嗨我想用ggplot包含一系列带有截距和斜率的直线。这是我的df，这是我的第一个情节：
df
x y
1 5.767382e-04 -0.0179316822
2 5.382526e-04 -0.0064160490
3 3.283479e-04 0.0118829440
4 5.515362e-04 -0.0082115912
5 6.214837e-04 -0.0171607214
6 9.149512e-04 0.0162441461
7 1.096518e-03 0.0096310500
8 1.231807e-03 0.0039493755
9 1.291489e-03 -0.0373589167
10 1.290377e-03 -0.0047063656
11 1.346938e-03 0.0236590046
12 1.561922e-03 0.0063475614
13 1.421441e-03 -0.0070059435
14 1.546511e-03 -0.0169201470
15 -1.674733e-04 0.0015561601
16 1.954316e-03 0.0249988252
17 1.450279e-03 -0.0229110197
18 3.647360e-03 0.0196769910
19 -9.657013e-04 -0.0232707201
20 9.267196e-03 -0.0147603533
21 -2.330904e-03 0.0146431308
22 1.690023e-03 0.0144978266
23 -1.899995e-03 -0.0079710747
24 -3.216321e-03 0.0127135418
25 7.389830e-04 0.0251444785
26 5.605426e-04 -0.0076547396
27 7.745684e-04 0.0026117673
28 -4.348211e-04 0.0042256186
29 8.814315e-04 0.0169941536
30 -2.446243e-03 -0.0273569406
31 0.000000e+00 0.0074404985
32 1.252793e-02 -0.0239050021
33 1.859272e-03 0.0036494591
34 -3.281863e-05 0.0298095265
35 -5.894445e-03 -0.0118560733
36 -1.450642e-04 -0.0056893640
37 8.910996e-04 0.0263777220
38 -6.954649e-03 -0.0104581718
39 5.741444e-04 0.0193070220
40 -1.564921e-04 0.0458305555
41 -2.394386e-03 -0.0127560808
42 -4.719316e-03 0.0107511726
43 -4.819214e-03 0.0289323344
44 -8.151265e-04 -0.0160308497
45 -5.028173e-04 0.0180222018
46 -1.118739e-03 0.0240456897
47 -4.347277e-03 -0.0523667661
48 -6.275963e-04 0.0221451602
49 5.550606e-04 0.0129487351
50 4.902597e-04 0.0143767869
51 4.406320e-04 -0.0063753077
52 -3.324976e-03 0.0071569408
53 -7.674161e-04 0.0100497676
54 1.096383e-03 -0.0132797308
55 1.722997e-03 -0.0175982918
56 1.796998e-03 -0.0131551067
57 1.674058e-03 0.0105408021
58 2.762772e-04 0.0195710544
59 4.198189e-03 -0.0203275722
60 6.759233e-03 0.0293425895
61 1.623984e-03 -0.0007776215
62 2.687013e-04 -0.0067553441
63 1.764196e-03 -0.0231203356
64 2.067229e-03 -0.0040448936
65 7.313363e-04 -0.0409024742
66 1.827486e-03 -0.0245661039
67 -4.117068e-03 0.0098401969
68 1.761924e-03 -0.0164702633
69 1.468973e-02 0.0051605124
70 6.409529e-03 0.0054452684
71 2.160516e-03 -0.0340426755
72 5.874911e-03 -0.0207890110
73 -4.931250e-05 0.0426363179
74 -3.308259e-04 -0.0035186580
75 1.001841e-02 -0.0110782883
76 6.817267e-03 -0.0247737276
77 1.898683e-03 -0.0071688431
78 1.879390e-03 0.0262043639
79 -3.978326e-03 0.0161207659
80 2.038382e-03 -0.0491302989
81 2.952938e-03 -0.0211728961
82 -5.091002e-05 -0.0133934387
83 2.233294e-03 -0.0400734285
84 7.845835e-03 -0.0166611856
85 2.290868e-03 -0.0193531306
86 2.576246e-03 0.0097141933
87 8.855930e-03 0.0357254985
88 8.331340e-04 -0.0333207907
89 2.746399e-03 0.0109406292
90 5.894602e-03 -0.0027239561
