# CCplot: Concordance Correlation Plot In nanostringr: Performs Quality Control, Data Normalization, and Batch Effect Correction for 'NanoString nCounter' Data

 CCplot R Documentation

## 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.

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)
```

nanostringr documentation built on Aug. 20, 2022, 1:05 a.m.