使用数据框中的标准误差围绕多条线图生成平滑误差条
Produce smooth error bar around multiple line graph using standard error in data frame
使用数据框中可用的标准误差在多线图周围生成平滑的误差条。我已经在数据框中有标准错误，所以我可以使用数据 +/- se.
使用数据框中可用的标准误差在多线图周围生成平滑的误差条。我已经在数据框中有标准错误，所以我可以使用数据 +/- se.
data10 <- structure(list(Group = c("Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered"), Condition = c("CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS"), test = c("Pre-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test"), trial = c(1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16), Variables = c("Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time"), Eye_Mx = c(1.150583333, 1.273916667, 1.213083333,
1.065166667, 1.2373, 1.19925, 0.93675, 0.950833333, 0.616916667,
0.440416667, 0.598083333, 0.618583333, 0.693545455, 0.667583333,
0.873666667, 0.51825, 1.220454545, 1.034583333, 0.874583333,
1.015166667, 0.532222222, 0.714454545, 0.905583333, 0.898333333,
0.641666667, 0.787666667, 0.609833333, 0.623583333, 0.69925,
0.7188, 0.61725, 0.661166667, 1.349, 1.585416667, 1.0145, 1.201090909,
0.810545455, 0.591090909, 1.1416, 0.697166667, 0.431166667, 0.804583333,
0.289666667, 0.63875, 0.46825, 0.633, 0.418833333, 0.691166667,
1.219125, 0.7033, 0.524666667, 0.724818182, 0.648583333, 0.639181818,
0.596583333, 0.509416667, 0.576272727, 0.483222222, 0.388222222,
0.647, 0.42575, 0.269818182, 0.488333333, 0.5903, 1.869083333,
2.066181818, 2.124166667, 2.31525, 2.0943, 1.93625, 1.786916667,
1.922583333, 1.470833333, 1.421454545, 1.519083333, 1.508833333,
1.575909091, 1.5135, 1.8025, 1.541, 1.800454545, 1.888666667,
1.85575, 2.201666667, 1.55725, 1.7781, 1.748, 1.767583333, 1.489333333,
1.4259, 1.436916667, 1.5855, 1.535666667, 1.4013, 1.3855, 1.356666667,
1.852888889, 2.463636364, 2.031, 2.195727273, 1.804454545, 1.709090909,
2.1938, 1.97625, 1.256833333, 1.704363636, 1.418083333, 1.371166667,
1.459166667, 1.46725, 1.183666667, 1.407, 2.348625, 1.8981, 1.973583333,
1.746727273, 1.6805, 1.963, 1.68075, 1.872583333, 1.345636364,
1.339222222, 1.311222222, 1.316833333, 1.215833333, 1.053636364,
1.415916667, 1.2292), sd = c(0.948671172, 0.678775831, 0.820965004,
0.771358286, 1.11350558, 0.598444974, 0.794668727, 0.824723627,
0.481933503, 0.314103185, 0.469586754, 0.576648697, 0.629203681,
0.528873667, 0.975212642, 0.406696922, 0.986302019, 0.821480975,
0.776634401, 0.804389643, 0.52690957, 0.881839936, 0.881676756,
0.842954149, 0.49820502, 0.551171205, 0.611370269, 0.630794947,
0.605911653, 0.612136659, 0.504005614, 0.478993231, 0.896792758,
1.545713396, 1.479810742, 1.481512366, 1.016337185, 0.827241616,
1.987092303, 0.874371549, 0.557526165, 1.312183015, 0.163762763,
1.081580084, 0.682258832, 0.99675364, 0.582176455, 1.069035235,
1.352635886, 1.003522136, 0.705413397, 0.93395362, 0.764277848,
0.989686599, 0.875251492, 0.582424316, 0.618786084, 0.971365119,
0.4453251, 1.057255968, 0.710771044, 0.157439397, 0.584064339,
