新数据在predict.coxph中的困难

时间:2018-08-08 18:26:55

标签: r predict survival

我有一个名为“ df”的具有结构的数据框。

'data.frame':   800 obs. of  4 variables:
 $ status: num  1 1 1 1 1 1 0 1 0 1 ...
 $ time  : num  4.97 1.53 3.98 2.75 3.86 ...
 $ uo    : chr  "C" "B" "C" "B" ...
 $ q3    : Factor w/ 4 levels "1","2","3","4": 4 1 1 3 3 3 2 3 2 1 ...

我实现了以下模型:

 sm1<-with( df, Surv(time,status))
 mod<-coxph(sm1~q3+strata(uo),data=df)

并预测

 new<-data.frame(q3=as.factor(rep(1:4,3)),uo=rep(LETTERS[1:3],each=4),stringsAsFactors=FALSE)
 pred<-predict(mod,newdata=new,type="lp")

获得以下警告:

newx中的错误-xmeans [match(newstrat,row.names(xmeans)),]:   数组不兼容

新的data.frame具有与原始df data.frame相同的名称和结构的相同列

任何人都可以帮助发现问题出在哪里?

谢谢 Filippo Pinzone

附录:df结构

df<-structure(list(status = c(1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 
1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 
0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 
1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 
1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 
1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 
0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 0, 
1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1), time = c(4.97333938980073, 
1.5335568005848, 3.97779635439093, 2.7459845744055, 3.8588892334092, 
2.54603219837931, 2.23372017027795, 3.42909940372004, 0.546441584169012, 
3.42961103414892, 4.13547035553943, 2.38974577815685, 3.63257703315934, 
4.52600017937921, 5.66841141763255, 0.897149198268734, 2.85064324456642, 
8.42369773602478, 2.55650588307482, 4.49307137525081, 1.61965325022512, 
2.24141041229007, 5.35115751878801, 0.258364639095974, 5.50295673773204, 
2.64924689468436, 1.43746977225737, 3.1851375871176, 1.47733323600212, 
3.07032651520212, 1.54783508621948, 3.02572237015449, 4.31482324352246, 
5.20246701830851, 7.10539801632626, 1.26567689018055, 3.12634544718299, 
4.25817073628868, 2.36798185108861, 2.9415872287492, 1.26159759501473, 
2.12715698922699, 1.46897481995762, 2.11118622466991, 1.09062191276148, 
5.53411546789772, 4.21764686029, 5.17825583557616, 5.55419429184471, 
1.17404030675649, 1.88173317316144, 3.21247299265059, 4.68079893941827, 
1.16337110806226, 4.58779282982402, 3.22280916289992, 3.75453112814264, 
2.84206420672571, 7.5301126370096, 6.49208441463532, 10.695513943654, 
5.8395208500808, 5.40933926419788, 2.75480193582422, 0.232036271442484, 
3.28471030284603, 0.970477490873351, 10.6677636849117, 1.53263429007799, 
2.28397550058292, 4.52246455309742, 5.87194714078361, 6.40447839676533, 
2.92042689942323, 1.74279636791905, 2.72246832426798, 2.20236870315485, 
4.66574704994628, 2.42944647221066, 0.584719775125415, 5.49438815582561, 
4.98920493534394, 6.94483846692817, 1.21991782009358, 12.2612774225453, 
