R의 RPI의 pdIndent 함수

Question

lmeSplines tutorial의 코드를 RPy로 변환하려고합니다.

이제 다음 줄에 붙어 : 내가 전에 nlme.lme 일한

fit1s <- lme(y ~ time, data=smSplineEx1,random=list(all=pdIdent(~Zt - 1))) 

하고 잘 작품을 다음

from rpy2.robjects.packages import importr 
nlme = importr('nlme')  
nlme.lme(r.formula('y ~ time'), data=some_data, random=r.formula('~1|ID')) 

그러나 이것이 다른 random 할당을 가지고을 . 나는이 비트를 어떻게 번역 할 수 있고 내 RPy 코드에도 넣을 수 있는지 궁금하다. list(all=pdIdent(~Zt - 1)).

smSplineEx1이 닮은 (전처리) 예시적인 데이터 구조 (. ZT와 * 98까지)

time   y y.true all   Zt.1   Zt.2   Zt.3 
1  1 5.797149 4.235263 1 1.168560e+00 2.071261e+00 2.944953e+00 
2  2 5.469222 4.461302 1 1.487859e-01 1.072013e+00 1.948857e+00 
3  3 4.567237 4.678477 1 -5.449190e-02 7.276623e-02 9.527613e-01 
4  4 3.645763 4.887137 1 -5.364552e-02 -1.359115e-01 -4.333438e-02 
5  5 5.094126 5.087615 1 -5.279913e-02 -1.337708e-01 -2.506194e-01 
6  6 4.636121 5.280233 1 -5.195275e-02 -1.316300e-01 -2.466158e-01 
7  7 5.501538 5.465298 1 -5.110637e-02 -1.294892e-01 -2.426123e-01 
8  8 5.011509 5.643106 1 -5.025998e-02 -1.273485e-01 -2.386087e-01 
9  9 6.114037 5.813942 1 -4.941360e-02 -1.252077e-01 -2.346052e-01 
10 10 5.696472 5.978080 1 -4.856722e-02 -1.230670e-01 -2.306016e-01 
11 11 6.615363 6.135781 1 -4.772083e-02 -1.209262e-01 -2.265980e-01 
12 12 8.002526 6.287300 1 -4.687445e-02 -1.187854e-01 -2.225945e-01 
13 13 6.887444 6.432877 1 -4.602807e-02 -1.166447e-01 -2.185909e-01 
14 14 6.319205 6.572746 1 -4.518168e-02 -1.145039e-01 -2.145874e-01 
15 15 6.482771 6.707130 1 -4.433530e-02 -1.123632e-01 -2.105838e-01 
16 16 7.938015 6.836245 1 -4.348892e-02 -1.102224e-01 -2.065802e-01 
17 17 7.585533 6.960298 1 -4.264253e-02 -1.080816e-01 -2.025767e-01 
18 18 7.560287 7.079486 1 -4.179615e-02 -1.059409e-01 -1.985731e-01 
19 19 7.571020 7.194001 1 -4.094977e-02 -1.038001e-01 -1.945696e-01 
20 20 8.922418 7.304026 1 -4.010338e-02 -1.016594e-01 -1.905660e-01 
21 21 8.241394 7.409737 1 -3.925700e-02 -9.951861e-02 -1.865625e-01 
22 22 7.447076 7.511303 1 -3.841062e-02 -9.737785e-02 -1.825589e-01 
23 23 7.317292 7.608886 1 -3.756423e-02 -9.523709e-02 -1.785553e-01 
24 24 7.077333 7.702643 1 -3.671785e-02 -9.309633e-02 -1.745518e-01 
25 25 8.268601 7.792723 1 -3.587147e-02 -9.095557e-02 -1.705482e-01 
26 26 8.216013 7.879272 1 -3.502508e-02 -8.881481e-02 -1.665447e-01 
27 27 8.968495 7.962427 1 -3.417870e-02 -8.667405e-02 -1.625411e-01 
28 28 9.085605 8.042321 1 -3.333232e-02 -8.453329e-02 -1.585375e-01 
29 29 9.002575 8.119083 1 -3.248593e-02 -8.239253e-02 -1.545340e-01 
30 30 8.763187 8.192835 1 -3.163955e-02 -8.025177e-02 -1.505304e-01 
31 31 8.936370 8.263695 1 -3.079317e-02 -7.811101e-02 -1.465269e-01 
32 32 9.033403 8.331776 1 -2.994678e-02 -7.597025e-02 -1.425233e-01 
33 33 8.248328 8.397188 1 -2.910040e-02 -7.382949e-02 -1.385198e-01 
34 34 5.961721 8.460035 1 -2.825402e-02 -7.168873e-02 -1.345162e-01 
35 35 8.400489 8.520418 1 -2.740763e-02 -6.954797e-02 -1.305126e-01 
36 36 6.855125 8.578433 1 -2.656125e-02 -6.740721e-02 -1.265091e-01 
37 37 9.798931 8.634174 1 -2.571487e-02 -6.526645e-02 -1.225055e-01 
38 38 8.862758 8.687729 1 -2.486848e-02 -6.312569e-02 -1.185020e-01 
39 39 7.282970 8.739184 1 -2.402210e-02 -6.098493e-02 -1.144984e-01 
40 40 7.484208 8.788621 1 -2.317572e-02 -5.884417e-02 -1.104949e-01 
41 41 8.404670 8.836120 1 -2.232933e-02 -5.670341e-02 -1.064913e-01 
42 42 8.880734 8.881756 1 -2.148295e-02 -5.456265e-02 -1.024877e-01 
43 43 8.826189 8.925603 1 -2.063657e-02 -5.242189e-02 -9.848418e-02 
44 44 9.827906 8.967731 1 -1.979018e-02 -5.028113e-02 -9.448062e-02 
45 45 8.528795 9.008207 1 -1.894380e-02 -4.814037e-02 -9.047706e-02 
46 46 9.484073 9.047095 1 -1.809742e-02 -4.599961e-02 -8.647351e-02 
