我正在尝试将指数函数拟合到 R 中的图形
I am trying to fit an exponential function to my graph in R
在 R 中，我有一个包含 1000 个模拟的大型数据框，呈指数分布。
当我使用 gg_plot 时，我得到的图表如下所示：
我正在尝试估计此图的指数函数值，然后使用这些值绘制一条线。
我对堆栈溢出还很陌生。回到图表，我首先将 y 和 x 值（分别为 q 和 t）转换为对数形式并执行线性回归。看起来像这样：
surscript$logq<-log(surscript$q)
surscript$logt<-log(surscript$t)
linearmod<-lm(surscript$logq~surscript$logt)
我的 y 截距为 -14.273，增长率为 1.717。
为了将其转换回指数，我对这些值执行了指数函数。
expmodint<-exp(-14.273)
expmodgrowth<-exp(1.717)
然后我用指数方程y=a*e^t来描述一个q值：
temp<-expmodint*(expmodgrowth^surscript$t)
然后我创建了一个数据框，其中包含这个指数方程和来自原始数据框的 t：
temp1<-data.frame(temp,surscript$t)
然后我尝试使用 geom_line:
将此行添加到我现有的图表中，如上所示
p+geom_line(temp1,aes(x=temp,y=surscript$t))
但是我返回错误 "mapping must be created by "aes"".
任何人都可以建议为什么会出现此错误，以及计算和拟合指数曲线的更好方法吗？
编辑： 这里我包含了两个模拟的数据框，因此，您将能够使用一些数据。
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-0.00200200267067308, -0.00100050033358353, -0.00100050033358353,
-0.00100050033358353, -0.00100050033358353, -0.00100050033358353,
-0.00100050033358353, -0.00100050033358353, -0.00100050033358353,
-0.00100050033358353, -0.00100050033358353, 0), logt = c(3.04452243772342,
3.93182563272433, 4.39444915467244, 4.70953020131233, 4.94875989037817,
5.14166355650266, 5.30330490805908, 5.44241771052179, 5.56452040732269,
5.67332326717149, 5.77144112313002, 5.86078622346587, 5.9427993751267,
6.01859321449623, 6.08904487544685, 6.15485809401642, 6.21660610108486,
6.27476202124194, 6.3297209055227, 6.3818160174061, 6.43133108193348,
6.47850964220857, 6.52356230614951, 6.56667242980324, 6.60800062529609,
6.64768837356333, 6.68586094706836, 6.72262979485545, 6.75809450442773,
6.79234442747081, 6.82546003625531, 6.85751406254539, 6.88857245956536,
6.91869521902047, 6.94793706861497, 6.97634807044775, 7.00397413672268,
7.03085747611612, 7.05703698169789, 7.0825485693553, 7.1074254741107,
7.13169851046691, 7.15539630189673, 7.1785454837637, 7.20117088328168,
7.22329567956231, 7.24494154633701, 7.26612877955645, 7.2868764117507,
7.30720231476474, 7.32712329225929, 7.34665516317654, 7.36581283720947,
7.38461038317697, 7.40306109109009, 7.42117752859539, 7.43897159239586,
7.45645455517621, 7.47363710849621, 7.49052940206071, 7.50714107972761,
7.5234813125735, 7.53955882930103, 7.55538194424027, 7.57095858316901,
7.58629630715272, 7.60140233458373, 7.61628356158038, 7.63094658089046,
7.64539769942863, 7.65964295456468, 7.67368812926773, 7.68753876620163,
7.70120018085745, 7.71467747380093, 7.72797554210556, 7.74109909003537,
7.75405263903576, 7.76684053708551, 7.77946696745832, 7.79193595693806,
7.80425138352811, 7.8164169836918, 7.82843635915759, 7.84031298332016,
7.85205020726589, 7.86365126544865, 7.87511928104029, 7.88645727097769,
7.89766815072691, 7.90875473878325, 7.91971976092457, 7.93056585423396,
7.94129557090653, 7.95191138185419, 7.96241568012106, 7.9728107841214,
7.98309894071089, 7.99328232810159, 8.00336305862995, 8.01334318138667,
8.02322468471667, 8.03300949859667, 8.04269949689764, 8.05229649953865,
8.06180227453835, 8.07121853996986, 8.0805469658245, 8.08978917578932,
8.09894674894334, 8.10802122137675, 8.11701408773731, 8.12592680270789,
8.13476078241865, 8.14351740579748, 8.15219801586179, 8.16080392095467,
8.16933639592839, 8.17779668327778, 8.18618599422608, 8.19450550976564,
8.20275638165564, 8.21093973337902, 8.2190566610606, 8.22710823434815,
