predict.nls {stats} R Documentation

## Predicting from Nonlinear Least Squares Fits

### Description

`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.

### Usage

```## S3 method for class 'nls':
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

`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

### 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). As from R 2.0.0 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`.

### Examples

```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)
```

[Package stats version 2.1.0 Index]