GroupProj {kimod} | R Documentation |
GroupProj
of SelectVar objectThis function calculates the relashionship between genes and samples from SelectVar method. Also, computes agglomerative hierarchical clustering of the dataset.
GroupProj(object, NGroups = 2, metric = c("euclidean", "manhattan"), method = c("ward", "single", "complete", "weighted", "flexible"), ...) ## S4 method for signature 'SelectVar' GroupProj(object, NGroups = 2, metric = c("euclidean", "manhattan"), method = c("ward", "single", "complete", "weighted", "flexible"), ...)
object |
it is an object of SelectVar Class. |
NGroups |
An integer scalar or vector with the desired number of clusters. |
metric |
The character string specifying the metric to be used to calculate dissimilarities between observations. The currently available options are "euclidean" and "manhattan". Euclidean distances are root sum-of-squares of differences, and manhattan distances are the sum of absolute differences. |
method |
character string defining the clustering method.
The methods implemented are "average" ([unweighted pair-]
group [arithMetic] average method, aka b
"single" (single linkage),
"complete" (complete linkage),
"ward" (Ward's method),
"weighted" (weighted average linkage, aka b
its generalization "flexible" which uses
(a constant version of) the Lance-Williams
formula and the par.method argument, and "gaverage"
a generalized "average" aka b
also using the Lance-Williams formula and par.method. (See |
... |
Additional parameters for |
GroupProj |
GroupProj with the corresponding completed slots |
M L Zingaretti, J A Demey-Zambrano, J L Vicente Villardon, J R Demey
{ data(NCI60Selec) Z1<-DiStatis(NCI60Selec) M1<-SelectVar(Z1,Crit="R2-Adj") A1<-GroupProj(M1,method="ward",metric="euclidean",NGroups=6) data(winesassesors) Z2<-DiStatis(winesassesors) M2<-SelectVar(Z2,Crit="R2-Adj") A2<-GroupProj(M2,method="ward",metric="euclidean",NGroups=6) }