`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.

```
# S3 method for nls
predict(object, newdata , se.fit = FALSE, scale = NULL, df = Inf,
interval = c("none", "confidence", "prediction"),
level = 0.95, …)
```

object

An object that inherits from class `nls`

.

newdata

A named list or data frame in which to look for variables with
which to predict. If `newdata`

is
missing the fitted values at the original data points are returned.

se.fit

A logical value indicating if the standard errors of the
predictions should be calculated. Defaults to `FALSE`

. At
present this argument is ignored.

scale

A numeric scalar. If it is set (with optional
`df`

), it is used as the residual standard deviation in the
computation of the standard errors, otherwise this information is
extracted from the model fit. At present this argument is ignored.

df

A positive numeric scalar giving the number of degrees of
freedom for the `scale`

estimate. At present this argument is
ignored.

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:

vector or matrix as above

standard error of predictions

residual standard deviations

degrees of freedom for residual

# NOT RUN { 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 first-order curve", xlim = c(0,7), ylim = c(0, 20) ) tt <- seq(0, 8, length = 101) lines(tt, predict(fm, list(Time = tt))) par(opar) # }