# predict.nls

##### Predicting from Nonlinear Least Squares Fits

`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.

- Keywords
- models, regression, nonlinear

##### Usage

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

##### Arguments

- 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.

##### Value

- fit
- vector or matrix as above
- se.fit
- standard error of predictions
- residual.scale
- residual standard deviations
- df
- degrees of freedom for residual

`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:##### Note

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.

##### See Also

##### Examples

`library(stats)`

```
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)
```

*Documentation reproduced from package stats, version 3.2.2, License: Part of R 3.2.2*