Compare Nested EVD Objects
Compute an analysis of deviance table for two or more nested evd objects.
## S3 method for class 'evd' anova(object, object2, ..., half = FALSE)
object |
An object of class |
object2 |
An object of class |
... |
Further successively nested objects. |
half |
For some non-regular tesing problems the deviance
difference is known to be one half of a chi-squared random
variable. Set |
An object of class c("anova", "data.frame"), with one
row for each model, and the following five columns
M.Df |
The number of parameters. |
Deviance |
The deviance. |
Df |
The number of parameters of the model in the previous row minus the number of parameters. |
Chisq |
The deviance minus the deviance of the model
in the previous row (or twice this if |
Pr(>chisq) |
The p-value calculated by comparing the quantile
|
Circumstances may arise such that the asymptotic distribution of the test statistic is not chi-squared. In particular, this occurs when the smaller model is constrained at the edge of the parameter space. It is up to the user recognize this, and to interpret the output correctly.
In some cases the asymptotic distribution is known to be
one half of a chi-squared; you can set half = TRUE in
these cases.
uvdata <- rgev(100, loc = 0.13, scale = 1.1, shape = 0.2) trend <- (-49:50)/100 M1 <- fgev(uvdata, nsloc = trend) M2 <- fgev(uvdata) M3 <- fgev(uvdata, shape = 0) anova(M1, M2, M3) bvdata <- rbvevd(100, dep = 0.75, model = "log") M1 <- fbvevd(bvdata, model = "log") M2 <- fbvevd(bvdata, model = "log", dep = 0.75) M3 <- fbvevd(bvdata, model = "log", dep = 1) anova(M1, M2) anova(M1, M3, half = TRUE)
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