predict.nls {stats} | R Documentation |

`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 class '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:

`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`

.

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 2.5.0 Index]