plot.rmc | R Documentation |
plot.rmc
produces a scatterplot of measure1
on the x-axis and
measure2
on the y-axis, with a different color used for each subject.
Parallel lines are fitted to each subject's data.
## S3 method for class 'rmc'
plot(
x,
dataset = NULL,
overall = F,
palette = NULL,
xlab = NULL,
ylab = NULL,
overall.col = "gray60",
overall.lwd = 3,
overall.lty = 2,
...
)
x |
an object of class "rmc" generated from the |
dataset |
Deprecated: This argument is no longer required |
overall |
logical: if TRUE, plots the regression line between measure1 and measure2, ignoring the participant variable. |
palette |
the palette to be used. Defaults to the RColorBrewer "Paired" palette |
xlab |
label for the x axis, defaults to the variable name for measure1. |
ylab |
label for the y axis, defaults to the variable name for measure2. |
overall.col |
the color of the overall regression line |
overall.lwd |
the line thickness of the overall regression line |
overall.lty |
the line type of the overall regression line |
... |
additional arguments to |
rmcorr
## Bland Altman 1995 data
my.rmc <- rmcorr(participant = Subject, measure1 = PaCO2, measure2 = pH,
dataset = bland1995)
plot(my.rmc)
#using ggplot instead
if (requireNamespace("ggplot2", quietly = TRUE)){
ggplot2::ggplot(bland1995, ggplot2::aes(x = PaCO2, y = pH,
group = factor(Subject), color = factor(Subject))) +
ggplot2::geom_point(ggplot2::aes(colour = factor(Subject))) +
ggplot2::geom_line(ggplot2::aes(y = my.rmc$model$fitted.values),
linetype = 1)
}
## Raz et al. 2005 data
my.rmc <- rmcorr(participant = Participant, measure1 = Age, measure2 =
Volume, dataset = raz2005)
library(RColorBrewer)
blueset <- brewer.pal(8, 'Blues')
pal <- colorRampPalette(blueset)
plot(my.rmc, overall = TRUE, palette = pal, overall.col = 'black')
## Gilden et al. 2010 data
my.rmc <- rmcorr(participant = sub, measure1 = rt, measure2 = acc,
dataset = gilden2010)
plot(my.rmc, overall = FALSE, lty = 2, xlab = "Reaction Time",
ylab = "Accuracy")
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