将geom_smooth中的nls函数与ggplot2结合使用 [英] Combine nls function in geom_smooth with ggplot2
本文介绍了将geom_smooth中的nls函数与ggplot2结合使用的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
我有以下问题！
我用ggplot2创建了一个使用这个代码的图：
bi28 < - ggplot（Loc28，aes（patch， biovmean，shape = group，color = group，group = id））+
geom_errorbar（aes（ymin = biovmean- biovse，ymax = biovmean + biovse），
color =black，width =。 4，position = pd，size = .7）+
geom_point（size = 5，position = pd）+
ylab（paste（Log〜biovolume〜[，μm^ 3〜mL （Patch-Position）+
theme_bw（）+
scale_colour_manual（name =营养素供应，扩散，
标签= c（Grad，High，Grad，Low，Uniform，High，Uniform，Low），
values = c（black，darkgrey，black darkgrey））+
scale_shape_manual（name =Nutrient supply，Dispersal，
labels = c（Grad，High，Grad，Low，Uniform，High，Uniform，低），
值= c（19,19,17,17））+
主题（axis.title.y = element_text（size = 18，face =plain，color =black，
vjust = 0.3），axis.text.y = element_text（size = 12，face =bold，
color =black，angle = 90，hjust = 0.4））+
theme（axis.title.x = element_text（size = 18，face =plain，color =black，
vjust = 0），axis.text.x = element_text（size = 12，face =bold，
color =black））+
theme（legend.key = element_blank（））+主题（legend.position = c（0.6,0.14））+
主题（legend.box =horizontal）+
主题（axis.ticks = element_line（color =black，size = 1 ），
axis.ticks.length =单位（0.3，cm））+
主题（panel.border = element_rect（color =black，size = 1.5））+
theme（panel.grid.major = element_blank（），
panel.grid.minor = element_blank（））+
annotate（text，label =（a），x = 1，y = 18.5，size = 9）
现在我只想添加Grad ，高（黑色圆圈）和Grad，低度（灰色圆圈）非线性曲线，使用geom_smooth中的nls函数...
bi28 + geom_smooth（aes（group = 1），method =nls，
formula = biovmean〜a * patch ^ 2 + b * patch + c，start = list（a = .1，b = .5，c = .2），se = F，
linetype = 1，color =black）
...但是我收到了以下消息：
警告消息：
1：In min（x）：没有非缺少参数min;返回Inf
2：在max（x）中：没有非缺少参数为max;返回-Inf
以及唯一的曲折曲线。我知道我必须重复geom_smooth（aes（.....））两次才能得到两条曲线。然而，我不知道如何拟合一条曲线在图中。
在此先感谢您的任何建议，
Nils
ps我的数据框是最后一个抽样日期的一个子集（第28天）
structure（list（patch = c（1,1， 1,1,1,2,2,2,2,3,3,3,3，
4,4,4,5,5,5,5），结构=结构（c（1L，1L） ，2L，2L，
1L，1L，2L，2L，1L，1L，2L，2L，1L，1L，2L，2L，1L，1L，2L，2L
），。标签= c （grad，uniform），class =factor），disp = structure（c（1L，
2L，1L，2L，1L，2L，1L，2L，1L，2L，1L，2L ，1L，2L，1L，2L，1L，
2L，1L，2L），标签= c（高，低），等级=因子），days = structure（c ，
4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，4L，
4L，4L，4L） （16.1120455927322，15.3554188684282,17.3165093040388，
17.6218303026139,16.6099317757225,16.1923099447477）= c（7，14，21，28），class =factor），b $ b biovmean = c ，17.6138860131455，
17.929187639503,17.324281439863,17.6978828174639,17.1943857045216，
17.1990430007219,17.7599841920424,17.9527426970286,17.3901664732389，
17.38 85901759597，17.4077117222741，17.9359587186513，17.0632166374749，
17.3234243871302），biovsd = C（0.386139235419791，0.442996309054709，
0.605751887065899，0.443659180068705，0.299207353823959，
0.483598811629063，0.277191716108104，0.407960548494472，
0.684333385005494，0.960623164358474 ，0.245729109016748，
0.320988261651675，0.225089646245845，0.802478513571635，
0.406524651605786，0.445621562118268，0.309621120995582，
0.313963803402196，0.321686079165377，1.3844064397821），biovse = C（0.222937591514292，
0.255764038282747，0.349731015059621，0.256146747041115，
