Мне было интересно, можно ли запрограммировать Shiny для вставки значений из input на nomogram, нанесенного с помощью rms-package. Это намного выше моего уровня навыков, поэтому я также особенно заинтересован в альтернативных решениях.
Пожалуйста, найдите мои данные p и номограмму, хранящуюся в nom ниже.
Мой nomogram
Возьмите эту текущую ситуацию в моем Shinyapp:
Я хотел бы вставить , сделать , выделить , et c., Эти выбранные input - значения nomogram: n_fjernet==100, n_sygdom==5, ecs=="no", contra.pos=="Contra." и Total Point Score==52
Что-то вроде:
library(shiny)
library(rms)
ui <- fluidPage(
titlePanel("Nomogram\n"),
sidebarLayout(
sidebarPanel(
sliderInput("n_fjernet", "Lymph Nodal Yield", min = 2, max = 120, value = 40),
sliderInput("n_sygdom", "Number of positive lymph nodes", min = 0, max = 40, value = 0),
conditionalPanel(
condition = "input.n_sygdom >= 1",
radioButtons("ecs", "Extracapsular extension", c("No","Yes")),
radioButtons("contra.pos", "Neck involvement", c("Contra.","Ipsi.")
)
),
verbatimTextOutput("out.score"),
verbatimTextOutput("out.score.group")
),
mainPanel(
plotOutput("surv_plot")
)
)
)
server <- function(input, output, session) {
calc_score <- reactive({
round(nom$ecs$points[nom$ecs$ecs==input$ecs] +
nom$contra.pos$points[nom$contra.pos$contra.pos==input$contra.pos] +
nom$n_fjernet$points[nom$n_fjernet$n_fjernet==input$n_fjernet] +
nom$n_sygdom$points[nom$n_sygdom$n_sygdom==input$n_sygdom], digits=1)
})
observe(
updateSliderInput(
session = session,
inputId = "n_sygdom",
max = min(40, input$n_fjernet),
value = min(input$n_fjernet, input$n_sygdom)
)
)
rvs <- reactiveValues(n_sygdom = 0)
observeEvent(input$n_sygdom, {
if ((input$n_sygdom >= 1 && rvs$n_sygdom == 0) || (input$n_sygdom == 0)) {
updateRadioButtons(session, "ecs", selected = "No")
updateRadioButtons(session, "contra.pos", selected = "Contra.")
}
rvs$n_sygdom <- input$n_sygdom
})
calc_score_group <- function(score) {
cut(score, c(0,35.9,55.2,70.0,83.3,Inf), include.lowest = TRUE, labels = c("1","2","3","4","5"))
}
output$out.score <- renderText(calc_score())
output$out.score.group <- renderText(calc_score_group(calc_score()))
output$surv_plot <- renderPlot({
plot(nom)
})
}
shinyApp(ui, server)
И номограмма
nom <- structure(list(n_fjernet = structure(list(n_fjernet = c(2, 3,
4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,
53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,
69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100,
101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113,
114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126,
127, 128, 129, 130, 131, 132, 133), Xbeta = c(`1` = -0.0152562141665504,
`2` = -0.0228843212498257, `3` = -0.0305124283331009, `4` = -0.0381405354163761,
`5` = -0.0457686424996513, `6` = -0.0533967495829265, `7` = -0.0610248566662018,
`8` = -0.068652963749477, `9` = -0.0762810708327522, `10` = -0.0839091779160274,
`11` = -0.0915372849993026, `12` = -0.0991653920825779, `13` = -0.106793499165853,
`14` = -0.114421606249128, `15` = -0.122049713332404, `16` = -0.129677820415679,
`17` = -0.137305927498954, `18` = -0.144934034582229, `19` = -0.152562141665504,
