="Student t") # or equivalently with user-specified function # independence elliptic(conc, model=mu, preg=log(c(0.5,0.4,0.1))) # AR(1) elliptic(conc, model=mu, preg=log(c(0.5,0.4,0.1)), par=0.1) # add variance function elliptic(conc, model=mu, preg=log(c(0.5,0.4,0.1)), par=0.1, varfn=shape, pvar=log(c(0.5,0.2))) # multivariate elliptical distribution elliptic(conc, model=mu, preg=log(c(0.5,0.4,0.1)), par=0.1, varfn=shape, pvar=log(c(0.5,0.2)), pell=1) # multivariate Student t distribution elliptic(conc, model=mu, preg=log(c(0.5,0.4,0.1)), par=0.1, varfn=shape, pvar=log(c(0.5,0.2)), pell=5, distribution="Student t") # or with user-specified formula # independence elliptic(conc, model=~exp(absorption-volume)* dose/(exp(absorption)-exp(elimination))* (exp(-exp(elimination)*times)-exp(-exp(absorption)*times)), preg=list(absorption=log(0.5),elimination=log(0.4), volume=log(0.1))) # AR(1) elliptic(conc, model=~exp(absorption-volume)* dose/(exp(absorption)-exp(elimination))* (exp(-exp(elimination)*times)-exp(-exp(absorption)*times)), preg=list(absorption=log(0.5),elimination=log(0.4),volume=log(0.1)), par=0.1) # add variance function elliptic(conc, model=~exp(absorption-volume)* dose/(exp(absorption)-exp(elimination))* (exp(-exp(elimination)*times)-exp(-exp(absorption)*times)), preg=list(absorption=log(0.5),elimination=log(0.4),volume=log(0.1)), varfn=~exp(b1-b2)*times*dose*exp(-exp(b1)*times), par=0.1, pvar=list(b1=log(0.5),b2=log(0.2))) # multivariate elliptical distribution elliptic(conc, model=~exp(absorption-volume)* dose/(exp(absorption)-exp(elimination))* (exp(-exp(elimination)*times)-exp(-exp(absorption)*times)), preg=list(absorption=log(0.5),elimination=log(0.4),volume=log(0.1)), varfn=~exp(b1-b2)*times*dose*exp(-exp(b1)*times), par=0.1, pvar=list(b1=log(0.5),b2=log(0.2)), pell=1) # multivariate Student t distribution elliptic(conc, model=~exp(absorption-volume)* dose/(exp(absorption)-exp(elimination))* (exp(-exp(elimination)*times)-exp(-exp(absorption)*times)), preg=list(absorption=log(0.5),elimination=log(0.4),volume=log(0.1)), varfn=~exp(b1-b2)*times*dose*exp(-exp(b1)*times), par=0.1, pvar=list(b1=log(0.5),b2=log(0.2)), pell=5, distribution="Student t") # # generalized logistic regression with square-root transformation # and square link times <- rep(seq(10,200,by=10),2) mu <- function(p) { yinf <- exp(p[2]) yinf*(1+((yinf/exp(p[1]))^p[4]-1)*exp(-yinf^p[4] *exp(p[3])*times))^(-1/p[4])} y <- matrix(rnorm(40,sqrt(mu(c(2,1.5,0.05,-2))),0.05)^2,ncol=20,byrow=T) y[,2:20] <- y[,2:20]+0.5*(y[,1:19]-matrix(mu(c(2,1.5,0.05,-2)), ncol=20,byrow=T)[,1:19]) y <- ifelse(y>0,y,0.01) # with builtin function # independence elliptic(y, model="logistic", preg=c(2,1,0.1,-1), trans="sqrt", link="square") # the same model with AR(1) elliptic(y, model="logistic", preg=c(2,1,0.1,-1), trans="sqrt", link="square", par=0.4) # the same model with AR(1) and one component of variance elliptic(y, model="logistic", preg=c(2,1,0.1,-1), trans="sqrt", link="square", pre=1, par=0.4) # or equivalently with user-specified function # independence elliptic(y, model=mu, preg=c(2,1,0.1,-1), trans="sqrt", link="square") # the same model with AR(1) elliptic(y, model=mu, preg=c(2,1,0.1,-1), trans="sqrt", link="square", par=0.4) # the same model with AR(1) and one component of variance elliptic(y, model=mu, preg=c(2,1,0.1,-1), trans="sqrt", link="square", pre=1, par=0.4) # or equivalently with user-specified formula # independence elliptic(y, model=~exp(yinf)*(1+((exp(yinf-y0))^b4-1)* exp(-exp(yinf*b4+b3)*times))^(-1/b4), preg=list(y0=2,yinf=1,b3=0.1,b4=-1), trans="sqrt", link="square") # the same model with AR(1) elliptic(y, model=~exp(yinf)*(1+((exp(yinf-y0))^b4-1)* exp(-exp(yinf*b4+b3)*times))^(-1/b4), preg=list(y0=2,yinf=1,b3=0.1,b4=-1), trans="sqrt", link="square", par=0.1) # add one component of variance elliptic(y, model=~exp(yinf)*(1+((exp(yinf-y0))^b4-1)* exp(-exp(yinf*b4+b3)*times))^(-1/b4), preg=list(y0=2,yinf=1,b3=0.1,b4=-1), trans="sqrt", link="square", pre=1, par=0.1) # # multivariate elliptical and Student t distributions for outliers y <- matrix(rcauchy(40,mu(c(2,1.5,0.05,-2)),0.05),ncol=20,byrow=T) y[,2:20] <- y[,2:20]+0.5*(y[,1:19]-matrix(mu(c(2,1.5,0.05,-2)), ncol=20,byrow=T)[,1:19]) y <- ifelse(y>0,y,0.01) # first with normal distribution elliptic(y, model="logistic", preg=c(1,1,0.1,-1)) elliptic(y, model="logistic", preg=c(1,1,0.1,-1), par=0.5) # then elliptic elliptic(y, model="logistic", preg=c(1,1,0.1,-1), pell=1) elliptic(y, model="logistic", preg=c(1,1,0.1,-1), par=0.5, pell=1) # finally Student t elliptic(y, model="logistic", preg=c(1,1,0.1,-1), pell=1, distribution="Student t") elliptic(y, model="logistic", preg=c(1,1,0.1,-1), par=0.5, pell=1, distribution="Student t")