0.172747446273779，0.279205904073823，0.160036711912148，
0.235536132492031，0.39510006404837，0.554616042532155，0.141871767238546，
0.185322659271305，0.129955567851836，0.463311185829474，
0.23470711703682，0.257279728845683，0.178759837553593，0.181267086410056，
0.185725544400685， 0.79928743067605），richm ean = c（4,4.333333333333333，
4,5,4,4,333,3333333333333,4.666666666666667,5,4,5.33333333333333，
4.33333333333333,4.333333333333333,3.666666666666667,4.666666666666667，
4.66666666666667,4.333333333333333,3.333333333333333， 5，
4.33333333333333,4.666666666666667），richsd = c（0,1.15470053837925，
0,0,0.115470053837925,0.577350269189626,0,1,0.577350269189626，
0.577350269189626,0.577350269189626,1.15470053837925,1.52752523165195 ，
0.577350269189626,0.577350269189626,0.577350269189626，
0，0.577350269189626,0.577350269189626），richse = c（0,0.6666666666666667，
0,0,0,0.6666666666666667,0.3333333333333333,0,0.577350269189626，
0.333333333333333，0.333333333333333，0.333333333333333，
0.666666666666667，0.881917103688197，0.333333333333333，
0.333333333333333，0.333333333333333，0，0.333333333333333，
0.333333333333333），evenmean = C（0.756050730666667，0。 681044498666667，
0.596813298666667，0.553620488333333，0.777150884333333，
0.666109811666667，0.512830639333333，0.486772008666667，
0.613568058333333，0.410651973333333，0.642752105，0.500671924333333，
0.678079105666667，0.501918322，0.625104587333333，0.633017123，
0.625349769，0.312000794333333，0.792033323666667，0.663921120333333
）中，evensd = C（0.121297293578432，0.095557876163764，0.220113939363098，
0.163036349226814，0.0834972915863553，0.0784293891088788，
0.12115524832891，0.0364888610238083，0.173556485434428，
0.194946745237399，0.144495991615472 ，0.216288511672041，
0.166484618361109，0.354598600750826，0.181924005800042，
0.175899869477491，0.173365773474122，0.0401721093066354，
0.100316932087287，0.198268629314692），evense = C（0.070031025099481，
0.0551703655263381，0.127082842143674，0.0941290801137949，
0.0482071837739869，0.0452812289143891， 0.0699490152364324，
0.0210668537345185，0.100202883585172，0.112552555840454，
0.0834247996560147，0.124874230436476，0.0961199392267183，
0.204727597597754，0.105033873720709，0.10155583699325，0.100092775983552，
0.023193378122101，0.0579180077448726，0.114470446506696），
rueNmean = C（16.5572610572757，15.708421773738，19.5444508541557，
19.8497718526503，17.9816839091709，17.6482664544002，19.8418275631974，
20.157129189519，19.0790959759891，19.6778832709719，19.4223272545649，
19.4269845507481，19.7917875822563，20.2757124037856，19.618108023301，
19.6165317260058 ，19.3928384979227，20.1569434904781，19.29115818742，
19.5513659372153），rueNsd = C（0.386139235342985，0.442996308940832，
0.605751886963905，0.443659180100931，0.299207353800656，
0.483598811677173，0.277191716150326，0.40796054848411，0.684333384989912，
0.960623164542173， 0.245729108852386，0.320988261 711241，
0.225089646157831，0.802478513467233，0.40652465162401，0.44562156214825，
0.309621121136794，0.313963803485894，0.321686079153626，
1.38440643994791），rueNse = C（0.222937591469949，0.255764038217，
0.349731015000734，0.256146747059721，0.172747446260324，
0.279205904101599，0.160036711936525，0.235536132486048，
