Description Usage Arguments Details Value Author(s) See Also Examples

Calculates predicted values for `regr`

objects.
The possible results depend on the class of the fitted model.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
## S3 method for class 'regr'
predict(object, newdata = NULL, scale = object$sigma,
type = NULL, ...)
## S3 method for class 'polr'
predict(object, newdata=NULL,
type = c("class", "probs", "link"), ...)
## S3 method for class 'mlm'
predict(object, newdata=NULL, se.fit = FALSE,
scale = NULL, df = Inf,
interval = c("none", "confidence", "prediction"), level = 0.95,
type = c("response", "terms"), terms = NULL, na.action = na.pass,
pred.var = NULL, weights = 1, ...)
``` |

`object` |
Object of class |

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

`se.fit` |
if TRUE, standard errors will be calculated if possible |

`scale` |
Scale parameter for std.err. calculation |

`df` |
Degrees of Freedom to be used, if |

`interval` |
Type of interval requested |

`level` |
Confidence level |

`type` |
Type of prediction: response or model term |

`terms` |
If |

`na.action` |
function determining what should be done with missing values
in |

`pred.var, weights` |
see |

`...` |
further arguments passed to specific methods |

`regr`

is a "super class" which includes many specific model
classes such as "lm", "glm", "polr", ... .
`predict.regr`

is a wrapper function that calls the specific methods
corresponding to the specific model class.

vector of predictions, or matrix with columns `fit`

, `lwr`

,
and `upr`

if `interval`

is set.

If `se.fit`

is `TRUE`

, a list with the
following components is returned:

`fit` |
vector or matrix as above |

`se.fit` |
standard error of predicted means |

`residual.scale` |
residual standard deviations |

`df` |
degrees of freedom for residual~Describe the value returned |

Werner A. Stahel, ETH Zurich

1 2 3 4 5 6 7 8 9 10 11 12 |

regr0 documentation built on May 31, 2017, 2:26 a.m.

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