Lag Sequential Analysis Across Several Lags

Fits lsa() at each requested lag and returns the fits together, so you can compare a transition's strength across lags (a lag profile). Each element is an ordinary lsa fit.

Usage

lsa_lags(data, lags = 1:3, ...)

Arguments

data

Sequence input (any form accepted by lsa()).

lags

Integer vector of lags. May include negative lags (predecessors) and 0. Default 1:3.

...

Passed to lsa() (e.g. engine, alpha, structural_zeros).

Value

An object of class c("lsa_lags", "list"): a named list of lsa fits (names "lag1", "lag2", ...), with a lags attribute. as.data.frame() on it row-binds transitions() of every fit (each already carries its lag column) into one tidy long frame with the same columns as transitions().

See also

lsa()

Examples

prof <- lsa_lags(engagement, lags = 1:3)
prof
#> <lsa_lags>
#>   engine: classical
#>   lags:   1, 2, 3
#>     lag 1    7 of 9 transitions significant
#>     lag 2    8 of 9 transitions significant
#>     lag 3    8 of 9 transitions significant
# Track one transition across lags with the dedicated verb:
lag_profile(engagement, from = "Active", to = "Average", lags = 1:3)
#>   lag   from      to count      prob    adj_res            p significant
#> 1   1 Active Average   176 0.2674772 -11.319132 1.055115e-29        TRUE
#> 2   2 Active Average   174 0.2824675  -9.949353 2.538293e-23        TRUE
#> 3   3 Active Average   175 0.3075571  -8.341561 7.331604e-17        TRUE