47 47 8.911947 9.084459 1 -1.725103e-02 -4.385885e-02 -8.246995e-02 
48 48 10.201343 9.120358 1 -1.640465e-02 -4.171809e-02 -7.846639e-02 
49 49 8.908016 9.154849 1 -1.555827e-02 -3.957733e-02 -7.446283e-02 
50 50 8.202368 9.187988 1 -1.471188e-02 -3.743657e-02 -7.045927e-02 
51 51 7.432851 9.219828 1 -1.386550e-02 -3.529581e-02 -6.645572e-02 
52 52 8.063268 9.250419 1 -1.301912e-02 -3.315505e-02 -6.245216e-02 
53 53 10.155756 9.279810 1 -1.217273e-02 -3.101429e-02 -5.844860e-02 
54 54 7.905281 9.308049 1 -1.132635e-02 -2.887353e-02 -5.444504e-02 
55 55 9.688337 9.335181 1 -1.047997e-02 -2.673277e-02 -5.044148e-02 
56 56 9.437176 9.361249 1 -9.633582e-03 -2.459201e-02 -4.643793e-02 
57 57 9.165873 9.386295 1 -8.787198e-03 -2.245125e-02 -4.243437e-02 
58 58 9.120195 9.410358 1 -7.940815e-03 -2.031049e-02 -3.843081e-02 
59 59 9.955840 9.433479 1 -7.094432e-03 -1.816973e-02 -3.442725e-02 
60 60 9.314230 9.455692 1 -6.248048e-03 -1.602897e-02 -3.042369e-02 
61 61 9.706852 9.477035 1 -5.401665e-03 -1.388821e-02 -2.642014e-02 
62 62 9.615765 9.497541 1 -4.555282e-03 -1.174746e-02 -2.241658e-02 
63 63 7.918843 9.517242 1 -3.708898e-03 -9.606695e-03 -1.841302e-02 
64 64 9.352892 9.536172 1 -2.862515e-03 -7.465935e-03 -1.440946e-02 
65 65 9.722685 9.554359 1 -2.016132e-03 -5.325176e-03 -1.040590e-02 
66 66 9.186888 9.571832 1 -1.169748e-03 -3.184416e-03 -6.402346e-03 
67 67 8.652299 9.588621 1 -3.233650e-04 -1.043656e-03 -2.398788e-03 
68 68 8.681421 9.604751 1 5.230184e-04 1.097104e-03 1.604770e-03 
69 69 10.279181 9.620249 1 1.369402e-03 3.237864e-03 5.608328e-03 
70 70 9.314963 9.635140 1 2.215785e-03 5.378623e-03 9.611886e-03 
71 71 6.897151 9.649446 1 3.062168e-03 7.519383e-03 1.361544e-02 
72 72 9.343135 9.663191 1 3.908552e-03 9.660143e-03 1.761900e-02 
73 73 9.273135 9.676398 1 4.754935e-03 1.180090e-02 2.162256e-02 
74 74 10.041796 9.689086 1 5.601318e-03 1.394166e-02 2.562612e-02 
75 75 9.724713 9.701278 1 6.447702e-03 1.608242e-02 2.962968e-02 
76 76 8.593517 9.712991 1 7.294085e-03 1.822318e-02 3.363323e-02 
77 77 7.401988 9.724244 1 8.140468e-03 2.036394e-02 3.763679e-02 
78 78 10.258688 9.735057 1 8.986852e-03 2.250470e-02 4.164035e-02 
79 79 10.037192 9.745446 1 9.833235e-03 2.464546e-02 4.564391e-02 
80 80 9.637510 9.755427 1 1.067962e-02 2.678622e-02 4.964747e-02 
81 81 8.887625 9.765017 1 1.152600e-02 2.892698e-02 5.365102e-02 
82 82 9.922013 9.774230 1 1.237239e-02 3.106774e-02 5.765458e-02 
83 83 10.466709 9.783083 1 1.321877e-02 3.320850e-02 6.165814e-02 
84 84 11.132830 9.791588 1 1.406515e-02 3.534926e-02 6.566170e-02 
85 85 10.154038 9.799760 1 1.491154e-02 3.749002e-02 6.966526e-02 
86 86 10.433068 9.807612 1 1.575792e-02 3.963078e-02 7.366881e-02 
87 87 9.666781 9.815156 1 1.660430e-02 4.177154e-02 7.767237e-02 
88 88 9.478004 9.822403 1 1.745069e-02 4.391230e-02 8.167593e-02 
89 89 10.002749 9.829367 1 1.829707e-02 4.605306e-02 8.567949e-02 
90 90 7.593259 9.836058 1 1.914345e-02 4.819382e-02 8.968305e-02 
91 91 10.915754 9.842486 1 1.998984e-02 5.033458e-02 9.368660e-02 
92 92 8.855580 9.848662 1 2.083622e-02 5.247534e-02 9.769016e-02 
93 93 8.884683 9.854596 1 2.168260e-02 5.461610e-02 1.016937e-01 
94 94 9.757451 9.860298 1 2.252899e-02 5.675686e-02 1.056973e-01 
95 95 10.222361 9.865775 1 2.337537e-02 5.889762e-02 1.097008e-01 
96 96 9.090410 9.871038 1 2.422175e-02 6.103838e-02 1.137044e-01 
97 97 8.837872 9.876095 1 2.506814e-02 6.317914e-02 1.177080e-01 
98 98 9.413135 9.880953 1 2.591452e-02 6.531990e-02 1.217115e-01 
99 99 9.295531 9.885621 1 2.676090e-02 6.746066e-02 1.257151e-01 
100 100 9.698118 9.890106 1 2.760729e-02 6.960142e-02 1.297186e-01 