0.395100064039374，0.554616042638214，0.141871767143652，
0.185322659305695，0.129955567801021，0.463311185769197，
0.234707117047341，0.257279728862994，0.178759837635122，
0.181267086458379，0.185725544393901，0.799287430771776）
ruePmean = C（21.4280385379173，20.8031653238865，22.2268468768554，
22.5321678753869，22.3060626586716，22.1474816795235，22.5242235859877，
22.8395252122633，22.5865691209464，23.1211788972626，22.1047232773335，
22.109380573508，22.5340390228808，22.6670960859443，22.3005040460323，
22.2989277486916,20.7093329343 06，21.1214532969654，21.9735542101992，
22.2337619600135），ruePsd = C（0.386139235311824，0.442996309033614，
0.605751887006541，0.443659180111885，0.299207353795987，
0.48359881169521，0.277191716106355，0.407960548510935，0.684333384965727，
0.960623164323019，0.245729109018956 ，0.320988261625588，
0.225089646230825，0.802478513511538，0.406524651558781，
0.445621562198412，0.309621121115937，0.313963803594971，
0.321686079221144，1.38440643963915），ruePse = C（0.222937591451958，
0.255764038270568，0.34973101502535，0.256146747066045，0.172747446257629，
0.279205904112013，0.160036711911138，0.235536132501536，
0.39510006402541，0.554616042511685，0.141871767239821，0.185322659256243，
0.129955567843164，0.463311185794777，0.234707117009681，
0.257279728891954，0.17875983762308，0.181267086521355，0.185725544432883，
0.799287430593515） ，id = 1:20，group = c（gradhigh， gradlow，
uniformhigh，uniformlow，gradhigh，gradlow，uniformhigh，
uniformlow，gradhigh，gradlow，uniformhigh，uniformlowlow ，
gradhigh，gradlow，uniformhigh，uniformlow，gradhigh，
gradlow，uniformhigh，uniformlow）），.Names = c（补丁，
treat，disp，days，biovmean，biovsd，biovse，richmean，
richsd，richse ，evensd，evense，rueNmean，
rueNsd，rueNse，ruePmean，ruePsd，ruePse，id，group
）， row.names = c（3L，7L，11L，15L，19L，23L，27L，31L，35L，39L，
43L，47L，51L，55L，59L，63L，67L，71L，75L，79L） ，class =data.frame）
解决方案
geom_smooth 的公式，您需要引用美学 x 和 y ，而不是映射到它们的变量。因此，公式应该是'y〜a * x ^ 2 + b * x + c'（它可以帮助将公式放在引号中）
如果您想通过group变量进行分组，然后只绘制一部分数据（即 group ='gradhigh'或 group ='uniformhigh'），您可以使用子集参数（并且 group = group`）
例如
library（plyr）＃for`.`函数
bi28 + geom_smooth（aes（group = group），method =nls，formula ='y〜a * x ^ 2 + b * x + c'，
start = list （a = .1，b = .5，c = .2），se = FALSE，linetype = 1，color =black，
subset =。（％c（'gradhigh'，' ））
这会给出一个类似于
（定义 pd ='dodge'，因为这不是问题）
你似乎适合二次多项式。你可以使用一个线性模型来拟合相同的模型，其中x =
bi28 + geom_smooth（aes（group = group））上的多项式。 method =lm，formula ='y〜poly（x，2）'，
se = FALSE，linetype = 1，color =black，
subset =。 （'gradhigh'，'uniformhigh'）））
I have following problem! I create a figure with ggplot2 using this code:
bi28 <- ggplot(Loc28, aes(patch, biovmean, shape=group,colour=group,group=id)) +
geom_errorbar(aes(ymin=biovmean- biovse,ymax=biovmean+biovse),
colour="black",width=.4,position=pd,size=.7) +
geom_point(size=5,position=pd) +
ylab(expression(paste(Log~biovolume~"[",µm^3~mL^-1,"]"))) +
xlab("Patch-Position") +
theme_bw() +
scale_colour_manual(name="Nutrient supply, Dispersal",
labels=c("Grad, High","Grad, Low", "Uniform, High", "Uniform, Low"),
values=c("black","darkgrey","black","darkgrey")) +
scale_shape_manual(name="Nutrient supply, Dispersal",
