AIC.lcc | R Documentation |
lcc
ObjectCalculates the Akaike Information Criterion (AIC) or the Bayesian Information Criterion (BIC)
for a fitted longitudinal concordance correlation model represented by an lcc
object.
Calculates the Bayesian Information Criterion (BIC) for a fitted
longitudinal concordance correlation model represented by an lcc
object.
BIC is used for model selection, with lower values indicating a better model.
## S3 method for class 'lcc'
AIC(object, ..., k = 2)
## S3 method for class 'lcc'
BIC(object, ...)
object |
An object of class |
... |
Optional arguments passed to the underlying |
k |
Numeric value used as a penalty coefficient for the number of
parameters in the fitted model; the default |
The function computes AIC or BIC values as a measure of the relative quality of
statistical models for a given set of data. Lower AIC or BIC values indicate a
better model fit with fewer parameters. For more information, refer to the methods
for AIC
objects.
The function computes BIC as a measure of the trade-off between model fit and
complexity. It is particularly useful for comparing models with different numbers
of parameters. For more information, refer to the documentation for BIC
.
lcc
, summary.lcc
,
coef.lcc
, vcov.lcc
lcc
, summary.lcc
,
coef.lcc
, vcov.lcc
, AIC.lcc
## Not run:
fm1 <- lcc(data = hue, subject = "Fruit", resp = "H_mean",
method = "Method", time = "Time", qf = 2, qr = 2)
AIC(fm1)
## End(Not run)
## Not run:
attach(simulated_hue)
fm6 <- lcc(data = simulated_hue, subject = "Fruit",
resp = "Hue", method = "Method", time = "Time",
qf = 2, qr = 1, components = TRUE,
time_lcc = list(n=50, from=min(Time), to=max(Time)))
AIC(fm6)
BIC(fm6)
## End(Not run)
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