AIC.gips | R Documentation |
gips
classAkaike's An Information Criterion for gips
class
## S3 method for class 'gips'
AIC(object, ..., k = 2)
## S3 method for class 'gips'
BIC(object, ...)
object |
An object of class |
... |
Further arguments will be ignored. |
k |
Numeric, the penalty per parameter to be used.
The default |
AIC.gips()
returns calculated Akaike's An Information Criterion
When the multivariate normal model does not exist
(number_of_observations < n0
), it returns NULL
.
When the multivariate normal model cannot be reasonably approximated
(output of project_matrix()
is singular), it returns Inf
.
In both failure situations, shows a warning.
More information can be found in the Existence of likelihood
section of logLik.gips()
.
BIC.gips()
returns calculated
Schwarz's Bayesian Information Criterion.
BIC(gips)
: Schwarz's Bayesian Information Criterion
For more details and used formulas, see
the Information Criterion - AIC and BIC section in
vignette("Theory", package = "gips")
or its
pkgdown page.
AIC()
, BIC()
- Generic functions
this AIC.gips()
and BIC.gips()
extend.
find_MAP()
- Usually, the AIC.gips()
and BIC.gips()
are called on the output of find_MAP()
.
logLik.gips()
- Calculates the log-likelihood for
the gips
object. An important part of the Information Criteria.
S <- matrix(c(
5.15, 2.05, 3.10, 1.99,
2.05, 5.09, 2.03, 3.07,
3.10, 2.03, 5.21, 1.97,
1.99, 3.07, 1.97, 5.13
), nrow = 4)
g <- gips(S, 14)
g_map <- find_MAP(g, optimizer = "brute_force")
AIC(g) # 238
AIC(g_map) # 224 < 238, so g_map is better than g according to AIC
# ================================================================================
BIC(g) # 244
BIC(g_map) # 226 < 244, so g_map is better than g according to BIC
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