R/ri.R

In gamlss: Generalized Additive Models for Location Scale and Shape

##------------------------------------------------------------------------------
# New Ridge Lasso 
# ri : fit a ridge type regression in gamlss using QR and svd
#-------------------------------------------------------------------------------
# QUESTIONS
# 1) do we need to stardise? Bob thinks YES
#   taking out the mean improves but I am not sure if we should scale
# 
#-------------------------------------------------------------------------------
# TO DO
#  to translate to svd algorithm done 
#  save the lanbda as in pb done
#  I have taken out GCV because I am not sure it should work
#  should I allowed formula?
#-------------------------------------------------------------------------------
# this has a new experimental function to fit ridge
#-------------------------------------------------------------------------------
#------------------------------------------------------------------------------
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ri <- function(X = NULL,
            x.vars = NULL,
                df = NULL, 
            lambda = NULL,
            method = c("ML","GAIC"),
             order = 0, 
             start = 10,  
                Lp = 2,
             kappa = 0.00001, 
              iter = 100,  # no of iterations 
            c.crit = 1.0e-6,
                 k = 2) 
{
     scall <- deparse(sys.call(), width.cutoff = 500L)
    method <- match.arg(method)
  # check for standarized matric
     rexpr <- grepl("gamlss",sys.calls()) ## 
for (i in length(rexpr):1) { 
      position <- i # get the position
      if (rexpr[i]==TRUE) break
    }
gamlss.env <- sys.frame(position) #gamlss or predict.gamlss
## get the data
if (sys.call(position)[1]=="predict.gamlss()") { # if predict is used 
      Data <- get("data", envir=gamlss.env)
    } else if (sys.call(position)[1]=="gamlss()") { # if gamlss() is used
      if (is.null(get("gamlsscall", envir=gamlss.env)$data)) { # if no data argument but the formula can be interpreted
        Data <- data.frame(X)	
    } else {# data argument in gamlss 
        Data <- get("gamlsscall", envir=gamlss.env)$data
    }
    } else {
      Data <- get("data", envir=gamlss.env)
    }
    Data <- data.frame(eval(substitute(Data)))
    # 
    if (is.null(X)&&is.null(x.vars)) stop("X or x.vars has to be set in gnet")
    if (is.null(X)&&!is.null(x.vars)) X <- as.matrix(Data[, x.vars])
    if (!is.null(X)&&is.null(x.vars)) warning("For prediction use the x.vars argument")
     X <- scale(X)
  #if (any(abs(apply(X,2, "mean")>.5))) warning("The design matrix X should be standarized")
  # if (any(abs(apply(X,2, "sd")>.5))) warning("The design matrix X should be standarized")
         p <- ncol(X)
         n <- nrow(X)
    namesX <- if (is.null(colnames(X)))  as.character(1:p) else colnames(X)
  if(!is.null(lambda))
  {
    if(lambda<0)
    {
    lambda <- 0
      warning(paste("lambda was negative; have used ", lambda))
    }
  } 
  if(!is.null(df))
  {
    if (df > p)  warning("the df are set to p") 
         df <- if (df > p)  p  else  df  
    if (df < 0)  warning("the df are set to 0") 
         df <- if (df < 0)  0  else  df  
  }
  ## here we get the gamlss environment and a random name to save
  ## the starting values for lambda within gamlss()
  ## get gamlss environment
  # #--------
  #     rexpr <- regexpr("gamlss",sys.calls())
  # for (i in 1:length(rexpr)){ 
  #   position <- i 
  #   if (rexpr[i]==1) break}
  # gamlss.environment <- sys.frame(position)
  #--------
  ## get a random name to use it in the gamlss() environment
  #--------
               sl <- sample(letters, 4)
      fourLetters <- paste(paste(paste(sl[1], sl[2], sep=""), sl[3], sep=""),sl[4], sep="")
  startLambdaName <- paste("start.Lambda",fourLetters, sep=".")
  ## put the starting values in the gamlss()environment
  #--------
  assign(startLambdaName, start, envir=gamlss.env)
  #--------
  # this is included here for generality  
                         D <- if(order==0) diag(p) else diff(diag(p), diff=order)
                         x <- rep(0, n)
              attr(x, "X") <- X
           attr(x, "call") <- substitute(gamlss.ri(data[[scall]], z, w)) 
         attr(x, "namesX") <- namesX
              attr(x, "D") <- D
         attr(x, "lambda") <- lambda
             attr(x, "df") <- df
         attr(x, "method") <- method
             attr(x, "Lp") <- Lp
          attr(x, "kappa") <- kappa
           attr(x, "iter") <- iter
         attr(x, "c.crit") <- c.crit
           attr(x,"order") <- order
         attr(x, "start")  <- start 
             attr(x, "k")  <- k 
    attr(x, "gamlss.env")  <- gamlss.env
  attr(x, "NameForLambda") <- startLambdaName
                 class(x)  <- c("smooth", class(x))  
  x
}
#-------------------------------------------------------------------------------
gamlss.ri <- function(x, y, w, xeval = NULL, ...)
