The fasthplus R package provides fast approximations for metrics of discordance or dissimilarity.

The metric G+ was introduced by W. T. Williams in 1971 as a way to measure the discordance or dissimilarity between two different classifications (where the classification consists of distance matrix and a set of predicted labels for each observation).

Here, we introduce the H+, a discordance metric modified from G+. This metric can be used (1) to evaluate the discordance between two arbitrary sets or (2) to evaluate label fitness (clustering) for a generalized dissimilarity matrix.

Installing fasthplus

At present, our package is available only via github installation using the devtools package.

install_github(repo="ntdyjack/fasthplus", ref = "main")

After installation, the package can be loaded into R


Using fasthplus

The main functions in the fasthplus package are hpe() and hpb().

The hpe() function accepts either (1) two sets (A and B) or (2) a distance matrix (D) and set of labels (L). With additional arguments alg (algorithm choice) alphaand gammas (see vignette). The hpb() function accepts (1) a data matrix (D) and set of labels (L).

To run the hpe() function with two sets (A and B) and the number of p + 1 percentiles:

a <- rnorm(n=500,mean=0)
b <- rnorm(n=500,mean=1)
h <- hpe(A=a,B=b,p=101,alg=1)

To run the hpe() and hpb() with D (dissimilarity or data respectively) and set of labels (L):

# Two sets
a <- sapply(1:100, function(i) rnorm(n=50,mean=0.0,sd=1))
b <- sapply(1:100, function(i) rnorm(n=50,mean=0.0,sd=1))
x <- t(cbind(a,b))

# Create a set of labels
l <- c(rep(0,100),rep(1,100))

#hpb estimate

# Calculate dissimilarity matrix
d <- dist(x)
#hpe estimate

Issues and bug reports

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