ggplot2-在两个变量的多面图中的点周围添加误差线？
[英]ggplot2 - adding error bars around points in muti-facet plot of two variables?
问题描述
I have an R ggplot2 plot like so: 
我有一个R ggplot2图，如下所示：
(It is the same plot that my other question is also based on) （这是我的另一个问题也基于的情节）
As you can see, this plot has facets and plots points representing two variables (as seen in the legend). 如您所见，此图具有多个小平面并绘制了代表两个变量的点（如在图例中所示）。
Here is my ggplot call to make the plot: 这是我的ggplot调用以进行绘图：
ggplot(data = my_data) +
geom_point(mapping = aes(x = frame_size, y = psi_hat_mean, colour = "psi_hat")) +
geom_point(mapping = aes(x = frame_size, y = p_hat_mean, colour = "p_hat")) +
facet_grid(facets = psi ~ p, labeller = label_both) +
scale_x_continuous("Frame size") +
scale_y_continuous("Parameter estimates") +
scale_colour_manual("Estimated parameter",
values = c("psi_hat" = "grey", "p_hat" = "black"),
breaks = c("p_hat", "psi_hat"),
labels = c(expression(paste(hat(p))),
expression(paste(hat(psi))))) +
theme(legend.title=element_blank())
In my my_data data frame, I have additional columns representing the standard deviations around the points already plotted, but I don't know the correct/canonical way to plot those standard deviations as error bars around the points. 在my_data数据框中，我还有其他列表示已绘制点周围的标准偏差，但我不知道将这些标准偏差绘制为围绕点的误差线的正确/规范方法。 I tried geom_errorbar and geom_pointrange to no avail. 我尝试了geom_errorbar和geom_pointrange无济于事。
Here is the glimpse() of my_data : 这是my_data的glimpse() ：
Observations: 99
Variables: 7
$ frame_size <int> 1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30, 1, 2, 3, 4, 5, 6, 10, 12, 15, 20, 30, 1, 2, 3, 4, ...
$ psi <dbl> 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1...
$ p <dbl> 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.10, 0.10, 0.10, 0.10...
$ psi_hat_mean <dbl> 0.13435605, 0.13448677, 0.13873620, 0.13880599, 0.13870936, 0.13865080, 0.14379677, 0.13...
$ psi_hat_SD <dbl> 1.535874e-01, 1.530718e-01, 1.643022e-01, 1.645519e-01, 1.646508e-01, 1.641912e-01, 1.74...
$ p_hat_mean <dbl> 0.04938056, 0.09689240, 0.14150134, 0.18462472, 0.22528098, 0.26249276, 0.39490852, 0.45...
$ p_hat_SD <dbl> 2.067107e-02, 4.059717e-02, 5.951006e-02, 7.724016e-02, 9.234872e-02, 1.065262e-01, 1.59...
I want to use psi_hat_SD and p_hat_SD which are the standard deviations to construct the error bars so that in the end the points would look something like this: 我想使用psi_hat_SD和p_hat_SD ，它们是构建误差线的标准偏差，因此最终这些点看起来像这样：
How do I do this correctly? 如何正确执行此操作？ Thank you! 谢谢！
UPDATE : Here is how I tried to do the above with geom_errorbar : 更新 ：这是我尝试使用geom_errorbar进行上述geom_errorbar ：
# doesn't work
ggplot(data = my_data) +
geom_point(mapping = aes(x = frame_size, y = psi_hat_mean, colour = "psi_hat")) +
geom_errorbar(aes(ymin = psi_hat_mean - psi_hat_SD,
ymax = psi_hat_mean + psi_hat_SD)) +
geom_point(mapping = aes(x = frame_size, y = p_hat_mean, colour = "p_hat")) +
facet_grid(facets = psi ~ p, labeller = label_both) +
scale_x_continuous("Frame size") +
scale_y_continuous("Parameter estimates") +
scale_colour_manual("Estimated parameter",
values = c("psi_hat" = "grey", "p_hat" = "black"),
breaks = c("p_hat", "psi_hat"),
labels = c(expression(paste(hat(p))),
expression(paste(hat(psi))))) +
theme(legend.title=element_blank())
It gives me this error: 它给了我这个错误：
Error in eval(substitute(list(...)), `_data`, parent.frame()) : object 'x' not found In addition: Warning messages: 1: In min(x, na.rm
= na.rm) : no non-missing arguments to min; returning Inf 2: In max(x, na.rm = na.rm) : no non-missing arguments to max; returning
-Inf 3: In min(diff(sort(x))) : no non-missing arguments to min; returning Inf
If I replace geom_errorbar with geom_pointrange I get this error: 如果将geom_errorbar替换为geom_errorbar ， geom_pointrange此错误：
