data.frame (mm [, -1], surv.t = surv.t, cens = cens)의 오류 : 인수가 다른 행 수를 의미 함 :

Question

R에서 BMA 패키지를 실행하여 CoxPH를 수행했습니다. 테스트. 나는이 문제가 "행 수의 차이를 의미 함 : 146, 0"을 해결할 수 있도록 데이터를 편집해야하는지 궁금합니다.

library(BMA) 
data <- read.csv("Test1.csv", header = TRUE) 

x<- data[1:146,] 

x <- data[,c( "dom_econ_2","llgdp", "pcrdbofgdp")] 
surv.t<- x$crisis1 
cens<- x$cen1 

test.bic.surv<- bic.surv(x, surv.t, cens, factor.type=Ture, strict=FALSE, nbest=200) 

Error in data.frame(mm[, -1], surv.t = surv.t, cens = cens) : 
    arguments imply differing number of rows: 146, 0 

구성 data.

data <- read.table(text=" country Start crisis1 cen1  llgdp pcrdbofgdp dom_econ_2 
1    Algeria 1988  48 1 90.537788 65.226883  0.00 
2    Algeria 1994  24 1 43.727940 5.994088  14.25 
3   Argentina 1985  96 0 12.049210 12.676220  0.00 
4   Argentina 2002  12 1 27.514610 18.335609  14.96 
5   Australia 1985  12 0 36.909191 30.567970  0.00 
6   Australia 1997  12 1 60.054508 69.576698  104.06 
7   Australia 2000  12 1 64.405777 80.765381  89.13 
8   Australia 2008  12 1 95.728081 115.909699  237.16 
9    Austria 2005  12 1 91.344994 108.155701  82.14 
10    Belgium 2005  12 1 102.885399 71.527367  114.55 
11    Bolivia 1985  12 0 4.461628 4.868293  0.00 
12    Bolivia 1987  12 1 13.480320 13.259240  0.00 
13    Bolivia 1989  12 1 17.370689 17.162399  0.00 
14    Brazil 1985  132 0 7.082396 22.242729  0.00 
15    Brazil 1999  12 1 40.434750 30.275040  153.22 
16    Brazil 2001  24 1 45.114819 30.151600  133.65 
17    Brazil 2008  12 1 57.924221 47.755600  409.57 
18    canada 2008  12 1 119.428703 126.900398  225.36 
19    Chile 1985  12 0 0.000000 0.000000  0.00 
20    Chile 1987  12 1 0.000000 0.000000  0.00 
21    Chile 1989  12 1 0.000000 0.000000  0.00 
22    Chile 2008  12 1 0.000000 0.000000  35.17 
23  Cote D'lvoire 1994  12 1 25.643181 22.177429  2.10 
24  Cote D'lvoire 2011  24 1 41.235161 19.288630  4.68 
25    china 1986  12 1 0.000000 0.000000  0.00 
26    china 1989  12 1 62.773560 71.162529  0.00 
27    china 1994  12 1 83.825783 76.370827  67.21 
28   Colombia 1985  84 0 29.268551 32.937222  0.00 
29   Colombia 1995  12 1 30.042919 30.603430  12.56 
30   Colombia 1997  48 1 31.537670 34.393360  17.34 
31   Colombia 2002  12 1 16.778780 22.066490  17.12 
32   Costa Rica 1987  12 1 35.334270 17.252380  0.00 
33   Costa Rica 1991  12 1 30.253300 10.472690  1.01 
34   Costa Rica 1995  12 1 25.711729 10.946140  1.88 
35 Dominican Republic 1985  12 0 22.065741 38.200081  0.00 
36 Dominican Republic 1987  24 1 27.200859 41.605549  0.00 
37 Dominican Republic 1990  12 1 23.815241 35.062832  0.77 
38 Dominican Republic 2002  24 1 20.893270 38.377579  3.62 
39    Ecuador 1985  96 0 24.365290 25.992100  0.00 
40    Ecuador 1995  72 1 25.012659 25.226681  3.30 
41    Egypt 1989  36 1 0.000000 0.000000  0.00 
42    Egypt 2001  12 1 0.000000 0.000000  21.36 
43    Egypt 2003  12 1 0.000000 0.000000  21.67 
44   El Salvador 1988  12 1 5.249366 4.249679  0.00 
45    Finland 1992  12 1 61.804680 93.284843  51.87 
46    France 2005  12 1 73.674927 90.176163 1144.92 