{  
#-------------------------------------------------------------------------------
# local functions
#-------------------------------------------------------------------------------
  regpen <- function(sm,  D,  P0, lambda)
  {
    for (it in 1: iter) 
    {  
         RD <- rbind(R,sqrt(lambda)*sqrt(omega.)*D) #
      svdRD <- svd(RD)  
       rank <- sum(svdRD$d>max(svdRD$d)*.Machine$double.eps^.8)
         np <- min(p,N)# new MS 7-5-17
         U1 <- svdRD$u[1:np,1:rank] # new MS 7-5-17
         y1 <- t(U1)%*%Qy
       beta <- svdRD$v[,1:rank] %*%(y1/svdRD$d[1:rank])
         dm <- max(abs(sm - beta))
         sm <- beta
     omega. <- c(1 / (abs(sm)^(2-Lp) + kappa ^ 2)) # L_p
      #   Omega <- diag(omega.)
      if (dm < c.crit) break # if difference small stop    
    }
         HH <- (svdRD$u)[1:p,1:rank]%*%t(svdRD$u[1:p,1:rank])
        edf <- sum(diag(HH))
          fv <- X%*%beta
row.names(beta) <- namesX
        out <-  list(fv=fv, beta=beta, edf=edf, omega=omega.)  
  }
  #-------------------------------------------------------------------------------
  #-------------------------------------------------------------------------------
  # ## function to find lambdas miimizing the local GAIC        
  fnGAIC <- function(lambda, k)
  {
     fit <- regpen(sm,  D, P0, lambda)
      fv <- fit$fv         
    GAIC <- sum(w*(y-fv)^2)+k*fit$edf 
    # cat("GAIC", GAIC, "\n")
    GAIC   
  }
  #-------------------------------------------------------------------------------
  #-------------------------------------------------------------------------------
  # main function starts here 
  # ------------------------------------------------------------------------------
              X <-  if (is.null(xeval)) as.matrix(attr(x,"X"))
                   else as.matrix(attr(x,"X"))[seq(1,length(y)),]
         namesX <- as.character(attr(x,"namesX")) 
              D <- as.matrix(attr(x,"D"))
          order <- as.vector(attr(x,"order"))
         lambda <- as.vector(attr(x,"lambda"))
             df <- as.vector(attr(x,"df"))  
             Lp <- as.vector(attr(x,"Lp")) 
          kappa <- as.vector(attr(x,"kappa")) 
           iter <- as.vector(attr(x,"iter")) 
              k <- as.vector(attr(x,"k")) 
         c.crit <- as.vector(attr(x,"c.crit"))
         method <- as.character(attr(x,"method")) 
     gamlss.env <- as.environment(attr(x, "gamlss.env"))
startLambdaName <- as.character(attr(x, "NameForLambda")) 
              N <- sum(w!=0) # DS+FDB 3-2-14
              n <- nrow(X)
              p <- ncol(X)
             aN <- nrow(D)
             ap <- ncol(D)  
            qrX <- qr(sqrt(w)*X, tol=.Machine$double.eps^.8)  
              R <- qr.R(qrX)
              Q <- qr.Q(qrX) 
             Qy <- t(Q)%*%(sqrt(w)*y)
  # 
  if(p!=ap) stop("the dimensions of the penalty matrix and of the design matrix are incompatible")
            P0 <- diag(p) * 1e-6
  ## starting values for smoothing function 
            sm <- rep(0, p) 
        omega. <- rep(1, p)
          tau2 <- sig2 <- NULL
  # now the action depends on the values of lambda and df
  #------------------------------------------------------------------------------- 
        lambdaS <- get(startLambdaName, envir=gamlss.env) ## geting the starting value
  if (lambdaS>=1e+07) lambda <- 1e+07 # MS 19-4-12
  if (lambdaS<=1e-07) lambda <- 1e-07 # MS 19-4-12
  # cat(lambda, "\n")
  # case 1: if lambda is known just fit -----------------------------------------
  if (is.null(df)&&!is.null(lambda)||!is.null(df)&&!is.null(lambda))
  {
         fit <- regpen(sm,  D, P0, lambda)
          fv <- fit$fv        
  } # case 2: if lambda is estimated -------------------------------------------- 