0.966582301, 0.807429305, 0.578682092, 0.911954428, 1.146678771,
0.977409848, 0.7173858, 0.692368328, 0.84760684, 0.426626052,
0.392027133, 0.463031406, 0.346331904, 0.435984278, 0.625301164,
0.733525794, 0.468399014, 0.911551574, 0.845252338, 0.560227896,
1.191183013, 0.503701088, 0.686482249, 0.812501692, 0.649220856,
0.448065201, 0.520082782, 0.465629478, 0.601450142, 0.498518229,
0.432112652, 0.422273393, 0.374147354, 0.631002663, 1.659917846,
1.024954525, 1.202822771, 0.652806306, 0.768222032, 1.742846509,
0.782477781, 0.398411581, 0.98639944, 0.580826286, 0.781519247,
0.683742619, 0.717473487, 0.26632937, 0.748351886, 1.884740371,
0.875399141, 0.661320505, 0.703044393, 0.49535084, 0.954243365,
0.645801986, 1.293963499, 0.649359573, 0.623769945, 0.256283426,
0.8611224, 0.495113363, 0.158687285, 0.522609442, 0.635988959
), se = c(0.273857778, 0.195945704, 0.236992183, 0.222671957,
0.352121382, 0.172756183, 0.229401102, 0.238077204, 0.139122219,
0.090673779, 0.135558019, 0.16646414, 0.189712048, 0.152672677,
0.281519641, 0.117403289, 0.297381248, 0.237141131, 0.22419504,
0.232207288, 0.175636523, 0.265884745, 0.254518156, 0.243339902,
0.143819401, 0.159109422, 0.176487395, 0.182094816, 0.174911628,
0.193574608, 0.145493889, 0.138273435, 0.298930919, 0.446209023,
0.467957245, 0.446692786, 0.306437191, 0.249422732, 0.62837376,
0.252409325, 0.160943941, 0.378794609, 0.047274238, 0.312225276,
0.19695116, 0.287737991, 0.168059866, 0.30860389, 0.478229004,
0.317341563, 0.203635307, 0.281597612, 0.220628011, 0.298401737,
0.252663342, 0.168131418, 0.186571024, 0.323788373, 0.1484417,
0.305203509, 0.205181927, 0.047469764, 0.168604852, 0.305660162,
0.233084763, 0.174479216, 0.263258567, 0.331017649, 0.309084133,
0.207091442, 0.19986952, 0.244683019, 0.123156333, 0.118200628,
0.133665654, 0.099977409, 0.131454206, 0.180508898, 0.211750657,
0.135215148, 0.274843141, 0.244003332, 0.161723863, 0.343864917,
0.178085227, 0.217084748, 0.244978478, 0.187413918, 0.129345282,
0.164464616, 0.134415652, 0.173623701, 0.143909817, 0.136646019,
0.121899828, 0.108007038, 0.210334221, 0.500484062, 0.32411908,
0.362664711, 0.196828507, 0.231627658, 0.551136458, 0.225881879,
0.115011517, 0.297410621, 0.167670106, 0.225605174, 0.197379493,
0.207116755, 0.076882667, 0.216030581, 0.666356349, 0.276825515,
0.190906786, 0.21197586, 0.14299547, 0.2877152, 0.186426975,
0.373535087, 0.195789278, 0.207923315, 0.085427809, 0.248584625,
0.142926917, 0.047846017, 0.150864351, 0.201117368), ci = c(0.602756906,
0.431273588, 0.521616278, 0.490097673, 0.796553907, 0.380233796,
0.504908421, 0.524004393, 0.306205939, 0.199571642, 0.298361189,
0.366385102, 0.422704785, 0.336030297, 0.619620551, 0.258402896,
0.662606712, 0.52194411, 0.493449956, 0.511084796, 0.405018549,
0.59242813, 0.560190685, 0.535587514, 0.316544368, 0.350197476,
0.388446137, 0.400787988, 0.384977898, 0.437896186, 0.320229889,
0.304337779, 0.689335936, 0.982099437, 1.058592834, 0.99529355,
