cat("this will be hidden; use for general initializations.") library(superb) library(ggplot2)
In paired-sample designs ---also called within-subject designs--- the same participants are measured more than once. In that case, asking whether a factor influenced the scores is the same as asking if the factor influenced all the participants. If all the participants turn out to be influenced in the same manner, it can be safely concluded that the factor influenced the group of participants.
Consider a study trying to establish the benefit of using exercises to improve visuo-spatial abilities onto scores in statistics reasoning, as measured by a standardized test with scores ranging from 50 to 150. The design is a within-subject design, specifically a pre-exercises measure and a post-exercises measure of statistics reasoning.
The data are available in dataFigure2
; here is a snapshot of it
head(dataFigure2)
There is a large variation in the scores obtained and as such a t test where the scores are treated as independent will fail to detect a difference:
t.test(dataFigure2$pre, dataFigure2$post, var.equal=TRUE)
However, when the proper, paired-sample t-test is used, we find that the difference is quite important:
t.test(dataFigure2$pre, dataFigure2$post, var.equal=TRUE, paired = TRUE)
What is going on? how can we make a plot that properly display this difference?
Let's examine the data using a plot in which each participant's scores are shown with a line. We get the following:
library(reshape2) # first transform the data in long format; the pre-post scores will go into column "variable" dl <- melt(dataFigure2, id="id") # add transparency when pre is smaller or equal to post dl$trans = ifelse(dataFigure2$pre <= dataFigure2$post,0.9,1.0) # make a plot, with transparent lines when the score increased ggplot(data=dl, aes(x=variable, y=value, group=id, alpha = trans)) + geom_line( ) + coord_cartesian( ylim = c(70,150) ) + geom_abline(intercept = 102.5, slope = 0, colour = "red", linetype=2)
As seen, except for 5 participants, a vast majority of the participants have an upward trend in their results. Thus, this upward trend is probably a reality in this dataset.
One solution used in @c05 is to center the participants' data on the participants' mean. It consists in computing for each participant their mean score and replace that participant's mean score with the overall mean. With this manipulation, all the participants will now hover around the overall mean (here 102.5, shown with a red dashed line).
The following realizes this subject-centered plot for each participant.
# use subjectCenteringTransform function library(superb) df2 <- subjectCenteringTransform(dataFigure2, c("pre","post")) # tranform into long format library(reshape2) dl2 <- melt(df2, id="id") # make the plot ggplot(data=dl2, aes(x=variable, y=value, colour=id, group=id)) + geom_line()+ coord_cartesian( ylim = c(70,150) ) + geom_abline(intercept = 102.5, slope = 0, colour = "red", size = 0.5, linetype=2)
Here again, we see that for 5 participants, their scores went down. For the 20 remaining ones, the trend is upward. Thus, there is clear tendency for the exercices to be beneficial.
Running the adequate paired t test, we find indeed that the difference is strongly significant (t(24) = 2.9, p = .008):
t.test(dataFigure2$pre, dataFigure2$post, paired=TRUE)
The above suggests that within-subject designs can be much more powerful than between- subject design. As long as there is a general trend visible in most participants, the paired design will afford more statistical power. How to we know that there is a general trend? An easy solution is to compute the correlation across the pairs of scores.
In R, you can run the following:
cor(dataFigure2$pre, dataFigure2$post)
and you find out that in the present dataset, correlation is actually quite high, with $r \approx .8$. Whenever correlation is positive, statistical power benefits from correlation.
Because increased power means higher level of precision, the error bars should be shortened by positive correlation. Estimating the adjusted length of the error bars from correlation is a process called decorrelation [@c19].
To this date, three techniques have been proposed to decorrelate the measures.
CM
: This method uses subject-centering followed by a bias-correction step (otherwise
the error bars are slightly overestimated) [@c05; @m08].
LM
: This method also uses subject-centering and bias-correction
but when there are more than two measurements, it also equalizes the length of
the error bars using a technique akin to pooled standard deviation measure [@lm94].
CA
: this is the newest proposal. It directly uses the correlation (or the mean
pairwise correlation when there are more than two measurement) to adjust the error bar
length. In a nutshell, the error bar length are adjusted using a multiplicative term
$\sqrt{1-r}$. As an example, when $ r = .8$, the adjustment is $\sqrt{1-.8} = 0.44$.
That means that the error bars are 44% the length of the unadjusted error bars
(that is, less than half).
Whichever method you choose have very little bearing on the actual result. As shown in @c19, all three methods are based on the same general concepts and they generate very little difference in the amount of adjustments.
In the present dataset, the error bar are more than shorten by half! which clearly shows the benefit of the within-subject design on precision.
With suberb
, all the decorrelation techniques are available using the adjustment
decorrelation
[@cgh21].
The simplest code is the following:
superbPlot(dataFigure2, WSFactors = "Moment(2)", adjustments = list( purpose = "difference", decorrelation = "CA" ## NEW! use a decorrelation technique ), variables = c("pre","post"), plotStyle = "line" )
This will result in a basic plot, but what matters is that the confidence intervals are adjusted so that you can compare bars even if they are repeated measures. Note that the confidence interval of one point does not contain the other point, suggesting, as it should be, a significant differencee.
