AIC | R Documentation |
This function computes the Akaike Information Criterion (AIC) of a Gaussian mixture model or graphical model:
AIC = logLik - n_{par}
where logLik is the log-likelihood and n_{par} the number of free parameters.
## S3 method for class 'gmm' AIC(object, data, y = NULL, regul = 0.01, ...) ## S3 method for class 'gmbn' AIC(object, data, col_seq = NULL, ...) ## S3 method for class 'gmdbn' AIC(object, data, col_seq = NULL, ...)
object |
An object of class |
data |
A data frame containing the data used to compute the AIC. Its
columns must explicitly be named after the variables (or nodes) of
|
y |
A character vector containing the dependent variables if a
conditional AIC is computed. If |
regul |
A positive numeric value corresponding to the regularization
constant if a penalty term is added for Bayesian regularization. If
|
... |
Unused arguments from the generic function. |
col_seq |
A character vector containing the column names of |
If object
is a gmm
object, a numeric value
corresponding to the AIC.
If object
is a gmbn
or gmdbn
object, a list with
elements:
global |
A numeric value corresponding to the global AIC. |
local |
For a |
BIC
, logLik
data(gmm_body, data_body) aic_1 <- AIC(gmm_body, data_body) aic_2 <- AIC(gmm_body, data_body, y = "WAIST") data(gmbn_body, data_body) aic_3 <- AIC(gmbn_body, data_body) data(gmdbn_air, data_air) aic_4 <- AIC(gmdbn_air, data_air, col_seq = "DATE")
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