![]() Ggplot (data_box, aes (x = group, y = value ) ) + test ( value ~ group, data = subset (data_box, group %in % c ( "A", "D" ) ) ) test ( value ~ group, data = subset (data_box, group %in % c ( "A", "C" ) ) ) test ( value ~ group, data = subset (data_box, group %in % c ( "A", "B" ) ) ) frame ( group = rep (LETTERS, each = 100 ), Always make sure that the statistical methods you’re using are appropriate for your data and your research question.ĭata_box <- data. Please note that both these approaches are based on assumptions about your data and your specific use case. You’ll need to adjust the y values and sizes according to your actual data and aesthetic preferences. In this case, we’ve added a significance asterisk “***” manually between 5 and 10 on the log scale. Geom_smooth (aes (x = tframe, y = trimSlope ), method = "loess", col = "black" ) + ![]() Ggplot (df, aes (x = tframe, y = trimSlope ) ) + This is a workaround and might need adjustment depending on the actual data: Option 2: If you still want to use a continuous x-axis, you may manually add lines and text to signify the significant difference. Note that this will change the x-axis to a categorical variable, and the scale_x_log10() will no longer be applicable. background = element_rect (fill = "antiquewhite1", colour = "blue" ) ) Labs (x = "time frame in seconds (log scale)", y = "mean indice values (trim slope) ± SE" ) + Geom_hline (yintercept = 1, linetype = "dashed", color = "red" ) + Geom_signif (comparisons = list (c ( "5", "10" ) ), map_signif_level = TRUE ) + Geom_errorbar (aes (ymin = trimSlope - trimSD, ymax = trimSlope + trimSD ), width =. factor (tframe ), y = trimSlope ), method = "loess", col = "black" ) + Can someone please write this code for me? Replyĭata <-data. I’m just not there in my coding life to write the code quickly and am a PhD student with too much work already. The labels will need to be assigned to a comparison, then a logical applied to either is or is not significant and the correct label applied to the x and y at the properly spaced distance from the line. I know what I want to do is get filter the highest y value (of the error bars) at each time point to transform by adding a fixed value for space for the label (a, b, c, d, e, or f ) above the error bar. I’ve started using a sequence of ggpubr::stat_compare_means where each instance is a different group and manually adjusting the y position to stack the marks but this is not sophisticated. This is not a fast process but all the ggplot2 modifier packages are not compatible with geom_line. I have been using ggpubr::compare_means to get the comparison results and then calling annotate() to manually adjust a vector of 8 x and y values to add a label of a,b,c,d,e,f. How can I visually represent significant differences from a multiple comparisons t.test between 4 independent groups at 8 time points on a line plot? Basically, I want to add (*) to stat_summary(geom = “line”).
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