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). 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

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 2.5.0 Index]