nlr {gnlm} | R Documentation |
nlr
fits a user-specified nonlinear regression equation by
least squares (normal) or its generalization for the gamma and inverse
Gauss distributions.
A nonlinear regression model can be supplied as a formula 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 parameter estimates, standard errors, and correlations.
nlr(y, mu=NULL, pmu=NULL, distribution="normal", wt=1, delta=1, envir=parent.frame(), print.level=0, typsiz=abs(pmu), ndigit=10, gradtol=0.00001, stepmax=10*sqrt(pmu%*%pmu), steptol=0.00001, iterlim=100, fscale=1)
y |
The response vector or an object of
class, response (created by restovec ) or
repeated (created by rmna or
lvna ). |
mu |
A function of p giving the regression equation for
the mean or a formula beginning with ~, specifying either a linear
regression function in the Wilkinson and Rogers notation or a general
nonlinear function with named unknown parameters. |
pmu |
Vector of initial estimates of the 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. |
distribution |
The distribution to be used: normal, gamma, or inverse Gauss. |
wt |
Weight vector. |
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 . If the response is transformed,
this must be multiplied by the Jacobian. For example, with a log
transformation, delta=1/y . |
envir |
Environment in which model formulae are to be
interpreted or a data object of class, repeated , tccov ,
or tvcov . If y has class repeated , it is used as
the environment. |
others |
Arguments controlling nlm . |
A list of class nlr
is returned that contains all of the
relevant information calculated, including error codes.
J.K. Lindsey
finterp
, fmr
, glm
,
glmm
, gnlmm
,
gnlr
, gnlr3
, lm
,
nls
.
x <- c(3,5,0,0,0,3,2,2,2,7,4,0,0,2,2,2,0,1,3,4) y <- c(5.8,11.6,2.2,2.7,2.3,9.4,11.7,3.3,1.5,14.6,9.6,7.4,10.7,6.9, 2.6,17.3,2.8,1.2,1.0,3.6) # rgamma(20,2,scale=0.2+2*exp(0.1*x)) # linear least squares regression mu1 <- function(p) p[1]+p[2]*x summary(lm(y~x)) nlr(y, mu=mu1, pmu=c(3,0)) # or nlr(y, mu=~x, pmu=c(3,0)) # or nlr(y, mu=~b0+b1*x, pmu=c(3,0)) # linear gamma regression nlr(y, dist="gamma", mu=~x, pmu=c(3,0)) # nonlinear regression mu2 <- function(p) p[1]+p[2]*exp(p[3]*x) nlr(y, mu=mu2, pmu=c(0.2,3,0.2)) # or nlr(y, mu=~b0+c0*exp(c1*x), pmu=list(b0=0.2,c0=3,c1=0.2)) # with gamma distribution nlr(y, dist="gamma", mu=~b0+c0*exp(c1*x), pmu=list(b0=0.2,c0=3,c1=0.2))