47    Germany 1997  12 1 69.414650 107.758598 1048.86 
48    Germany 1999  12 1 85.617897 115.610901 1037.57 
49    Germany 2005  12 1 105.417099 111.763199 1297.82 
50    Greece 1985  24 0 58.569908 37.887230  0.00 
51    Greece 1990  12 1 68.117287 34.083881  30.32 
52    Greece 1999  36 1 55.327202 36.298470  44.28 
53    Greece 2005  12 1 85.200127 73.185272  77.85 
54   Guatemala 1986  12 1 23.963770 14.939860  0.00 
55   Guatemala 1989  24 1 22.968491 14.576470  0.00 
56   Honduras 1990  12 1 31.085350 29.356951  0.60 
57   Honduras 1993  24 1 29.533979 25.364269  0.91 
58   Honduras 1996  12 1 28.978729 22.788309  0.86 
59    Hungary 1989  12 1 39.513908 44.371880  0.00 
60    Hungary 1991  12 1 44.693378 42.222179  18.29 
61    Hungary 1993  12 1 52.589550 28.814779  21.60 
62    Hungary 1995  36 1 44.789848 21.890961  21.87 
63    Hungary 1999  12 1 44.038410 24.015810  21.43 
64    Iceland 1985  24 0 21.419769 34.361641  0.00 
65    Iceland 1988  24 1 25.819929 34.976372  0.00 
66    Iceland 2008  12 1 93.622017 184.647003  0.00 
67    India 1988  12 1 40.268990 28.615240  0.00 
68    India 1991  12 1 40.929920 23.150181  55.40 
69    India 1993  12 1 42.146000 22.969900  53.35 
70    India 2008  12 1 69.759697 44.396610  207.09 
71   Indonesia 1997  24 1 50.021770 53.528721  40.59 
72   Indonesia 2000  12 1 49.576542 17.631670  27.06 
73   Indonesia 2008  12 1 36.236462 23.411659  101.12 
74    Ireland 1993  12 1 46.543369 42.833199  16.32 
75    Ireland 1997  12 1 69.748718 72.668739  22.49 
76    Ireland 2005  12 1 87.587280 141.341995  51.42 
77    Italy 1992  12 1 61.862431 57.690781  537.05 
78    Italy 2005  12 1 58.811539 85.478607  856.04 
79   Malaysia 1997  12 1 116.673599 139.381607  21.01 
80    Mexico 1985  36 0 23.277300 10.972870  0.00 
81    Mexico 1989  12 1 12.128950 11.774920  0.00 
82    Mexico 1994  24 1 27.620720 33.321041  64.37 
83    Mexico 1998  12 1 31.633909 22.903950  60.87 
84    Mexico 2008  12 1 25.276720 20.486820  175.60 
85    Morocco 1985  12 0 46.630791 28.247660  0.00 
86   Netherlands 2005  12 1 111.478996 159.227707  196.86 
87   New Zealand 1997  12 1 81.314529 96.649277  20.87 
88   New Zealand 2008  12 1 91.273071 143.887497  40.38 
89   Nicaragua 1985  24 0 0.000000 0.000000  0.00 
90   Nicaragua 1988  48 1 0.000000 0.000000  0.00 
91   Nicaragua 1993  12 1 0.000000 0.000000  0.54 
92    Nigeria 1985  72 0 33.616810 15.274050  0.00 
93    Nigeria 1999  12 1 18.795080 12.470600  10.26 
94    Norway 1986  12 1 52.509472 65.354111  0.00 
95    Norway 2008  12 1 0.000000 0.000000  138.04 
96   Paraguay 1985  24 0 19.059549 13.474090  0.00 
97   Paraguay 1989  12 1 18.109470 13.592000  0.00 
98   Paraguay 1992  24 1 28.895550 20.640970  0.88 
99   Paraguay 1998  24 1 27.359171 27.806259  1.41 
100   Paraguay 2001  24 1 27.472139 27.111059  1.27 
101    Peru 1985  12 0 18.312740 12.587190  0.00 
102    Peru 1987  84 1 14.426420 9.529409  0.00 
103    Peru 1998  12 1 29.766150 26.084431  9.76 
104  Philippines 1990  12 1 32.946239 19.481730  8.97 