1.64387919962076, 3.11351158768676, 6.02012568402022, 2.84048913941756, 
15.1330748995984, 12.1532553821213, 7.92672786859241, 5.08613142306492, 
2.47404484270418, 7.97464891193322, 6.0469647581773, 4.37937394517663, 
7.21515646353254, 8.35511772464242, 4.78918650706861, 4.55511484971807, 
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1.52138900599624, 8.18334131390905, 1.30815978905887, 2.89759827956571, 
4.32935233740533, 2.6169313180985, 5.79338694507468, 1.58346536833831, 
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10.8788952130521, 0.919054794572684, 1.70022647469967, 9.87332722560451, 
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1.35856211893161, 2.12545413807233, 1.71729183424993, 1.49909126666628, 
1.38215870951835, 7.2409648216671, 2.08210126766111, 7.12008489425891, 
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0.965708997216683, 2.67614106156811, 2.40706858382392, 0.837537990866297, 
4.63379635636901, 1.13044389676465, 2.56102616236792, 5.11885834111707, 
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4.48783174804656, 0.456137994851575, 3.49003530879005, 7.44148383909282, 
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1.63023486386103, 1.78106271525285, 3.19134611937137, 2.65017544879692, 
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2.16235638692237, 5.33392965905542, 2.08231249256378, 6.30672627694159, 
6.30916514402266, 3.15707578331759, 3.90762211046578, 1.24081147703082, 
2.70943646092296, 5.75837593653837, 1.41393765283164, 4.20912657664402, 
3.57171871711402, 14.1575149244147, 6.08447434725717, 5.68272466880187, 
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1.9289475466796, 3.00792278832052, 0.798065700948247, 2.45720678002809, 
1.94277580308899, 0.981822967256262, 5.94925042626186, 7.68642343976035, 
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1.97843479971945, 1.45479536669226, 9.48521002890528, 1.60268988182326, 
5.92675054606929, 4.32264259966141, 7.44182367197277, 4.38933463672153, 
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11.7115166321232, 0.75032407528675, 2.47212017931738, 1.29234048801447, 
11.2066822359374, 2.27551434371306, 0.973634469430681, 1.86384151732567, 
1.75754742737153, 3.38235081963136, 12.3128541873023, 0.127401995820925, 
2.53038521138808, 7.65475581833248, 3.50148205173299), uo = c("C", 
"B", "C", "B", "A", "B", "C", "A", "A", "A", "A", "C", "C", "B", 
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"B", "C", "A", "C", "C", "C", "B", "A", "C", "A", "A", "A", "A", 
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"C", "B", "B", "C", "C", "B", "B", "B", "C", "A", "A", "C", "B", 