91 2.760225e-03 0.0107796305
92 5.507580e-03 -0.0058818392
93 7.966446e-03 -0.0124912857
94 2.269484e-03 -0.0423110888
95 1.147931e-02 -0.0043869382
96 1.343029e-03 0.0177541412
97 3.332866e-03 -0.0207729389
98 7.214568e-03 0.0189228345
99 5.126573e-03 -0.0106763932
100 1.545817e-03 0.0468064583
101 6.923315e-04 0.0166643412
102 -5.572046e-03 0.0179240781
103 8.799100e-04 -0.0213355187
104 8.703026e-03 0.0165175080
105 5.011657e-03 -0.0006547804
106 -3.404681e-03 0.0227698214
107 7.859670e-03 0.0079283421
108 1.201645e-04 -0.0346891934
109 5.052620e-03 0.0147842433
110 6.510928e-03 0.0095857959
111 -6.805175e-03 -0.0468008862
112 -4.363319e-04 -0.0290832355
113 -8.097971e-03 0.0116832017
114 1.943374e-02 -0.0094214974
115 1.306860e-04 -0.0186188622
116 -2.380523e-03 0.0181519870
117 -4.110306e-04 -0.0208185537
118 2.305092e-03 -0.0308729855
119 8.793202e-03 0.0050425386
120 -2.153084e-04 -0.0001370091
121 -2.057392e-03 -0.0011947316
122 -2.924294e-03 -0.0008234326
123 5.883910e-05 -0.0194284696
124 -9.804020e-04 0.0109390697
125 1.857671e-03 -0.0141840698
126 3.811320e-04 -0.0127408263
127 4.298727e-03 0.0074098408
128 1.451233e-03 -0.0100142738
129 6.517445e-04 -0.0194408567
130 5.964974e-03 0.0102715503
131 -2.243700e-03 0.0323884648
132 -2.588121e-04 0.0013129366
133 1.569282e-03 -0.0006164677
134 1.709266e-03 0.0008549983
135 1.370448e-03 0.0035117342
136 6.829333e-03 0.0021959986
137 2.250341e-03 -0.0070402575
138 1.590869e-03 0.0046659934
139 3.164434e-03 0.0825476969
140 2.186351e-03 0.0230247530
141 -1.470575e-02 0.0981854863
142 -4.647970e-03 0.0516244670
143 -1.381099e-04 0.0650898115
144 -2.663386e-03 -0.0027354677
145 3.891808e-04 0.0113962573
146 1.282737e-04 -0.0055887228
147 1.006854e-02 -0.0332557676
148 3.609098e-03 -0.0845451850
149 9.674767e-05 -0.0210540954
150 -4.803852e-03 -0.0066589253
151 5.373557e-03 0.0170621188
152 -7.173718e-04 0.0188572127
153 3.751451e-03 -0.0301665839
154 1.813012e-04 0.0044078294
155 -4.622137e-03 0.0161162531
156 8.778474e-03 0.0077374492
157 2.243122e-04 0.0512243884
158 2.435534e-04 0.0036167504
159 4.313842e-03 -0.0090206012
160 2.140608e-03 0.0520850888
161 4.855610e-03 -0.0292653473
162 -6.378769e-03 -0.0457181372
163 1.409388e-04 0.0188185729
164 -6.677023e-03 -0.0249923532
165 -2.350696e-03 -0.0144915120
166 2.238066e-04 0.0262718350
167 2.985634e-03 -0.0053246849
168 1.733721e-02 -0.0227874598
169 1.110229e-02 -0.0153335154
170 1.218916e-02 0.0145804128
171 -1.636347e-03 -0.0397472603
172 4.168994e-04 0.0132717818
173 4.443091e-04 0.0249194075
174 2.261120e-03 -0.0222072628
175 3.282455e-04 0.0081088595
176 7.487101e-04 0.0442237830
177 -6.156654e-03 -0.0355019963
178 1.291197e-04 0.0259225094
