Count and format stages transitions.

transitions(
  hypnogram,
  stages = c("AWA", "REM", "N1", "N2", "N3", "NREM"),
  format = "vector"
)

Arguments

hypnogram: A hypnogram dataframe. Dataframe must contain begin (POSIXt), end (POSIXt) and event (character) columns.
stages: Stages to include in transitions Defaults to c("N1", "N2", "N3", "N4", "REM").
format: Set the return format. 'vector', 'dataframe' or 'heatmap'.

Value

Count of stages transitions in selected format.

References

Swihart BJ, Punjabi NM, Crainiceanu CM. Modeling sleep fragmentation in sleep hypnograms: An instance of fast, scalable discrete-state, discrete-time analyses. Comput Stat Data Anal. 2015 Sep;89:1-11. doi: 10.1016/j.csda.2015.03.001. PMID: 27182097; PMCID: PMC4865264.

Examples

tryCatch({
download.file("https://rsleep.org/data/hypnodensity.csv", "hypnodensity.csv")

hypnodensity <- read.csv2("hypnodensity.csv")

unlink("hypnodensity.csv")

events <- hypnogram(hypnodensity)

transitions(events)

transitions(events, format = "dataframe")

transitions(events, format = "heatmap")

# 3 Dimensions sleep transitions
levels(events$event)[levels(events$event)=="N1"] <- "NREM"
levels(events$event)[levels(events$event)=="N2"] <- "NREM"
levels(events$event)[levels(events$event)=="N3"] <- "NREM"

round(
  transitions(
    events, 
    format = "dataframe")/(
      sum(transitions(events)))*100,2)
}, error = function(e) {
  print("Error executing this example, check your internet connection.")
  })
#>       AWA   REM  NREM
#> AWA  8.16  0.61  3.06
#> REM  1.77 18.29  5.91
#> NREM 1.84  7.07 53.30