predict.nls
produces predicted values, obtained by evaluating
the regression function in the frame newdata
. If the logical
se.fit
is TRUE
, standard errors of the predictions are
calculated. If the numeric argument scale
is set (with
optional df
), it is used as the residual standard deviation in
the computation of the standard errors, otherwise this is extracted
from the model fit. Setting intervals
specifies computation of
confidence or prediction (tolerance) intervals at the specified
level
.
At present se.fit
and interval
are ignored.
1 2 3 4 
object 
An object that inherits from class 
newdata 
A named list or data frame in which to look for variables with
which to predict. If 
se.fit 
A logical value indicating if the standard errors of the
predictions should be calculated. Defaults to 
scale 
A numeric scalar. If it is set (with optional

df 
A positive numeric scalar giving the number of degrees of
freedom for the 
interval 
A character string indicating if prediction intervals or a confidence interval on the mean responses are to be calculated. At present this argument is ignored. 
level 
A numeric scalar between 0 and 1 giving the confidence level for the intervals (if any) to be calculated. At present this argument is ignored. 
... 
Additional optional arguments. At present no optional arguments are used. 
predict.nls
produces a vector of predictions.
When implemented, interval
will produce a matrix of
predictions and bounds with column names fit
, lwr
, and
upr
. When implemented, if se.fit
is
TRUE
, a list with the following components will be returned:
fit 
vector or matrix as above 
se.fit 
standard error of predictions 
residual.scale 
residual standard deviations 
df 
degrees of freedom for residual 
Variables are first looked for in newdata
and then searched for
in the usual way (which will include the environment of the formula
used in the fit). A warning will be given if the
variables found are not of the same length as those in newdata
if it was supplied.
The model fitting function nls
,
predict
.
1 2 3 4 5 6 7 8 9 10 11 12  require(graphics)
fm < nls(demand ~ SSasympOrig(Time, A, lrc), data = BOD)
predict(fm) # fitted values at observed times
## Form data plot and smooth line for the predictions
opar < par(las = 1)
plot(demand ~ Time, data = BOD, col = 4,
main = "BOD data and fitted firstorder curve",
xlim = c(0,7), ylim = c(0, 20) )
tt < seq(0, 8, length = 101)
lines(tt, predict(fm, list(Time = tt)))
par(opar)

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