`20` = -0.16019024874878, `21` = -0.167818355832055, `22` = -0.17544646291533,
`23` = -0.183074569998605, `24` = -0.19070267708188, `25` = -0.198330784165156,
`26` = -0.205958891248431, `27` = -0.213586998331706, `28` = -0.221215105414981,
`29` = -0.228843212498257, `30` = -0.236471319581532, `31` = -0.244099426664807,
`32` = -0.251727533748082, `33` = -0.259355640831357, `34` = -0.266983747914633,
`35` = -0.274611854997908, `36` = -0.282239962081183, `37` = -0.289868069164458,
`38` = -0.297496176247734, `39` = -0.305124283331009, `40` = -0.312752390414284,
`41` = -0.320380497497559, `42` = -0.328008604580834, `43` = -0.33563671166411,
`44` = -0.343264818747385, `45` = -0.35089292583066, `46` = -0.358521032913935,
`47` = -0.366149139997211, `48` = -0.373777247080486, `49` = -0.381405354163761,
`50` = -0.389033461247036, `51` = -0.396661568330311, `52` = -0.404289675413587,
`53` = -0.411917782496862, `54` = -0.419545889580137, `55` = -0.427173996663412,
`56` = -0.434802103746687, `57` = -0.442430210829963, `58` = -0.450058317913238,
`59` = -0.457686424996513, `60` = -0.465314532079788, `61` = -0.472942639163064,
`62` = -0.480570746246339, `63` = -0.488198853329614, `64` = -0.495826960412889,
`65` = -0.503455067496165, `66` = -0.51108317457944, `67` = -0.518711281662715,
`68` = -0.52633938874599, `69` = -0.533967495829265, `70` = -0.541595602912541,
`71` = -0.549223709995816, `72` = -0.556851817079091, `73` = -0.564479924162366,
`74` = -0.572108031245641, `75` = -0.579736138328917, `76` = -0.587364245412192,
`77` = -0.594992352495467, `78` = -0.602620459578742, `79` = -0.610248566662018,
`80` = -0.617876673745293, `81` = -0.625504780828568, `82` = -0.633132887911843,
`83` = -0.640760994995118, `84` = -0.648389102078394, `85` = -0.656017209161669,
`86` = -0.663645316244944, `87` = -0.671273423328219, `88` = -0.678901530411494,
`89` = -0.68652963749477, `90` = -0.694157744578045, `91` = -0.70178585166132,
`92` = -0.709413958744595, `93` = -0.717042065827871, `94` = -0.724670172911146,
`95` = -0.732298279994421, `96` = -0.739926387077696, `97` = -0.747554494160972,
`98` = -0.755182601244247, `99` = -0.762810708327522, `100` = -0.770438815410797,
`101` = -0.778066922494072, `102` = -0.785695029577348, `103` = -0.793323136660623,
`104` = -0.800951243743898, `105` = -0.808579350827173, `106` = -0.816207457910448,
`107` = -0.823835564993724, `108` = -0.831463672076999, `109` = -0.839091779160274,
`110` = -0.846719886243549, `111` = -0.854347993326825, `112` = -0.8619761004101,
`113` = -0.869604207493375, `114` = -0.87723231457665, `115` = -0.884860421659926,
`116` = -0.892488528743201, `117` = -0.900116635826476, `118` = -0.907744742909751,
`119` = -0.915372849993026, `120` = -0.923000957076302, `121` = -0.930629064159577,
`122` = -0.938257171242852, `123` = -0.945885278326127, `124` = -0.953513385409402,
`125` = -0.961141492492678, `126` = -0.968769599575953, `127` = -0.976397706659228,
`128` = -0.984025813742503, `129` = -0.991653920825779, `130` = -0.999282027909054,
`131` = -1.00691013499233, `132` = -1.0145382420756), points = c(`1` = 36.9376457881459,
