plot.rmc {rmcorr} | 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 |
## 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")