CRAN Package Check Results for Package robeth

Last updated on 2024-05-19 22:50:48 CEST.

Flavor Version Tinstall Tcheck Ttotal Status Flags
r-devel-linux-x86_64-debian-clang 2.7-8 50.97 63.64 114.61 ERROR
r-devel-linux-x86_64-debian-gcc 2.7-8 22.49 45.80 68.29 OK
r-devel-linux-x86_64-fedora-clang 2.7-8 149.78 ERROR
r-devel-linux-x86_64-fedora-gcc 2.7-8 124.84 OK
r-devel-windows-x86_64 2.7-8 25.00 93.00 118.00 OK
r-patched-linux-x86_64 2.7-8 31.94 60.89 92.83 OK
r-release-linux-x86_64 2.7-8 25.35 59.32 84.67 OK
r-release-macos-arm64 2.7-8 37.00 OK
r-release-windows-x86_64 2.7-8 25.00 91.00 116.00 OK
r-oldrel-macos-arm64 2.7-8 40.00 OK
r-oldrel-macos-x86_64 2.7-8 93.00 OK
r-oldrel-windows-x86_64 2.7-8 27.00 102.00 129.00 OK

Additional issues

clang17 Intel

Check Details

Version: 2.7-8
Check: examples
Result: ERROR Running examples in ‘robeth-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: robeth-package > ### Title: Interface for the FORTRAN programs developed at the ETH-Zuerich > ### and then at IUMSP-Lausanne > ### Aliases: robeth-package robeth > ### Keywords: package robust > > ### ** Examples > > library(robeth) > > # > # ------------- Examples of Chapter 1: Location problems ------------------------------ > # > y <- c(6.0,7.0,5.0,10.5,8.5,3.5,6.1,4.0,4.6,4.5,5.9,6.5) > n <- 12 > dfvals() NULL > #----------------------------------------------------------------------- > # M-estimate (tm) of location and confidence interval (tl,tu) > # > dfrpar(as.matrix(y),"huber") $itypw [1] 0 $itype [1] 1 $isigma [1] 1 > libeth() $bta [1] 0.3550823 attr(,"Csingle") [1] TRUE > s <- lilars(y); t0 <- s$theta; s0 <- s$sigma > s <- lyhalg(y=y,theta=t0,sigmai=s0) > tm <- s$theta; vartm <- s$var > s <- quant(0.975) > tl <- tm-s$x*sqrt(vartm) > tu <- tm+s$x*sqrt(vartm) > #----------------------------------------------------------------------- > # Hodges and Lehmann estimate (th) and confidence interval (zl,zu) > # > m <- n*(n+1)/2 # n even > k1 <- m/2; k2 <- k1+1 > z1 <- lyhdle(y=y,k=k1); z2 <- lyhdle(y=y,k=k2) > th <- (z1$hdle+z2$hdle)/2. > ku <- liindh(0.95,n); kl <- liindh(0.05,n) > zu <- lyhdle(y=y,k=ku$k); zl <- lyhdle(y=y,k=kl$k) > #....................................................................... > { + cat(" tm, tl, tu : ",round(c(tm,tl,tu),3),"\n") + cat(" th, zl, zu : ",round(c(th,zl$hdle,zu$hdle),3),"\n") + } tm, tl, tu : 5.809 4.748 6.87 th, zl, zu : 5.85 5 7 > # tm, tl, tu : 5.809 4.748 6.87 > # th, zl, zu : 5.85 5 7 > #======================================================================= > # > # Two sample problem > # > y <- c(17.9,13.3,10.6,7.6,5.7,5.6,5.4,3.3,3.1,0.9) > n <- 10 > x <- c(7.7,5.0,1.7,0.0,-3.0,-3.1,-10.5) > m <- 7 > #----------------------------------------------------------------------- > # Estimate (dm) and confidence interval [dl,du] based on Mann-Whitney > # > k1 <- m*n/2; k2 <- k1+1 > s1 <- lymnwt(x=x,y=y,k=k1); s2 <- lymnwt(x=x,y=y,k=k2) > dm <- (s1$tmnwt+s2$tmnwt)/2.0 > sl <- liindw(0.05,m,n); kl <- sl$k > s <- lymnwt(x=x,y=y,k=kl); dl <- s$tmnwt > s <- lymnwt(x=x,y=y,k=m*n-kl+1); du <- s$tmnwt > #----------------------------------------------------------------------- > # Tau-test . P-value (P) > # > z <- c(x,y) > dfrpar(as.matrix(z),"huber") $itypw [1] 0 $itype [1] 1 $isigma [1] 1 > libeth() $bta [1] 0.3550823 attr(,"Csingle") [1] TRUE > s <- lytau2(z=z,m=m,n=n) > P <- s$p > # > # estimate (tm) and confidence interval (tl,tu) > # > tm <- s$deltal > c22<- s$cov[3] > s <- quant(0.975) > tl <- tm-s$x*sqrt(c22) > tu <- tm+s$x*sqrt(c22) > #....................................................................... > { + cat("dm, dl, du:",round(c(dm,dl,du),3),"\n") + cat("P, tm, tl, tu:",round(c(P,tm,tl,tu),3),"\n") + } dm, dl, du: 6.35 2.9 12.9 P, tm, tl, tu: 0.014 6.918 1.562 12.273 > # dm, dl, du: 6.35 2.9 