The number of observations to which a VLMC model is fitted is notably used for computing the Bayesian information criterion
BIC or the Akaike information criterion with correction for finite sample sizes
## S4 method for signature 'PSTf' nobs(object)
A PST, that is an object of class
This is the method for the generic
nobs function provided by the
stats4 package. The number of observations to which a VLMC model is fitted is the total number of symbols in the learning sample. If the learning sample contains missing values and the model is learned without including missing values (see
pstree), the total number of symbols is the number of non-missing states in the sequence(s). This information is used to compute the Bayesian information criterion of a fitted VLMC model. The
BIC generic function calls the
nobs methods for class
PSTf. For more details, see Gabadinho 2016.
An integer containing the number of symbols in the learning sample.
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.
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data(s1) s1.seq <- seqdef(s1) S1 <- pstree(s1.seq, L=3) nobs(S1) ## Self rated health sequences ## Loading the 'SRH' data frame and 'SRH.seq' sequence object data(SRH) ## model without considering missing states ## model with max. order 2 to reduce computing time ## nobs is the same whatever L and nmin m1 <- pstree(SRH.seq, L=2, nmin=30, ymin=0.001) nobs(m1) ## considering missing states, hence nobs is higher m2 <- pstree(SRH.seq, L=2, nmin=30, ymin=0.001, with.missing=TRUE) nobs(m2)
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