`2` = 36.6556790264043, `3` = 36.3737122646627, `4` = 36.0917455029211,
`5` = 35.8097787411796, `6` = 35.527811979438, `7` = 35.2458452176964,
`8` = 34.9638784559549, `9` = 34.6819116942133, `10` = 34.3999449324717,
`11` = 34.1179781707301, `12` = 33.8360114089886, `13` = 33.554044647247,
`14` = 33.2720778855054, `15` = 32.9901111237639, `16` = 32.7081443620223,
`17` = 32.4261776002807, `18` = 32.1442108385391, `19` = 31.8622440767976,
`20` = 31.580277315056, `21` = 31.2983105533144, `22` = 31.0163437915729,
`23` = 30.7343770298313, `24` = 30.4524102680897, `25` = 30.1704435063481,
`26` = 29.8884767446066, `27` = 29.606509982865, `28` = 29.3245432211234,
`29` = 29.0425764593819, `30` = 28.7606096976403, `31` = 28.4786429358987,
`32` = 28.1966761741571, `33` = 27.9147094124156, `34` = 27.632742650674,
`35` = 27.3507758889324, `36` = 27.0688091271909, `37` = 26.7868423654493,
`38` = 26.5048756037077, `39` = 26.2229088419661, `40` = 25.9409420802246,
`41` = 25.658975318483, `42` = 25.3770085567414, `43` = 25.0950417949999,
`44` = 24.8130750332583, `45` = 24.5311082715167, `46` = 24.2491415097751,
`47` = 23.9671747480336, `48` = 23.685207986292, `49` = 23.4032412245504,
`50` = 23.1212744628089, `51` = 22.8393077010673, `52` = 22.5573409393257,
`53` = 22.2753741775841, `54` = 21.9934074158426, `55` = 21.711440654101,
`56` = 21.4294738923594, `57` = 21.1475071306179, `58` = 20.8655403688763,
`59` = 20.5835736071347, `60` = 20.3016068453931, `61` = 20.0196400836516,
`62` = 19.73767332191, `63` = 19.4557065601684, `64` = 19.1737397984269,
`65` = 18.8917730366853, `66` = 18.6098062749437, `67` = 18.3278395132021,
`68` = 18.0458727514606, `69` = 17.763905989719, `70` = 17.4819392279774,
`71` = 17.1999724662359, `72` = 16.9180057044943, `73` = 16.6360389427527,
`74` = 16.3540721810111, `75` = 16.0721054192696, `76` = 15.790138657528,
`77` = 15.5081718957864, `78` = 15.2262051340449, `79` = 14.9442383723033,
`80` = 14.6622716105617, `81` = 14.3803048488201, `82` = 14.0983380870786,
`83` = 13.816371325337, `84` = 13.5344045635954, `85` = 13.2524378018539,
`86` = 12.9704710401123, `87` = 12.6885042783707, `88` = 12.4065375166291,
`89` = 12.1245707548876, `90` = 11.842603993146, `91` = 11.5606372314044,
`92` = 11.2786704696629, `93` = 10.9967037079213, `94` = 10.7147369461797,
`95` = 10.4327701844381, `96` = 10.1508034226966, `97` = 9.868836660955,
`98` = 9.58686989921343, `99` = 9.30490313747186, `100` = 9.02293637573028,
`101` = 8.74096961398871, `102` = 8.45900285224714, `103` = 8.17703609050557,
`104` = 7.895069328764, `105` = 7.61310256702243, `106` = 7.33113580528086,
`107` = 7.04916904353929, `108` = 6.76720228179771, `109` = 6.48523552005614,
`110` = 6.20326875831457, `111` = 5.921301996573, `112` = 5.63933523483143,
`113` = 5.35736847308986, `114` = 5.07540171134829, `115` = 4.79343494960671,
`116` = 4.51146818786514, `117` = 4.22950142612357, `118` = 3.947534664382,
`119` = 3.66556790264043, `120` = 3.38360114089885, `121` = 3.10163437915728,
`122` = 2.81966761741571, `123` = 2.53770085567414, `124` = 2.25573409393257,
`125` = 1.973767332191, `126` = 1.69180057044943, `127` = 1.40983380870786,
