Produce graphics of calibration data, the fitted model as well as
confidence, and, for unweighted regression, prediction bands.

```
calplot(
object,
xlim = c("auto", "auto"),
ylim = c("auto", "auto"),
xlab = "Concentration",
ylab = "Response",
legend_x = "auto",
alpha = 0.05,
varfunc = NULL
)
```

## Arguments

- object
A univariate model object of class `lm`

or
`rlm`

with model formula `y ~ x`

or ```
y ~ x -
1
```

.

- xlim
The limits of the plot on the x axis.

- ylim
The limits of the plot on the y axis.

- xlab
The label of the x axis.

- ylab
The label of the y axis.

- legend_x
An optional numeric value for adjusting the x coordinate of
the legend.

- alpha
The error tolerance level for the confidence and prediction
bands. Note that this includes both tails of the Gaussian distribution,
unlike the alpha and beta parameters used in `lod`

(see note
below).

- varfunc
The variance function for generating the weights in the
model. Currently, this argument is ignored (see note below).

## Value

A plot of the calibration data, of your fitted model as well as
lines showing the confidence limits. Prediction limits are only shown for
models from unweighted regression.

## Note

Prediction bands for models from weighted linear regression require
weights for the data, for which responses should be predicted. Prediction
intervals using weights e.g. from a variance function are currently not
supported by the internally used function `predict.lm`

,
therefore, `calplot`

does not draw prediction bands for such models.

It is possible to compare the `calplot`

prediction bands with
the `lod`

values if the `lod()`

alpha and beta parameters
are half the value of the `calplot()`

alpha parameter.

## Examples

```
data(massart97ex3)
m <- lm(y ~ x, data = massart97ex3)
calplot(m)
```