lineup (version 0.34-1)

plot.lineupdist: Plot summary of inter-individual distances

Description

Plot histograms of self-self and self-nonself distances from a distance matrix calculated by distee or disteg.

Usage

## S3 method for class 'lineupdist':
plot(x, breaks, add.rug=TRUE, what=c("both", "ss", "sn"), ...)

Arguments

x

Output of distee or disteg.

breaks

Optional vector of breaks, passed to hist, though if it is length 1, we interpret it as the number of breaks and ensure that both histograms use the same set of breaks.

add.rug

If true, also include rug below histograms.

what

Indicates whether to plot both self-self and self-nonself distances (or correlations) or just one or the other. ("ss" indicates self-self and "sn" indicates self-nonself.)

...

Ignored at this point.

Value

• None.

Details

We call pulldiag and omitdiag to get the self-self and self-nonself distances.

If all of the self-self distances are missing, we plot just the self-nonself distances.

See Also

pulldiag, distee, plot2dist

Examples

# simulate MVN, 100 individuals, 40 measurements (of which 20 are just noise)
V <- matrix(0.3, ncol=20, nrow=20) + diag(rep(0.5, 20)) 
D <- chol(V)
z <- matrix(rnorm(20*100), ncol=20)
# create two data matrices as z + noise
x <- cbind(z + rnorm(20*100, 0, 0.2), matrix(rnorm(20*100), ncol=20))
y <- cbind(z + rnorm(20*100, 0, 0.2), matrix(rnorm(20*100), ncol=20))

# permute some rows
x[51:53,] <- x[c(52,53,51),]
y[41:42,] <- y[42:41,]

# add column and row names
dimnames(x) <- dimnames(y) <- list(paste("ind", 1:100, sep=""),
                                   paste("gene", 1:40, sep=""))

# calculate correlations between cols of x and cols of y
thecor <- corbetw2mat(x, y)

# subset x and y, taking only columns with corr > 0.75
xs <- x[,thecor > 0.8]
ys <- y[,thecor > 0.8]

# calculate distance (using "RMS difference" as a measure)
d1 <- distee(xs, ys, d.method="rmsd", labels=c("x","y"))

# calculate distance (using "correlation" as a measure...really similarity)
d2 <- distee(xs, ys, d.method="cor", labels=c("x", "y"))

# pull out the smallest 8 self-self correlations
sort(pulldiag(d2))[1:8]

# summary of results
summary(d1)
summary(d2)

# order to put matches together
summary(d2, reorder="alignmatches")

# plot histograms of RMS distances
plot(d1)

# plot histograms of correlations
plot(d2)

# plot distances against one another
plot2dist(d1, d2)