| seqindic {TraMineR} | R Documentation | 
Sequence indicators
Description
Table of per sequence values of selected indicators.
Usage
seqindic(seqdata, indic=c("visited","trans","entr","cplx","turb2n"),
    with.missing=FALSE, ipos.args=list(), prec.args=list(), w=.5)
Arguments
| seqdata | a state sequence object (class  | 
| indic | vector of character strings. List of selected indicators among  | 
| with.missing | logical: should non-void missing values be treated as a regular state? If  | 
| ipos.args | list: when any of  | 
| prec.args | list: when any of  | 
| w | real in range [0,1]: when  | 
Details
The number of visited states is the number of different elements in the sequence, e.g. 2 for aababba. The recurrence index 'recu' is the average number of visits of visited states, i.e. Dlgth/Visited, the number of spells on the number of visited states.
The sequence length, number of transitions, longitudinal entropy, duration standard deviation, volatility, complexity, turbulence, degradation, badness, precarity, and insecurity are computed respectively with functions seqlength, seqtransn, seqient, seqivardur,  seqivolatility, seqici, seqST, seqidegrad, seqibad,  seqprecarity, and  seqinsecurity. The proportion of positive states, normative volatility, and integrative potential are computed with seqipos. See corresponding help pages for details.
The proportion of positive states ('ppos') and the normative volatility ('nvolat') are the proportions of positive elements in respectively the original sequences and the DSS. They ignore the value of dss in the ipos.args list.
The with.missing argument applies to all indicators but the length. 'lgth' returns the length obtained with with.missing=TRUE, and 'nonm' the length obtained with with.missing=FALSE.
Value
A data frame with the selected indicators. Names are:
 Lght: Length of the sequence
 NonM: Number of non-missing elements
 Dlgth: Number of spells (length of DSS)
 Visited: Number of visited states
 Visitp: Proportion of states visited
 Recu: Recurrence: average number of visits to visited states
 Trans: Number of transitions (state changes)
 Transp: Number of state changes as a proportion of maximum number of transitions
 Entr: Longitudinal entropy
 Meand: Mean spell duration
 Dustd: Duration standard deviation
 Meand2: Mean spell duration taking non visited states into account
 Dustd2: Duration standard deviation taking non visited states into account
 Nsubs: Number of subsequences of the DSS sequence
 Volat: Objective volatility
 Cplx: Complexity
 Turb: Turbulence
 Turbn: Normalized turbulence
 Turb2: Turbulence taking non visited states into account
 Turbn2: Normalized turbulence taking non visited states into account
 Ppos: Proportion of positive states
 Nvolat: Normative volatility (proportion of positive spells)
 Vpos: Objective volatility of positive-negative state sequences
 Integr: Integrative capacity (potential)
 Degrad: Degradation
 Bad: Badness
 Prec: Precarity
 Insec: Insecurity
Author(s)
Gilbert Ritschard
References
Ritschard, G. (2023), "Measuring the nature of individual sequences", Sociological Methods and Research, 52(4), 2016-2049. doi:10.1177/00491241211036156.
See Also
seqlength, seqtransn, seqient, seqivardur,  seqivolatility, seqici, seqST, seqidegrad, seqibad,  seqprecarity,  seqinsecurity, seqipos.
Examples
data(ex1)
sx <- seqdef(ex1[,1:13], right="DEL")
print(sx, format='SPS')
seqindic(sx, indic=c("lgth","nonm","visited","turbn","cplx"))
seqindic(sx, indic=c("lgth","nonm","visited","turbn","cplx"), with.missing=TRUE)
seqindic(sx, indic=c("lgth","dlgth","ppos","integr","prec"), with.missing=TRUE,
          ipos.args=list(pos.states=c("A","B")),
          prec.args=list(state.order=c("A","B","C"), state.equiv=list(c("C","D"))))
seqindic(sx, indic=c("volat","binary"), ipos.args=list(pos.states=c("A","B")))
seqindic(sx, indic=c("basic","integr"), ipos.args=list(pos.states="D"))