predict.nls               package:nls               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:

     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 with values of the input variables
          for the model in `object'.  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 or a matrix of
     predictions and bounds with column names `fit', `lwr', and `upr'
     if `interval' is set.  If `se.fit' is `TRUE', a list with the
     following components is returned:

     fit: vector or matrix as above

  se.fit: standard error of predictions

residual.scale: residual standard deviations

      df: degrees of freedom for residual

See Also:

     The model fitting function `nls', `predict'.

Examples:

     data( BOD )
     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)