`128` = 1.12786704696629, `129` = 0.845900285224714, `130` = 0.563933523483143,
`131` = 0.281966761741567, `132` = 0)), info = list(nfun = 2L,
predictor = "n_fjernet", effect.name = "n_fjernet", type = "main")),
n_sygdom = structure(list(n_sygdom = c(0, 1, 2, 3, 4, 5,
6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21,
22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
37, 38, 39, 40), Xbeta = c(`133` = 0, `134` = 0.289782888103146,
`135` = 0.597659631547995, `136` = 0.847429207013489, `137` = 1.02887445686286,
`138` = 1.15566024621933, `139` = 1.24145144020613, `140` = 1.29991290394648,
`141` = 1.3447095025636, `142` = 1.38722862366018, `143` = 1.42974774475677,
`144` = 1.47226686585336, `145` = 1.51478598694994, `146` = 1.55730510804654,
`147` = 1.59982422914312, `148` = 1.64234335023972, `149` = 1.6848624713363,
`150` = 1.72738159243285, `151` = 1.76990071352946, `152` = 1.81241983462605,
`153` = 1.85493895572266, `154` = 1.89745807681922, `155` = 1.93997719791581,
`156` = 1.98249631901238, `157` = 2.02501544010901, `158` = 2.06753456120562,
`159` = 2.11005368230218, `160` = 2.15257280339881, `161` = 2.19509192449543,
`162` = 2.23761104559199, `163` = 2.2801301666886, `164` = 2.32264928778508,
`165` = 2.36516840888183, `166` = 2.40768752997815, `167` = 2.45020665107478,
`168` = 2.49272577217142, `169` = 2.53524489326801, `170` = 2.57776401436475,
`171` = 2.62028313546134, `172` = 2.66280225655792, `173` = 2.70532137765463
), points = c(`133` = 0, `134` = 10.7115882976674, `135` = 22.0920012122972,
`136` = 31.3245300175082, `137` = 38.0315058078179, `138` = 42.7180391862067,
`139` = 45.8892407556548, `140` = 48.0502211191424, `141` = 49.7060908796498,
`142` = 51.2777755396602, `143` = 52.8494601996709, `144` = 54.4211448596817,
`145` = 55.9928295196922, `146` = 57.5645141797031, `147` = 59.1361988397135,
`148` = 60.7078834997246, `149` = 62.2795681597352, `150` = 63.8512528197446,
`151` = 65.422937479756, `152` = 66.9946221397666, `153` = 68.5663067997782,
`154` = 70.1379914597879, `155` = 71.7096761197985, `156` = 73.2813607798086,
`157` = 74.8530454398212, `158` = 76.4247300998324, `159` = 77.9964147598421,
`160` = 79.5680994198545, `161` = 81.1397840798663, `162` = 82.7114687398759,
`163` = 84.2831533998875, `164` = 85.8548380598941, `165` = 87.4265227199109,
`166` = 88.9982073799118, `167` = 90.5698920399238, `168` = 92.1415766999367,
`169` = 93.7132613599473, `170` = 95.2849460199637, `171` = 96.8566306799743,
`172` = 98.428315339985, `173` = 100)), info = list(nfun = 2L,
predictor = "n_sygdom", effect.name = "n_sygdom", type = "main")),
ecs = structure(list(ecs = c("No", "Yes"), Xbeta = c(`174` = 0,
`175` = 0.311111953690113), points = c(`174` = 0, `175` = 11.4999998247835
)), info = list(nfun = 2L, predictor = "ecs", effect.name = "ecs",
type = "main")), contra.pos = structure(list(contra.pos = c("Ipsi.",
"Contra."), Xbeta = c(`176` = 0, `177` = -0.139442361056046
), points = c(`176` = 5.15437323668122, `177` = 0)), info = list(
nfun = 2L, predictor = "contra.pos", effect.name = "contra.pos",
