R/zOneDimLogit.r

In SGL: Fit a GLM (or Cox Model) with a Combination of Lasso and Group Lasso Regularization

oneDimLogit <- function(data, index, thresh = 0.0001, lambdas=NULL, beta.naught = rep(0,ncol(data$x)), inner.iter = 100, outer.iter = 100, outer.thresh = 0.0001, gamma = 0.8, step = 1, reset = 10, alpha = 0.95, min.frac= 0.05, nlam = 20, verbose = FALSE){

  if(is.null(lambdas)){
  lambdas <- betterPathCalc(data = data, index = index, alpha=alpha, min.frac = min.frac, nlam = nlam, type = "logit")
  }

  X <- data$x
  y <- data$y
  n <- nrow(X)
  p <- ncol(X)

  ## Setting up group lasso stuff ##
     
  ord <- order(index)
  index <- index[ord]
  X <- X[,ord]
  unOrd <- match(1:length(ord),ord)

  ## Coming up with other C++ info ##
    
  groups <- unique(index)
  num.groups <- length(groups)
  range.group.ind <- rep(0,(num.groups+1))
  for(i in 1:num.groups){
    range.group.ind[i] <- min(which(index == groups[i])) - 1
  }
  range.group.ind[num.groups + 1] <- ncol(X)
  
  group.length <- diff(range.group.ind)
  beta.naught <- rep(0,ncol(X))
  beta <- beta.naught
  
  beta.is.zero <- rep(1, num.groups)
  beta.old <- rep(0, ncol(X))
  beta <- matrix(0, nrow = ncol(X), ncol = nlam)

  eta <- rep(0,n)

  intercepts <- rep(log(sum(y)) - log(n-sum(y)), nlam)

  eta = eta + intercepts[1]

  

  beta.is.zero <- rep(1, num.groups)
  beta.old <- rep(0, ncol(X))
 # eta <- rep(0,n)
    for(i in 1:nlam){

    junk <- .C("logitNest", X = as.double(as.vector(X)), y = as.integer(y), index = as.integer(index), nrow = as.integer(nrow(X)), ncol = as.integer(ncol(X)), numGroup = as.integer(num.groups), rangeGroupInd = as.integer(range.group.ind), groupLen = as.integer(group.length), lambda1 = as.double(alpha*lambdas[i]), lambda2 = as.double((1-alpha)*lambdas[i]), beta = as.double(beta.old), innerIter = as.integer(inner.iter), outerIter = as.integer(outer.iter), thresh = as.double(thresh), outerThresh = as.double(outer.thresh), eta = as.double(eta), gamma = as.double(gamma), betaIsZero = as.integer(beta.is.zero), betaZero = as.double(intercepts[i]), step = as.double(step))

    intercepts[i] = junk$betaZero
    if(i < nlam){
      intercepts[i+1] = intercepts[i]
    }
    beta.new <- junk$beta
    beta[,i] <- beta.new
    beta.is.zero <- junk$betaIsZero
    eta <- junk$eta
    beta.old <- beta.new
    if(verbose == TRUE){
      write(paste("***Lambda", i, "***"),"")
      }
    }




  return(list(beta = beta[unOrd,], lambdas = lambdas, intercepts = intercepts))
}