Error: geom_pointrange requires the following missing aesthetics: x, y
Here is the actual dput() output of the data frame: 这是数据帧的实际dput()输出：
structure(list(frame_size = c(1L, 2L, 3L, 4L, 5L, 6L, 10L, 12L,
15L, 20L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 10L, 12L, 15L, 20L, 30L,
1L, 2L, 3L, 4L, 5L, 6L, 10L, 12L, 15L, 20L, 30L, 1L, 2L, 3L,
4L, 5L, 6L, 10L, 12L, 15L, 20L, 30L, 1L, 2L, 3L, 4L, 5L, 6L,
10L, 12L, 15L, 20L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 10L, 12L, 15L,
20L, 30L, 1L, 2L, 3L, 4L, 5L, 6L, 10L, 12L, 15L, 20L, 30L, 1L,
2L, 3L, 4L, 5L, 6L, 10L, 12L, 15L, 20L, 30L, 1L, 2L, 3L, 4L,
5L, 6L, 10L, 12L, 15L, 20L, 30L), psi = c(0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,
0.4, 0.4, 0.4, 0.4), p = c(0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2,
0.2, 0.2, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05,
0.05, 0.05, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1,
0.1, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.05,
0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05, 0.1,
0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.2, 0.2, 0.2,
0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2, 0.2), psi_hat_mean = c(0.134356048382177,
0.134486773572389, 0.13873620166692, 0.138805987584267, 0.13870935995785,
0.138650800195541, 0.143796766183063, 0.13916560213237, 0.143315740756538,
0.169921216208113, 0.197659424623347, 0.100613069549282, 0.100635588260763,
0.100597940900849, 0.100694725369573, 0.100675516255325, 0.100745841907948,
0.100815988120923, 0.100979695039558, 0.101142533641973, 0.101443977996904,
0.115373528295039, 0.0999755153064313, 0.0999979752698177, 0.0998962157963402,
0.100005898203748, 0.100003047766876, 0.100019378162055, 0.100011289299875,
0.100012663699438, 0.100017130290604, 0.100019688944185, 0.100002352877339,
0.250280871706711, 0.20814054675288, 0.207769046277594, 0.208090970914499,
0.208532916287244, 0.208859991544016, 0.209252480710842, 0.210811715962647,
0.211537486757659, 0.212108292997122, 0.221164186075537, 0.200847254811565,
0.200882290085097, 0.200895194559799, 0.200963134246294, 0.200935377663199,
0.200986349050379, 0.201026021866445, 0.20133815021031, 0.201285819726138,
0.201771848718818, 0.202132030705017, 0.200000863248174, 0.200013219984772,
0.20001223270007, 0.199994520753463, 0.200008287620153, 0.200004330046502,
0.199998072041364, 0.20000498181904, 0.199884681785292, 0.199925907752792,
0.200030218291904, 0.46909003266665, 0.404543079367836, 0.405542037009819,
0.405473359663344, 0.406450221494248, 0.407016892401095, 0.407804741068077,
0.408998802872545, 0.409059879710074, 0.412281980391505, 0.417686374689605,
0.40047111166792, 0.400465155003593, 0.4004738934807, 0.400494312554866,
0.400470923112942, 0.400528527625076, 0.400573788345039, 0.400647311390814,
0.400818156817758, 0.400819981298249, 0.401099396871977, 0.399988878019095,
0.400032636565743, 0.400001592936094, 0.400001283207262, 0.40000165174745,
0.399995790514786, 0.400004582819455, 0.400021723478689, 0.400009633788358,
0.400003950520113, 0.400026826367252), psi_hat_SD = c(0.153587439714877,
0.153071750905185, 0.16430223183556, 0.164551851214726, 0.164650834475015,
0.164191178124105, 0.174995748335211, 0.163948969973203, 0.174791156358891,
0.227232415724057, 0.272815709195867, 0.00416873072294715, 0.00418449905149223,
0.00408281532783013, 0.00413066011578574, 0.00411030244674003,
0.00415794082443098, 0.00428705188869137, 0.00447385303686605,
0.00512309080419838, 0.00538878394819733, 0.109059513881393,
8.10448978287042e-05, 3.25412047710771e-05, 0.000229019038524637,
1.32554791740984e-05, 3.28644816689232e-05, 2.89433268844505e-05,
2.95023799151163e-05, 4.38564452461257e-05, 6.39815108451877e-05,
9.1881375010522e-05, 4.78209458970029e-13, 0.183002870467444,