type = "main")), total.points = list(x = c(0, 20, 40,
60, 80, 100, 120, 140, 160)), `Probability of 1 year survival` = list(
x = c(136.918624113452, 126.189430356567, 116.096408880892,
105.780027398013, 94.4980129305566, 81.2203725094768,
63.883800820975, 36.1448211060803), x.real = c(0.2, 0.3,
0.4, 0.5, 0.6, 0.7, 0.8, 0.9), fat = c("0.2", "0.3",
"0.4", "0.5", "0.6", "0.7", "0.8", "0.9"), which = c(FALSE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE
)), `Probability of 5 years survival` = list(x = c(100.730409459798,
87.4917755218773, 76.7625776915112, 66.6695587818235, 56.3531782563681,
45.0711658878166, 31.7935142409663, 14.4569590604092), x.real = c(0.1,
0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8), fat = c("0.1", "0.2",
"0.3", "0.4", "0.5", "0.6", "0.7", "0.8"), which = c(FALSE,
TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, TRUE, FALSE))), info = list(
fun = list(function (x)
surv(12, x), function (x)
surv(60, x)), lp = FALSE, lp.at = c(-1.5, -1, -0.5, 0, 0.5,
1, 1.5, 2, 2.5), discrete = c(n_fjernet = FALSE, n_sygdom = FALSE,
ecs = TRUE, contra.pos = TRUE, studie = TRUE), funlabel = c("Probability of 1 year survival",
"Probability of 5 years survival"), fun.at = NULL, fun.lp.at = NULL,
Abbrev = list(), minlength = 4, conf.int = FALSE, R = structure(c(-1.0145382420756,
-0.0152562141665504, 0, 2.70532137765463, 0, 0.311111953690113,
-0.139442361056046, 0), .Dim = c(2L, 4L), .Dimnames = list(
NULL, c("n_fjernet", "n_sygdom", "ecs", "contra.pos"))),
sc = 36.9641850413701, maxscale = 100, Intercept = -1.37882973459195,
nint = 10, space.used = c(main = 4, ia = 0)), class = "nomogram")
А p
p <- structure(list(contra.pos = structure(c(1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 2L,
1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L), .Label = c("Ipsi.", "Contra."), class = "factor"),
ecs = structure(c(1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L,
2L, 2L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L,
2L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 1L, 1L, 1L,
2L, 1L, 1L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 2L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L,
1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 1L, 2L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 1L, 1L, 1L, 2L, 2L, 1L, 1L,
1L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 2L, 1L, 2L, 2L, 2L, 1L, 2L,
2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L
), .Label = c("No", "Yes"), class = "factor"), n_fjernet = c(22L,
61L, 50L, 47L, 30L, 60L, 82L, 60L, 33L, 67L, 35L, 56L, 15L,
37L, 44L, 124L, 41L, 30L, 31L, 35L, 36L, 28L, 39L, 54L, 25L,
27L, 69L, 53L, 24L, 33L, 52L, 77L, 51L, 7L, 22L, 53L, 26L,
58L, 28L, 83L, 39L, 15L, 37L, 27L, 9L, 17L, 32L, 26L, 44L,
52L, 22L, 62L, 53L, 68L, 52L, 38L, 50L, 21L, 41L, 74L, 15L,
26L, 36L, 37L, 34L, 22L, 31L, 53L, 13L, 44L, 43L, 51L, 20L,
21L, 63L, 40L, 25L, 17L, 43L, 47L, 35L, 21L, 4L, 23L, 35L,
50L, 69L, 24L, 38L, 45L, 37L, 35L, 25L, 19L, 43L, 19L, 33L,
38L, 50L, 21L, 40L, 100L, 45L, 53L, 41L, 7L, 75L, 48L, 20L,
11L, 72L, 37L, 34L, 70L, 20L, 47L, 44L, 45L, 48L, 23L, 27L,