179 7.723982e-03 0.0119600885
180 2.952351e-04 -0.0152999892
181 2.366808e-04 -0.0122095048
182 2.076462e-04 -0.0001917117
183 1.917117e-04 -0.0162039464
184 2.110784e-04 0.0535713964
185 1.539030e-04 -0.0188620346
186 7.841969e-05 0.0010503132
187 3.133568e-05 -0.0136614578
188 1.270608e-04 -0.0165115654
189 9.688358e-05 0.0023226192
190 9.665883e-05 0.0052383062
191 3.205231e-05 -0.0141548721
192 8.127108e-05 0.0249463151
193 1.585314e-04 0.0434959064
194 1.821383e-04 -0.0003188219
195 1.518326e-04 -0.0246884369
196 2.023110e-04 0.0262360490
197 2.122305e-04 -0.0003942620
198 2.123142e-04 0.0004245834
199 2.273813e-04 -0.0201528956
200 2.320096e-04 -0.0458005930
201 2.590716e-04 0.0079359095
202 2.248996e-04 -0.0193856392
203 2.293015e-04 -0.0110522023
204 1.987314e-04 0.0077707135
205 1.971933e-04 -0.0195328110
206 2.345923e-04 0.0052150485
207 2.167046e-04 -0.0127680943
208 2.363707e-04 0.0066644474
209 2.180312e-04 -0.0191872308
210 2.393490e-04 0.0126747424
211 2.532137e-04 0.0164598299
212 2.324770e-04 0.0241359061
213 2.593487e-04 0.0132217626
214 2.399482e-04 0.0213523358
215 1.722505e-04 0.0043441641
216 2.494504e-04 0.0374752187
217 1.802127e-04 -0.0285494293
218 2.317837e-04 0.0402110643
219 2.078076e-04 -0.0346015566
220 2.151232e-04 0.0070594834
221 2.136100e-04 -0.0301762391
222 2.044298e-04 0.0077859198
223 2.184462e-04 0.0145314016
224 1.537870e-04 0.0152173697
225 1.514647e-04 -0.0193785840
226 1.698697e-04 0.0256451675
227 -1.806522e-04 0.0210763020
228 -8.843818e-05 0.0383149856
229 -2.837040e-05 0.0177152551
230 -1.254312e-04 -0.0308801034
231 -8.624159e-05 -0.0190227662
232 -8.789792e-05 0.0064394355
ggplot(df, aes(x=df[,1], y=df[,2]))+ geom_point(shape=1) + xlim(-0.015,0.021) + ylim(-0.1,0.1)
现在我想直截了当地截取下面的斜坡和斜坡。也就是说，有19个拦截和19个斜坡，那么我将有19条线：
intercept_slope
intercept slope
tau= 0.05 -0.0382845875 0.89386498
tau= 0.10 -0.0289286773 0.35422172
tau= 0.15 -0.0212578889 -0.08822473
tau= 0.20 -0.0193409306 -0.19879373
tau= 0.25 -0.0163342536 -0.37221704
tau= 0.30 -0.0130620457 -0.20459475
tau= 0.35 -0.0088296144 -0.45186425
tau= 0.40 -0.0058704748 -0.51982456
tau= 0.45 -0.0002945895 -0.46964248
tau= 0.50 0.0018460162 -0.57979144
tau= 0.55 0.0050322871 -0.74374709
tau= 0.60 0.0074404985 -0.70151564
tau= 0.65 0.0106591381 -0.63874335
tau= 0.70 0.0133004843 -0.63371325
tau= 0.75 0.0160149735 -0.89770765
tau= 0.80 0.0194564185 -0.97319036
tau= 0.85 0.0244394584 -1.30573010
tau= 0.90 0.0275024680 -1.26220009
tau= 0.95 0.0438769348 -2.40348955
我正在尝试，但错了：
ggplot(df, aes(x=df[,1], y=df[,2]))+ geom_point(shape=1) + xlim(-0.015,0.021) + ylim(-0.1,0.1)+
for (i in 1:19) {
geom_abline(intercept = intercept_slope[i,1], slope = intercept_slope[i,2])
}
出错了什么？有什么帮助吗？