stats (version 3.4.3)

predict.nls: 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 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

See Also

The model fitting function nls, predict.

Examples

# NOT RUN {
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)
# }