I am using the following line of code to produce boxplot with statistical test
accuracy_boxplot <- ggboxplot(df, x = "Modality", y = "Accuracy",
color = "Modality", # Color by modality
palette = "jco", # Use the jco color palette
add = "jitter", # Add jittered points for visibility
title = "Sensor Comparison Stir",
xlab = "Modality",
width = 0.7,
ylab = "Accuracy")+
stat_compare_means(comparisons = list(c("1", "2"), c("4","1"), c("2","3_A")),
method = "wilcox.test", # You can specify the statistical test method
label = "p.signif")
# Print the boxplot
print(accuracy_boxplot)
I would like to make the line of the boxplot thicker, someone know the parameter? The structure of the dataset is the following:
structure(list(Modality = c("4", "4", "4", "4", "4", "4", "4",
"4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4", "4",
"4", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1", "1",
"1", "1", "1", "1", "1", "1", "1", "1", "1", "2", "2", "2", "2",
"2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2", "2",
"2", "2", "2", "2", "3_A", "3_A", "3_A", "3_A", "3_A", "3_A",
"3_A", "3_A", "3_A", "3_A", "3_A", "3_A", "3_A", "3_A", "3_A",
"3_A", "3_A", "3_A", "3_A", "3_A", "3_A", "3_B", "3_B", "3_B",
"3_B", "3_B", "3_B", "3_B", "3_B", "3_B", "3_B", "3_B", "3_B",
"3_B", "3_B", "3_B", "3_B", "3_B", "3_B", "3_B", "3_B", "3_B"
), Accuracy = c(0.926919291, 0.957405615, 0.93502734, 0.913872655,
0.868427488, 0.966666667, 0.951881015, 0.890891535, 0.967990516,
0.973490025, 0.987417615, 0.977457169, 0.9555131, 0.954411765,
0.838779956, 0.980553919, 0.950308733, 0.902105551, 0.957419018,
0.95808705, 0.896860987, 0.863681102, 0.8583414, 0.827275651,
0.886496484, 0.728784561, 0.880116959, 0.877515311, 0.780495655,
0.868109069, 0.846132823, 0.88615936, 0.940787496, 0.917030568,
0.857647059, 0.570079884, 0.918680024, 0.885033814, 0.844134537,
0.864420063, 0.928264374, 0.7382287, 0.856299213, 0.928041304,
0.902862657, 0.818209782, 0.85448765, 0.937134503, 0.905220181,
0.915674284, 0.930349733, 0.904892047, 0.973037747, 0.953411482,
0.938591703, 0.926470588, 0.761801017, 0.893635828, 0.927080271,
0.800656276, 0.891588297, 0.874529823, 0.838845291, 0.914616142,
0.95095192, 0.938887102, 0.924023377, 0.830031793, 0.962865497,
0.947214931, 0.88381075, 0.975992887, 0.936594698, 0.973337328,
0.96964232, 0.95360262, 0.961764706, 0.864197531, 0.91661756,
0.967068509, 0.828274542, 0.954806688, 0.94545943, 0.908352018,
0.843257874, 0.93502734, 0.868446446, 0.894494002, 0.86989484,
0.828362573, 0.936424614, 0.972320566, 0.960580913, 0.916097294,
0.982624326, 0.974150887, 0.947598253, 0.956470588, 0.849673203,
0.958161461, 0.945604234, 0.901832103, 0.942789969, 0.917786136,
0.850336323), Precision = c(0.936582392, 0.959110073, 0.940141008,
0.915944914, 0.874800619, 0.96673115, 0.95325237, 0.920688922,
0.968687127, 0.974399691, 0.987427002, 0.977338056, 0.957024819,
0.957633477, 0.943452169, 0.981046576, 0.951057388, 0.91193623,
0.958413204, 0.957964941, 0.899529262, 0.867865457, 0.873232392,
0.851084268, 0.891556096, 0.74836074, 0.900411402, 0.886028998,
0.86314785, 0.884470041, 0.849555348, 0.904116554, 0.94077121,
0.916577708, 0.877791411, 0.920643029, 0.923748579, 0.893670704,
0.881460026, 0.874873974, 0.927756128, 0.832820261, 0.886167226,
0.937507816, 0.900465967, 0.845151133, 0.860264489, 0.937570635,
0.916234574, 0.9306372, 0.931854587, 0.921565613, 0.973922866,
0.955706368, 0.940202556, 0.927643652, 0.900108492, 0.916059831,
0.93572824, 0.828610831, 0.897034089, 0.889602619, 0.854149083,
