Plot dyadic categorical state strips — plot.dyadic

Displays each categorical sequence retained by a dyadicMarkov result as a horizontal state strip. A strip consists of adjacent coloured intervals: each interval represents one measurement occasion, and its colour identifies the observed state. Consecutive occasions in the same state therefore form a continuous run. Vertical position identifies the member and, for bivariate results, the variable.

Usage

# S3 method for class 'dyadic_pattern'
plot(x, col = NULL, main = NULL, cex = NULL, ...)

# S3 method for class 'dyadic_case'
plot(x, col = NULL, main = NULL, cex = NULL, ...)

Arguments

x: A dyadic_pattern or dyadic_case object.
col: Character vector containing exactly one distinct, non-fully-transparent colour per state, or NULL for an accessible default palette. Colours identify states consistently across every strip.
main: Optional main title. If NULL, a title appropriate to the univariate or bivariate sequence is used.
cex: Optional positive text scaling factor. If NULL, the default is 1.
...: Additional arguments are not currently supported.

Value

Invisibly returns the validated plotting specification used to draw the state strips.

Details

Univariate results contain two strips, one for each member, and support any integer \(\mathrm{states} \ge 2\). Bivariate results contain four strips for the two members on the main and second variables; the currently developed bivariate method supports states = 2 only. The plot displays observed sequences, not fitted probabilities or inferred dependency structures. Statistical identification results remain available through print() and summary().

References

Tueller, S. J., Van Dorn, R. A., and Bobashev, G. V. (2016). Visualization of categorical longitudinal and time series data. Methods Report RTI Press, 2016. doi:10.3768/rtipress.2016.mr.0033.1602 .

Examples

chainFM <- c(1L, 2L, 1L, 2L, 2L, 1L)
chainSM <- c(2L, 1L, 2L, 1L, 1L, 2L)
univariate <- univariatePattern(
  chainFM,
  chainSM,
  states = 2L
)
plot(univariate)


chainFM_V2 <- c(1L, 1L, 2L, 2L, 1L, 2L)
chainSM_V2 <- c(2L, 2L, 1L, 1L, 2L, 1L)
empirical <- countEmpBivariate(
  chainFM,
  chainSM,
  chainFM_V2,
  chainSM_V2,
  states = 2L
)
bivariate <- bivariateCase(empirical)
plot(bivariate)