# A small bogus cohort
xcoh <- structure( list( id = c("A", "B", "C"),
birth = c("14/07/1952", "01/04/1957", "10/06/1987"),
entry = c("04/08/1965", "08/09/1972", "23/12/1991"),
exit = c("27/06/1997", "23/05/1995", "24/07/1998"),
fail = c(1, 0, 1) ),
.Names = c("id", "birth", "entry", "exit", "fail"),
row.names = c("1", "2", "3"),
class = "data.frame" )
# Convert the character dates into numerical variables (fractional years)
xcoh$bt <- cal.yr( xcoh$birth, format="%d/%m/%Y" )
xcoh$en <- cal.yr( xcoh$entry, format="%d/%m/%Y" )
xcoh$ex <- cal.yr( xcoh$exit , format="%d/%m/%Y" )
# See how it looks
xcoh
# Define as Lexis object with timescales calendar time and age
Lcoh <- Lexis( entry = list( per=en ),
exit = list( per=ex, age=ex-bt ),
exit.status = fail,
data = xcoh )
# Default plot of follow-up
plot( Lcoh )
# Show follow-up time
PY.ann( Lcoh )
# Show exit status
plot( Lcoh, type="b" )
# Same but failures only
plot( Lcoh, type="b", pch=c(NA,16)[Lcoh$fail+1] )
# With a grid and deaths as endpoints
plot( Lcoh, grid=0:10*10, col="black" )
points( Lcoh, pch=c(NA,16)[Lcoh$lex.Xst+1] )
# With a lot of bells and whistles:
plot( Lcoh, grid=0:20*5, col="black", xaxs="i", yaxs="i",
xlim=c(1960,2010), ylim=c(0,50), lwd=3, las=1 )
points( Lcoh, pch=c(NA,16)[Lcoh$lex.Xst+1], col="red", cex=1.5 )
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