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H A D | splitLexis.Rd | 1 \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" ) )
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H A D | time.band.Rd | 7 \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" )
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H A D | occup.Rd | 30 sx <- splitLexis( lx, seq(1940,1960,5), "per" ) 31 sx <- splitLexis( sx, seq( 40, 60,5), "age" )
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H A D | addCov.Lexis.Rd | 75 \code{\link{splitLexis}}, 116 Lb <- addCov.Lexis(splitLexis(Lx, 123 La <- splitLexis(addCov.Lexis( Lx,
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H A D | cutLexis.Rd | 64 \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" )
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H A D | rbind.Lexis.Rd | 81 sL <- splitLexis( Lcoh, time.scale="age", breaks=0:20*5 ) 82 sD <- splitLexis( Dcoh, time.scale="tfe", breaks=0:50*2 )
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H A D | time.scales.Rd | 35 \seealso{\code{\link{Lexis}}, \code{\link{splitLexis}}}
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H A D | rcutLexis.Rd | 43 \code{\link{splitLexis}}
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H A D | plot.Lexis.Rd | 63 \code{splitLexis}, then vertical or horizontal grid lines are plotted 114 \seealso{\code{\link{Lexis}}, \code{\link{splitLexis}}}
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H A D | stack.Lexis.Rd | 70 \code{\link{splitLexis}}
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H A D | bootLexis.Rd | 70 Lx <- splitLexis( Lx, breaks=0:10*10, "age" )
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H A D | Termplot.Rd | 69 dms <- splitLexis( dml, time.scale="Age", breaks=0:100 )
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H A D | mcutLexis.Rd | 61 \code{\link{splitLexis}}
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H A D | N2Y.Rd | 67 \code{\link{splitLexis}}, \code{\link{apc.fit}}
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H A D | matshade.Rd | 76 sL <- splitLexis( mL, breaks=0:100, time.scale="age")
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H A D | simLexis.Rd | 166 \code{\link{splitLexis}} 182 Si <- splitLexis( dmi, 0:30/2, "DMdur" )
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H A D | ci.cum.Rd | 105 sL <- splitLexis( lungL, "tfd", breaks=seq(0,1100,10) )
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H A D | ci.Crisk.Rd | 142 Sdm <- splitLexis(factorize(subset(Mdm, lex.Cst == "DM")),
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/dports/science/R-cran-Epi/Epi/R/ |
H A D | splitLexis.R | 60 splitLexis <- function(lex, breaks, time.scale=1, tol= .Machine$double.eps^0.5) function
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/dports/science/R-cran-Epi/Epi/vignettes/ |
H A D | flup.R | 70 dmS1 <- splitLexis( dmL, "age", breaks=seq(0,100,5) ) 86 dmS2 <- splitLexis( dmS1, "tfD", breaks=c(0,1,2,5,10,20,30,40) )
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H A D | flup.rnw | 252 \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}
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/dports/science/R-cran-Epi/Epi/inst/doc/ |
H A D | flup.R | 70 dmS1 <- splitLexis( dmL, "age", breaks=seq(0,100,5) ) 86 dmS2 <- splitLexis( dmS1, "tfD", breaks=c(0,1,2,5,10,20,30,40) )
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/dports/science/R-cran-Epi/Epi/ |
H A D | MD5 | 78 358b6fa60093d8bbee969a76a88797c0 *R/splitLexis.R 219 a22aa8901f1f717b6d3a7ed086bc8e02 *man/splitLexis.Rd
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H A D | NAMESPACE | 89 splitLexis,
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H A D | CHANGES | 775 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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