lineup (version 0.34-1)

omitdiag: Replace the diagonal in a distance matrix with missing values

Description

Replace the diagonal (that is, self-self distances) from a distance matrix calculated by distee or disteg with missing values (so that only self-nonself distances are left).

Usage

omitdiag(d)

Arguments

d

A distance matrix calculated by distee or disteg.

Value

• A matrix of the same form as the input, but with self-self distances replaced with NA.

Details

We use the row and column names to identify which entries are self-self.

See Also

pulldiag, distee, disteg, summary.lineupdist, plot2dist, plot.lineupdist

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)