CCRseqk {cluscov} | R Documentation |
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, ...)
Y |
vector of dependent variable Y |
X |
design matrix (without intercept) |
k |
number of clusters |
nC |
first |
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 |
reltol |
a logical for relative tolerance instead of level. Defaults at TRUE |
rndcov |
seed for randomising assignment of covariates to partitions; default |
report |
number of iterations after which to report progress; default |
... |
additional arguments to be passed to the model |
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
set.seed(14) #Generate data N = 1000; (bets = rep(-2:2,4)/2); p = length(bets); X = matrix(rnorm(N*p),N,p) 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 (bets = c(del[1:nC],(del[-c(1:nC)])[zg$clus])) #construct beta