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"B", "A", "A", "B", "B", "C", "B", "B", "B", "C", "C", "A", "A", 
"B", "B", "B", "A", "A", "C", "B", "B", "B", "B", "A", "B", "C", 
"C", "A", "A", "C", "A", "C", "B", "C", "B", "B", "B", "A", "A", 
"A", "B", "C", "B", "A", "C", "A", "B", "C", "A", "C", "C", "A", 
"A", "B", "C", "A", "C", "C", "A", "C", "C", "B", "B", "B", "A", 
"B", "A", "C", "C", "A", "A", "A", "C", "C", "A", "A", "C", "A", 
"B", "B", "A", "B", "B", "A", "A", "B", "B", "B", "B", "A", "C", 
"A", "A", "B", "A", "B", "C", "B", "B", "B", "A", "A", "B", "B", 
"B", "B", "B", "B", "A", "A", "C", "A", "B", "C", "A", "B", "C", 
"C", "A", "A", "C", "A", "A", "C", "A", "A", "C", "A", "C", "C", 
"C", "B", "C", "B", "A", "B", "B", "A", "C", "A", "A", "C", "B", 
"A", "A", "C", "A", "A", "B", "A", "C", "C", "A", "A", "A", "B", 
"C", "C", "C", "C", "A", "A", "B", "B", "A", "A", "A", "B", "A", 
"A", "A", "A", "B", "A", "B", "C", "A", "B", "A", "B", "B", "A", 
"A", "C", "B", "C", "A", "A", "A", "A", "C", "B", "A", "B", "A", 
"A", "C", "C", "C", "B", "A", "C", "A", "A", "A", "A", "B", "C", 
"B", "A", "A", "B", "A", "A", "B", "B", "B", "A", "B", "A", "A", 
"B", "C", "C", "C", "C", "A", "C", "C", "B", "A", "B", "A", "A", 
"A", "A", "B", "A", "B", "C", "C", "A", "A", "C", "B", "B", "B", 
"A", "B", "B", "B", "A", "B", "C", "C", "C", "C", "A", "C", "C", 
"A", "B", "B", "A", "B", "C", "C", "C", "A", "A", "A", "C", "A", 
"A", "C", "C", "A", "B", "A", "B", "A", "A", "A", "A", "B", "A", 
"B", "A", "B", "B", "C", "C", "C", "A", "C", "B", "B", "A", "A", 
"A", "A", "A", "B", "B", "A", "A", "A", "C", "C", "A", "B", "B", 
"B", "C", "A", "A", "A", "A", "C", "A", "A", "B", "C", "A", "A", 
"A", "A", "A", "C", "C", "B", "A", "A", "A", "B", "B", "B", "C", 
"B", "C", "C", "C", "C", "A", "B", "B", "A", "C", "C", "A", "B", 
"C", "A", "A", "B", "B", "A"), q3 = structure(c(4L, 1L, 1L, 3L, 
3L, 3L, 2L, 3L, 2L, 1L, 3L, 4L, 3L, 1L, 1L, 1L, 4L, 2L, 3L, 2L, 
3L, 3L, 2L, 3L, 4L, 1L, 4L, 2L, 1L, 2L, 1L, 4L, 3L, 2L, 3L, 3L, 
4L, 3L, 3L, 3L, 3L, 3L, 4L, 3L, 3L, 1L, 1L, 4L, 2L, 1L, 2L, 2L, 
4L, 1L, 1L, 2L, 2L, 3L, 2L, 4L, 2L, 2L, 3L, 2L, 3L, 1L, 3L, 2L, 
1L, 3L, 1L, 3L, 2L, 4L, 2L, 3L, 3L, 3L, 2L, 4L, 4L, 4L, 3L, 2L, 
3L, 3L, 4L, 4L, 1L, 4L, 2L, 4L, 3L, 2L, 4L, 2L, 4L, 1L, 4L, 2L, 
1L, 2L, 3L, 3L, 1L, 1L, 3L, 1L, 3L, 3L, 3L, 4L, 3L, 3L, 2L, 3L, 
2L, 1L, 3L, 1L, 2L, 2L, 2L, 2L, 2L, 1L, 4L, 1L, 4L, 1L, 4L, 1L, 
4L, 1L, 2L, 3L, 2L, 2L, 4L, 1L, 4L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 
3L, 2L, 2L, 1L, 2L, 3L, 2L, 1L, 4L, 2L, 2L, 1L, 3L, 2L, 2L, 4L, 
3L, 2L, 3L, 4L, 4L, 4L, 2L, 3L, 1L, 4L, 3L, 3L, 3L, 2L, 4L, 1L, 
4L, 4L, 2L, 3L, 3L, 4L, 3L, 3L, 2L, 3L, 4L, 3L, 3L, 2L, 1L, 1L, 
1L, 3L, 3L, 4L, 3L, 2L, 1L, 1L, 4L, 3L, 2L, 2L, 2L, 3L, 2L, 4L, 
