Sequential CCR

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

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. \(min(n,p)\)
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

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)
#>  [1] -2 -1  0  1  2 -2 -1  0  1  2 -2 -1  0  1  2 -2 -1  0  1  2
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)
#> Iter =8fval =171.638146623588
#> Iter =16fval =-2021.84752122029
#> Iter =24fval =-4238.60896304071
#> Iter =32fval =-6142.19086056517
#> Iter =40fval =-7774.8680459318
#> Iter =48fval =-9587.03501749175
#> Iter =56fval =-11390.6671213327
#> Iter =64fval =-13005.2920283862
#> Iter =72fval =-14775.7226280329
#> Iter =80fval =-16533.2707748432
#> Iter =88fval =-18396.9020228272
#> Iter =96fval =-20283.2123805922
#> Iter =104fval =-21899.352834383
#> Iter =112fval =-23697.1933466147
#> Iter =120fval =-25467.9532300146
#> Iter =128fval =-27271.2886822776
#> Iter =136fval =-29144.2744340554
#> $betas
#>   (Intercept)                                                         
#>  5.000000e-01 -2.000000e+00 -1.000000e+00  9.545726e-15  1.000000e+00 
#>                                                                       
#>  2.000000e+00 -2.000000e+00 -1.000000e+00 -2.844662e-15  1.000000e+00 
#>                                                                       
#>  2.000000e+00 -2.000000e+00 -1.000000e+00 -5.262962e-16  1.000000e+00 
#>                                                                       
#>  2.000000e+00 -2.000000e+00 -1.000000e+00 -1.949927e-15  1.000000e+00 
#>               
#>  2.000000e+00 
#> 
#> $iter
#> [1] 139
#> 
#> $dev
#> 'log Lik.' -0.00236367 (df=5)
#> 
#> $fval
#> 'log Lik.' -29814.17 (df=5)
#>