使用資料框中可用的標準誤差在多線圖周圍生成平滑的誤差條。我已經在資料框中存在標準錯誤，因此我可以使用資料 /- se。
使用資料框中可用的標準誤差在多線圖周圍生成平滑的誤差條。我已經在資料框中存在標準錯誤，因此我可以使用資料 /- 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))
uj5u.com熱心網友回復：
由于您的標準誤差已經包含在您的資料中，您可以通過geom_ribbon如下方式在您的線條周圍添加一個置信區間：
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))
uj5u.com熱心網友回復：
是否有必要使用您的資料？您可以簡單地使用geom_smooth和設定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))
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