3L, 1L, 4L, 3L, 3L, 2L, 2L, 1L, 4L, 2L, 3L, 3L, 2L, 1L, 1L, 2L, 
4L, 3L, 4L, 4L, 3L, 2L, 3L, 2L, 1L, 3L, 3L, 2L, 4L, 1L, 3L, 2L, 
2L, 1L, 3L, 3L, 2L, 3L, 1L, 1L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 4L, 
4L, 2L, 1L, 1L, 3L, 2L, 2L, 1L, 2L, 3L, 1L, 2L, 4L, 2L, 3L, 2L, 
3L, 1L, 4L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 3L, 1L, 2L, 1L, 2L, 2L, 
1L, 3L, 2L, 4L, 2L, 2L, 2L, 2L, 3L, 4L, 2L, 2L, 3L, 3L, 3L, 3L, 
1L, 3L, 3L, 2L, 3L, 2L, 2L, 3L, 1L, 2L, 3L, 3L, 4L, 2L, 4L, 2L, 
3L, 3L, 2L, 1L, 4L, 2L, 2L, 3L, 1L, 3L, 3L, 3L, 1L, 2L, 4L, 2L, 
3L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 4L, 2L, 4L, 2L, 2L, 4L, 3L, 
2L, 2L, 4L, 1L, 1L, 4L, 2L, 1L, 2L, 1L, 2L, 2L, 2L, 3L, 4L, 3L, 
4L, 4L, 4L, 3L, 2L, 2L, 2L, 2L, 4L, 3L, 3L, 3L, 4L, 3L, 3L, 2L, 
3L, 2L, 2L, 3L, 3L, 2L, 2L, 3L, 2L, 1L, 1L, 4L, 4L, 4L, 3L, 3L, 
3L, 3L, 2L, 3L, 4L, 2L, 3L, 1L, 1L, 1L, 2L, 2L, 3L, 2L, 2L, 1L, 
2L, 2L, 1L, 4L, 1L, 1L, 4L, 4L, 2L, 2L, 1L, 3L, 3L, 1L, 4L, 2L, 
2L, 4L, 1L, 3L, 1L, 1L, 2L, 2L, 4L, 2L, 3L, 3L, 2L, 1L, 4L, 3L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 1L, 2L, 1L, 1L, 3L, 3L, 2L, 4L, 2L, 
3L, 1L, 2L, 4L, 4L, 2L, 3L, 2L, 2L, 4L, 1L, 4L, 1L, 2L, 2L, 2L, 
2L, 2L, 3L, 2L, 2L, 3L, 2L, 1L, 1L, 2L, 1L, 4L, 1L, 2L, 2L, 1L, 
2L, 4L, 3L, 3L, 4L, 4L, 3L, 3L, 2L, 2L, 1L, 3L, 2L, 1L, 4L, 3L, 
2L, 3L, 2L, 4L, 1L, 3L, 1L, 2L, 3L, 3L, 2L, 2L, 1L, 1L, 1L, 2L, 
1L, 2L, 1L, 2L, 2L, 4L, 3L, 3L, 2L, 2L, 3L, 4L, 3L, 4L, 3L, 4L, 
3L, 1L, 2L, 1L, 4L, 3L, 4L, 3L, 4L, 1L, 1L, 4L, 3L, 1L, 2L, 3L, 
3L, 4L, 2L, 1L, 2L, 4L, 1L, 2L, 2L, 4L, 4L, 3L, 3L, 2L, 4L, 2L, 
4L, 1L, 2L, 3L, 2L, 3L, 1L, 2L, 2L, 2L, 4L, 1L, 2L, 3L, 4L, 3L, 
2L, 2L, 4L, 1L, 3L, 4L, 2L, 3L, 1L, 2L, 3L, 2L, 1L, 3L, 4L, 1L, 
2L, 3L, 2L, 3L, 3L, 3L, 1L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 3L, 3L, 
3L, 1L, 2L, 2L, 1L, 1L, 3L, 3L, 3L, 3L, 3L, 2L, 3L, 3L, 2L, 2L, 
3L, 2L, 4L, 2L, 1L, 2L, 2L, 2L, 3L, 2L, 2L, 4L, 3L, 1L, 4L, 2L, 
2L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 4L, 3L, 3L, 3L, 4L, 2L, 3L, 1L, 
2L, 2L, 4L, 2L, 2L, 4L, 3L, 2L, 1L, 4L, 3L, 4L, 4L, 2L, 3L, 3L, 
1L, 2L, 2L, 1L, 3L, 2L, 4L, 2L, 4L, 4L, 4L, 1L, 1L, 4L, 1L, 2L, 
2L, 4L, 1L, 4L, 2L, 3L, 3L, 4L, 2L, 3L, 2L, 2L, 2L, 1L, 3L, 3L, 
2L, 3L, 4L, 1L, 3L, 3L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 4L, 1L, 4L, 
3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 1L, 4L, 3L, 3L, 1L, 4L, 1L, 3L, 
4L, 1L, 4L, 2L, 3L, 3L, 3L, 4L, 2L, 1L, 2L, 2L, 3L, 3L, 4L, 4L,      
4L, 4L, 4L, 2L, 2L, 1L, 2L, 4L, 4L, 1L, 4L, 2L, 2L, 3L, 2L, 1L, 
2L, 4L, 2L, 2L, 3L, 3L, 1L, 2L, 4L, 2L, 4L, 2L), .Label = c("1", 
"2", "3", "4"), class = "factor")), .Names = c("status", "time", 
"uo", "q3"), class = "data.frame", row.names = c(NA, 800L))

0 个答案:

没有答案