Answer 1

넌 reval() 방법을 사용 R의 지구 환경에 list(all=pdIdent(~Zt - 1)) 넣을 수

을

In [55]: 

import rpy2.robjects as ro 
import pandas.rpy.common as com 
mydata = ro.r['data.frame'] 
read = ro.r['read.csv'] 
head = ro.r['head'] 
summary = ro.r['summary'] 
library = ro.r['library'] 
In [56]: 

formula = '~ time' 
library('lmeSplines') 
ro.reval('data(smSplineEx1)') 
ro.reval('smSplineEx1$all <- rep(1,nrow(smSplineEx1))') 
ro.reval('smSplineEx1$Zt <- smspline(~ time, data=smSplineEx1)') 
ro.reval('rnd <- list(all=pdIdent(~Zt - 1))') 
#result = ro.r.smspline(formula=ro.r(formula), data=ro.r.smSplineEx1) #notice: data=ro.r.smSplineEx1 
result = ro.r.lme(ro.r('y~time'), data=ro.r.smSplineEx1, random=ro.r.rnd) 
In [57]: 

print com.convert_robj(result.rx('coefficients')) 
{'coefficients': {'random': {'all':   Zt1  Zt2  Zt3  Zt4  Zt5  Zt6  Zt7 \ 
1 0.000509 0.001057 0.001352 0.001184 0.000869 0.000283 -0.000424 

     Zt8  Zt9  Zt10 ...   Zt89  Zt90  Zt91 \ 
1 -0.001367 -0.002325 -0.003405 ... -0.001506 -0.001347 -0.000864 

     Zt92  Zt93  Zt94  Zt95  Zt96  Zt97  Zt98 
1 -0.000631 -0.000569 -0.000392 -0.000049 0.000127 0.000114 0.000071 

[1 rows x 98 columns]}, 'fixed': (Intercept) 6.498800 
time   0.038723 
dtype: float64}} 

결과가 약간 변형되었습니다. 기본적으로는 pandas.DataFrame으로 변환 할 수없는 중첩 된 사전입니다. 당신이 R에서 그렇게하는 것처럼

당신은 smsSplineEx1$y에, ro.r.smSplineEx1.rx('y')으로 smsSplineEx 비슷한을 y에 액세스 할 수 있습니다.

은 이제

result = ro.r.lme(ro.r('y~time'), data=ro.r.smSplineEx1, random=ro.r.rnd) 

에 의해 생성 python에서 result 변수를 가지고 있고 (대신, 말, matplotlib을 사용하여 플롯의), R를 사용하여 플롯하려는 경우, 당신이 그것을 할당해야 할 말 R 작업 공간 변수에 :

ro.R().assign('result', result) 

지금 R 작업 공간에서 result라는 변수가, 당신은 012를 사용하여 액세스 할 수 있습니다.

