| selfStart {stats} | R Documentation |
Construct self-starting nonlinear models.
selfStart(model, initial, parameters, template)
model |
a function object defining a nonlinear model or
a nonlinear formula object of the form ~expression. |
initial |
a function object, taking three arguments: mCall,
data, and LHS, representing, respectively, a matched
call to the function model, a data frame in
which to interpret the variables in mCall, and the expression
from the left-hand side of the model formula in the call to nls.
This function should return initial values for the parameters in
model. |
parameters |
a character vector specifying the terms on the right
hand side of model for which initial estimates should be
calculated. Passed as the namevec argument to the
deriv function. |
template |
an optional prototype for the calling sequence of the
returned object, passed as the function.arg argument to the
deriv function. By default, a template is generated with the
covariates in model coming first and the parameters in
model coming last in the calling sequence. |
This function is generic; methods functions can be written to handle specific classes of objects.
a function object of class "selfStart", for the formula
method obtained by applying
deriv to the right hand side of the model formula. An
initial attribute (defined by the initial argument) is
added to the function to calculate starting estimates for the
parameters in the model automatically.
Jose Pinheiro and Douglas Bates
## self-starting logistic model
SSlogis <- selfStart(~ Asym/(1 + exp((xmid - x)/scal)),
function(mCall, data, LHS)
{
xy <- sortedXyData(mCall[["x"]], LHS, data)
if(nrow(xy) < 4) {
stop("Too few distinct x values to fit a logistic")
}
z <- xy[["y"]]
if (min(z) <= 0) { z <- z + 0.05 * max(z) } # avoid zeroes
z <- z/(1.05 * max(z)) # scale to within unit height
xy[["z"]] <- log(z/(1 - z)) # logit transformation
aux <- coef(lm(x ~ z, xy))
parameters(xy) <- list(xmid = aux[1], scal = aux[2])
pars <- as.vector(coef(nls(y ~ 1/(1 + exp((xmid - x)/scal)),
data = xy, algorithm = "plinear")))
value <- c(pars[3], pars[1], pars[2])
names(value) <- mCall[c("Asym", "xmid", "scal")]
value
}, c("Asym", "xmid", "scal"))
# 'first.order.log.model' is a function object defining a first order
# compartment model
# 'first.order.log.initial' is a function object which calculates initial
# values for the parameters in 'first.order.log.model'
# self-starting first order compartment model
## Not run:
SSfol <- selfStart(first.order.log.model, first.order.log.initial)
## End(Not run)