labels=c("Grad, High","Grad, Low", "Uniform, High", "Uniform, Low"),
values=c(19,19,17,17)) +
theme(axis.title.y=element_text(size=18, face="plain", colour="black",
vjust=0.3),axis.text.y=element_text(size=12,face="bold",
colour="black",angle=90,hjust=0.4)) +
theme(axis.title.x=element_text(size=18,face="plain",colour="black",
vjust=0),axis.text.x=element_text(size=12,face="bold",
colour="black")) +
theme(legend.key=element_blank())+theme(legend.position=c(0.6,0.14)) +
theme(legend.box="horizontal") +
theme(axis.ticks=element_line(colour="black",size=1),
axis.ticks.length=unit(0.3,"cm")) +
theme(panel.border=element_rect(colour="black",size=1.5)) +
theme(panel.grid.major=element_blank(),
panel.grid.minor=element_blank()) +
annotate("text",label="(a)",x=1,y=18.5,size=9)
Now I would like to add only for Grad, high (black circle) and for Grad, low (grey circle) a non-linear curve using the nls function in geom_smooth...
bi28 + geom_smooth(aes(group=1), method="nls",
formula=biovmean~a*patch^2+b*patch+c,start=list(a=.1,b=.5,c=.2),se=F,
linetype=1, colour="black")
...but I got this message:
Warning messages:
1: In min(x) : no non-missing arguments to min; returning Inf
2: In max(x) : no non-missing arguments to max; returning -Inf
and the only a zigzag curve. I know that I have to repeat geom_smooth(aes(.....)) twice to get both curves.
However, I have no idea how to fit a curve in the figure.
Thanks in advance for any suggestions,
Nils
p.s. My dataframe, a subset of the last sampling date (day 28)
structure(list(patch = c(1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3,
4, 4, 4, 4, 5, 5, 5, 5), treat = structure(c(1L, 1L, 2L, 2L,
1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L, 1L, 1L, 2L, 2L
), .Label = c("grad", "uniform"), class = "factor"), disp = structure(c(1L,
2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L,
2L, 1L, 2L), .Label = c("high", "low"), class = "factor"), days = structure(c(4L,
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L), .Label = c("7", "14", "21", "28"), class = "factor"),
biovmean = c(16.1120455927322, 15.3554188684282, 17.3165093040388,
17.6218303026139, 16.6099317757225, 16.1923099447477, 17.6138860131455,
17.929187639503, 17.324281439863, 17.6978828174639, 17.1943857045216,
17.1990430007219, 17.7599841920424, 17.9527426970286, 17.3901664732389,
17.3885901759597, 17.4077117222741, 17.9359587186513, 17.0632166374749,
17.3234243871302), biovsd = c(0.386139235419791, 0.442996309054709,
0.605751887065899, 0.443659180068705, 0.299207353823959,
0.483598811629063, 0.277191716108104, 0.407960548494472,
0.684333385005494, 0.960623164358474, 0.245729109016748,
0.320988261651675, 0.225089646245845, 0.802478513571635,
0.406524651605786, 0.445621562118268, 0.309621120995582,
0.313963803402196, 0.321686079165377, 1.3844064397821), biovse = c(0.222937591514292,
0.255764038282747, 0.349731015059621, 0.256146747041115,
0.172747446273779, 0.279205904073823, 0.160036711912148,
0.235536132492031, 0.39510006404837, 0.554616042532155, 0.141871767238546,
0.185322659271305, 0.129955567851836, 0.463311185829474,