  else if (is.null(df)&&is.null(lambda)) 
  {
    lambda <- lambdaS  # MS 19-4-12
    switch(method,
           "ML"={
             for (it in 1:20) 
             {
               fit  <- regpen(sm,  D, P0, lambda)
             gamma. <- D %*% as.vector(fit$beta)*sqrt(fit$omega) 
                 fv <- X %*% fit$beta
               sig2 <- sum(w * (y - fv) ^ 2) / (N - fit$edf)
               tau2 <- sum(gamma. ^ 2) / (fit$edf-order)# Tuesday, March 17, 2009 at 11:57
         lambda.old <- lambda
             lambda <- sig2 / tau2
if (abs(lambda-lambda.old) < 0.0001||lambda>100000) break
               #cat("lambda",lambda, '\n')
             }
           },
  "GAIC"=  #--------------------------------------------------------------- GAIC
{
            lambda <- nlminb(lambda, fnGAIC,  lower = 1.0e-7, upper = 1.0e7, k=k)$par 
              fit  <- regpen(sm,  D,  P0, lambda)
                fv <- fit$fv     
  assign(startLambdaName, lambda, envir=gamlss.env)
},
    )
  }
else # case 3 : if df are required
{ 
  #method 2 from Simon Wood (2006) pages 210-211, and 360 
  ## local function to get df using eigen values
  edf1_df <- function(lambda)
  {
     edf <-  sum(1/(1+lambda*UDU$values))
     (edf-df)
  }
  
     Rinv <- solve(R)
      S   <- t(D)%*%D
      UDU <- eigen(t(Rinv)%*%S%*%Rinv)           
   lambda <- if (sign(edf1_df(0))==sign(edf1_df(100000))) 100000  # in case they have the some sign
             else  uniroot(edf1_df, c(0,100000))$root
  # if (any(class(lambda)%in%"try-error")) {lambda<-100000}  
     fit  <- regpen(sm, D, P0, lambda)
       fv <- fit$fv
}      
     waug <- as.vector(c(w, rep(1,nrow(D))))
     xaug <- as.matrix(rbind(X,sqrt(lambda)*D))
      lev <- hat(sqrt(waug)*xaug,intercept=FALSE)[1:n] # get the hat matrix
      var <- lev/w # the variance of the smoother
  coefSmo <- list(coef = fit$beta, 
                lambda = lambda, 
                   edf = fit$edf, 
                 sigb2 = tau2, 
                 sige2 = sig2,
                  sigb = if (is.null(tau2)) NA else sqrt(tau2),
                  sige = if (is.null(sig2)) NA else sqrt(sig2),
                fv = as.vector(fv),  
                se = sqrt(var),
                Lp = Lp)
class(coefSmo) <- "ri"
#----------------------------------------------
if (is.null(xeval))
{
  list(fitted.values=as.vector(fv), residuals=y-fv, var=var, nl.df =fit$edf-1,
       lambda=lambda, coefSmo=coefSmo )
}
else 
{
  ll <- dim(as.matrix(attr(x,"X")))[1]
  nx <- as.matrix(attr(x,"X"))[seq(length(y)+1,ll),]
  pred <- drop(nx %*% fit$beta)
  pred
}    
}
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# methods for ri object
plot.ri <- function(x, ...)
{
  alist <- list(...)
main  <-  if (!is.null(alist$main)) alist$main 
else paste("Lp =", paste(x$Lp), sep=" ")
  plot(x$coef, type="h", xlab="x-variables", ylab="coefficients", 
       main=main)  
  abline(h=0)
}
#-------------------------------------------------------------------------------
coef.ri <- function(object, ...)
{
         x <- as.vector(object$coef)
  names(x) <-  row.names(object$coef)
  x
}
#-------------------------------------------------------------------------------
fitted.ri<- function(object, ...)
{
  as.vector(object$fv)
}
#------------------------------------------------------------------------------
print.ri  <- function (x, digits = max(3, getOption("digits") - 3), ...) 
{   
  cat("Ridge fit using the gamlss function ri() \n")
  cat("Degrees of Freedom for the fit :", x$edf, "\n")
  cat("Random effect parameter sigma_b:", format(signif(x$sigb)), "\n")  
  cat("Smoothing parameter lambda     :", format(signif(x$lambda)), "\n") 
}
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