0.682784611, 0.555748479, 1.421480202, 0.555549178, 0.354235225,
0.833721312, 0.104049895, 0.6872032, 0.433486581, 0.633307048,
0.369897272, 0.679232583, 1.1308319, 0.71787649, 0.448198289,
0.627438579, 0.485598977, 0.664880504, 0.556108267, 0.370054755,
0.415706148, 0.746657327, 0.342307174, 0.671748394, 0.451602376,
0.105769226, 0.371096776, 0.691451324, 0.513016105, 0.388763919,
0.5794282, 0.728564932, 0.699196885, 0.455805192, 0.439909848,
0.538543693, 0.271065261, 0.263367411, 0.29419612, 0.220048794,
0.292898224, 0.397297405, 0.466060055, 0.297606535, 0.61238868,
0.537047714, 0.355951823, 0.756841578, 0.421104648, 0.491079817,
0.545846064, 0.412495252, 0.284687047, 0.37204481, 0.295846856,
0.382143188, 0.316743371, 0.30911477, 0.268299713, 0.237721887,
0.485031584, 1.115147982, 0.733208298, 0.808067333, 0.438561244,
0.516098584, 1.246757287, 0.497162663, 0.253138642, 0.66267216,
0.369039416, 0.49655364, 0.434429334, 0.455860905, 0.169217609,
0.475480104, 1.575682382, 0.626222821, 0.420183003, 0.47231165,
0.314730908, 0.641069416, 0.410323006, 0.822145184, 0.436245697,
0.479472024, 0.19699688, 0.54713107, 0.314580023, 0.106607569,
0.332050198, 0.454959094)), class = c("spec_tbl_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -128L), spec = structure(list(
cols = list(Group = structure(list(), class = c("collector_character",
"collector")), Condition = structure(list(), class = c("collector_character",
"collector")), test = structure(list(), class = c("collector_character",
"collector")), trial = structure(list(), class = c("collector_double",
"collector")), Variables = structure(list(), class = c("collector_character",
"collector")), Eye_Mx = structure(list(), class = c("collector_double",
"collector")), sd = structure(list(), class = c("collector_double",
"collector")), se = structure(list(), class = c("collector_double",
"collector")), ci = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1), class = "col_spec"))
p <- ggplot(data10, aes(x = trial, y = Eye_Mx)) +
geom_line(aes(color = Variables, linetype = Variables), lwd=1.2) +
scale_color_manual(values = c("darkred", "steelblue")) + facet_grid(Condition ~ Group)+ theme_bw() + xlab("Trial Pre- / Post-test") + ylab("Hand and Eye Movement time (s)") +
scale_x_continuous(limits = c(1,16), breaks = seq(1,16,1)) + theme(axis.text.x = element_text(size = 10,face="bold", angle = 90),#, angle = 10, hjust = .5, vjust = .5),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.y = element_text(vjust= 1.8, size = 16),
axis.title.x = element_text(vjust= -0.5, size = 16),
axis.title = element_text(face = "bold")) + theme(legend.position="top")+
geom_vline(xintercept=8.5, linetype="dashed", color = "black", size=1.5)
p + guides(fill=guide_legend(title="Variables:")) + theme(legend.text=element_text(size=14),legend.title=element_text(size=14) ) +
theme(strip.text = element_text(face="bold", size=12))
是否有必要使用您的数据？您可以简单地使用 geom_smooth 并设置 se=TRUE.
