C Output from Public domain Ratfor, version 1.0 subroutine sslvrg(penalt,dofoff,x,y,w,ssw, n, knot,nk,coef, * sz,lev, crit,icrit, lambda, xwy, hs0,hs1,hs2,hs3, * sg0,sg1,sg2,sg3, abd,p1ip,p2ip,ld4,ldnk,info) C Purpose : C Compute smoothing spline for smoothing parameter lambda C and compute one of three `criteria' (OCV , GCV , "df match"). C See comments in ./sbart.f from which this is called integer n,nk,icrit,ld4,ldnk,info DOUBLE precision penalt,dofoff,x(n),y(n),w(n),ssw, & knot(nk+4), coef(nk),sz(n),lev(n), crit, lambda, * xwy(nk), hs0(nk),hs1(nk),hs2(nk),hs3(nk), * sg0(nk),sg1(nk),sg2(nk),sg3(nk), abd(ld4,nk), & p1ip(ld4,nk),p2ip(ldnk,nk) EXTERNAL bvalue double precision bvalue C local variables double precision vnikx(4,1),work(16) integer i,ileft,j,mflag, lenkno, ilo double precision b0,b1,b2,b3,eps, xv,rss,df, sumw c c integer interv c external interv lenkno = nk+4 ileft = 1 eps = 1d-11 ilo = 1 C compute the coefficients coef() of estimated smooth do 1 i=1,nk coef(i) = xwy(i) abd(4,i) = hs0(i)+lambda*sg0(i) 1 continue do 4 i=1,(nk-1) abd(3,i+1) = hs1(i)+lambda*sg1(i) 4 continue do 6 i=1,(nk-2) 6 abd(2,i+2) = hs2(i)+lambda*sg2(i) do 8 i=1,(nk-3) 8 abd(1,i+3) = hs3(i)+lambda*sg3(i) c factorize banded matrix abd: call dpbfa(abd,ld4,nk,3,info) if(info.ne.0) then C matrix could not be factorized -> ier := info return endif c solve linear system (from factorize abd): call dpbsl(abd,ld4,nk,3,coef) C Value of smooth at the data points do 12 i=1,n xv = x(i) 12 sz(i) = bvalue(knot,coef,nk,4,xv,0) C Compute the criterion function if requested if(icrit .eq. 0)then return else C --- Ordinary or Generalized CV or "df match" --- C Get Leverages First call sinerp(abd,ld4,nk,p1ip,p2ip,ldnk,0) do 16 i=1,n xv = x(i) call interv(knot(1), nk+1, xv, ileft, mflag) if(mflag .eq. -1) then ileft = 4 xv = knot(4)+eps else if(mflag .eq. 1) then ileft = nk xv = knot(nk+1) - eps endif j=ileft-3 C call bspvd(knot,4,1,xv,ileft,4,vnikx,work) call bsplvd(knot,lenkno,4,xv,ileft,work,vnikx,1) b0=vnikx(1,1) b1=vnikx(2,1) b2=vnikx(3,1) b3=vnikx(4,1) lev(i) = ( & p1ip(4,j)*b0**2 + 2.d0*p1ip(3,j)*b0*b1 + * 2.d0*p1ip(2,j)*b0*b2 + 2.d0*p1ip(1,j)*b0*b3 + * p1ip(4,j+1)*b1**2 + 2.d0*p1ip(3,j+1)*b1*b2 + * 2.d0*p1ip(2,j+1)*b1*b3 + p1ip(4,j+2)*b2**2 + & 2.d0*p1ip(3,j+2)*b2*b3 + p1ip(4,j+3)*b3**2 & )*w(i)**2 16 continue C Evaluate Criterion if(icrit .eq. 1)then C Generalized CV rss = ssw df = 0d0 sumw = 0d0 c w(i) are sqrt( wt[i] ) weights scaled in ../R/smspline.R such c that sumw = number of observations with w(i) > 0 do 24 i=1,n rss = rss + ((y(i)-sz(i))*w(i))**2 df = df + lev(i) sumw = sumw + w(i)**2 24 continue crit = (rss/sumw)/((1d0-(dofoff + penalt*df)/sumw)**2) c call dblepr("spar", 4, spar, 1) c call dblepr("crit", 4, crit, 1) else if(icrit .eq. 2) then C Ordinary CV crit = 0d0 do 30 i = 1,n 30 crit = crit + (((y(i)-sz(i))*w(i))/(1-lev(i)))**2 crit = crit/n c call dblepr("spar", 4, spar, 1) c call dblepr("crit", 4, crit, 1) else C df matching crit = 0d0 do 32 i=1,n 32 crit = crit+lev(i) crit = 3 + (dofoff-crit)**2 endif return endif C Criterion evaluation end