0.0293818799338775, 0.0288402656743286, 0.0288165177620824, 0.0297634852739387,
0.0299527800584211, 0.0347846993392747, 0.0372990730196757, 0.035669047956326,
0.0368368101376486, 0.0705751689361306, 0.00203121177367882,
0.00192575329570197, 0.00184269195613544, 0.00240720598274821,
0.00214770293122925, 0.00226807525752776, 0.00188169304328542,
0.00340586369727475, 0.00345333835319253, 0.00394664917443553,
0.00872196151289955, 8.6094454437387e-06, 1.71106169362863e-05,
3.23287049318278e-05, 3.53049297358841e-05, 2.1492246131839e-05,
6.2288439810166e-06, 1.44667033948848e-05, 1.2515352244187e-05,
0.00012029349894011, 0.000171331269685535, 0.000293960412588456,
0.195750328230943, 0.0432980747152544, 0.0448046192056449, 0.0440846647061932,
0.0485042955171366, 0.0482979217319834, 0.0522379989209262, 0.0579890416481406,
0.0531009498688177, 0.0630117285310315, 0.0713821778815143, 0.00623289291854068,
0.006221899243366, 0.00624538209117939, 0.00628328531688065,
0.00627909851780257, 0.00634871536458593, 0.00637069626931915,
0.00644027718251307, 0.00670664538203358, 0.00674945522402437,
0.00672507992540975, 1.28897443961884e-05, 4.4027910050059e-05,
2.80088266440853e-06, 4.4937052942413e-06, 3.89369731696281e-06,
1.90379259756568e-05, 1.15262840907433e-05, 3.12553218039823e-05,
2.25050991263921e-05, 5.46276800711688e-05, 0.00028230060200018
), p_hat_mean = c(0.0493805572043857, 0.0968923979425734, 0.141501343959094,
0.184624723464391, 0.225280979633726, 0.262492758502991, 0.39490851538299,
0.45154142965489, 0.525853334795089, 0.622498582290085, 0.744750596464598,
0.0951102054450773, 0.181157836797368, 0.258547631613774, 0.329852164417611,
0.392734430296655, 0.452845308468353, 0.634656661079906, 0.700411111167911,
0.775401595177942, 0.852473557888133, 0.927365458017404, 0.201075086977168,
0.361245627511475, 0.490362348231505, 0.593823984188126, 0.675404456152514,
0.741475393843094, 0.896180595647868, 0.930911156580922, 0.969228130789834,
0.993980061494683, 0.999933401786537, 0.0476141340798173, 0.0949703678299418,
0.139111305167333, 0.179581265249894, 0.218955133012378, 0.255643703659721,
0.390317309763656, 0.442577946011462, 0.517324051736444, 0.616089655559368,
0.742460394002412, 0.0991474592154088, 0.188345493237174, 0.268690852765111,
0.340630181377103, 0.404700993857244, 0.466171060122403, 0.646753481555216,
0.710048127892561, 0.790236156784217, 0.86402129317977, 0.953873990924048,
0.202356758942191, 0.363873473395577, 0.493936650336295, 0.597901898041952,
0.680296139765208, 0.747366703193019, 0.903322005155783, 0.939981567846486,
0.966801836293303, 0.988586306636356, 0.999243451951223, 0.0463364925613695,
0.096186827643031, 0.13997233747503, 0.181749771931483, 0.222523649736396,
0.259499892174649, 0.394045900835822, 0.45083186052406, 0.527845241576527,
0.625777508556727, 0.765427947503516, 0.0993534376856661, 0.189237207621871,
0.269758186956382, 0.342693314139248, 0.409295462208863, 0.467030278808092,
0.651622635973668, 0.718795315510802, 0.793143513356861, 0.879655463405415,
0.957032974716723, 0.201367021664948, 0.362466628523926, 0.490623284961033,
0.595997765341907, 0.676872347808209, 0.73980714214723, 0.892075883852205,
0.930114063779856, 0.963579844299096, 0.985988067825672, 0.99830794696651
), p_hat_SD = c(0.020671073048708, 0.0405971695814201, 0.0595100648300367,
0.077240162880486, 0.0923487236898059, 0.106526196932577, 0.159729823310882,
0.176228877737896, 0.200751656004054, 0.254670828390244, 0.287381559859864,
0.0253741193781226, 0.0471638768455461, 0.0655493558111445, 0.0839136353773245,
0.100142912148987, 0.110911181108964, 0.140636755877925, 0.15125797466698,
0.1555013613506, 0.149459455618193, 0.166850371924508, 0.0344233871624597,
0.0575241859626709, 0.0765279598077096, 0.088251660637436, 0.0907468071979005,
