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.

## Author

Johannes Ranke
jranke@uni-bremen.de

## Examples