R 多元非线性回归

我试图通过估计的总成本和工作的持续时间来预测工作的每月成本。 我有一个原始数据，包括工作的开始日期、结束日期、总成本、与工作相关的所有成本以及这些成本的生效日期。 我认为日期没有多大意义，所以我找到了5%时间的数字，然后找到了在时间增量中产生的成本。当我尝试散点图时，我得到了类似于图片所示的东西。我的问题是，如何让逃逸数据点成行堆积？ 当我绘制总成本与每月成本的对比图时，我会遇到同样的问题，因为在一个确切的工作期间，所有付款的总成本都是相同的

---

这是R还是Python？看起来像R，编辑了你的问题，如果相关的话，可能值得添加你用来生成图像的代码。这是R，只是看一些帮助我修复数据的想法。dput（问题）只是为了我可以发布数据。非常感谢。老实说，我不知道这里有什么问题。这一部分不清楚“我如何获得成列堆积的逃逸数据点”。不知道那是什么意思。你在寻找如何使情节抖动吗？一般来说，预测时，取总数减去当前支出，然后根据预期的项目完成情况将剩余的部分分配到剩余的月份，如果你对此没有任何要求，只需将其除以平均的月份数。谢谢你，Reeza，我确实将我的数据分成了5%的时间段，感觉我正在取得进展。

