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[1,"==> quarto preview schedule.qmd --to html --no-watch-inputs --no-browse\n\n"] | ||
[1,"\r\n\r\nprocessing file: schedule.qmd\r\n\r | \r | | 0%\r | \r |.......... | 20% \r | \r |..................... | 40% [unnamed-chunk-1]"] | ||
[1,"\r | \r |............................... | 60% \r | \r |.......................................... | 80% [unnamed-chunk-2]"] | ||
[1,"\r | \r |....................................................| 100% \r \r\noutput file: schedule.knit.md\r\n\r\n"] | ||
[1,"pandoc \n to: html\n output-file: schedule.html\n standalone: true\n title-prefix: Demystifying ML Rostock November 5-6\n section-divs: true\n html-math-method: mathjax\n wrap: none\n default-image-extension: png\n css:\n - styles.css\n toc: true\n \nmetadata\n document-css: false\n link-citations: true\n date-format: long\n lang: en\n style: docked\n title: Schedule\n \n"] | ||
[1,"Output created: docs\\schedule.html\n\nPreparing to preview\n"] | ||
[1,"\nWatching files for changes\nBrowse at http://localhost:6805/schedule.html\n"] | ||
[1,"GET: /schedule.html\n"] |
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.Rproj.user/781306AB/sources/session-35d87fc4/799D6713-contents
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## Load packages | ||
library(dtms) | ||
library(ggplot2) | ||
library(tidyverse) | ||
## Define model: Absorbing and transient states, time scale | ||
hrs <- dtms(transient=c("Working","Non-working","Retired"), | ||
absorbing="Dead", | ||
timescale=50:99) | ||
|
||
## Quick look at data | ||
head(hrsdata) | ||
#> ID Gender Age State | ||
#> 1 1 1 50 <NA> | ||
#> 2 1 1 51 <NA> | ||
#> 3 1 1 52 <NA> | ||
#> 4 1 1 53 <NA> | ||
#> 5 1 1 54 <NA> | ||
#> 6 1 1 55 <NA> | ||
|
||
## Reshape | ||
estdata <- dtms_format(data=hrsdata, | ||
dtms=hrs, | ||
idvar="ID", | ||
timevar="Age", | ||
statevar="State") | ||
|
||
## Drop dead-to-dead transitions etc | ||
estdata <- dtms_clean(data=estdata, | ||
dtms=hrs) | ||
#> Dropping 0 rows not in state space | ||
#> Dropping 0 rows not in time range | ||
#> Dropping 98287 rows starting or ending in NA | ||
#> Dropping 51935 rows starting in absorbing state | ||
|
||
## Overview | ||
summary(estdata) | ||
#> from to COUNT PROP PROB | ||
#> 1 Non-working Dead 197 0.001974383 0.013936050 | ||
#> 2 Non-working Non-working 10635 0.106586622 0.752334465 | ||
#> 3 Non-working Retired 1900 0.019042274 0.134408602 | ||
#> 4 Non-working Working 1404 0.014071238 0.099320883 | ||
#> 5 Retired Dead 2602 0.026077893 0.051556401 | ||
#> 6 Retired Non-working 606 0.006073483 0.012007371 | ||
#> 7 Retired Retired 46423 0.465262884 0.919831976 | ||
#> 8 Retired Working 838 0.008398645 0.016604252 | ||
#> 9 Working Dead 306 0.003066808 0.008699855 | ||
#> 10 Working Non-working 2066 0.020705967 0.058738237 | ||
#> 11 Working Retired 2178 0.021828459 0.061922497 | ||
#> 12 Working Working 30623 0.306911343 0.870639411 | ||
|
||
## Basic censoring | ||
dtms_censoring(data=estdata, | ||
dtms=hrs) | ||
#> Units with left censoring: 2036 | ||
#> Units with gaps: 1720 | ||
#> Units with right censoring: 1323 | ||
