Description Usage Arguments Details Value Author(s) References See Also Examples

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 `AICc`

.

1 2 | ```
## S4 method for signature 'PSTf'
nobs(object)
``` |

`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 `logLik`

and `nobs`

methods for class `PSTf`

. For more details, see Gabadinho 2016.

An integer containing the number of symbols in the learning sample.

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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
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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