8.23509549725836, 8.24301946898925, 8.25088114470065, 8.25868149626424,
8.26642147298455, 8.27410200229233, 8.28172399041139, 8.28928832300032,
8.29679586577005, 8.30424746507847, 8.31164394850298, 1.38629436111989,
3.52636052461616, 4.15888308335967, 4.54329478227, 4.82028156560504,
5.03695260241363, 5.21493575760899, 5.36597601502185, 5.4971682252932,
5.61312810638807, 5.71702770140622, 5.8111409929767, 5.89715386763674,
5.97635090929793, 6.04973345523196, 6.11809719804135, 6.18208490671663,
6.24222326545517, 6.29894924685594, 6.35262939631957, 6.40357419793482,
6.45204895443723, 6.49828214947643, 6.5424719605068, 6.58479139238572,
6.62539236800796, 6.66440902035041, 6.70196036600254, 6.73815249459596,
6.77308037565554, 6.80682936039218, 6.83947643822884, 6.87109129461055,
6.90173720665657, 6.93147180559945, 6.96034772910131, 6.98841318199959,
7.01571242048723, 7.04228617193974, 7.06817200038804, 7.09340462586877,
7.11801620446533, 7.1420365747068, 7.16549347506085, 7.18841273649695,
7.21081845347222, 7.23273313617761, 7.25417784645652, 7.27517231945277,
7.29573507274928, 7.31588350450979, 7.3356339819272, 7.35500192110526,
7.37400185935016, 7.39264752072162, 7.41095187558364, 7.42892719480227,
7.44658509915773, 7.46393660446893, 7.48099216286952, 7.49776170062257,
7.51425465281641, 7.53047999524554, 7.54644627374602, 7.56216163122565,
7.57763383260273, 7.59287028784482, 7.60787807327851, 7.6226639513236,
7.63723438878947, 7.6515955738576, 7.6657534318617, 7.67971363996637,
7.69348164083518, 7.70706265537047, 7.72046169459972, 7.7336835707759,
7.74673290775362, 7.7596141506969, 7.77233157516961, 7.7848892956551,
7.79729127354747, 7.80954132465341, 7.82164312623998, 7.8336002236611,
7.84541603659248, 7.85709386490249, 7.86863689418417, 7.88004820097158,
7.89133075766189, 7.90248743716286, 7.91352101728389, 7.92443418488756,
7.93522953981691, 7.94590959861313, 7.95647679803678, 7.96693349840484,
7.97728198675515, 7.98752447984877, 7.9976631270201, 8.00770001288403,
8.01763715990848, 8.02747653086048, 8.03722003113301, 8.04686951095958,
8.05642676752298, 8.06589354696427, 8.07527154629746, 8.0845624152353,
8.09376775793108, 8.10288913464087, 8.11192806331074, 8.12088602109284,
8.12976444579417, 8.13856473726163, 8.14728825870662, 8.15593633797239,
8.16451026874704, 8.17301131172497, 8.18144069571937, 8.18979961872823,
8.19808924895612, 8.20631072579402, 8.21446516075919, 8.22255363839696,
8.23057721714645, 8.23853693017177, 8.24643378616036, 8.25426877009018,
8.26204284396694, 8.26975694753298, 8.277411998949, 8.28500889544988,
8.29254851397576, 8.30003171177957, 8.30745932701195, 8.31483217928456,
8.3221510702129, 8.32941678393932, 8.33663008763715, 8.34379173199684,
8.35090245169481, 8.35796296584568, 8.36497397843873, 8.3719361787591,
8.37885024179449, 8.38571682862785, 8.39253658681668, 8.39931015075952,
8.40603814205008, 8.41272116981953, 8.41935983106747, 8.42595471098197
)), row.names = c(NA, 289L), class = "data.frame")
情节的定义存在一些问题。
一个是因为组因子没有应用到所有 geom_line() 或被重新定义，它应该从 ggplot 函数移动到特定的 geom_line() 定义。
此外，由于您要将新数据框添加到定义中，因此请显式添加 "data=" 函数。
此外，根据您的模型：lm(surscript$logq~surscript$logt) "temp" 的等式不正确，应该是：q_predicted =exp(-14.273)*(t^1.717)。如果你真的想要 y=b*a^t，那么你的模型应该是：lm(surscript$logq~surscript$t) 和 q_predicted = exp(intercept)*exp(slope)^t.