|
||
## More advanced censoring example | ||
estdata <- dtms_censoring(data=estdata, | ||
dtms=hrs, | ||
add=T, | ||
addtype="obs") | ||
#> Units with left censoring: 2036 | ||
#> Units with gaps: 1720 | ||
#> Units with right censoring: 1323 | ||
|
||
estdata |> | ||
subset(subset=to!="Dead",select=c(RIGHT,to)) |> | ||
table() |> | ||
prop.table(margin=1) | ||
#> to | ||
#> RIGHT Non-working Retired Working | ||
#> FALSE 0.13846880 0.51950708 0.34202412 | ||
#> TRUE 0.07860922 0.73015873 0.19123205 | ||
|
||
## Add age squared | ||
estdata$time2 <- estdata$time^2 | ||
estdata$time2 <- ifelse(estdata$time > 65, 1, 0)*estdata$time | ||
|
||
means <- estdata %>% | ||
group_by(time) %>% | ||
summarize(mean_value = mean(time2)) | ||
|
||
## Fit model | ||
fit <- dtms_fit(data=estdata, | ||
formula=to~from+time+time2+Gender) | ||
|
||
## Transition probabilities by gender | ||
|
||
# Men | ||
probs_m <- dtms_transitions(dtms=hrs, | ||
model = fit, | ||
controls = list(Gender=0, | ||
time =50:98, | ||
time2 = means$mean_value), | ||
CI=TRUE) | ||
|
||
# Women | ||
probs_w <- dtms_transitions(dtms=hrs, | ||
model = fit, | ||
controls = list(Gender=1, | ||
time =50:98, | ||
time2 =(50:98)^2), | ||
CI=TRUE) | ||
|
||
# Overview | ||
summary(probs_m) | ||
#> from to MIN MINtime MAX MAXtime MEDIAN MEAN | ||
#> 1 Non-working Dead 0.0064 50 0.7701 98 0.1080 0.2055 | ||
#> 4 Non-working Non-working 0.0000 98 0.8173 54 0.1402 0.3186 | ||
#> 7 Non-working Retired 0.0356 50 0.7089 79 0.3904 0.3880 | ||
#> 10 Non-working Working 0.0238 98 0.1668 50 0.1008 0.0879 | ||
#> 2 Retired Dead 0.0231 58 0.4588 98 0.0364 0.0930 | ||
#> 5 Retired Non-working 0.0000 98 0.1627 50 0.0025 0.0324 | ||
#> 8 Retired Retired 0.5365 98 0.9492 73 0.8855 0.8381 | ||
#> 11 Retired Working 0.0047 98 0.2132 50 0.0121 0.0365 | ||
#> 3 Working Dead 0.0013 50 0.4725 98 0.0288 0.0926 | ||
#> 6 Working Non-working 0.0000 98 0.0704 58 0.0104 0.0252 | ||
#> 9 Working Retired 0.0075 50 0.2443 85 0.1524 0.1375 | ||
#> 12 Working Working 0.3990 98 0.9456 50 0.7862 0.7447 | ||
summary(probs_w) | ||
#> from to MIN MINtime MAX MAXtime MEDIAN MEAN | ||
#> 1 Non-working Dead 0.0036 50 0.6979 98 0.0743 0.1649 | ||
#> 4 Non-working Non-working 0.0000 98 0.8565 54 0.1780 0.3471 | ||
#> 7 Non-working Retired 0.0296 50 0.7504 80 0.4309 0.4090 | ||
#> 10 Non-working Working 0.0297 98 0.1312 50 0.0822 0.0791 | ||
#> 2 Retired Dead 0.0156 57 0.3678 98 0.0253 0.0687 | ||
#> 5 Retired Non-working 0.0000 98 0.2013 50 0.0032 0.0404 | ||
#> 8 Retired Retired 0.5857 50 0.9600 74 0.9001 0.8567 | ||
#> 11 Retired Working 0.0052 98 0.1966 50 0.0115 0.0341 | ||
#> 3 Working Dead 0.0009 50 0.3908 98 0.0208 0.0718 | ||
#> 6 Working Non-working 0.0000 98 0.0922 58 0.0138 0.0332 | ||
#> 9 Working Retired 0.0078 50 0.2633 86 0.1700 0.1488 | ||
#> 12 Working Working 0.4542 98 0.9311 50 0.7815 0.7462 | ||
|
||
# Plotting, men as example | ||
probs_m |> dtms_simplify() |> | ||
ggplot(aes(x=time,y=P,color=to)) + | ||
geom_ribbon(aes(ymin = CIlow, ymax = CIup,fill=to),alpha=0.5) + | ||
geom_line() + | ||
facet_wrap(~from) |
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