12.9 > # P, tm, tl, tu: 0.014 6.918 1.562 12.273 > > # > # Examples of Chapter 2: M-estimates of coefficients and scale in linear regression > # > # Read data; declare the vector wgt; load defaults > # > z <- c(-1, -2, 0, 35, 1, 0, -3, 20, + -1, -2, 0, 30, 1, 0, -3, 39, + -1, -2, 0, 24, 1, 0, -3, 16, + -1, -2, 0, 37, 1, 0, -3, 27, + -1, -2, 0, 28, 1, 0, -3, -12, + -1, -2, 0, 73, 1, 0, -3, 2, + -1, -2, 0, 31, 1, 0, -3, 31, + -1, -2, 0, 21, 1, 0, -1, 26, + -1, -2, 0, -5, 1, 0, -1, 60, + -1, 0, 0, 62, 1, 0, -1, 48, + -1, 0, 0, 67, 1, 0, -1, -8, + -1, 0, 0, 95, 1, 0, -1, 46, + -1, 0, 0, 62, 1, 0, -1, 77, + -1, 0, 0, 54, 1, 0, 1, 57, + -1, 0, 0, 56, 1, 0, 1, 89, + -1, 0, 0, 48, 1, 0, 1, 103, + -1, 0, 0, 70, 1, 0, 1, 129, + -1, 0, 0, 94, 1, 0, 1, 139, + -1, 0, 0, 42, 1, 0, 1, 128, + -1, 2, 0, 116, 1, 0, 1, 89, + -1, 2, 0, 105, 1, 0, 1, 86, + -1, 2, 0, 91, 1, 0, 3, 140, + -1, 2, 0, 94, 1, 0, 3, 133, + -1, 2, 0, 130, 1, 0, 3, 142, + -1, 2, 0, 79, 1, 0, 3, 118, + -1, 2, 0, 120, 1, 0, 3, 137, + -1, 2, 0, 124, 1, 0, 3, 84, + -1, 2, 0, -8, 1, 0, 3, 101) > xx <- matrix(z,ncol=4, byrow=TRUE) > dimnames(xx) <- list(NULL,c("z2","xS","xT","y")) > z2 <- xx[,"z2"]; xS <- xx[,"xS"]; xT <- xx[,"xT"] > x <- cbind(1, z2, xS+xT, xS-xT, xS^2+xT^2, xS^2-xT^2, xT^3) > y <- xx[,"y"] > wgt <- vector("numeric",length(y)) > n <- 56; np <- 7 > dfvals() NULL > # Set parameters for Huber estimate > dfrpar(x, "huber") $itypw [1] 0 $itype [1] 1 $isigma [1] 1 > # Compute the constants beta, bet0, epsi2 and epsip > ribeth(wgt) $d [1] 1.345 attr(,"Csingle") [1] TRUE $bta [1] 0.3550823 attr(,"Csingle") [1] TRUE > ribet0(wgt) $bt0 [1] 0.6741892 attr(,"Csingle") [1] TRUE > s <- liepsh() > epsi2 <- s$epsi2; epsip <- s$epsip > # > # Least squares solution (theta0,sigma0) > # > z <- riclls(x, y) > theta0<- z$theta; sigma0 <- z$sigma > # Preliminary estimate of the covariance matrix of the coefficients > cv <- kiascv(z$xt, fu=epsi2/epsip^2, fb=0.) > cov <- cv$cov > #----------------------------------------------------------------------- > # Solution (theta1,sigma1) by means of RYHALG. > # > zr <- ryhalg(x,y,theta0,wgt,cov,sigmai=sigma0,ic=0) > theta1<- zr$theta[1:np]; sigma1 <- zr$sigmaf; nit1 <- zr$nit > #----------------------------------------------------------------------- > # Solution (theta2,sigma2) by means of RYWALG (recompute cov) > # > cv <- ktaskv(x, f=epsi2/epsip^2) > zr <- rywalg(x, y, theta0, wgt, cv$cov, sigmai=sigma0) > theta2 <- zr$theta[1:np]; sigma2 <- zr$sigmaf; nit2 <- zr$nit > #----------------------------------------------------------------------- > # Solution (theta3,sigma3) by means of RYSALG with ISIGMA=2. > # > zr <- rysalg(x,y, theta0, wgt, cv$cov, sigma0, isigma=2) > theta3 <- zr$theta[1:np]; sigma3 <- zr$sigmaf; nit3 <- zr$nit > #----------------------------------------------------------------------- > # Solution (theta4,sigma4) by means of RYNALG with ICNV=2 and ISIGMA=0. > # > # Invert cov > covm1 <- cv$cov > zc <- mchl(covm1,np) > zc <- minv(zc$a, np) > zc <- mtt1(zc$r,np) > covm1 <- zc$b > zr <- rynalg(x,y, theta0, wgt, covm1, sigmai=sigma3, + iopt=1, isigma=0, icnv=2) > theta4 <- zr$theta[1:np]; sigma4 <- zr$sigmaf; nit4 <- zr$nit > #....................................................................... > { + cat("theta0 : ",round(theta0[1:np],3),"\n") + cat("sigma0 : ",round(sigma0,3),"\n") + cat("theta1 : ",round(theta1,3),"\n") + cat("sigma1, nit1 : ",round(sigma1,3),nit1,"\n") + cat("theta2 : ",round(theta2,3),"\n") + cat("sigma2, nit2 : ",round(sigma2,3),nit2,"\n") + cat("theta3 : ",round(theta3,3),"\n") + cat("sigma3, nit3 : ",round(sigma3,3),nit3,"\n") + cat("theta4 : ",round(theta4,3),"\n") + cat("sigma4, nit4 : ",round(sigma4,3),nit4,"\n") + } theta0 : 68.634 3.634 24.081 -8.053 -0.446 -0.179 -1.634 sigma0 : 26.635 theta1 : 70.006 5.006 