In [17]: 

ro.reval('plot(smSplineEx1$time,smSplineEx1$y,pch="o",type="n", \ 
      main="Spline fits: lme(y ~ time, random=list(all=pdIdent(~Zt-1)))", \ 
      xlab="time",ylab="y")') 
Out[17]: 
rpy2.rinterface.NULL 
In [21]: 

ro.reval('lines(smSplineEx1$time, fitted(result),col=2)') 
Out[21]: 
rpy2.rinterface.NULL 

또는 당신은 R에서 모든 작업을 수행 할 수 있습니다 :

R 사용하여 플로팅

ro.reval('result <- lme(y ~ time, data=smSplineEx1,random=list(all=pdIdent(~Zt - 1)))') 
ro.reval('plot(smSplineEx1$time,smSplineEx1$y,pch="o",type="n", \ 
     main="Spline fits: lme(y ~ time, random=list(all=pdIdent(~Zt-1)))", \ 
     xlab="time",ylab="y")') 
ro.reval('lines(smSplineEx1$time, fitted(result),col=2)') 

및 사용 R 변수에 액세스 : ro.r.smSplineEx1.rx2('time') 또는 ro.r.result

을

편집

공지 사항 일부 R 오브젝트는 데이터 구조의 혼합물에 의한 그대로 pandas.dataFrame로 변환 할 수 없습니다 : 우리가 몇 벡터를 가지고 있지만 마지막 하나는 Matrix입니다

In [62]: 

ro.r["smSplineEx1"] 
Out[62]: 
<DataFrame - Python:0x108525518/R:0x109e5da38> 
[FloatVe..., FloatVe..., FloatVe..., FloatVe..., Matrix] 
    time: <class 'rpy2.robjects.vectors.FloatVector'> 
    <FloatVector - Python:0x10807e518/R:0x1022599e0> 
[1.000000, 2.000000, 3.000000, ..., 98.000000, 99.000000, 100.000000] 
    y: <class 'rpy2.robjects.vectors.FloatVector'> 
    <FloatVector - Python:0x108525a70/R:0x102259d30> 
[5.797149, 5.469222, 4.567237, ..., 9.413135, 9.295531, 9.698118] 
    y.true: <class 'rpy2.robjects.vectors.FloatVector'> 
    <FloatVector - Python:0x1085257a0/R:0x10225dfb0> 
[4.235263, 4.461302, 4.678477, ..., 9.880953, 9.885621, 9.890106] 
    all: <class 'rpy2.robjects.vectors.FloatVector'> 
    <FloatVector - Python:0x1085258c0/R:0x10225e300> 
[1.000000, 1.000000, 1.000000, ..., 1.000000, 1.000000, 1.000000] 
    Zt: <class 'rpy2.robjects.vectors.Matrix'> 
    <Matrix - Python:0x108525908/R:0x103e8ba00> 
[1.168560, 0.148786, -0.054492, ..., -0.030141, -0.030610, 0.757597] 

공지 사항. smSplineEx을 파이썬으로 두 부분으로 변환해야합니다.

In [63]: 

ro.r["smSplineEx1"].names 
Out[63]: 
<StrVector - Python:0x108525dd0/R:0x1042ca7c0> 
['time', 'y', 'y.true', 'all', 'Zt'] 
In [64]: 

print com.convert_robj(ro.r["smSplineEx1"].rx(ro.IntVector(range(1, 5)))).head() 
    time   y y.true all 
1  1 5.797149 4.235263 1 
2  2 5.469222 4.461302 1 
3  3 4.567237 4.678477 1 
4  4 3.645763 4.887137 1 
5  5 5.094126 5.087615 1 
In [65]: 

print com.convert_robj(ro.r["smSplineEx1"].rx2('Zt')).head(2) 
     0   1   2   3   4   5   6 \ 
1 1.168560 2.071261 2.944953 3.782848 4.584037 5.348937 6.078121 
2 0.148786 1.072013 1.948857 2.789264 3.593423 4.361817 5.095016 

     7   8   9  ...   88  89  90 \ 
1 6.772184 7.431719 8.057321 ...  0.933947 0.769591 0.619420 
2 5.793601 6.458153 7.089255 ...  0.904395 0.745337 0.599976 

     91  92  93  94  95  96  97 
1 0.484029 0.36401 0.259959 0.172468 0.102133 0.049547 0.015305 
2 0.468893 0.35267 0.251890 0.167135 0.098986 0.048026 0.014836 

[2 rows x 98 columns] 

com.convert_robj(ro.r["smSplineEx1"])은 혼합 된 데이터 구조 문제로 인해 작동하지 않습니다.