library(ggplot2)
expmodint<- exp(-14.273)
expmodgrowth<- (1.717)
#q=exp(-14.273)*(t^1.717)
temp<-expmodint*(surscript$t^expmodgrowth)
temp1<-data.frame(temp,surscript$t)
head(temp1) #notice the name change
ggplot(surscript, aes(x=t, y=q)) +
geom_line(aes(group=sim)) +
geom_line(data=temp1, aes(x=surscript.t, y=temp), color="blue")
在 R 中，我有一个包含 1000 个模拟的大型数据框，呈指数分布。
当我使用 gg_plot 时，我得到的图表如下所示：
我正在尝试估计此图的指数函数值，然后使用这些值绘制一条线。
我对堆栈溢出还很陌生。回到图表，我首先将 y 和 x 值（分别为 q 和 t）转换为对数形式并执行线性回归。看起来像这样：
surscript$logq<-log(surscript$q)
surscript$logt<-log(surscript$t)
linearmod<-lm(surscript$logq~surscript$logt)
我的 y 截距为 -14.273，增长率为 1.717。 为了将其转换回指数，我对这些值执行了指数函数。
expmodint<-exp(-14.273)
expmodgrowth<-exp(1.717)
然后我用指数方程y=a*e^t来描述一个q值：
temp<-expmodint*(expmodgrowth^surscript$t)
然后我创建了一个数据框，其中包含这个指数方程和来自原始数据框的 t：
temp1<-data.frame(temp,surscript$t)
然后我尝试使用 geom_line:
将此行添加到我现有的图表中，如上所示p+geom_line(temp1,aes(x=temp,y=surscript$t))
但是我返回错误 "mapping must be created by "aes"".
任何人都可以建议为什么会出现此错误，以及计算和拟合指数曲线的更好方法吗？
编辑： 这里我包含了两个模拟的数据框，因此，您将能够使用一些数据。
structure(list(sim = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("simulation 1",
"simulation 2"), class = "factor"), m = c(0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), q = c(0.001,
0.001, 0.002, 0.002, 0.005, 0.005, 0.006, 0.007, 0.011, 0.016,
0.02, 0.026, 0.033, 0.035, 0.041, 0.045, 0.05, 0.052, 0.054,
0.058, 0.064, 0.066, 0.071, 0.075, 0.08, 0.087, 0.097, 0.111,
0.124, 0.132, 0.143, 0.154, 0.162, 0.176, 0.188, 0.205, 0.216,
0.225, 0.231, 0.241, 0.252, 0.269, 0.287, 0.301, 0.32, 0.345,
0.358, 0.383, 0.406, 0.429, 0.454, 0.479, 0.493, 0.521, 0.541,
0.565, 0.587, 0.606, 0.635, 0.654, 0.677, 0.694, 0.712, 0.729,
0.744, 0.766, 0.783, 0.805, 0.815, 0.824, 0.835, 0.849, 0.859,
0.866, 0.881, 0.888, 0.9, 0.912, 0.92, 0.925, 0.934, 0.941, 0.944,
0.949, 0.954, 0.959, 0.963, 0.965, 0.97, 0.974, 0.977, 0.978,
0.98, 0.981, 0.984, 0.986, 0.987, 0.988, 0.989, 0.989, 0.99,
0.99, 0.99, 0.99, 0.991, 0.992, 0.992, 0.992, 0.992, 0.993, 0.995,
0.996, 0.997, 0.998, 0.998, 0.998, 0.998, 0.998, 0.998, 0.998,
0.998, 0.998, 0.998, 0.998, 0.998, 0.998, 0.998, 0.998, 0.998,
0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 1, 0.001, 0.002, 0.003,
0.003, 0.005, 0.007, 0.011, 0.016, 0.022, 0.026, 0.028, 0.031,
0.036, 0.043, 0.044, 0.051, 0.056, 0.059, 0.064, 0.072, 0.076,
0.083, 0.083, 0.088, 0.096, 0.099, 0.103, 0.111, 0.118, 0.125,
0.129, 0.139, 0.145, 0.148, 0.156, 0.166, 0.175, 0.184, 0.191,
0.197, 0.2, 0.207, 0.211, 0.219, 0.224, 0.238, 0.25, 0.265, 0.273,
0.289, 0.3, 0.313, 0.328, 0.344, 0.354, 0.368, 0.378, 0.387,
0.399, 0.411, 0.424, 0.439, 0.455, 0.468, 0.48, 0.497, 0.503,
0.513, 0.52, 0.53, 0.539, 0.552, 0.56, 0.569, 0.581, 0.588, 0.596,
0.604, 0.618, 0.626, 0.64, 0.654, 0.672, 0.688, 0.705, 0.723,
0.733, 0.747, 0.765, 0.781, 0.795, 0.807, 0.818, 0.825, 0.836,
0.843, 0.854, 0.863, 0.872, 0.875, 0.883, 0.889, 0.901, 0.906,
