Generic prediction method for various types of fitted models. (For internal use only.)

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
predFit(object, ...)
## S3 method for class 'lm'
predFit(object, newdata, se.fit = FALSE, interval = c("none",
"confidence", "prediction"), level = 0.95, adjust = c("none",
"Bonferroni", "Scheffe"), k, ...)
## S3 method for class 'nls'
predFit(object, newdata, se.fit = FALSE, interval = c("none",
"confidence", "prediction"), level = 0.95, adjust = c("none",
"Bonferroni", "Scheffe"), k, ...)
## S3 method for class 'lme'
predFit(object, newdata, se.fit = FALSE, ...)
``` |

`object` |
An object that inherits from class |

`...` |
Additional optional arguments. At present, no optional arguments are used. |

`newdata` |
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used. |

`se.fit` |
A logical vaue indicating if standard errors are required. Default
is |

`interval` |
Type of interval to be calculated. Can be one of "none"
(default), "confidence", or "prediction". Default is |

`level` |
A numeric scalar between 0 and 1 giving the confidence level for
the intervals (if any) to be calculated. Default is |

`adjust` |
A logical value indicating if an adjustment should be made to
the critical value used in calculating the confidence interval. This is
useful for when the calibration curve is to be used multiple, say k, times.
Default is |

`k` |
The number times the calibration curve is to be used for computing
a confidence interval. Only needed when |

investr documentation built on May 29, 2017, 3:24 p.m.

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