Nonlinear Regression
Usage
nlr(y, mu=NULL, p=NULL, dist="normal", wt=1, delta=1,
print.level=0, typsiz=abs(p), ndigit=10, gradtol=0.00001,
stepmax=10*sqrt(p%*%p), steptol=0.00001, iterlim=100, fscale=1)
Arguments
y
|
The response vector.
|
mu
|
A function of p giving the regression equation for the mean.
|
p
|
Vector of initial estimates of the parameters.
|
dist
|
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 .
|
others
|
Arguments controlling nlm .
|
Description
nlr
fits a user-specified nonlinear regression equation by
least squares (normal) or its generalization for the gamma and inverse
Gauss distributions.Value
A list of class nlr is returned.
The printed output includes the -log likelihood (not the deviance),
the corresponding AIC, the parameter estimates, standard
errors, and correlations. A list is returned that contains all of the
relevant information calculated, including error codes.See Also
lm
, glm
, glmm
,
gnlmm
, gnlr
, gnlr3
,
fmr
.Examples
# linear regression
mu1 <- function(p) p[1]+p[2]*x
summary(lm(y~x))
nlr(y,mu=mu1,p=c(3,2))
# nonlinear regression
mu2 <- function(p) p[1]+p[2]*x^p[3]
nlr(y,mu=mu2,p=c(3,2,1))