24.742 -6.246 -0.079 0.434 -1.487 sigma1, nit1 : 23.564 7 theta2 : 70.006 5.006 24.742 -6.245 -0.079 0.434 -1.487 sigma2, nit2 : 23.563 7 theta3 : 69.993 5.002 24.766 -6.214 -0.055 0.44 -1.48 sigma3, nit3 : 22.249 3 theta4 : 69.993 5.002 24.766 -6.214 -0.055 0.44 -1.48 sigma4, nit4 : 22.249 3 > # theta0 : 68.634 3.634 24.081 -8.053 -0.446 -0.179 -1.634 > # sigma0 : 26.635 > # theta1 : 70.006 5.006 24.742 -6.246 -0.079 0.434 -1.487 > # sigma1, nit1 : 23.564 7 > # theta2 : 70.006 5.006 24.742 -6.245 -0.079 0.434 -1.487 > # sigma2, nit2 : 23.563 7 > # theta3 : 69.993 5.002 24.766 -6.214 -0.055 0.44 -1.48 > # sigma3, nit3 : 22.249 3 > # theta4 : 69.993 5.002 24.766 -6.214 -0.055 0.44 -1.48 > # sigma4, nit4 : 22.249 3 > > > # > # ---- Examples of Chapter 3: Weights for bounded influence regression ------ > # > > #======================================================================= > rbmost <- function(x,y,cc,usext=userfd) { + n <- nrow(x); np <- ncol(x); dfcomn(xk=np) + .dFvPut(1,"itw") + z <- wimedv(x) + z <- wyfalg(x, z$a, y, exu=usext); nitw <- z$nit + wgt <- 1/z$dist; wgt[wgt>1.e6] <- 1.e6 + z <- comval() + bto <- z$bt0; ipso <- z$ipsi; co <- z$c + z <- ribet0(wgt, itype=2, isqw=0) + xt <- x*wgt; yt <- y * wgt + z <- rilars(xt, yt) + theta0 <- z$theta; sigma0 <- z$sigma + rs <- z$rs/wgt; r1 <- rs/sigma0 + dfcomn(ipsi=1,c=cc) + z <- liepsh(cc) + den <- z$epsip + g <- Psp(r1)/den # (see Psi in Chpt. 14) + dfcomn(ipsi=ipso, c=co, bet0=bto) + list(theta=theta0, sigma=sigma0, rs=rs, g=g, nitw=nitw) + } > #----------------------------------------------------------------------- > # Mallows-standard estimate (with wyfalg and rywalg) > # > Mal.Std <- function(x, y, b2=-1, cc=-1, isigma=2) { + n <- length(y); np <- ncol(x) + dfrpar(x, "Mal-Std", b2, cc); .dFv <- .dFvGet() + if (isigma==1) {dfcomn(d=.dFv$ccc); .dFvPut(1,"isg")} + # Weights + z <- wimedv(x) + z <- wyfalg(x, z$a, y); nitw <- z$nit + wgt <- Www(z$dist) # See Www in Chpt. 14 + # Initial cov. matrix of coefficient estimates + z <- kiedch(wgt) + cov <- ktaskw(x, z$d, z$e, f=1/n) + # Initial theta and sigma + z <- rbmost(x,y,1.5,userfd) + theta0 <- z$theta; sigma0 <- z$sigma; nitw0 <- z$nitw + # Final theta and sigma + if (isigma==1) ribeth(wgt) else ribet0(wgt) + z <- rywalg(x, y,theta0,wgt,cov$cov, sigmai=sigma0) + theta1 <- z$theta[1:np]; sigma1 <- z$sigmaf; nit1 <- z$nit + # Covariance matrix of coefficient estimates + z <- kfedcc(wgt, z$rs, sigma=sigma1) + cov <- ktaskw(x, z$d, z$e, f=(sigma1^2)/n) + sd1 <- NULL; for (i in 1:np) { j <- i*(i+1)/2 + sd1 <- c(sd1,cov$cov[j]) } + sd1 <- sqrt(sd1) + #....................................................................... + { + cat("rbmost: theta0 : ",round(theta0[1:np],3),"\n") + cat("rbmost: sigma0, nitw : ",round(sigma0,3),nitw0,"\n") + cat("Mallows-Std: theta1 : ",round(theta1,3),"\n") + cat("Mallows-Std: sd1 : ",round(sd1,3),"\n") + cat("Mallows-Std: sigma1, nitw, nit1 : ",round(sigma1,3),nitw,nit1,"\n") + } + + #....................................................................... + list(theta0=theta0[1:np], sigma0=sigma0, nitw=nitw, + theta1=theta1, sigma1=sigma1, nit1=nit1, sd1=sd1)} > #----------------------------------------------------------------------- > # Krasker-Welsch estimate (with wynalg and rynalg) > # > Kra.Wel <- function(x, y, ckw=-1, isigma=2) { + n <- length(y); np <- ncol(x) + dfrpar(x, "Kra-Wel", ckw); .dFv <- .dFvGet() + if (isigma==1) {dfcomn(d=.dFv$ccc); .dFvPut(1,"isg")} + # Weights + z <- wimedv(x) + z <- wynalg(x, z$a); nitw <- z$nit + wgt <- Www(z$dist) # See Www in Chpt. 14 + # Initial cov. matrix of coefficient estimates + z <- kiedch(wgt) + cov <- ktaskw(x, z$d, z$e, f=1/n) + # Initial theta and sigma + z <- rbmost(x, y, cc=1.5) + theta0 <- z$theta; sigma0 <- z$sigma; nitw0 <- z$nitw + # Final theta