24L, 39L, 9L, 34L, 22L, 89L, 40L, 35L, 34L, 61L, 28L, 27L,
62L, 47L, 13L, 20L, 9L, 27L, 38L, 44L, 15L, 33L, 65L, 31L,
49L, 53L, 15L, 26L, 17L, 24L, 20L, 25L, 12L, 34L, 22L, 27L,
14L, 27L, 31L, 26L, 15L, 16L, 30L, 19L, 51L, 12L, 33L, 68L,
26L, 20L, 34L, 31L, 7L, 76L, 7L, 24L, 36L, 22L, 27L, 35L,
64L, 18L, 38L, 10L, 27L, 26L, 47L, 15L, 30L, 30L, 21L, 31L,
14L, 14L, 22L, 28L, 13L, 17L, 16L, 7L, 11L, 37L, 55L, 13L,
26L, 17L, 12L, 44L, 58L, 20L, 28L, 7L, 24L, 10L, 42L, 39L,
14L, 31L, 49L, 87L, 18L, 26L, 24L, 20L, 41L, 31L, 13L, 41L,
25L, 16L, 18L, 26L, 35L, 36L, 22L, 20L, 16L, 10L, 19L, 46L,
6L, 49L, 70L, 46L, 55L, 25L, 22L, 37L, 28L), n_sygdom = c(1L,
2L, 1L, 3L, 1L, 0L, 3L, 0L, 2L, 1L, 4L, 4L, 1L, 0L, 2L, 2L,
1L, 0L, 0L, 4L, 0L, 0L, 1L, 1L, 0L, 1L, 4L, 3L, 1L, 0L, 8L,
1L, 1L, 1L, 1L, 1L, 0L, 1L, 2L, 1L, 0L, 2L, 1L, 0L, 2L, 0L,
3L, 0L, 1L, 1L, 1L, 2L, 0L, 3L, 2L, 1L, 0L, 0L, 0L, 2L, 0L,
3L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 4L, 0L, 0L, 2L, 2L, 1L,
1L, 0L, 0L, 3L, 1L, 6L, 0L, 0L, 0L, 3L, 2L, 2L, 4L, 0L, 3L,
27L, 0L, 2L, 1L, 0L, 0L, 1L, 1L, 2L, 2L, 5L, 1L, 0L, 0L,
1L, 0L, 5L, 0L, 0L, 2L, 10L, 0L, 6L, 2L, 1L, 2L, 0L, 0L,
0L, 0L, 4L, 0L, 0L, 1L, 5L, 2L, 2L, 1L, 2L, 1L, 0L, 0L, 1L,
13L, 0L, 1L, 0L, 1L, 0L, 1L, 1L, 0L, 23L, 0L, 2L, 2L, 0L,
2L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 2L, 3L, 1L, 4L, 0L, 1L, 0L,
5L, 5L, 4L, 0L, 0L, 4L, 0L, 1L, 1L, 0L, 2L, 5L, 1L, 3L, 6L,
1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L,
1L, 2L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 1L, 0L,
40L, 2L, 0L, 1L, 0L, 2L, 0L, 3L, 1L, 1L, 4L, 1L, 2L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), score = c(47.2,
47.5, 39.3, 72.2, 44.9, 25.7, 62.4, 25.7, 55.4, 34.5, 82.3,
76.4, 60.6, 32.2, 52.3, 36.1, 41.8, 34.2, 33.9, 65.7, 32.5,
34.8, 42.4, 38.1, 35.6, 45.8, 61.2, 59, 46.6, 33.4, 84, 26.5,
39, 62.9, 47.2, 38.4, 35.3, 37, 56.9, 30, 31.7, 60.5, 42.9,
35, 62.2, 37.9, 65, 35.3, 41, 38.7, 47.2, 58.8, 27.7, 54.8,
50.1, 42.7, 28.6, 36.7, 31.1, 43.9, 38.4, 78.1, 32.5, 32.2,
33.1, 47.2, 44.6, 27.7, 39, 41, 68.6, 28.3, 37, 58.8, 58.5,
42.1, 46.3, 37.9, 30.5, 60.7, 55, 77.5, 41.5, 36.2, 32.8,
71.4, 56.8, 58, 76.3, 30, 63.5, 123.9, 35.6, 59.4, 41.2,
37.3, 33.4, 37.5, 39.3, 58.8, 53.5, 52, 35.5, 27.7, 31.1,
51.4, 21.5, 83.3, 37, 39.6, 50.8, 91.4, 33.1, 63.7, 70.6,
51.6, 52.3, 30, 29.1, 36.2, 35, 85.4, 31.7, 40.1, 55.3, 90.7,
51.2, 53.5, 43.5, 55.2, 47.7, 34.8, 35, 35.9, 98.5, 39, 59.2,
40.1, 57.3, 31.9, 52.5, 49.1, 33.4, 92.5, 33.9, 50.9, 49.8,
38.4, 57.4, 37.9, 35.9, 47.7, 57.8, 39.3, 33.1, 36.5, 57.1,
70, 57.3, 71.9, 35.3, 60.6, 38.1, 71.8, 91.5, 61.2, 39.3,
33.4, 56.4, 35.3, 47.7, 43.8, 33.9, 62.8, 75.4, 51.4, 78.7,
78.4, 47.2, 45.8, 55, 24.6, 37.6, 42.7, 62, 35, 35.3, 51.6,
38.4, 34.2, 34.2, 42.3, 33.9, 49.4, 60.8, 36.5, 57, 61.2,
37.9, 38.1, 40.7, 61.8, 54.4, 27.1, 49.7, 46, 60.1, 39.3,
130.2, 59.9, 37, 40.3, 40.7, 69.5, 39.8, 73.6, 53.9, 60.9,
71.9, 51.1, 51.7, 37.6, 35.3, 35.9, 37, 31.1, 33.9, 39, 31.1,
35.6, 38.1, 37.6, 35.3, 32.8, 32.5, 36.5, 37, 38.1, 39.8,
37.3, 29.7, 41, 28.8, 22.9, 29.7, 27.1, 35.6, 36.5, 32.2,
34.8)), row.names = c(NA, 250L), class = "data.frame")