p <- ggplot(data10, aes(x = trial, y = Eye_Mx)) +
geom_line(aes(color = Variables, linetype = Variables), lwd=1.2) +
scale_color_manual(values = c("darkred", "steelblue")) + facet_grid(Condition ~ Group)+ theme_bw() + xlab("Trial Pre- / Post-test") + ylab("Hand and Eye Movement time (s)") +
scale_x_continuous(limits = c(1,16), breaks = seq(1,16,1)) + theme(axis.text.x = element_text(size = 10,face="bold", angle = 90),#, angle = 10, hjust = .5, vjust = .5),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.y = element_text(vjust= 1.8, size = 16),
axis.title.x = element_text(vjust= -0.5, size = 16),
axis.title = element_text(face = "bold")) + theme(legend.position="top")+
geom_vline(xintercept=8.5, linetype="dashed", color = "black", size=1.5) +
geom_smooth(method="loess", se=TRUE, fullrange=FALSE, level=0.95)
p + guides(fill=guide_legend(title="Variables:")) + theme(legend.text=element_text(size=14),legend.title=element_text(size=14) ) +
theme(strip.text = element_text(face="bold", size=12))
由于您的标准误差已包含在您的数据中，您可以通过 geom_ribbon 在您的线周围添加一个置信区间，如下所示：
library(ggplot2)
ggplot(data10, aes(x = trial, y = Eye_Mx)) +
geom_line(aes(color = Variables, linetype = Variables), lwd=1.2) +
geom_ribbon(aes(ymin = Eye_Mx - 1.96 * se, ymax = Eye_Mx + 1.96 * se, fill = Variables), alpha = .3) +
scale_color_manual(values = c("darkred", "steelblue")) + facet_grid(Condition ~ Group)+ theme_bw() + xlab("Trial Pre- / Post-test") + ylab("Hand and Eye Movement time (s)") +
scale_x_continuous(limits = c(1,16), breaks = seq(1,16,1)) + theme(axis.text.x = element_text(size = 10,face="bold", angle = 90),#, angle = 10, hjust = .5, vjust = .5),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.y = element_text(vjust= 1.8, size = 16),
axis.title.x = element_text(vjust= -0.5, size = 16),
axis.title = element_text(face = "bold")) + theme(legend.position="top")+
geom_vline(xintercept=8.5, linetype="dashed", color = "black", size=1.5) +
guides(fill=guide_legend(title="Variables:")) + theme(legend.text=element_text(size=14),legend.title=element_text(size=14) ) +
theme(strip.text = element_text(face="bold", size=12))
使用数据框中可用的标准误差在多线图周围生成平滑的误差条。我已经在数据框中有标准错误，所以我可以使用数据 +/- se.
使用数据框中可用的标准误差在多线图周围生成平滑的误差条。我已经在数据框中有标准错误，所以我可以使用数据 +/- se.
data10 <- structure(list(Group = c("Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered"), Condition = c("CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS"), test = c("Pre-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test"), trial = c(1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16), Variables = c("Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time"), Eye_Mx = c(1.150583333, 1.273916667, 1.213083333,
1.065166667, 1.2373, 1.19925, 0.93675, 0.950833333, 0.616916667,
0.440416667, 0.598083333, 0.618583333, 0.693545455, 0.667583333,
0.873666667, 0.51825, 1.220454545, 1.034583333, 0.874583333,
1.015166667, 0.532222222, 0.714454545, 0.905583333, 0.898333333,
0.641666667, 0.787666667, 0.609833333, 0.623583333, 0.69925,
0.7188, 0.61725, 0.661166667, 1.349, 1.585416667, 1.0145, 1.201090909,
0.810545455, 0.591090909, 1.1416, 0.697166667, 0.431166667, 0.804583333,
0.289666667, 0.63875, 0.46825, 0.633, 0.418833333, 0.691166667,
