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## ----echo = FALSE, warning=FALSE, message = FALSE, results = 'hide'-----------
cat("this will be hidden; use for general initializations.\n")
library(superb)
options("superb.feedback" = c("warnings","design"))
library(ggplot2)
dta = GRD(WSFactors="Moments(3)", SubjectsPerGroup =20,
Population = list(mean = 12, stddev = 1, rho = 0.45),
Effects = list("Moments" = custom(2,3,5) ),
RenameDV = "Score"
)
## -----------------------------------------------------------------------------
pCM <- superbPlot(dta, WSFactors = "moment(3)",
variables = c("Score.1","Score.2","Score.3"),
adjustments=list(decorrelation="none"),
preprocessfct = "subjectCenteringTransform",
postprocessfct = "biasCorrectionTransform",
plotStyle = "pointjitter",
errorbarParams = list(color="red", width= 0.1, position = position_nudge(-0.05) )
)
## -----------------------------------------------------------------------------
pLM <- superbPlot(dta, WSFactors = "moment(3)",
variables = c("Score.1","Score.2","Score.3"),
adjustments=list(decorrelation="none"),
preprocessfct = "subjectCenteringTransform",
postprocessfct = c("biasCorrectionTransform","poolSDTransform"),
plotStyle = "line",
errorbarParams = list(color="orange", width= 0.1, position = position_nudge(-0.0) )
)
## -----------------------------------------------------------------------------
pNKM <- superbPlot(dta, WSFactors = "moment(3)",
variables = c("Score.1","Score.2","Score.3"),
adjustments=list(decorrelation="none"),
preprocessfct = "subjectCenteringTransform",
postprocessfct = c("poolSDTransform"),
plotStyle = "line",
errorbarParams = list(color="blue", width= 0.1, position = position_nudge(+0.05) )
)
## ----fig.height=4, fig.width=7, fig.cap = "**Figure 1**. Plot of the tree decorrelation methods based on subject transformation."----
tlbl <- paste( "(red) Subject centering & Bias correction == CM\n",
"(orange) Subject centering, Bias correction & Pooling SDs == LM\n",
"(blue) Subject centering & Pooling SDs == NKM", sep="")
ornate <- list(
xlab("Group"),
ylab("Score"),
labs( title=tlbl),
coord_cartesian( ylim = c(12,18) ),
theme_light(base_size=10)
)
# the plots on top are made transparent
pCM2 <- ggplotGrob(pCM + ornate)
pLM2 <- ggplotGrob(pLM + ornate + makeTransparent() )
pNKM2 <- ggplotGrob(pNKM + ornate + makeTransparent() )
# put the grobs onto an empty ggplot
ggplot() +
annotation_custom(grob=pCM2) +
annotation_custom(grob=pLM2) +
annotation_custom(grob=pNKM2)
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