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 nls
predict(object, newdata , se.fit = FALSE, scale = NULL, df = Inf,
interval = c("none", "confidence", "prediction"),
level = 0.95, …)An object that inherits from class nls.
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.
A logical value indicating if the standard errors of the
predictions should be calculated. Defaults to FALSE. At
present this argument is ignored.
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.
A positive numeric scalar giving the number of degrees of
freedom for the scale estimate. At present this argument is
ignored.
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.
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:
vector or matrix as above
standard error of predictions
residual standard deviations
degrees of freedom for residual
# 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)
# }