1.219125, 0.7033, 0.524666667, 0.724818182, 0.648583333, 0.639181818,
0.596583333, 0.509416667, 0.576272727, 0.483222222, 0.388222222,
0.647, 0.42575, 0.269818182, 0.488333333, 0.5903, 1.869083333,
2.066181818, 2.124166667, 2.31525, 2.0943, 1.93625, 1.786916667,
1.922583333, 1.470833333, 1.421454545, 1.519083333, 1.508833333,
1.575909091, 1.5135, 1.8025, 1.541, 1.800454545, 1.888666667,
1.85575, 2.201666667, 1.55725, 1.7781, 1.748, 1.767583333, 1.489333333,
1.4259, 1.436916667, 1.5855, 1.535666667, 1.4013, 1.3855, 1.356666667,
1.852888889, 2.463636364, 2.031, 2.195727273, 1.804454545, 1.709090909,
2.1938, 1.97625, 1.256833333, 1.704363636, 1.418083333, 1.371166667,
1.459166667, 1.46725, 1.183666667, 1.407, 2.348625, 1.8981, 1.973583333,
1.746727273, 1.6805, 1.963, 1.68075, 1.872583333, 1.345636364,
1.339222222, 1.311222222, 1.316833333, 1.215833333, 1.053636364,
1.415916667, 1.2292), sd = c(0.948671172, 0.678775831, 0.820965004,
0.771358286, 1.11350558, 0.598444974, 0.794668727, 0.824723627,
0.481933503, 0.314103185, 0.469586754, 0.576648697, 0.629203681,
0.528873667, 0.975212642, 0.406696922, 0.986302019, 0.821480975,
0.776634401, 0.804389643, 0.52690957, 0.881839936, 0.881676756,
0.842954149, 0.49820502, 0.551171205, 0.611370269, 0.630794947,
0.605911653, 0.612136659, 0.504005614, 0.478993231, 0.896792758,
1.545713396, 1.479810742, 1.481512366, 1.016337185, 0.827241616,
1.987092303, 0.874371549, 0.557526165, 1.312183015, 0.163762763,
1.081580084, 0.682258832, 0.99675364, 0.582176455, 1.069035235,
1.352635886, 1.003522136, 0.705413397, 0.93395362, 0.764277848,
0.989686599, 0.875251492, 0.582424316, 0.618786084, 0.971365119,
0.4453251, 1.057255968, 0.710771044, 0.157439397, 0.584064339,
0.966582301, 0.807429305, 0.578682092, 0.911954428, 1.146678771,
0.977409848, 0.7173858, 0.692368328, 0.84760684, 0.426626052,
0.392027133, 0.463031406, 0.346331904, 0.435984278, 0.625301164,
0.733525794, 0.468399014, 0.911551574, 0.845252338, 0.560227896,
1.191183013, 0.503701088, 0.686482249, 0.812501692, 0.649220856,
0.448065201, 0.520082782, 0.465629478, 0.601450142, 0.498518229,
0.432112652, 0.422273393, 0.374147354, 0.631002663, 1.659917846,
1.024954525, 1.202822771, 0.652806306, 0.768222032, 1.742846509,
0.782477781, 0.398411581, 0.98639944, 0.580826286, 0.781519247,
0.683742619, 0.717473487, 0.26632937, 0.748351886, 1.884740371,
0.875399141, 0.661320505, 0.703044393, 0.49535084, 0.954243365,
0.645801986, 1.293963499, 0.649359573, 0.623769945, 0.256283426,
0.8611224, 0.495113363, 0.158687285, 0.522609442, 0.635988959
), se = c(0.273857778, 0.195945704, 0.236992183, 0.222671957,
0.352121382, 0.172756183, 0.229401102, 0.238077204, 0.139122219,
0.090673779, 0.135558019, 0.16646414, 0.189712048, 0.152672677,
0.281519641, 0.117403289, 0.297381248, 0.237141131, 0.22419504,
0.232207288, 0.175636523, 0.265884745, 0.254518156, 0.243339902,
0.143819401, 0.159109422, 0.176487395, 0.182094816, 0.174911628,
0.193574608, 0.145493889, 0.138273435, 0.298930919, 0.446209023,
0.467957245, 0.446692786, 0.306437191, 0.249422732, 0.62837376,
0.252409325, 0.160943941, 0.378794609, 0.047274238, 0.312225276,
0.19695116, 0.287737991, 0.168059866, 0.30860389, 0.478229004,
0.317341563, 0.203635307, 0.281597612, 0.220628011, 0.298401737,
