Sequential CCR with k clusters

CCRseqk runs regressions with potentially more covariates than observations with k clusters. See c_chmod() for the list of models supported.

CCRseqk(Y, X, k, nC = 1, kap = 0.1, modclass = "lm", tol = 1e-06,
  reltol = TRUE, rndcov = NULL, report = NULL, ...)

Arguments

Y

vector of dependent variable Y
X

design matrix (without intercept)
k

number of clusters
nC

first nC-1 columns in X not to cluster
kap

maximum number of parameters to estimate in each active sequential step, as a fraction of the less of total number of observations n or number of covariates p. i.e. \(min(n,p)\)
modclass

a string denoting the desired the class of model. See c_chmod for details.
tol

level of tolerance for convergence; default tol=1e-6
reltol

a logical for relative tolerance instead of level. Defaults at TRUE
rndcov

seed for randomising assignment of covariates to partitions; default NULL
report

number of iterations after which to report progress; default NULL
...

additional arguments to be passed to the model

Value

a list of objects

  • mobj low dimensional model object of class lm, glm, or rq (depending on modclass)

  • clus cluster assignments of covariates

  • iter number of iterations

  • dev decrease in the function value at convergence

Examples

set.seed(14) #Generate data
N = 1000; (bets = rep(-2:2,4)/2); p = length(bets); X = matrix(rnorm(N*p),N,p)
#>  [1] -1.0 -0.5  0.0  0.5  1.0 -1.0 -0.5  0.0  0.5  1.0 -1.0 -0.5  0.0  0.5  1.0
#> [16] -1.0 -0.5  0.0  0.5  1.0
Y = cbind(1,X)%*%matrix(c(0.5,bets),ncol = 1); nC=1
zg=CCRseqk(Y,X,k=5,nC=nC,kap=0.1,modclass="lm",tol=1e-6,reltol=TRUE,rndcov=NULL,report=8)
(del=zg$mobj$coefficients) # delta
#>   (Intercept)            X1            X2            X3            X4 
#>  5.000000e-01 -1.000000e+00 -5.000000e-01  1.944761e-16  5.000000e-01 
#>            X5 
#>  1.000000e+00 
(bets = c(del[1:nC],(del[-c(1:nC)])[zg$clus])) #construct beta
#>   (Intercept)            X1            X2            X3            X4 
#>  5.000000e-01 -1.000000e+00 -5.000000e-01  1.944761e-16  5.000000e-01 
#>            X5            X1            X2            X3            X4 
#>  1.000000e+00 -1.000000e+00 -5.000000e-01  1.944761e-16  5.000000e-01 
#>            X5            X1            X2            X3            X4 
#>  1.000000e+00 -1.000000e+00 -5.000000e-01  1.944761e-16  5.000000e-01 
#>            X5            X1            X2            X3            X4 
#>  1.000000e+00 -1.000000e+00 -5.000000e-01  1.944761e-16  5.000000e-01 
#>            X5 
#>  1.000000e+00