105  Philippines 1997  12 1 60.959930 55.599201  15.96 
106  Philippines 2000  12 1 57.644821 39.109230  14.52 
107    Poland 1985  108 0 38.214378 51.334850  0.00 
108    Poland 1995  36 1 27.932590 14.869600  51.27 
109    Poland 1999  12 1 37.415001 22.911200  32.18 
110    Poland 2008  12 1 48.807541 43.228100  178.28 
111   Portugal 2005  12 1 92.989853 135.765900  89.34 
112   Romania 1990  144 1 0.000000 0.000000  12.92 
113   Romania 2008  12 1 31.392929 36.600521  32.11 
114   Romania 2010  12 1 37.728611 45.040459  32.29 
115    Russia 1987  120 1 0.000000 0.000000  0.00 
116    Russia 1998  24 1 0.000000 0.000000  43.93 
117    Russia 2008  12 1 0.000000 0.000000  293.34 
118   Singapore 1997  12 1 109.437202 107.355103  29.25 
119  South Africa 1985  12 0 51.689949 66.574753  0.00 
120  South Africa 1988  12 1 49.117390 67.433647  0.00 
121  South Africa 1996  12 1 47.592419 112.563797  41.01 
122  South Africa 1998  12 1 53.312820 113.043098  36.40 
123  South Africa 2000  24 1 52.709499 127.040100  34.19 
124  South Africa 2008  12 1 46.246601 149.139099  80.10 
125    Spain 1993  12 1 73.074364 77.935318  129.39 
126    Spain 2005  12 1 100.510200 129.920197  159.93 
127   Sri Lanka 1989  12 1 35.501869 19.156321  0.00 
128    Sweden 1992  12 1 50.942661 124.471397  117.62 
129    Sweden 2005  12 1 46.589840 102.645203  97.60 
130    Sweden 2008  12 1 56.333191 124.272102  116.23 
131  Switzerland 1999  12 1 165.171402 159.786499  27.19 
132   Thailand 1997  12 1 90.951942 154.129700  27.92 
133   Thailand 2000  12 1 112.097000 116.628799  21.31 
134   Tunisia 1986  12 1 0.000000 0.000000  0.00 
135    Turkey 1985  204 0 20.020611 15.242030  0.00 
136    Turkey 2008  12 1 44.036678 29.615061  175.62 
137   Uruguay 1985  156 0 43.514191 34.115601  0.00 
138   Uruguay 2001  24 1 45.520069 49.360771  5.82 
139   Venezuela 1986  12 1 0.000000 0.000000  0.00 
140   Venezuela 1989  96 1 0.000000 0.000000  0.00 
141   Venezuela 2002  12 1 0.000000 0.000000  23.89 
142   Venezuela 2004  12 1 0.000000 0.000000  28.59 
143   Venezuela 2010  12 1 0.000000 0.000000  85.81 
144  United Kingdom 1993  12 1 59.609852 106.663597  409.43 
145  United Kingdom 2008  12 1 163.094299 197.386902 1093.45 
146  United States 2002  24 1 64.508629 169.231400 2012.69", 
    header=TRUE) 

Answer 1

문제는 surv.t & cens가 비어 있다는 것입니다. 방금 처음 146 개 행을 원하는 경우,

surv.t <- data$crisis1 
cens <- data$cen1 

마음에

surv.t <- data$crisis1[1:146] 
cens <- data$cen1[1:146] 

그러나

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, 곰을 사용

## NOTICE IN THIS LINE, YOU SELECT ONLY THREE SPECIFIC COLUMNS 
x <- data[,c( "dom_econ_2","llgdp", "pcrdbofgdp")] 

## Then in this line, you are trying to access a column that is not there. 
surv.t<- x$crisis1 

난 당신이 data 대신 x을 사용하는 의미 생각 함수에 대한 인수로 data$cen1 (등)을 사용할 수 있습니다. 당신이 함수에서 오류가 발생하는 경우와 첫 번째 단계 중 하나는 당신이 전달하는 인자를 확인하는 것입니다 왜 확실하지 않은 : 필요가 일반적인 문제 해결 팁으로 새로운 변수

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을 만들 수 없습니다 함수 (예 : 괄호 안의 내용을 확인하십시오)으로 이동 한 다음 해당 값과 예상되는 값이 있는지 확인하십시오.