0.252663342, 0.168131418, 0.186571024, 0.323788373, 0.1484417,
0.305203509, 0.205181927, 0.047469764, 0.168604852, 0.305660162,
0.233084763, 0.174479216, 0.263258567, 0.331017649, 0.309084133,
0.207091442, 0.19986952, 0.244683019, 0.123156333, 0.118200628,
0.133665654, 0.099977409, 0.131454206, 0.180508898, 0.211750657,
0.135215148, 0.274843141, 0.244003332, 0.161723863, 0.343864917,
0.178085227, 0.217084748, 0.244978478, 0.187413918, 0.129345282,
0.164464616, 0.134415652, 0.173623701, 0.143909817, 0.136646019,
0.121899828, 0.108007038, 0.210334221, 0.500484062, 0.32411908,
0.362664711, 0.196828507, 0.231627658, 0.551136458, 0.225881879,
0.115011517, 0.297410621, 0.167670106, 0.225605174, 0.197379493,
0.207116755, 0.076882667, 0.216030581, 0.666356349, 0.276825515,
0.190906786, 0.21197586, 0.14299547, 0.2877152, 0.186426975,
0.373535087, 0.195789278, 0.207923315, 0.085427809, 0.248584625,
0.142926917, 0.047846017, 0.150864351, 0.201117368), ci = c(0.602756906,
0.431273588, 0.521616278, 0.490097673, 0.796553907, 0.380233796,
0.504908421, 0.524004393, 0.306205939, 0.199571642, 0.298361189,
0.366385102, 0.422704785, 0.336030297, 0.619620551, 0.258402896,
0.662606712, 0.52194411, 0.493449956, 0.511084796, 0.405018549,
0.59242813, 0.560190685, 0.535587514, 0.316544368, 0.350197476,
0.388446137, 0.400787988, 0.384977898, 0.437896186, 0.320229889,
0.304337779, 0.689335936, 0.982099437, 1.058592834, 0.99529355,
0.682784611, 0.555748479, 1.421480202, 0.555549178, 0.354235225,
0.833721312, 0.104049895, 0.6872032, 0.433486581, 0.633307048,
0.369897272, 0.679232583, 1.1308319, 0.71787649, 0.448198289,
0.627438579, 0.485598977, 0.664880504, 0.556108267, 0.370054755,
0.415706148, 0.746657327, 0.342307174, 0.671748394, 0.451602376,
0.105769226, 0.371096776, 0.691451324, 0.513016105, 0.388763919,
0.5794282, 0.728564932, 0.699196885, 0.455805192, 0.439909848,
0.538543693, 0.271065261, 0.263367411, 0.29419612, 0.220048794,
0.292898224, 0.397297405, 0.466060055, 0.297606535, 0.61238868,
0.537047714, 0.355951823, 0.756841578, 0.421104648, 0.491079817,
0.545846064, 0.412495252, 0.284687047, 0.37204481, 0.295846856,
0.382143188, 0.316743371, 0.30911477, 0.268299713, 0.237721887,
0.485031584, 1.115147982, 0.733208298, 0.808067333, 0.438561244,
0.516098584, 1.246757287, 0.497162663, 0.253138642, 0.66267216,
0.369039416, 0.49655364, 0.434429334, 0.455860905, 0.169217609,
0.475480104, 1.575682382, 0.626222821, 0.420183003, 0.47231165,
0.314730908, 0.641069416, 0.410323006, 0.822145184, 0.436245697,
0.479472024, 0.19699688, 0.54713107, 0.314580023, 0.106607569,
0.332050198, 0.454959094)), class = c("spec_tbl_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -128L), spec = structure(list(
cols = list(Group = structure(list(), class = c("collector_character",
"collector")), Condition = structure(list(), class = c("collector_character",
"collector")), test = structure(list(), class = c("collector_character",
"collector")), trial = structure(list(), class = c("collector_double",
"collector")), Variables = structure(list(), class = c("collector_character",
"collector")), Eye_Mx = structure(list(), class = c("collector_double",
"collector")), sd = structure(list(), class = c("collector_double",
