CCplot: Concordance Correlation Plot

In TalhoukLab/nanostringr: Performs Quality Control, Data Normalization, and Batch Effect Correction for 'NanoString nCounter' Data

Concordance Correlation Plot

Description

Plotting function for reliability measure.

Usage

CCplot(
  method1,
  method2,
  Ptype = "None",
  metrics = FALSE,
  xlabel = "",
  ylabel = "",
  title = "",
  subtitle = NULL,
  xrange = NULL,
  yrange = NULL,
  MArange = c(-3.5, 5.5)
)

Arguments

method1	measurements obtained in batch 1 or using method 1
method2	measurements obtained in batch 2 or using method 2
Ptype	type of plot to be outputted c("scatter", "MAplot")
metrics	if TRUE, prints Rc, Ca, and R2 to console
xlabel	x-axis label for scatterplot
ylabel	y-axis label for scatterplot
title	title for the main plot
subtitle	subtitle of plot
xrange	range of x axis
yrange	range of y axis
MArange	MA range

Value

Either a scatterplot or MA plot showing concordance correlation.

Author(s)

Aline Talhouk

Examples

# Simulate normally distributed data
set.seed(12)
a1 <- rnorm(20) + 2
a2 <- a1 + rnorm(20, 0, 0.15)
a3 <- a1 + rnorm(20, 0, 0.15) + 1.4
a4 <- 1.5 * a1 + rnorm(20, 0, 0.15)
a5 <- 1.3 * a1 + rnorm(20, 0, 0.15) + 1
a6 <- a1 + rnorm(20, 0, 0.8)

# One scatterplot
CCplot(a1, a2, Ptype = "scatter")


m2 <- list(a1, a2, a3, a4, a5, a6)
mains <- c("Perfect Agreement", "Very Good Agreement", "Location Shift",
           "Scale Shift", "Location and Scale Shift", "Measurement Error")
subs <- letters[1:6]
par(mfrow = c(3, 2), mar = c(5.1, 4.1, 1.5, 1.5))

# Scatterplots
mapply(function(y, t, s)
  CCplot(method1 = a1, method2 = y, Ptype = "scatter",
         xlabel = "X", ylabel = "Y", title = t, subtitle = s),
  y = m2, t = mains, s = subs)

# MAplots and show metrics
mapply(function(y, t, s)
  CCplot(method1 = a1, method2 = y, Ptype = "MAplot",
         title = t, subtitle = s, metrics = TRUE),
  y = m2, t = mains, s = subs)

TalhoukLab/nanostringr documentation built on Aug. 24, 2023, 7:49 p.m.