0.23470711703682, 0.257279728845683, 0.178759837553593, 0.181267086410056,
0.185725544400685, 0.79928743067605), richmean = c(4, 4.33333333333333,
4, 5, 4, 4.33333333333333, 4.66666666666667, 5, 4, 5.33333333333333,
4.33333333333333, 4.33333333333333, 3.66666666666667, 4.66666666666667,
4.66666666666667, 4.33333333333333, 3.33333333333333, 5,
4.33333333333333, 4.66666666666667), richsd = c(0, 1.15470053837925,
0, 0, 0, 1.15470053837925, 0.577350269189626, 0, 1, 0.577350269189626,
0.577350269189626, 0.577350269189626, 1.15470053837925, 1.52752523165195,
0.577350269189626, 0.577350269189626, 0.577350269189626,
0, 0.577350269189626, 0.577350269189626), richse = c(0, 0.666666666666667,
0, 0, 0, 0.666666666666667, 0.333333333333333, 0, 0.577350269189626,
0.333333333333333, 0.333333333333333, 0.333333333333333,
0.666666666666667, 0.881917103688197, 0.333333333333333,
0.333333333333333, 0.333333333333333, 0, 0.333333333333333,
0.333333333333333), evenmean = c(0.756050730666667, 0.681044498666667,
0.596813298666667, 0.553620488333333, 0.777150884333333,
0.666109811666667, 0.512830639333333, 0.486772008666667,
0.613568058333333, 0.410651973333333, 0.642752105, 0.500671924333333,
0.678079105666667, 0.501918322, 0.625104587333333, 0.633017123,
0.625349769, 0.312000794333333, 0.792033323666667, 0.663921120333333
), evensd = c(0.121297293578432, 0.095557876163764, 0.220113939363098,
0.163036349226814, 0.0834972915863553, 0.0784293891088788,
0.12115524832891, 0.0364888610238083, 0.173556485434428,
0.194946745237399, 0.144495991615472, 0.216288511672041,
0.166484618361109, 0.354598600750826, 0.181924005800042,
0.175899869477491, 0.173365773474122, 0.0401721093066354,
0.100316932087287, 0.198268629314692), evense = c(0.070031025099481,
0.0551703655263381, 0.127082842143674, 0.0941290801137949,
0.0482071837739869, 0.0452812289143891, 0.0699490152364324,
0.0210668537345185, 0.100202883585172, 0.112552555840454,
0.0834247996560147, 0.124874230436476, 0.0961199392267183,
0.204727597597754, 0.105033873720709, 0.10155583699325, 0.100092775983552,
0.023193378122101, 0.0579180077448726, 0.114470446506696),
rueNmean = c(16.5572610572757, 15.708421773738, 19.5444508541557,
19.8497718526503, 17.9816839091709, 17.6482664544002, 19.8418275631974,
20.157129189519, 19.0790959759891, 19.6778832709719, 19.4223272545649,
19.4269845507481, 19.7917875822563, 20.2757124037856, 19.618108023301,
19.6165317260058, 19.3928384979227, 20.1569434904781, 19.29115818742,
19.5513659372153), rueNsd = c(0.386139235342985, 0.442996308940832,
0.605751886963905, 0.443659180100931, 0.299207353800656,
0.483598811677173, 0.277191716150326, 0.40796054848411, 0.684333384989912,
0.960623164542173, 0.245729108852386, 0.320988261711241,
0.225089646157831, 0.802478513467233, 0.40652465162401, 0.44562156214825,
0.309621121136794, 0.313963803485894, 0.321686079153626,
1.38440643994791), rueNse = c(0.222937591469949, 0.255764038217,
0.349731015000734, 0.256146747059721, 0.172747446260324,
0.279205904101599, 0.160036711936525, 0.235536132486048,
0.395100064039374, 0.554616042638214, 0.141871767143652,
0.185322659305695, 0.129955567801021, 0.463311185769197,
