# Instead of summarySEwithin, use summarySE, which treats condition as though it were a between-subjects variable The method in Morey (2008) and Cousineau (2005) essentially normalizes the data to remove the between-subject variability and calculates the variance from this normalized data. The graph of individual data shows that there is a consistent trend for the within-subjects variable condition, but this would not necessarily be revealed by taking the regular standard errors (or confidence intervals) for each group. The steps here are for explanation purposes only they are not necessary for making the error bars. This section explains how the within-subjects error bar values are calculated. See the section below on normed means for more information. The value and value_norm columns represent the un-normed and normed means. 1, aes ( ymin = value - ci, ymax = value + ci )) + geom_point ( shape = 21, size = 3, fill = "white" ) + ylim ( 40, 60 ) Ggplot ( dfwc, aes ( x = condition, y = value, group = 1 )) + geom_line () + geom_errorbar ( width =. Library ( ggplot2 ) # Make the graph with the 95% confidence interval Here is a data set (from Morey 2008) with one within-subjects variable: pre/post-test.ĭfwc condition N value value_norm sd se ci See these papers for a more detailed treatment of the issues involved in error bars with within-subjects variables. The method below is from Morey (2008), which is a correction to Cousineau (2005), which in turn is meant to be a simpler method of that in Loftus and Masson (1994). However, when there are within-subjects variables (repeated measures), plotting the standard error or regular confidence intervals may be misleading for making inferences about differences between conditions. When all variables are between-subjects, it is straightforward to plot standard error or confidence intervals. 9 )) + xlab ( "Dose (mg)" ) + ylab ( "Tooth length" ) + scale_fill_hue ( name = "Supplement type", # Legend label, use darker colorsīreaks = c ( "OJ", "VC" ), labels = c ( "Orange juice", "Ascorbic acid" )) + ggtitle ( "The Effect of Vitamin C on\nTooth Growth in Guinea Pigs" ) + scale_y_continuous ( breaks = 0 : 20 * 4 ) + theme_bw ()
Geom_errorbar ( aes ( ymin = len - se, ymax = len + se ), size =. Ggplot ( tgc2, aes ( x = dose, y = len, fill = supp )) + geom_bar ( position = position_dodge (), stat = "identity", colour = "black", # Use black outlines, The points are drawn last so that the white fill goes on top of the lines and error bars. 1, position = pd ) + geom_line ( position = pd ) + geom_point ( position = pd, size = 3 )Ī finished graph with error bars representing the standard error of the mean might look like this. Ggplot ( tgc, aes ( x = dose, y = len, colour = supp, group = supp )) + geom_errorbar ( aes ( ymin = len - ci, ymax = len + ci ), colour = "black", width =. 1, position = pd ) + geom_line ( position = pd ) + geom_point ( position = pd ) # Black error bars - notice the mapping of 'group=supp' - without it, the error Ggplot ( tgc, aes ( x = dose, y = len, colour = supp )) + geom_errorbar ( aes ( ymin = len - ci, ymax = len + ci ), width =. 1, position = pd ) + geom_line ( position = pd ) + geom_point ( position = pd ) # Use 95% confidence interval instead of SEM Ggplot ( tgc, aes ( x = dose, y = len, colour = supp )) + geom_errorbar ( aes ( ymin = len - se, ymax = len + se ), width =.
Pd <- position_dodge ( 0.1 ) # move them. 1 ) + geom_line () + geom_point () # The errorbars overlapped, so use position_dodge to move them horizontally
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