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/dports/science/R-cran-Epi/Epi/man/
H A DsplitLexis.Rd1 \name{splitLexis}
2 \alias{splitLexis}
5 The \code{splitLexis} function divides each row of a \code{Lexis}
10 splitLexis(lex, breaks, time.scale, tol=.Machine$double.eps^0.5)
33 The \code{splitLexis()} function divides follow-up time into intervals
77 ( x2 <- splitLexis( Lcoh, breaks = seq(1900,2000,5), time.scale="per") )
78 ( x2 <- splitLexis( x2, breaks = seq(0,80,5), time.scale="age" ) )
H A Dtime.band.Rd7 \code{splitLexis}) divide the follow-up intervals into time bands
39 defined by a call to \code{splitLexis}. The \code{timeBand}
56 diet.split <- splitLexis(diet.lex, breaks=seq(40,70,5), "age" )
H A Doccup.Rd30 sx <- splitLexis( lx, seq(1940,1960,5), "per" )
31 sx <- splitLexis( sx, seq( 40, 60,5), "age" )
H A DaddCov.Lexis.Rd75 \code{\link{splitLexis}},
116 Lb <- addCov.Lexis(splitLexis(Lx,
123 La <- splitLexis(addCov.Lexis( Lx,
H A DcutLexis.Rd64 \code{splitLexis} function. However, the \code{splitLexis} function
132 \code{\link{splitLexis}},
169 xs <- splitLexis( xx, breaks=seq(0,100,10), time.scale="age" )
175 xCs <- splitLexis( xC, breaks=seq(0,100,10), time.scale="age" )
H A Drbind.Lexis.Rd81 sL <- splitLexis( Lcoh, time.scale="age", breaks=0:20*5 )
82 sD <- splitLexis( Dcoh, time.scale="tfe", breaks=0:50*2 )
H A Dtime.scales.Rd35 \seealso{\code{\link{Lexis}}, \code{\link{splitLexis}}}
H A DrcutLexis.Rd43 \code{\link{splitLexis}}
H A Dplot.Lexis.Rd63 \code{splitLexis}, then vertical or horizontal grid lines are plotted
114 \seealso{\code{\link{Lexis}}, \code{\link{splitLexis}}}
H A Dstack.Lexis.Rd70 \code{\link{splitLexis}}
H A DbootLexis.Rd70 Lx <- splitLexis( Lx, breaks=0:10*10, "age" )
H A DTermplot.Rd69 dms <- splitLexis( dml, time.scale="Age", breaks=0:100 )
H A DmcutLexis.Rd61 \code{\link{splitLexis}}
H A DN2Y.Rd67 \code{\link{splitLexis}}, \code{\link{apc.fit}}
H A Dmatshade.Rd76 sL <- splitLexis( mL, breaks=0:100, time.scale="age")
H A DsimLexis.Rd166 \code{\link{splitLexis}}
182 Si <- splitLexis( dmi, 0:30/2, "DMdur" )
H A Dci.cum.Rd105 sL <- splitLexis( lungL, "tfd", breaks=seq(0,1100,10) )
H A Dci.Crisk.Rd142 Sdm <- splitLexis(factorize(subset(Mdm, lex.Cst == "DM")),
/dports/science/R-cran-Epi/Epi/R/
H A DsplitLexis.R60 splitLexis <- function(lex, breaks, time.scale=1, tol= .Machine$double.eps^0.5) function
/dports/science/R-cran-Epi/Epi/vignettes/
H A Dflup.R70 dmS1 <- splitLexis( dmL, "age", breaks=seq(0,100,5) )
86 dmS2 <- splitLexis( dmS1, "tfD", breaks=c(0,1,2,5,10,20,30,40) )
H A Dflup.rnw252 \texttt{splitLexis} (note that it is \emph{not} called
256 dmS1 <- splitLexis( dmL, "age", breaks=seq(0,100,5) )
278 dmS2 <- splitLexis( dmS1, "tfD", breaks=c(0,1,2,5,10,20,30,40) )
296 is not available in \texttt{splitLexis}, nevertheless this may be
371 Thus it does not matter in which order we use \texttt{splitLexis} and
372 \texttt{cutLexis}. Mathematicians would say that \texttt{splitLexis}
588 view. \texttt{splitLexis} and \texttt{splitMulti} will allocate the
1294 \item[\texttt{splitLexis}] split follow up along a time scale
1297 \texttt{splitLexis}
/dports/science/R-cran-Epi/Epi/inst/doc/
H A Dflup.R70 dmS1 <- splitLexis( dmL, "age", breaks=seq(0,100,5) )
86 dmS2 <- splitLexis( dmS1, "tfD", breaks=c(0,1,2,5,10,20,30,40) )
/dports/science/R-cran-Epi/Epi/
H A DMD578 358b6fa60093d8bbee969a76a88797c0 *R/splitLexis.R
219 a22aa8901f1f717b6d3a7ed086bc8e02 *man/splitLexis.Rd
H A DNAMESPACE89 splitLexis,
H A DCHANGES775 o splitLexis now allows NAs in the timescale on which you split.
989 o splitLexis uses the first timescale by default. Which in particular
1040 o splitLexis gave wrong results for factor states.
1065 o splitLexis amended so that lex.Xst is returned as a factor if
1066 lex.Cst is a factor. splitLexis crashed if lex.Cst and lex.Xst were factors.
1128 o splitLexis got state information wrong if breaks were not unique.
1141 splitLexis().

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