Demography and epidemiology: Practical use of the Lexis diagram ...

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22 Cox-sim-c.R Attaching package 'Lexis': The following object(s) are masked from package:Useful : ci.lin steplines The following object(s) are masked from package:Epi : interp lines.est nice plot.est points.est print.floated ROC ROC.tic steplines tabplot twoby2 weeks > > Sim.const
Practical use of the Lexis diagram 23 > > plt( "haz-c" ) > par( mar=c(3,3,1,1), mgp=c(3,1,0)/1.5 ) > plot( c(0,x), haz[c(1:10,10)], type="s", + ylim=c(0,0.7), lwd=3, xlab="Time", ylab="Hazard" ) > > Sim.haz( hz = haz, N=20 ) time fail ping pong 1 10.00000000 0 -2.36311150 1.7970969 2 10.00000000 0 -1.23325934 2.1082490 3 0.81456026 1 0.83391569 0.8713003 4 10.00000000 0 0.05474751 2.0151734 5 1.96800055 1 1.73760142 1.3494013 6 0.11602405 1 1.64865673 0.8118940 7 4.50134461 1 0.33638008 0.6004780 8 0.06443735 1 0.22310678 1.2611728 9 0.94988150 1 2.08837050 0.3567075 10 3.73566349 1 0.77143651 0.3887416 11 10.00000000 0 -3.19908283 0.3413883 12 1.54290184 1 1.66640833 0.3189720 13 8.37089382 1 1.22283849 1.0488130 14 0.32142153 1 2.45633169 0.1730299 15 1.46654335 1 0.19312621 0.4166438 16 10.00000000 0 -0.61977668 2.6851711 17 10.00000000 0 -0.70285032 0.8428157 18 1.66896876 1 0.62371878 0.3818595 19 6.84896792 1 0.97933566 2.1948316 20 0.75159817 1 0.16429308 2.1726228 > > si cx > # Number of datasets to simulate > n.sim # Array to collect results: for each dataset, type of model and > # regression parameter we collect estimates and standard errror. > res pct.cens > system.time( + for( i in 1:n.sim ) + { + si
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Demography and epidemiology: Practical use of the Lexis diagram ...

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