0.92683439, 0.953158545, 0.941801798, 0.928737617, 0.85111457,
0.96284554, 0.949215459, 0.908566894, 0.975992868, 0.939950014,
0.97436741, 0.970496042, 0.955470997, 0.962402791, 0.939975608,
0.929090583, 0.967452108, 0.871733053, 0.956625096, 0.946928181,
0.910830306, 0.900620924, 0.936491885, 0.893613832, 0.899530288,
0.876340119, 0.89673006, 0.938564279, 0.973274108, 0.961035246,
0.925380714, 0.982772262, 0.974341038, 0.950505318, 0.958764628,
0.917276958, 0.961677416, 0.949297089, 0.9044066, 0.945374046,
0.920208588, 0.870491435), Recall = c(0.926919291, 0.957405615,
0.93502734, 0.913872655, 0.868427488, 0.966666667, 0.951881015,
0.890891535, 0.967990516, 0.973490025, 0.987417615, 0.977457169,
0.9555131, 0.954411765, 0.838779956, 0.980553919, 0.950308733,
0.902105551, 0.957419018, 0.95808705, 0.896860987, 0.863681102,
0.8583414, 0.827275651, 0.886496463, 0.728784544, 0.880116959,
0.877515311, 0.780495655, 0.868109069, 0.846132823, 0.886159377,
0.940787496, 0.917030568, 0.857647059, 0.570079884, 0.918680024,
0.885033814, 0.844134537, 0.864420063, 0.928264374, 0.7382287,
0.856299213, 0.928041304, 0.902862657, 0.818209782, 0.85448765,
0.937134503, 0.905220181, 0.915674284, 0.930349733, 0.904892047,
0.973037747, 0.953411482, 0.938591703, 0.926470588, 0.761801017,
0.893635828, 0.927080271, 0.800656276, 0.891588297, 0.874529823,
0.838845291, 0.914616142, 0.95095192, 0.938887102, 0.924023377,
0.830031793, 0.962865497, 0.947214931, 0.88381075, 0.975992887,
0.936594698, 0.973337328, 0.96964232, 0.95360262, 0.961764706,
0.864197531, 0.91661756, 0.967068509, 0.828274542, 0.954806688,
0.94545943, 0.908352018, 0.843257874, 0.93502734, 0.868446446,
0.894494002, 0.86989484, 0.828362573, 0.936424614, 0.972320566,
0.960580913, 0.916097294, 0.982624326, 0.974150887, 0.947598253,
0.956470588, 0.849673203, 0.958161461, 0.945604234, 0.901832103,
0.942789969, 0.917786136, 0.850336323), F1_Score = c(0.929086174,
0.95651457, 0.936390561, 0.914309968, 0.869604989, 0.966450904,
0.950890327, 0.896758749, 0.967443864, 0.973482873, 0.987401104,
0.977293156, 0.955878114, 0.955182988, 0.884345796, 0.980491122,
0.950144228, 0.904601062, 0.957543302, 0.957707886, 0.893919439,
0.863595174, 0.859905233, 0.833963825, 0.886478636, 0.733980876,
0.884101889, 0.879400583, 0.798351879, 0.87177573, 0.845990446,
0.890633997, 0.940504634, 0.916311624, 0.863410963, 0.690762254,
0.92003212, 0.887147978, 0.852743674, 0.857495117, 0.927381366,
0.755635667, 0.861655353, 0.930003872, 0.898436501, 0.824412226,
0.856187851, 0.936464275, 0.907940847, 0.918630793, 0.930758235,
0.909233324, 0.97319852, 0.953854615, 0.938842591, 0.925984651,
0.824614766, 0.897527966, 0.928885867, 0.804426246, 0.893169776,
0.877120083, 0.840218695, 0.917504092, 0.950358859, 0.938879247,
0.924882343, 0.834406838, 0.962247039, 0.947218496, 0.888505229,
0.975915841, 0.93675, 0.973639264, 0.96951785, 0.954080138, 0.96111755,
0.900220206, 0.918819981, 0.96713785, 0.838567469, 0.954681127,
0.945187204, 0.90710247, 0.856396292, 0.935277045, 0.874020509,
0.895714873, 0.871827368, 0.842781664, 0.936014019, 0.972002524,
0.960251529, 0.917865952, 0.982532583, 0.974028301, 0.948398454,
0.956597019, 0.880740556, 0.958795345, 0.942991875, 0.9014725,
0.943259563, 0.915899246, 0.852445783), Model = c("LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM",
"LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM", "LSTM")), class = "data.frame", row.names = c(NA,
-105L))
I have added lwd = 2 to the parameter and this worked