CCRls {cluscov} | R Documentation |
Sequential CCR
Description
CCRls
runs regressions with potentially more covariates than observations.
See c_chmod()
for the list of models supported.
Usage
CCRls(Y, X, 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) |
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. |
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 |
Value
betas
parameter estimates (intercept first),
iter
number of iterations,
dev
increment in the objective function value at convergence
fval
objective function value at convergence
Examples
set.seed(14) #Generate data
N = 1000; (bets = rep(-2:2,4)); p = length(bets); X = matrix(rnorm(N*p),N,p)
Y = cbind(1,X)%*%matrix(c(0.5,bets),ncol = 1)
CCRls(Y,X,kap=0.1,modclass="lm",tol=1e-6,reltol=TRUE,rndcov=NULL,report=8)