"collector")), se = structure(list(), class = c("collector_double",
"collector")), ci = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1), class = "col_spec"))
p <- ggplot(data10, aes(x = trial, y = Eye_Mx)) +
geom_line(aes(color = Variables, linetype = Variables), lwd=1.2) +
scale_color_manual(values = c("darkred", "steelblue")) + facet_grid(Condition ~ Group)+ theme_bw() + xlab("Trial Pre- / Post-test") + ylab("Hand and Eye Movement time (s)") +
scale_x_continuous(limits = c(1,16), breaks = seq(1,16,1)) + theme(axis.text.x = element_text(size = 10,face="bold", angle = 90),#, angle = 10, hjust = .5, vjust = .5),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.y = element_text(vjust= 1.8, size = 16),
axis.title.x = element_text(vjust= -0.5, size = 16),
axis.title = element_text(face = "bold")) + theme(legend.position="top")+
geom_vline(xintercept=8.5, linetype="dashed", color = "black", size=1.5)
p + guides(fill=guide_legend(title="Variables:")) + theme(legend.text=element_text(size=14),legend.title=element_text(size=14) ) +
theme(strip.text = element_text(face="bold", size=12))
是否有必要使用您的数据？您可以简单地使用 geom_smooth 并设置 se=TRUE.
p <- ggplot(data10, aes(x = trial, y = Eye_Mx)) +
geom_line(aes(color = Variables, linetype = Variables), lwd=1.2) +
scale_color_manual(values = c("darkred", "steelblue")) + facet_grid(Condition ~ Group)+ theme_bw() + xlab("Trial Pre- / Post-test") + ylab("Hand and Eye Movement time (s)") +
scale_x_continuous(limits = c(1,16), breaks = seq(1,16,1)) + theme(axis.text.x = element_text(size = 10,face="bold", angle = 90),#, angle = 10, hjust = .5, vjust = .5),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.y = element_text(vjust= 1.8, size = 16),
axis.title.x = element_text(vjust= -0.5, size = 16),
axis.title = element_text(face = "bold")) + theme(legend.position="top")+
geom_vline(xintercept=8.5, linetype="dashed", color = "black", size=1.5) +
geom_smooth(method="loess", se=TRUE, fullrange=FALSE, level=0.95)
p + guides(fill=guide_legend(title="Variables:")) + theme(legend.text=element_text(size=14),legend.title=element_text(size=14) ) +
theme(strip.text = element_text(face="bold", size=12))
由于您的标准误差已包含在您的数据中，您可以通过 geom_ribbon 在您的线周围添加一个置信区间，如下所示：
library(ggplot2)
ggplot(data10, aes(x = trial, y = Eye_Mx)) +
geom_line(aes(color = Variables, linetype = Variables), lwd=1.2) +
geom_ribbon(aes(ymin = Eye_Mx - 1.96 * se, ymax = Eye_Mx + 1.96 * se, fill = Variables), alpha = .3) +
scale_color_manual(values = c("darkred", "steelblue")) + facet_grid(Condition ~ Group)+ theme_bw() + xlab("Trial Pre- / Post-test") + ylab("Hand and Eye Movement time (s)") +
scale_x_continuous(limits = c(1,16), breaks = seq(1,16,1)) + theme(axis.text.x = element_text(size = 10,face="bold", angle = 90),#, angle = 10, hjust = .5, vjust = .5),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.y = element_text(vjust= 1.8, size = 16),
axis.title.x = element_text(vjust= -0.5, size = 16),
axis.title = element_text(face = "bold")) + theme(legend.position="top")+
geom_vline(xintercept=8.5, linetype="dashed", color = "black", size=1.5) +
guides(fill=guide_legend(title="Variables:")) + theme(legend.text=element_text(size=14),legend.title=element_text(size=14) ) +
theme(strip.text = element_text(face="bold", size=12))