stats (version 3.6.2)

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

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

Examples

Run this code
``````# 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)
# }
``````

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