View source: R/DroppingInterval.R

anova.intRvals | R Documentation |

`intRvals`

objectsCompare model fits of `intRvals`

objects estimated on the same data.
If one object is provided, the results of a deviance test against a model without a missed event probability 'p'
is reported. If two objects are provided, the results of a deviance test between the model fits of the two objects is given.

## S3 method for class 'intRvals' anova( object, y = NULL, conf.level = 0.95, digits = max(3L, getOption("digits") - 3L), ... )

`object` |
an object of class |

`y` |
an (optional) object of class |

`conf.level` |
confidence level for the deviance test |

`digits` |
the number of digits for printing to screen |

`...` |
other arguments to be passed to low level functions |

A list of class "`anova.intRvals`

" with the best model (1 or 2), deviance statistic and test results

`best.model`

the index of the best model (1 is first argument, 2 is second)

`deviance`

the deviance between the two tested models

`p.value`

p-value for the deviance (likelihood-ratio) test

`conf.level`

assumed confidence level for the test

`model1.call`

call that generated model 1

`model2.call`

call that generated model 2

`AIC`

numeric 2-vector containg the AIC value for model 1 (first element) and model 2 (second element)

`loglik`

numeric 2-vector containg the log-likelihood value for model 1 (first element) and model 2 (second element)

data(goosedrop) model1=estinterval(goosedrop$interval,fun="gamma") # visually inspect model1 fit: plot(model1) # The observed distribution has intervals near zero. # We allow a small random baseline to reduce the effect # of intervals near zero on the fit result. model2=estinterval(goosedrop$interval,fun="gamma",fpp.method='auto') # model2 performs better than model1: anova(model1,model2)

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