0.914, 0.92, 0.928, 0.936, 0.943, 0.953, 0.958, 0.96, 0.965,
0.967, 0.968, 0.97, 0.973, 0.975, 0.979, 0.981, 0.982, 0.984,
0.984, 0.988, 0.99, 0.99, 0.992, 0.992, 0.992, 0.994, 0.994,
0.994, 0.995, 0.996, 0.998, 0.998, 0.998, 0.998, 0.998, 0.998,
0.998, 0.998, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999, 0.999,
0.999, 0.999, 0.999, 1), t = c(21, 51, 81, 111, 141, 171, 201,
231, 261, 291, 321, 351, 381, 411, 441, 471, 501, 531, 561, 591,
621, 651, 681, 711, 741, 771, 801, 831, 861, 891, 921, 951, 981,
1011, 1041, 1071, 1101, 1131, 1161, 1191, 1221, 1251, 1281, 1311,
1341, 1371, 1401, 1431, 1461, 1491, 1521, 1551, 1581, 1611, 1641,
1671, 1701, 1731, 1761, 1791, 1821, 1851, 1881, 1911, 1941, 1971,
2001, 2031, 2061, 2091, 2121, 2151, 2181, 2211, 2241, 2271, 2301,
2331, 2361, 2391, 2421, 2451, 2481, 2511, 2541, 2571, 2601, 2631,
2661, 2691, 2721, 2751, 2781, 2811, 2841, 2871, 2901, 2931, 2961,
2991, 3021, 3051, 3081, 3111, 3141, 3171, 3201, 3231, 3261, 3291,
3321, 3351, 3381, 3411, 3441, 3471, 3501, 3531, 3561, 3591, 3621,
3651, 3681, 3711, 3741, 3771, 3801, 3831, 3861, 3891, 3921, 3951,
3981, 4011, 4041, 4071, 4, 34, 64, 94, 124, 154, 184, 214, 244,
274, 304, 334, 364, 394, 424, 454, 484, 514, 544, 574, 604, 634,
664, 694, 724, 754, 784, 814, 844, 874, 904, 934, 964, 994, 1024,
1054, 1084, 1114, 1144, 1174, 1204, 1234, 1264, 1294, 1324, 1354,
1384, 1414, 1444, 1474, 1504, 1534, 1564, 1594, 1624, 1654, 1684,
1714, 1744, 1774, 1804, 1834, 1864, 1894, 1924, 1954, 1984, 2014,
2044, 2074, 2104, 2134, 2164, 2194, 2224, 2254, 2284, 2314, 2344,
2374, 2404, 2434, 2464, 2494, 2524, 2554, 2584, 2614, 2644, 2674,
2704, 2734, 2764, 2794, 2824, 2854, 2884, 2914, 2944, 2974, 3004,
3034, 3064, 3094, 3124, 3154, 3184, 3214, 3244, 3274, 3304, 3334,
3364, 3394, 3424, 3454, 3484, 3514, 3544, 3574, 3604, 3634, 3664,
3694, 3724, 3754, 3784, 3814, 3844, 3874, 3904, 3934, 3964, 3994,
4024, 4054, 4084, 4114, 4144, 4174, 4204, 4234, 4264, 4294, 4324,
4354, 4384, 4414, 4444, 4474, 4504, 4534, 4564), n = c(1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
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)), row.names = c(NA, 289L), class = "data.frame")
情节的定义存在一些问题。
一个是因为组因子没有应用到所有 geom_line() 或被重新定义，它应该从 ggplot 函数移动到特定的 geom_line() 定义。
此外，由于您要将新数据框添加到定义中，因此请显式添加 "data=" 函数。
此外，根据您的模型：lm(surscript$logq~surscript$logt) "temp" 的等式不正确，应该是：q_predicted =exp(-14.273)*(t^1.717)。如果你真的想要 y=b*a^t，那么你的模型应该是：lm(surscript$logq~surscript$t) 和 q_predicted = exp(intercept)*exp(slope)^t.
library(ggplot2)
expmodint<- exp(-14.273)
expmodgrowth<- (1.717)
#q=exp(-14.273)*(t^1.717)
temp<-expmodint*(surscript$t^expmodgrowth)
temp1<-data.frame(temp,surscript$t)
head(temp1) #notice the name change
ggplot(surscript, aes(x=t, y=q)) +
geom_line(aes(group=sim)) +
geom_line(data=temp1, aes(x=surscript.t, y=temp), color="blue")