anova.lax: Comparison of nested models
| anova.lax | R Documentation |
Comparison of nested models
Description
anova method for objects of class "lax".
Compares two or more nested models using the adjusted likelihood ratio
test statistic (ALRTS) described in Section 3.5 of Chandler and Bate (2007).
The nesting must result from the simple constraint that a subset of the
parameters of the larger model is held fixed.
Usage
## S3 method for class 'lax'
anova(object, object2, ...)
Arguments
object |
An object of class |
object2 |
An object of class |
... |
Further objects of class |
Details
The objects of class "lax" need not be provided in nested
order: they will be ordered inside anova.lax based on the
values of attr(., "p_current").
Value
An object of class "anova" inheriting from class
"data.frame", with four columns:
Model.Df |
The number of parameters in the model |
Df |
The decrease in the number of parameter compared the model in the previous row |
ALRTS |
The adjusted likelihood ratio test statistic |
Pr(>ALRTS) |
The p-value associated with the test that the model is a valid simplification of the model in the previous row. |
The row names are the names of the model objects.
References
Chandler, R. E. and Bate, S. (2007). Inference for clustered data using the independence loglikelihood. Biometrika, 94(1), 167-183. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/asm015")}
See Also
anova.chandwich: the anova method
on which anova.lax is based.
alogLik: loglikelihood adjustment for model fits.
Examples
got_evd <- requireNamespace("evd", quietly = TRUE)
if (got_evd) {
library(evd)
small <- fgev(ow$temp, nsloc = ow[, "loc"])
adj_small <- alogLik(small, cluster = ow$year)
tiny <- fgev(ow$temp)
adj_tiny <- alogLik(tiny, cluster = ow$year)
anova(adj_small, adj_tiny)
set.seed(4082019)
uvdata <- evd::rgev(100, loc = 0.13, scale = 1.1, shape = 0.2)
M0 <- fgev(uvdata)
M1 <- fgev(uvdata, nsloc = (-49:50)/100)
adj0 <- alogLik(M0)
adj1 <- alogLik(M1)
anova(adj1, adj0)
}
got_extRemes <- requireNamespace("extRemes", quietly = TRUE)
if (got_extRemes) {
library(extRemes)
large <- fevd(temp, ow, location.fun = ~ loc, scale.fun = ~ loc,
shape.fun = ~ loc)
medium <- fevd(temp, ow, location.fun = ~ loc, scale.fun = ~ loc)
small <- fevd(temp, ow, location.fun = ~ loc)
tiny <- fevd(temp, ow)
adj_large <- alogLik(large, cluster = ow$year)
adj_medium <- alogLik(medium, cluster = ow$year)
adj_small <- alogLik(small, cluster = ow$year)
adj_tiny <- alogLik(tiny, cluster = ow$year)
anova(adj_large, adj_medium, adj_small, adj_tiny)
}
