Linear regression via coordinate descent with covariate clustering

This function is a wrapper for linrclus. It requires less input.

CCRls.coord(Y, X, k, nC = 1, ...)

Arguments

Y

vector of outcome variable
X

matrix of covariates. Should not include 1's for the intercept
k

number of clusters
nC

first nC-1 covariates in X not to cluster. Must be at least 1 for the intercept
...

additional parameters to be passed to lm

Value

mobj the low dimension lm regression object

clus cluster assignments of covariates (excluding the first nC covariates - including the intercept 1)

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.coord(Y,X,k=5,nC=1)
#> Inner iter = 1, RSS = 660.88739, and reldev = 0.30341 
#> Inner iter = 2, RSS = 660.88739, and reldev = 0.00000 
#> $mobj
#> 
#> Call:
#> stats::lm(formula = Y ~ as.matrix(cbind(Xnc, X1)[, -1]))
#> 
#> Coefficients:
#>                     (Intercept)  as.matrix(cbind(Xnc, X1)[, -1])  
#>                        5.00e-01                        -2.00e+00  
#> as.matrix(cbind(Xnc, X1)[, -1])  as.matrix(cbind(Xnc, X1)[, -1])  
#>                       -1.00e+00                         4.01e-16  
#> as.matrix(cbind(Xnc, X1)[, -1])  as.matrix(cbind(Xnc, X1)[, -1])  
#>                        1.00e+00                         2.00e+00  
#> 
#> 
#> $clus
#>  [1] 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5
#>