0.09555580032782, 0.0910651633810324, 0.0787075972576783, 0.0610760339901177,
0.0314642360500455, 1.07277201318732e-11, 0.0174068050890887,
0.0296542146793441, 0.0420243108930666, 0.0538557925028335, 0.0650751964538859,
0.0748944574050911, 0.107149795097664, 0.126045003227699, 0.145148214248163,
0.161268452161263, 0.192544810714351, 0.0189081918124477, 0.0358817044204188,
0.0483995702453099, 0.0615944579033814, 0.0706741412337433, 0.0806577966830984,
0.101766144853803, 0.111253971769697, 0.108010267370669, 0.109406145445318,
0.0844663953398124, 0.0237717398485985, 0.0415592337691319, 0.0508739981443487,
0.055065018088554, 0.0607402227151579, 0.0644644110987824, 0.0599412871159953,
0.0537879467634287, 0.0473614192478991, 0.0335711724058262, 0.0100985164006129,
0.0148757945028784, 0.0223727872205404, 0.0328124061087913, 0.0416862513830743,
0.051522313637991, 0.0599641576237172, 0.0870524394338348, 0.100814653541668,
0.115655991730271, 0.131950868996117, 0.159736280003032, 0.0152671641785822,
0.0282221543538266, 0.0388769807365557, 0.0478383382638392, 0.0552111224929799,
0.0611073915392696, 0.0758076715431021, 0.0818244295408088, 0.0820682293454554,
0.0729034999789475, 0.0524286593814453, 0.0175508745752872, 0.0293973130349527,
0.0405068273531901, 0.0454857976255542, 0.0469496795934104, 0.0509564122496874,
0.0469363426205197, 0.0448122577291855, 0.0334480891840883, 0.0234468477023789,
0.0104300159826976)), class = c("tbl_df", "tbl", "data.frame"
), row.names = c(NA, -99L))
1 个解决方案
解决方案1
2 已采纳 2018-08-15 15:42:55
It is the first part of the error message that tells you what went wrong (but the additional warning messages were definitely distracting :-) ). 它是错误消息的第一部分，告诉您出了什么问题（但其他警告消息肯定会分散注意力:-)）。
Error in eval(substitute(list(...)),
_data, parent.frame()) :_data，parent.frame（））：
object 'x' not found
You didn't define the x axis in geom_errorbar() , so ggplot doesn't know where to put the bars along the x axis. 您没有在geom_errorbar()定义x轴，因此ggplot不知道沿x轴放置条形的位置。
You can add x specifically to this layer to plot the error bars: 您可以将x专门添加到此图层以绘制误差线：
geom_errorbar(aes(x = frame_size,
ymin = psi_hat_mean - psi_hat_SD,
ymax = psi_hat_mean + psi_hat_SD))
Another alternative is to map x globally by doing the mapping within ggplot() . 另一种选择是通过在ggplot()进行映射来全局映射x 。 This is useful in your case because you use the same x throughout the plot. 这在您的情况下很有用，因为在整个图中使用相同的x 。 You can then remove x from all geom layers, since anything set in ggplot() affects all the layers of the plot. 然后，您可以从所有geom图层中删除x ，因为ggplot()设置的任何内容ggplot()影响绘图的所有图层。
That would look like: 看起来像：
ggplot(data = my_data, aes(x = frame_size)) +
geom_point(mapping = aes(y = psi_hat_mean, colour = "psi_hat")) +
geom_errorbar(aes(ymin = psi_hat_mean - psi_hat_SD,
ymax = psi_hat_mean + psi_hat_SD)) +
geom_point(mapping = aes(y = p_hat_mean, colour = "p_hat")) +
facet_grid(facets = psi ~ p, labeller = label_both) +
scale_x_continuous("Frame size") +
scale_y_continuous("Parameter estimates") +
scale_colour_manual("Estimated parameter",
values = c("psi_hat" = "grey", "p_hat" = "black"),
breaks = c("p_hat", "psi_hat"),
labels = c(expression(paste(hat(p))),
expression(paste(hat(psi))))) +
theme(legend.title=element_blank())
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在ggplot中的散点图上向点添加双向误差线 - Adding bidirectional error bars to points on scatter plot in ggplot
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在 ggplot2 中将带点的线添加到 plot - Adding line with points to a plot in ggplot2
添加标签以分散 ggplot2 中的 plot 点 - Adding labels to scatter plot points in ggplot2
使用ggplot2向绘图添加点 - Adding points to plot using ggplot2
如何使用 ggplot2 中的折线图和误差条针对一个因子绘制多个连续变量？ - How to plot several continuous variables against a factor with a line plot and error bars in ggplot2?
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