distshape {clustEff} R Documentation

## Dissimilarity matrix

### Description

This function implements the dissimilarity matrix based on shape and distance of curves.

### Usage

distshape(Beta, alpha=.5, step=c("both", "shape", "distance"), trace=TRUE)


### Arguments

 Beta A matrix n x q. q represents the number of curves to cluster and n is either the length of percentiles used in the quantile regression or the length of the time vector. alpha It is the alpha-percentile used for computing the dissimilarity matrix. If not fixed, the algorithm choose alpha=.25 (cluster.effects=TRUE) or alpha=.5 (cluster.effects=FALSE). step The steps used in computing the dissimilarity matrix. Default is "both"=("shape" and "distance") trace If TRUE, some informations are printed.

### Value

The dissimilarity matrix of class “dist”.

### Author(s)

Gianluca Sottile gianluca.sottile@unipa.it

### References

Sottile, G., Adelfio, G. Clusters of effects curves in quantile regression models. Comput Stat 34, 551–569 (2019). https://doi.org/10.1007/s00180-018-0817-8

Sottile, G and Adelfio, G (2017). Clustering of effects through quantile regression. Proceedings 32nd International Workshop of Statistical Modeling, Groningen (NL), vol.2 127-130, https://iwsm2017.webhosting.rug.nl/IWSM_2017_V2.pdf.

Frumento, P., and Bottai, M. (2015). Parametric modeling of quantile regression coefficient functions. Biometrics, doi: 10.1111/biom.12410.

clustEff,summary.clustEff, plot.clustEff, for summary and plotting. extract.object to extract useful objects for the clustering algorithm through a quantile regression coefficient modeling in a multivariate case.

### Examples


set.seed(1234)
n <- 300
x <- 1:n/n

Y <- matrix(0, n, 30)

sigma2 <- 4*pmax(x-.2, 0) - 8*pmax(x-.5, 0) + 4*pmax(x-.8, 0)

mu <- sin(3*pi*x)
for(i in 1:10) Y[, i] <- mu + rnorm(length(x), 0, pmax(sigma2, 0))

mu <- cos(3*pi*x)
for(i in 11:23) Y[,i] <- mu + rnorm(length(x), 0, pmax(sigma2,0))

mu <- sin(3*pi*x)*cos(pi*x)
for(i in 24:28) Y[, i] <- mu + rnorm(length(x), 0, pmax(sigma2, 0))

mu <- 0 #sin(1/3*pi*x)*cos(2*pi*x)
for(i in 29:30) Y[, i] <- mu + rnorm(length(x), 0, pmax(sigma2, 0))

diss <- distshape(Y)
diss



[Package clustEff version 0.2.0 Index]