distshape | R Documentation |
This function implements the dissimilarity matrix based on shape and distance of curves.
distshape(Beta, alpha=.5, step=c("both", "shape", "distance"), trace=TRUE)
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. |
The dissimilarity matrix of class “dist
”.
Gianluca Sottile gianluca.sottile@unipa.it
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.
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
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