我用比例优势模型做模拟，设置参数Z=rnorm(100)，beta=1的值，假如设设G（t）=log(t).我应用
out=prop.odds(Surv(x,a1==0)~z,data=f,profile=1,n.sim=500)拟合，结果显示beta的估计=-0.5（原本设的为0.1），这不科学啊！不知道怎么回事，好着急！论文搞不粗来了！
比例优势模型（以下为公式）

library(survival)
data(sTRACE)
# Fits Proportional odds model
out<-prop.odds(Surv(time,status==9)~age+diabetes+chf+vf+sex,
sTRACE,max.time=7,n.sim=100)
summary(out)
运行上面R程序得到的结果是：

红色结果为参数beta的估计，那G(t)的估计呢？在哪里啊？
另外如果我模拟的话，我可以设定Z=rnorm(100)，beta=1的值，假如设设G（t）=log(t).我应用
out=prop.odds(Surv(x,a1==0)~z,data=f,profile=1,n.sim=500)拟合，结果显示beta的估计=-0.5（原本设的为0.1），这不科学啊！
哪位大神能帮我解决一下吗？
               x a1           z  [1,]   1.810059  1  1.09259603  [2,]   2.007997 -1  0.47955195  [3,]   6.008313  3  1.13684571  [4,]   1.136115  4  0.93162188  [5,]   2.938375  5  0.42419451  [6,]   2.118443  6 -2.03331829  [7,]   5.458736  7  1.40399180  [8,] 133.862857  0  0.53042776  [9,]   1.201311  9  0.65308888 [10,]  57.841493 10  0.83124419 [11,]   1.412959 11  0.18500374 [12,]  29.159761 -1  0.26182130 [13,]  18.188196 13  0.11006149 [14,]   1.530348 -1  2.27764079 [15,]   1.029573 15  1.71659100 [16,]   4.947406 -1 -0.68200276 [17,]   1.075174 17  0.27969654 [18,]   1.212033 18 -0.60528955 [19,]   1.138192 -1  1.79611574 [20,]   3.454956 20 -1.14942796 [21,]   1.776298 21  1.33147613 [22,]   1.471960 22  0.65926860 [23,]  96.076022  0  0.45825046 [24,]   1.359771 24  0.62660148 [25,]   1.108051 -1 -2.30447524 [26,]   1.038882 26  1.80092565 [27,]   1.719297 27 -0.01405962 [28,]   3.696800 -1 -0.33212669 [29,]   6.793580 29  0.28358103 [30,]   1.140321 30 -0.75380608 [31,]  69.411585 31 -0.34210455 [32,]  11.681829 32  1.39400218 [33,]   1.980047 33 -1.15812430 [34,]   6.521382 34 -1.59440032 [35,]   1.589795 35 -1.03585052 [36,]  71.669396  0 -0.06091484 [37,]  15.659469 37 -0.95993413 [38,]   1.928565 38  0.93385900 [39,]   1.138170 39  0.77540934 [40,]   1.939080 40  0.06628879 [41,]   2.092851 41  1.51795478 [42,]  10.322366 42  0.38394473 [43,]  62.058649  0 -0.11848924 [44,]  55.326818  0 -0.50033995 [45,]  22.866712  0 -1.22973080 [46,]   1.248777 46  1.50699171 [47,]   1.052387 47 -1.60512119 [48,]   1.304579 48 -0.94515289 [49,]   1.477914 -1  0.98440888 [50,]   1.364479 50 -1.52837184 [51,]   6.874675  0  0.88366645 [52,]   7.184373 -1 -1.73721796 [53,]   1.372746 53  1.79450340 [54,]  16.134302 -1  0.88973130 [55,]   2.619847 55  0.20157060 [56,]   3.079803 56 -0.21817915 [57,]  19.129748  0 -1.37019460 [58,]   2.364380 58  0.49073931 [59,]   4.376577 59  0.13280700 [60,]   1.108263 60  0.55740695 [61,]  99.767279  0  0.57441843 [62,]   1.401043 62  0.92237409 [63,]   2.638089 63  0.27917507 [64,]   1.809885 64 -0.83255871 [65,]   1.032053 65 -0.16200804 [66,]   1.124964 -1  0.57503182 [67,] 121.509653  0  0.87341865 [68,]   1.912180 68  0.50081941 [69,]   1.123240 69  1.66325449 [70,]  36.987249 -1 -0.24356056 [71,]   2.535763 71  0.21678123 [72,]   1.387551 72  1.30123798 [73,]   1.780667 -1 -1.15759830 [74,]   0.093716  0 -0.60943168 [75,]  45.763182 75 -0.03489918 [76,]   1.102329 76 -0.44264343 [77,]   1.225605 77 -0.02081011 [78,]   1.130118 78  0.35496530 [79,]   2.278332 79 -2.28353073 [80,]   1.367596 80  1.12561548 [81,]  36.542921  0 -0.22277303 [82,]   1.105251 82  0.38046687 [83,]  62.647017  0  0.15329150 [84,]   4.449773 84 -0.57321001 [85,]   1.122518 -1  0.40204439 [86,]   1.248854 86  2.35437648 [87,]   3.271822 87  0.36527550 [88,]   1.583235 88 -0.14255089 [89,]  36.259212 89  1.12899068 [90,]  26.257898  0  0.73035636 [91,]   2.465674 -1 -0.49969061 [92,]   2.037142 92  0.18011695 [93,]   1.076321 93 -2.59721574 [94,]  30.425119 94  1.00766624 [95,]   3.915904 95 -1.19791717 [96,]  28.153668  0  1.13879639 [97,]   1.008573 -1 -1.21757952 [98,]   1.976480 98 -0.40530044 [99,]   1.372950 99 -0.29324960[100,]  76.725819  0 -2.17550157