0.234707117047341, 0.257279728862994, 0.178759837635122,
0.181267086458379, 0.185725544393901, 0.799287430771776),
ruePmean = c(21.4280385379173, 20.8031653238865, 22.2268468768554,
22.5321678753869, 22.3060626586716, 22.1474816795235, 22.5242235859877,
22.8395252122633, 22.5865691209464, 23.1211788972626, 22.1047232773335,
22.109380573508, 22.5340390228808, 22.6670960859443, 22.3005040460323,
22.2989277486916, 20.709332934306, 21.1214532969654, 21.9735542101992,
22.2337619600135), ruePsd = c(0.386139235311824, 0.442996309033614,
0.605751887006541, 0.443659180111885, 0.299207353795987,
0.48359881169521, 0.277191716106355, 0.407960548510935, 0.684333384965727,
0.960623164323019, 0.245729109018956, 0.320988261625588,
0.225089646230825, 0.802478513511538, 0.406524651558781,
0.445621562198412, 0.309621121115937, 0.313963803594971,
0.321686079221144, 1.38440643963915), ruePse = c(0.222937591451958,
0.255764038270568, 0.34973101502535, 0.256146747066045, 0.172747446257629,
0.279205904112013, 0.160036711911138, 0.235536132501536,
0.39510006402541, 0.554616042511685, 0.141871767239821, 0.185322659256243,
0.129955567843164, 0.463311185794777, 0.234707117009681,
0.257279728891954, 0.17875983762308, 0.181267086521355, 0.185725544432883,
0.799287430593515), id = 1:20, group = c("gradhigh", "gradlow",
"uniformhigh", "uniformlow", "gradhigh", "gradlow", "uniformhigh",
"uniformlow", "gradhigh", "gradlow", "uniformhigh", "uniformlow",
"gradhigh", "gradlow", "uniformhigh", "uniformlow", "gradhigh",
"gradlow", "uniformhigh", "uniformlow")), .Names = c("patch",
"treat", "disp", "days", "biovmean", "biovsd", "biovse", "richmean",
"richsd", "richse", "evenmean", "evensd", "evense", "rueNmean",
"rueNsd", "rueNse", "ruePmean", "ruePsd", "ruePse", "id", "group"
), row.names = c(3L, 7L, 11L, 15L, 19L, 23L, 27L, 31L, 35L, 39L,
43L, 47L, 51L, 55L, 59L, 63L, 67L, 71L, 75L, 79L), class = "data.frame")
解决方案
When creating a formula for geom_smooth, you need to reference the aesthetics x and y, not the variables mapped to them. Therefore the formula should be 'y ~ a*x^2 + b*x +c' (it can help to put the formula in quotes as well).
If you want to group by the "group" variable, and then only plot a subset of the data (ie group = 'gradhigh' or group = 'uniformhigh'), you can use the subset argument (andgroup = group`)
eg
library(plyr) # for `.` function
bi28 + geom_smooth(aes(group=group), method = "nls", formula = 'y ~ a*x^2 + b*x +c',
start=list(a=.1,b=.5,c=.2),se = FALSE, linetype = 1, colour = "black",
subset = .(group %in% c('gradhigh', 'uniformhigh')))
which will give a plot something like
(having defined pd= 'dodge' as this was not in the question)
You appear to be fitting a quadratic polynomial. You could fit the same model using a linear model with a polynomial on x
bi28 + geom_smooth(aes(group=group), method = "lm", formula = 'y ~poly(x,2)',
se = FALSE, linetype = 1, colour = "black",
subset = .(group %in% c('gradhigh', 'uniformhigh')))
这篇关于将geom_smooth中的nls函数与ggplot2结合使用的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
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