structure(list(`5% Days` = c(58.5500000000029, 58.5500000000029, 
58.5500000000029, 58.5500000000029, 58.5500000000029, 58.5500000000029, 
58.5500000000029, 58.5500000000029, 58.5500000000029, 58.5500000000029, 
58.5500000000029, 58.5500000000029, 58.5500000000029, 58.5500000000029, 
58.5500000000029, 58.5500000000029, 58.5500000000029, 58.5500000000029, 
58.5500000000029, 58.5500000000029, 32.1999999999971, 32.1999999999971, 
32.1999999999971, 32.1999999999971, 32.1999999999971, 32.1999999999971, 
32.1999999999971, 32.1999999999971, 32.1999999999971, 32.1999999999971, 
32.1999999999971, 32.1999999999971, 32.1999999999971, 32.1999999999971, 
32.1999999999971, 32.1999999999971, 32.1999999999971, 32.1999999999971, 
32.1999999999971, 32.1999999999971, 45.4000000000015, 45.4000000000015, 
45.4000000000015, 45.4000000000015, 45.4000000000015, 45.4000000000015, 
45.4000000000015, 45.4000000000015, 45.4000000000015, 45.4000000000015, 
45.4000000000015, 45.4000000000015, 45.4000000000015, 45.4000000000015, 
45.4000000000015, 45.4000000000015, 45.4000000000015, 45.4000000000015, 
45.4000000000015, 45.4000000000015, 51.5500000000029, 51.5500000000029, 
51.5500000000029, 51.5500000000029, 51.5500000000029, 51.5500000000029, 
51.5500000000029, 51.5500000000029, 51.5500000000029, 51.5500000000029, 
51.5500000000029, 51.5500000000029, 51.5500000000029, 51.5500000000029, 
51.5500000000029, 51.5500000000029, 51.5500000000029, 51.5500000000029, 
51.5500000000029, 51.5500000000029, 29.5999999999985, 29.5999999999985, 
29.5999999999985, 29.5999999999985, 29.5999999999985, 29.5999999999985, 
29.5999999999985, 29.5999999999985, 29.5999999999985, 29.5999999999985, 
29.5999999999985, 29.5999999999985, 29.5999999999985, 29.5999999999985, 
29.5999999999985, 29.5999999999985, 29.5999999999985, 29.5999999999985, 
29.5999999999985, 29.5999999999985, 30.6999999999971, 30.6999999999971, 
30.6999999999971, 30.6999999999971, 30.6999999999971, 30.6999999999971, 
30.6999999999971, 30.6999999999971, 30.6999999999971, 30.6999999999971, 
30.6999999999971, 30.6999999999971, 30.6999999999971, 30.6999999999971, 
30.6999999999971, 30.6999999999971, 30.6999999999971, 30.6999999999971, 
30.6999999999971, 30.6999999999971, 42.9499999999971, 42.9499999999971, 
42.9499999999971, 42.9499999999971, 42.9499999999971, 42.9499999999971, 
42.9499999999971, 42.9499999999971, 42.9499999999971, 42.9499999999971, 
42.9499999999971, 42.9499999999971, 42.9499999999971, 42.9499999999971, 
42.9499999999971, 42.9499999999971, 42.9499999999971, 42.9499999999971, 
42.9499999999971, 42.9499999999971), Intervals = c(1, 2, 3, 4, 
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 
2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 
20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 
18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 
16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 
14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 
12, 13, 14, 15, 16, 17, 18, 19, 20, 1, 2, 3, 4, 5, 6, 7, 8, 9, 
10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20), Cost = c(37393.26, 
94402.72, 254472.52, 68352.09, 57305.47, 145057.39, 109348.68, 
117798.31, 562743.58, 1254942.13, 1257165.4, 1608822.4716, 1464465.2218, 
1151072.14, 1260578.39, 727788.660000001, 328083.95, 20033.62, 
-37691.85, -4519.71, 9356, 13592, 637, 48872.73, 13436.04, 5690.07, 
3359.42, 5017.5, 20604.02, 11311.08, 58289.9, 66368.69, 92337.93, 
7531.47, 721.87, 35828.79626, 5409.97339, -1547.71, 89.72, -3124.27, 
13654.84, 18547.55, 37470.14, 53262.8, 119556.16, 19213.39, 42285.05, 
118176.77, 155294.74, 84179.25, 103324.74, 41346.01626, 6567.06064, 
1846.78759, 668.51, -4937.56, 0, 757.36, 0, 3187.4, 522530.62, 
456349.31, 453759.73, 1261379.87, 1750672.05, 662884.16, 522026.4, 
832515.01, 465700.53, 513119.36, 112372.6, 42677.03, 19558.67, 
-11399.45, -17538.46, -686.83, -103.4, 238.14, 178.49, 146.11, 
360.8, 3777.79474, 3592.31615, 5621.01113, 8845.72825, 23488.33373, 
13649.75, 26835.24, 6962.24, 12252.71, 8114.44, 13961.85, 22113, 
8078.51, 27797.78, 28399.15, 36292.99, 9173.92, 4772.47, 3459.84, 
874.51, 7357.22, 4524.49, 1569.4, 4549.69, 746.22, 1270.88, 15734.31, 
1768, 10088.35, 16825.78, 15214.86, 19643.4, 43737.74, 45669.93, 
17960.44, 363.89, 5251.72, -131123.53, 624, 141061.78, 776803.76, 
14324.23, 15211.05, 30669.85, 125067.3, 363648.07, 192211.84049, 
617111.48037, 404960.99069, 561975.96322, 440356.85, 348916.26, 
185208.47, 137126.1, 46848.08, 17561.12, -15884.29, 9698.93, 
11595.22), `Total Cost` = c(10477614.4434, 10477614.4434, 10477614.4434, 
10477614.4434, 10477614.4434, 10477614.4434, 10477614.4434, 10477614.4434, 
10477614.4434, 10477614.4434, 10477614.4434, 10477614.4434, 10477614.4434, 
10477614.4434, 10477614.4434, 10477614.4434, 10477614.4434, 10477614.4434, 
10477614.4434, 10477614.4434, 392916.679650001, 392916.679650001, 
392916.679650001, 392916.679650001, 392916.679650001, 392916.679650001, 
392916.679650001, 392916.679650001, 392916.679650001, 392916.679650001, 
392916.679650001, 392916.679650001, 392916.679650001, 392916.679650001, 
392916.679650001, 392916.679650001, 392916.679650001, 392916.679650001, 
392916.679650001, 392916.679650001, 814401.00449, 814401.00449, 
814401.00449, 814401.00449, 814401.00449, 814401.00449, 814401.00449, 
814401.00449, 814401.00449, 814401.00449, 814401.00449, 814401.00449, 
814401.00449, 814401.00449, 814401.00449, 814401.00449, 814401.00449, 
814401.00449, 814401.00449, 814401.00449, 7586379.94, 267549.874, 
86735.7000000001, 4426382.97477, 305531.76, 1623521.98576, 2023575.50399, 
878403.537809998, 272291.81984, 57808.97944, 502983.580000001, 
10632667.0823001, 884170.511820001, 70206.80899, 4491048.47898997, 
284114.110000001, 44222.37, 1948932.00513, 299710.95, 722706.59595, 
267549.874, 86735.7000000001, 4426382.97477, 305531.76, 1623521.98576, 
2023575.50399, 878403.537809998, 272291.81984, 57808.97944, 502983.580000001, 
10632667.0823001, 884170.511820001, 70206.80899, 4491048.47898997, 
284114.110000001, 44222.37, 1948932.00513, 299710.95, 722706.59595, 
3257899.22349, 86735.7000000001, 86735.7000000001, 86735.7000000001, 
86735.7000000001, 86735.7000000001, 86735.7000000001, 86735.7000000001, 
86735.7000000001, 86735.7000000001, 86735.7000000001, 86735.7000000001, 
86735.7000000001, 86735.7000000001, 86735.7000000001, 86735.7000000001, 
86735.7000000001, 86735.7000000001, 86735.7000000001, 86735.7000000001, 
86735.7000000001, 4426382.97477, 4426382.97477, 4426382.97477, 
4426382.97477, 4426382.97477, 4426382.97477, 4426382.97477, 4426382.97477, 
4426382.97477, 4426382.97477, 4426382.97477, 4426382.97477, 4426382.97477, 
4426382.97477, 4426382.97477, 4426382.97477, 4426382.97477, 4426382.97477, 
4426382.97477, 4426382.97477)), row.names = c(NA, -140L), class = c("tbl_df", 
"tbl", "data.frame"))