logLik | R Documentation |
Retrieve the log-likelihood of a fitted VLMC. This is the logLik
method for objects of class PSTf
returned by the pstree
and prune
functions.
## S4 method for signature 'PSTf'
logLik(object)
object |
a probabilistic suffix tree, i.e., an object of class |
The likelihood of a learning sample containing n
sequences, given a model S
fitted to it, is
L(S)=\prod_{i=1}^{n} P^{S}(x^{i})
where P^{S}(x^{i})
is the probability of the i
th observed sequence predicted by S
.
Note that the log-likelihood of a VLMC model is not used in the estimation of the model's parameters (see pstree
). It is obtained once the model is estimated by calling the predict
function. The value is stored in the logLik
slot of the probabilistic suffix tree representing the model (a PSTf
object returned by the pstree
or prune
function).
The AIC
and BIC
values can also be obtained with the corresponding generic functions, which call logLik
and use its result. For more details, see Gabadinho 2016.
An object of class logLik
, a negative numeric value with the df
(degrees of freedom) attribute containing the number of free parameters of the model.
Alexis Gabadinho
Gabadinho, A. & Ritschard, G. (2016). Analyzing State Sequences with Probabilistic Suffix Trees: The PST R Package. Journal of Statistical Software, 72(3), pp. 1-39.
AIC
, BIC
## activity calendar for year 2000
## from the Swiss Household Panel
## see ?actcal
data(actcal)
## selecting individuals aged 20 to 59
actcal <- actcal[actcal$age00>=20 & actcal$age00 <60,]
## defining a sequence object
actcal.lab <- c("> 37 hours", "19-36 hours", "1-18 hours", "no work")
actcal.seq <- seqdef(actcal,13:24,labels=actcal.lab)
## building a PST
actcal.pst <- pstree(actcal.seq, nmin=2, ymin=0.001)
logLik(actcal.pst)
## Cut-offs for 5% and 1% (see ?prune)
C99 <- qchisq(0.99,4-1)/2
## pruning
actcal.pst.C99 <- prune(actcal.pst, gain="G2", C=C99)
## Comparing AIC
AIC(actcal.pst, actcal.pst.C99)
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