With the following code, we can decorate the plot a little bit, and show side-by-side both the unadjusted plot (which give an erroneous result) and the adjusted plot.
options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages library(gridExtra) ## realize the plot with unadjusted (left) and ajusted (right) 95\% confidence intervals plt2a <- superbPlot(dataFigure2, WSFactors = "Moment(2)", adjustments = list(purpose = "difference"), variables = c("pre","post"), plotStyle = "line" ) + xlab("Group") + ylab("Score") + labs(title="Difference-adjusted\n95% confidence intervals") + coord_cartesian( ylim = c(85,115) ) + theme_gray(base_size=10) + scale_x_discrete(labels=c("1" = "Collaborative games", "2" = "Unstructured activity")) plt2b <- superbPlot(dataFigure2, WSFactors = "Moment(2)", adjustments = list(purpose = "difference", decorrelation = "CA"), #only difference variables = c("pre","post"), plotStyle = "line" ) + xlab("Group") + ylab("Score") + labs(title="Correlation and difference-adjusted\n95% confidence intervals") + coord_cartesian( ylim = c(85,115) ) + theme_gray(base_size=10) + scale_x_discrete(labels=c("1" = "Collaborative games", "2" = "Unstructured activity")) plt2 <- grid.arrange(plt2a,plt2b,ncol=2)
In the above plot, I used decorrelation = "CA"
. Alternatively, you could
use decorrelation = "UA"
or ``decorrelation = "CM").
When there is only two conditions, all the techniques return identical
error bars.
As another example illustrating the differences between the techniques, I generated random data for 5 measures with an amount of correlation of 0.8 in the population. In Figure 4 below, all error bars are superimposed on the same plot. As seen, there is only minor differences between the three techniques. The green lines all have the same length; this is the main characteristic of the Loftus and Masson approach, in contrast with the other two techniques.
# using GRD to generate data with correlation of .8 and a moderate effect options(superb.feedback = 'none') # shut down 'warnings' and 'design' interpretation messages test <- GRD(WSFactors = "Moment(5)", Effects = list("Moment" = extent(10) ), Population = list(mean = 100, stddev = 25, rho = 0.8) ) # the common label to all 4 plots tlbl <- paste( "(red) Difference-adjusted only\n", "(blue) Difference adjusted and decorrelated with CM\n", "(green) Difference-adjusted and decorrelated with LM\n", "(orange) Difference-adjusted and decorrelated with CA\n", "(bisque) Difference-adjusted and decorrelated with UA", sep="") # to make the plots all identical except for the decorrelation method makeplot <- function(dataset, decorrelationmethod, color, nudge, dir) { superbPlot(dataset, WSFactors = "Moment(5)", variables = c("DV.1","DV.2","DV.3","DV.4","DV.5"), adjustments=list(purpose = "difference", decorrelation = decorrelationmethod), errorbarParams = list(color=color, width= 0.1, position = position_nudge(nudge), direction = dir ), plotStyle="line" ) + xlab("Moment") + ylab("Score") + labs(subtitle=tlbl) + coord_cartesian( ylim = c(85,115) ) + theme_gray(base_size=10) } theme_transparent <- theme( panel.grid.major = element_blank(), panel.grid.minor = element_blank(), panel.background = element_rect(fill = "transparent",colour = NA), plot.background = element_rect(fill = "transparent",colour = NA) ) # generate the plots, nudging the error bars and using distinct colors pltrw <- makeplot(test, "none", "red", 0.0, "both") pltCM <- makeplot(test, "CM", "blue", -0.2, "left") pltLM <- makeplot(test, "LM", "chartreuse3", -0.1, "left") pltCA <- makeplot(test, "CA", "orange", +0.1, "right") pltUA <- makeplot(test, "UA", "bisque4", +0.2, "right") # transform the ggplots into "grob" so that they can be manipulated pltrwg <- ggplotGrob(pltrw) pltCMg <- ggplotGrob(pltCM + theme_transparent) pltLMg <- ggplotGrob(pltLM + theme_transparent) pltCAg <- ggplotGrob(pltCA + theme_transparent) pltUAg <- ggplotGrob(pltUA + theme_transparent) # put the grobs onto an empty ggplot ggplot() + annotation_custom(grob=pltrwg) + annotation_custom(grob=pltCMg) + annotation_custom(grob=pltLMg) + annotation_custom(grob=pltCAg) + annotation_custom(grob=pltUAg)
In superb
, it is possible to ask a certain type of plot. The plotStyle
used so
far is "line"
(the default is "bar"
). Another basic style is "point"
(no line
connecting the means).
Other types of plot exists that are apt at showing the summary statistics but also
the individual scores (the [6th Vignette]
(https://dcousin3.github.io/superb/articles/Vignette6.html) shows how to
develop custom-made layouts).
For illustrating individual differences, a style proposed is pointindividualline
which --- as per Figure 1--- will show the individual scores along with the summary
statistics and the error bars. For example:
superbPlot(dataFigure2, WSFactors = "Moment(2)", adjustments = list(purpose = "difference", decorrelation = "CM"), variables = c("pre","post"), plotStyle = "pointindividualline" ) + xlab("Group") + ylab("Score") + labs(subtitle="Correlation- and Difference-adjusted\n95% confidence intervals") + coord_cartesian( ylim = c(70,150) ) + theme_gray(base_size=10) + scale_x_discrete(labels=c("1" = "Collaborative games", "2" = "Unstructured activity"))
The major obstacle to the use of adjusted error bars was the difficulty to obtain them. None of the statistical software (e.g., SPSS, SAS) provide these adjustments. A way around is to compute these manually. Although not that complicated, it requires manipulations, whether they are done in EXCEL, or through macros [e.g., WSPlot, @oc14].
The present function renders all the adjustments a mere option in a function.
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