and sigma + if (isigma==1) ribeth(wgt) else ribet0(wgt) + z <- rynalg(x, y,theta0,wgt,cov$cov, sigmai=sigma0) + theta2 <- z$theta[1:np]; sigma2 <- z$sigma; nit2 <- z$nit + # + # Covariance matrix of coefficient estimates + # + z <- kfedcc(wgt, z$rs, sigma=sigma2) + cov <- ktaskw(x, z$d, z$e, f=(sigma2^2)/n) + sd2 <- NULL; for (i in 1:np) { j <- i*(i+1)/2 + sd2 <- c(sd2,cov$cov[j]) } + sd2 <- sqrt(sd2) + #....................................................................... + { + cat("rbmost: theta0 : ",round(theta0[1:np],3),"\n") + cat("rbmost: sigma0, nitw : ",round(sigma0,3),nitw0,"\n") + cat("Krasker-Welsch: theta2 : ",round(theta2,3),"\n") + cat("Krasker-Welsch: sd2 : ",round(sd2,3),"\n") + cat("Krasker-Welsch: sigma2, nitw, nit2 : ",round(sigma2,3),nitw,nit2,"\n") + } + #....................................................................... + list(theta0=theta0[1:np], sigma0=sigma0, nitw=nitw, + theta2=theta2, sigma2=sigma2, nit2=nit2, sd2=sd2)} > #----------------------------------------------------------------------- > # Read data; load defaults > # > z <- c( 8.2, 4, 23.005, 1, 7.6, 5, 23.873, 1, + 4.6, 0, 26.417, 1, 4.3, 1, 24.868, 1, + 5.9, 2, 29.895, 1, 5.0, 3, 24.200, 1, + 6.5, 4, 23.215, 1, 8.3, 5, 21.862, 1, + 10.1, 0, 22.274, 1, 13.2, 1, 23.830, 1, + 12.6, 2, 25.144, 1, 10.4, 3, 22.430, 1, + 10.8, 4, 21.785, 1, 13.1, 5, 22.380, 1, + 13.3, 0, 23.927, 1, 10.4, 1, 33.443, 1, + 10.5, 2, 24.859, 1, 7.7, 3, 22.686, 1, + 10.0, 0, 21.789, 1, 12.0, 1, 22.041, 1, + 12.1, 4, 21.033, 1, 13.6, 5, 21.005, 1, + 15.0, 0, 25.865, 1, 13.5, 1, 26.290, 1, + 11.5, 2, 22.932, 1, 12.0, 3, 21.313, 1, + 13.0, 4, 20.769, 1, 14.1, 5, 21.393, 1) > x <- matrix(z, ncol=4, byrow=TRUE) > y <- c( 7.6, 7.7, 4.3, 5.9, 5.0, 6.5, 8.3, 8.2, 13.2, 12.6, + 10.4, 10.8, 13.1, 12.3, 10.4, 10.5, 7.7, 9.5, 12.0, 12.6, + 13.6, 14.1, 13.5, 11.5, 12.0, 13.0, 14.1, 15.1) > dfvals() NULL > dfcomn(xk=4) $ipsi [1] -9 $iucv [1] -1 $iwww [1] -1 > cat("Results\n") Results > z1 <- Mal.Std(x, y) rbmost: theta0 : 0.674 -0.171 -0.678 20.043 rbmost: sigma0, nitw : 0.846 20 Mallows-Std: theta1 : 0.721 -0.174 -0.654 18.868 Mallows-Std: sd1 : 0.052 0.145 0.166 4.285 Mallows-Std: sigma1, nitw, nit1 : 0.774 38 7 > z2 <- Kra.Wel(x, y) rbmost: theta0 : 0.674 -0.171 -0.678 20.043 rbmost: sigma0, nitw : 0.846 20 Krasker-Welsch: theta2 : 0.68 -0.177 -0.679 19.974 Krasker-Welsch: sd2 : 0.047 0.067 0.141 3.664 Krasker-Welsch: sigma2, nitw, nit2 : 0.732 21 4 > > > # > # ---- Examples of Chapter 4: Covariance matrix of the coefficient estimates ------ > # > > # > # Read x[1:4] and then set x[,4] <- 1 > # > z <- c(80, 27, 89, 1, 80, 27, 88, 1, 75, 25, 90, 1, + 62, 24, 87, 1, 62, 22, 87, 1, 62, 23, 87, 1, + 62, 24, 93, 1, 62, 24, 93, 1, 58, 23, 87, 1, + 58, 18, 80, 1, 58, 18, 89, 1, 58, 17, 88, 1, + 58, 18, 82, 1, 58, 19, 93, 1, 50, 18, 89, 1, + 50, 18, 86, 1, 50, 19, 72, 1, 50, 19, 79, 1, + 50, 20, 80, 1, 56, 20, 82, 1, 70, 20, 91, 1) > x <- matrix(z, ncol=4, byrow=TRUE) > n <- 21; np <- 4; ncov <- np*(np+1)/2 > dfvals() NULL > # Cov. matrix of Huber-type estimates > dfrpar(x, "huber") $itypw [1] 0 $itype [1] 1 $isigma [1] 1 > s <- liepsh() > epsi2 <- s$epsi2; epsip <- s$epsip > z <- rimtrf(x) > xt <- z$x; sg <- z$sg; ip <- z$ip > zc <- kiascv(xt, fu=epsi2/epsip^2, fb=0.) > covi <- zc$cov # Can be used in ryhalg with ic=0 > zc <- kfascv(xt, covi, f=1, sg=sg, ip=ip) > covf <- zc$cov > #....................................................................... > str <- rep(" ", ncov); str[cumsum(1:np)] <- "\n" > { + cat("covf:\n") + cat(round(covf,6),sep=str) + } covf: 0.00182 -0.003653 0.013553 -0.000715 1e-06 0.002444 0.028778 -0.065222 -0.167742 14.16076 > > > # > # ---- Examples of Chapter 5: Asymptotic relative efficiency ------ > # > #----------------------------------------------------------------------- > # Huber > # > dfcomn(ipsi=1,c=1.345,d=1.345) $ipsi [1] 1 $iucv [1] -1 $iwww [1] -1 > .dFvPut(1,"ite") NULL > z <- airef0(mu=3, ialfa=1, sigmx=1) > #....................................................................... > { + cat(" airef0 : Huber\n reff, alfa, beta, nit: ") + cat(round(c(z$reff,z$alfa,z$beta,z$nit),3),sep=c(", ",", ",", ","\n")) + } airef0 : Huber reff, alfa, beta, nit: 0.95, 0, 0, 0 > #----------------------------------------------------------------------- > # Schweppe: Krasker-Welsch > # > dfcomn(ipsi=1,c=3.76,iucv=3,ckw=3.76,iwww=1) $ipsi [1] 1 $iucv [1] 3 $iwww [1] 1 > .dFvPut(3,"ite") NULL > z <- airef0(mu=3, ialfa=1, sigmx=1) > #....................................................................... > { + cat(" airef0 : Krasker-Welsch\n reff, alfa, beta, nit: ") + cat(round(c(z$reff,z$alfa,z$beta,z$nit),3),sep=c(", ",", ",", ","\n")) + } airef0 : Krasker-Welsch reff, alfa, beta, nit: 0.95, 1.091, 1.17, 6 > #----------------------------------------------------------------------- > # Mallows-Standard > # > dfcomn(ipsi=1,c=1.5,iucv=1,a2=0,b2=6.16,iww=3) $ipsi [1] 1 $iucv [1] 1 $iwww [1] 3 > .dFvPut(2,"ite") NULL > z <- airef0(mu=3, ialfa=1, sigmx=1) > #....................................................................... > { + cat(" airef0 : Mallows-Std \n reff, alfa, beta, nit: ") + cat(round(c(z$reff,z$alfa,z$beta,z$nit),3),sep=c(", ",", ",", ","\n")) + } airef0 : Mallows-Std reff, alfa, beta, nit: 0.95, 1.031, 1.09, 5 > #======================================================================= > z <- c(1, 0, 0, + 1, 0, 0, + 1, 0, 0, + 1, 0, 0, + 0, 1, 0, + 0, 1, 0, + 0, 1, 0, + 0, 1, 0, + 0, 0, 1, + 0, 0, 1, + 0, 0, 1, + 0, 0, 1) > tt <- matrix(z,ncol=3,byrow=TRUE) > n <- nrow(tt); mu <- 2 > nu <- ncol(tt) > > #----------------------------------------------------------------------- > # Huber > # > dfrpar(tt,"Huber") $itypw [1] 0 $itype [1] 1 $isigma [1] 1 > z <- airefq(tt, mu=mu, sigmx=1) > #....................................................................... > { + cat(" airefq : Huber\n reff, beta, nit: ") + cat(round(c(z$reff,z$beta,z$nit),3),sep=c(", ",", ",", ","\n")) + } airefq : Huber reff, beta, nit: 0.95, 0, 0 > #----------------------------------------------------------------------- > # Krasker-Welsch > # > dfrpar(tt,"kra-wel",upar=3.755) $itypw [1] 1 $itype [1] 3 $isigma [1] 2 > z <- airefq(tt, mu=mu, sigmx=1,init=1) Floating point exception Flavor: r-devel-linux-x86_64-debian-clang

Version: 2.7-8
Check: examples
Result: ERROR Running examples in ‘robeth-Ex.R’ failed The error most likely occurred in: > ### Name: robeth-package > ### Title: Interface for the FORTRAN programs developed at the ETH-Zuerich > ### and then at IUMSP-Lausanne > ### Aliases: robeth-package robeth > ### Keywords: package robust > > ### ** Examples > > library(robeth) > > # > # ------------- Examples of Chapter 1: Location problems ------------------------------ > # > y <- c(6.0,7.0,5.0,10.5,8.5,3.5,6.1,4.0,4.6,4.5,5.9,6.5) > n <- 12 > dfvals() NULL > #----------------------------------------------------------------------- > # M-estimate (tm) of location and confidence interval (tl,tu) > # > dfrpar(as.matrix(y),"huber") $itypw [1] 0 $itype [1] 1 $isigma [1] 1 > libeth() $bta [1] 0.3550823 attr(,"Csingle") [1] TRUE > s <- lilars(y); t0 <- s$theta; s0 <- s$sigma > s <- lyhalg(y=y,theta=t0,sigmai=s0) > tm <- s$theta; vartm <- s$var > s <- quant(0.975) > tl <- tm-s$x*sqrt(vartm) > tu <- tm+s$x*sqrt(vartm) > #----------------------------------------------------------------------- > # Hodges and Lehmann estimate (th) and confidence interval (zl,zu) > # > m <- n*(n+1)/2 # n even > k1 <- m/2; k2 <- k1+1 > z1 <- lyhdle(y=y,k=k1); z2 <- lyhdle(y=y,k=k2) > th <- (z1$hdle+z2$hdle)/2. > ku <- liindh(0.95,n); kl <- liindh(0.05,n) > zu <- lyhdle(y=y,k=ku$k); zl <- lyhdle(y=y,k=kl$k) > #....................................................................... > { + cat(" tm, tl, tu : ",round(c(tm,tl,tu),3),"\n") + cat(" th, zl, zu : ",round(c(th,zl$hdle,zu$hdle),3),"\n") + } tm, tl, tu : 5.809 4.748 6.87 th, zl, zu : 5.85 5 7 > # tm, tl, tu : 5.809 4.748 6.87 > # th, zl, zu : 5.85 5 7 > #======================================================================= > # > # Two sample problem > # > y <- c(17.9,13.3,10.6,7.6,5.7,5.6,5.4,3.3,3.1,0.9) > n <- 10 > x <- c(7.7,5.0,1.7,0.0,-3.0,-3.1,-10.5) > m <- 7 > #----------------------------------------------------------------------- > # Estimate (dm) and confidence interval [dl,du] based on Mann-Whitney > # > k1 <- m*n/2; k2 <- k1+1 > s1 <- lymnwt(x=x,y=y,k=k1); s2 <- lymnwt(x=x,y=y,k=k2) > dm <- (s1$tmnwt+s2$tmnwt)/2.0 > sl <- liindw(0.05,m,n); kl <- sl$k > s <- lymnwt(x=x,y=y,k=kl); dl <- s$tmnwt > s <- lymnwt(x=x,y=y,k=m*n-kl+1); du <- s$tmnwt > #----------------------------------------------------------------------- > # Tau-test . P-value (P) > # > z <- c(x,y) > dfrpar(as.matrix(z),"huber") $itypw [1] 0 $itype [1] 1 $isigma [1] 1 > libeth() $bta [1] 0.3550823 attr(,"Csingle") [1] TRUE > s <- lytau2(z=z,m=m,n=n) > P <- s$p > # > # estimate (tm) and confidence interval (tl,tu) > # > tm <- s$deltal > c22<- s$cov[3] > s <- quant(0.975) > tl <- tm-s$x*sqrt(c22) > tu <- tm+s$x*sqrt(c22) > #....................................................................... > { + cat("dm, dl, du:",round(c(dm,dl,du),3),"\n") + cat("P, tm, tl, tu:",round(c(P,tm,tl,tu),3),"\n") + } dm, dl, du: 6.35 2.9 12.9 P, tm, tl, tu: 0.014 6.918 1.562 12.273 > # dm, dl, du: 6.35 2.9 12.9 > # P, tm, tl, tu: 0.014 6.918 1.562 12.273 > > # > # Examples of Chapter 2: M-estimates of coefficients and scale in linear regression > # > # Read data; declare the vector wgt; load defaults > # > z <- c(-1, -2, 0, 35, 1, 0, -3, 20, + -1, -2, 0, 30, 1, 0, -3, 39, + -1, -2, 0, 24, 1, 0, -3, 16, + -1, -2, 0, 37, 1, 0, -3, 27, + -1, -2, 0, 28, 1, 0, -3, -12, + -1, -2, 0, 73, 1, 0, -3, 2, + -1, -2, 0, 31, 1, 0, -3, 31, + -1, -2, 0, 21, 1, 0, -1, 26, + -1, -2, 0, -5, 1, 0, -1, 60, + -1, 0, 0, 62, 1, 0, -1, 48, + -1, 0, 0, 67, 1, 0, -1, -8, + -1, 0, 0, 95, 1, 0, -1, 46, + -1, 0, 0, 62, 1, 0, -1, 77, + -1, 0, 0, 54, 1, 0, 1, 57, + -1, 0, 0, 56, 1, 0, 1, 89, + -1, 0, 0, 48, 1, 0, 1, 103, + -1, 0, 0, 70, 1, 0, 1, 129, + -1, 0, 0, 94, 1, 0, 1, 139, + -1, 0, 0, 42, 1, 0, 1, 128, + -1, 2, 0, 116, 1, 0, 1, 89, + -1, 2, 0, 105, 1, 0, 1, 86, + -1, 2, 0, 91, 1, 0, 3, 140, + -1, 2, 0, 94, 1, 0, 3, 133, + -1, 2, 0, 130, 1, 0, 3, 142, + -1, 2, 0, 79, 1, 0, 3, 118, + -1, 2, 0, 120, 1, 0, 3, 137, + -1, 2, 0, 124, 1, 0, 3, 84, + -1, 2, 0, -8, 1, 0, 3, 101) > xx <- matrix(z,ncol=4, byrow=TRUE) > dimnames(xx) <- list(NULL,c("z2","xS","xT","y")) > z2 <- xx[,"z2"]; xS <- xx[,"xS"]; xT <- xx[,"xT"] > x <- cbind(1, z2, xS+xT, xS-xT, xS^2+xT^2, xS^2-xT^2, xT^3) > y <- xx[,"y"] > wgt <- vector("numeric",length(y)) > n <- 56; np <- 7 > dfvals() NULL > # Set parameters for Huber estimate > dfrpar(x, "huber") $itypw [1] 0 $itype [1] 1 $isigma [1] 1 > # Compute the constants beta, bet0, epsi2 and epsip > ribeth(wgt) $d [1] 1.345 attr(,"Csingle") [1] TRUE $bta [1] 0.3550823 attr(,"Csingle") [1] TRUE > ribet0(wgt) $bt0 [1] 0.6741892 attr(,"Csingle") [1] TRUE > s <- liepsh() > epsi2 <- s$epsi2; epsip <- s$epsip > # > # Least squares solution (theta0,sigma0) > # > z <- riclls(x, y) > theta0<- z$theta; sigma0 <- z$sigma > # Preliminary estimate of the covariance matrix of the coefficients > cv <- kiascv(z$xt, fu=epsi2/epsip^2, fb=0.) > cov <- cv$cov > #----------------------------------------------------------------------- > # Solution (theta1,sigma1) by means of RYHALG. > # > zr <- ryhalg(x,y,theta0,wgt,cov,sigmai=sigma0,ic=0) > theta1<- zr$theta[1:np]; sigma1 <- zr$sigmaf; nit1 <- zr$nit > #----------------------------------------------------------------------- > # Solution (theta2,sigma2) by means of RYWALG (recompute cov) > # > cv <- ktaskv(x, f=epsi2/epsip^2) > zr <- rywalg(x, y, theta0, wgt, cv$cov, sigmai=sigma0) > theta2 <- zr$theta[1:np]; sigma2 <- zr$sigmaf; nit2 <- zr$nit > #----------------------------------------------------------------------- > # Solution (theta3,sigma3) by means of RYSALG with ISIGMA=2. > # > zr <- rysalg(x,y, theta0, wgt, cv$cov, sigma0, isigma=2) > theta3 <- zr$theta[1:np]; sigma3 <- zr$sigmaf; nit3 <- zr$nit > #----------------------------------------------------------------------- > # Solution (theta4,sigma4) by means of RYNALG with ICNV=2 and ISIGMA=0. > # > # Invert cov > covm1 <- cv$cov > zc <- mchl(covm1,np) > zc <- minv(zc$a, np) > zc <- mtt1(zc$r,np) > covm1 <- zc$b > zr <- rynalg(x,y, theta0, wgt, covm1, sigmai=sigma3, + iopt=1, isigma=0, icnv=2) > theta4 <- zr$theta[1:np]; sigma4 <- zr$sigmaf; nit4 <- zr$nit > #....................................................................... > { + cat("theta0 : ",round(theta0[1:np],3),"\n") + cat("sigma0 : ",round(sigma0,3),"\n") + cat("theta1 : ",round(theta1,3),"\n") + cat("sigma1, nit1 : ",round(sigma1,3),nit1,"\n") + cat("theta2 : ",round(theta2,3),"\n") + cat("sigma2, nit2 : ",round(sigma2,3),nit2,"\n") + cat("theta3 : ",round(theta3,3),"\n") + cat("sigma3, nit3 : ",round(sigma3,3),nit3,"\n") + cat("theta4 : ",round(theta4,3),"\n") + cat("sigma4, nit4 : ",round(sigma4,3),nit4,"\n") + } theta0 : 68.634 3.634 24.081 -8.053 -0.446 -0.179 -1.634 sigma0 : 26.635 theta1 : 70.006 5.006 24.742 -6.246 -0.079 0.434 -1.487 sigma1, nit1 : 23.564 7 theta2 : 70.006 5.006 24.742 -6.245 -0.079 0.434 -1.487 sigma2, nit2 : 23.563 7 theta3 : 69.993 5.002 24.766 -6.214 -0.055 0.44 -1.48 sigma3, nit3 : 22.249 3 theta4 : 69.993 5.002 24.766 -6.214 -0.055 0.44 -1.48 sigma4, nit4 : 22.249 3 > # theta0 : 68.634 3.634 24.081 -8.053 -0.446 -0.179 -1.634 > # sigma0 : 26.635 > # theta1 : 70.006 5.006 24.742 -6.246 -0.079 0.434 -1.487 > # sigma1, nit1 : 23.564 7 > # theta2 : 70.006 5.006 24.742 -6.245 -0.079 0.434 -1.487 > # sigma2, nit2 : 23.563 7 > # theta3 : 69.993 5.002 24.766 -6.214 -0.055 0.44 -1.48 > # sigma3, nit3 : 22.249 3 > # theta4 : 69.993 5.002 24.766 -6.214 -0.055 0.44 -1.48 > # sigma4, nit4 : 22.249 3 > > > # > # ---- Examples of Chapter 3: Weights for bounded influence regression ------ > # > > #======================================================================= > rbmost <- function(x,y,cc,usext=userfd) { + n <- nrow(x); np <- ncol(x); dfcomn(xk=np) + .dFvPut(1,"itw") + z <- wimedv(x) + z <- wyfalg(x, z$a, y, exu=usext); nitw <- z$nit + wgt <- 1/z$dist; wgt[wgt>1.e6] <- 1.e6 + z <- comval() + bto <- z$bt0; ipso <- z$ipsi; co <- z$c + z <- ribet0(wgt, itype=2, isqw=0) + xt <- x*wgt; yt <- y * wgt + z <- rilars(xt, yt) + theta0 <- z$theta; sigma0 <- z$sigma + rs <- z$rs/wgt; r1 <- rs/sigma0 + dfcomn(ipsi=1,c=cc) + z <- liepsh(cc) + den <- z$epsip + g <- Psp(r1)/den # (see Psi in Chpt. 14) + dfcomn(ipsi=ipso, c=co, bet0=bto) + list(theta=theta0, sigma=sigma0, rs=rs, g=g, nitw=nitw) + } > #----------------------------------------------------------------------- > # Mallows-standard estimate (with wyfalg and rywalg) > # > Mal.Std <- function(x, y, b2=-1, cc=-1, isigma=2) { + n <- length(y); np <- ncol(x) + dfrpar(x, "Mal-Std", b2, cc); .dFv <- .dFvGet() + if (isigma==1) {dfcomn(d=.dFv$ccc); .dFvPut(1,"isg")} + # Weights + z <- wimedv(x) + z <- wyfalg(x, z$a, y); nitw <- z$nit + wgt <- Www(z$dist) # See Www in Chpt. 14 + # Initial cov. matrix of coefficient estimates + z <- kiedch(wgt) + cov <- ktaskw(x, z$d, z$e, f=1/n) + # Initial theta and sigma + z <- rbmost(x,y,1.5,userfd) + theta0 <- z$theta; sigma0 <- z$sigma; nitw0 <- z$nitw + # Final theta and sigma + if (isigma==1) ribeth(wgt) else ribet0(wgt) + z <- rywalg(x, y,theta0,wgt,cov$cov, sigmai=sigma0) + theta1 <- z$theta[1:np]; sigma1 <- z$sigmaf; nit1 <- z$nit + # Covariance matrix of coefficient estimates + z <- kfedcc(wgt, z$rs, sigma=sigma1) + cov <- ktaskw(x, z$d, z$e, f=(sigma1^2)/n) + sd1 <- NULL; for (i in 1:np) { j <- i*(i+1)/2 + sd1 <- c(sd1,cov$cov[j]) } + sd1 <- sqrt(sd1) + #....................................................................... + { + cat("rbmost: theta0 : ",round(theta0[1:np],3),"\n") + cat("rbmost: sigma0, nitw : ",round(sigma0,3),nitw0,"\n") + cat("Mallows-Std: theta1 : ",round(theta1,3),"\n") + cat("Mallows-Std: sd1 : ",round(sd1,3),"\n") + cat("Mallows-Std: sigma1, nitw, nit1 : ",round(sigma1,3),nitw,nit1,"\n") + } + + #....................................................................... + list(theta0=theta0[1:np], sigma0=sigma0, nitw=nitw, + theta1=theta1, sigma1=sigma1, nit1=nit1, sd1=sd1)} > #----------------------------------------------------------------------- > # Krasker-Welsch estimate (with wynalg and rynalg) > # > Kra.Wel <- function(x, y, ckw=-1, isigma=2) { + n <- length(y); np <- ncol(x) + dfrpar(x, "Kra-Wel", ckw); .dFv <- .dFvGet() + if (isigma==1) {dfcomn(d=.dFv$ccc); .dFvPut(1,"isg")} + # Weights + z <- wimedv(x) + z <- wynalg(x, z$a); nitw <- z$nit + wgt <- Www(z$dist) # See Www in Chpt. 14 + # Initial cov. matrix of coefficient estimates + z <- kiedch(wgt) + cov <- ktaskw(x, z$d, z$e, f=1/n) + # Initial theta and sigma + z <- rbmost(x, y, cc=1.5) + theta0 <- z$theta; sigma0 <- z$sigma; nitw0 <- z$nitw + # Final theta and sigma + if (isigma==1) ribeth(wgt) else ribet0(wgt) + z <- rynalg(x, y,theta0,wgt,cov$cov, sigmai=sigma0) + theta2 <- z$theta[1:np]; sigma2 <- z$sigma; nit2 <- z$nit + # + # Covariance matrix of coefficient estimates + # + z <- kfedcc(wgt, z$rs, sigma=sigma2) + cov <- ktaskw(x, z$d, z$e, f=(sigma2^2)/n) + sd2 <- NULL; for (i in 1:np) { j <- i*(i+1)/2 + sd2 <- c(sd2,cov$cov[j]) } + sd2 <- sqrt(sd2) + #....................................................................... + { + cat("rbmost: theta0 : ",round(theta0[1:np],3),"\n") + cat("rbmost: sigma0, nitw : ",round(sigma0,3),nitw0,"\n") + cat("Krasker-Welsch: theta2 : ",round(theta2,3),"\n") + cat("Krasker-Welsch: sd2 : ",round(sd2,3),"\n") + cat("Krasker-Welsch: sigma2, nitw, nit2 : ",round(sigma2,3),nitw,nit2,"\n") + } + #....................................................................... + list(theta0=theta0[1:np], sigma0=sigma0, nitw=nitw, + theta2=theta2, sigma2=sigma2, nit2=nit2, sd2=sd2)} > #----------------------------------------------------------------------- > # Read data; load defaults > # > z <- c( 8.2, 4, 23.005, 1, 7.6, 5, 23.873, 1, + 4.6, 0, 26.417, 1, 4.3, 1, 24.868, 1, + 5.9, 2, 29.895, 1, 5.0, 3, 24.200, 1, + 6.5, 4, 23.215, 1, 8.3, 5, 21.862, 1, + 10.1, 0, 22.274, 1, 13.2, 1, 23.830, 1, + 12.6, 2, 25.144, 1, 10.4, 3, 22.430, 1, + 10.8, 4, 21.785, 1, 13.1, 5, 22.380, 1, + 13.3, 0, 23.927, 1, 10.4, 1, 33.443, 1, + 10.5, 2, 24.859, 1, 7.7, 3, 22.686, 1, + 10.0, 0, 21.789, 1, 12.0, 1, 22.041, 1, + 12.1, 4, 21.033, 1, 13.6, 5, 21.005, 1, + 15.0, 0, 25.865, 1, 13.5, 1, 26.290, 1, + 11.5, 2, 22.932, 1, 12.0, 3, 21.313, 1, + 13.0, 4, 20.769, 1, 14.1, 5, 21.393, 1) > x <- matrix(z, ncol=4, byrow=TRUE) > y <- c( 7.6, 7.7, 4.3, 5.9, 5.0, 6.5, 8.3, 8.2, 13.2, 12.6, + 10.4, 10.8, 13.1, 12.3, 10.4, 10.5, 7.7, 9.5, 12.0, 12.6, + 13.6, 14.1, 13.5, 11.5, 12.0, 13.0, 14.1, 15.1) > dfvals() NULL > dfcomn(xk=4) $ipsi [1] -9 $iucv [1] -1 $iwww [1] -1 > cat("Results\n") Results > z1 <- Mal.Std(x, y) rbmost: theta0 : 0.674 -0.171 -0.678 20.043 rbmost: sigma0, nitw : 0.846 20 Mallows-Std: theta1 : 0.721 -0.174 -0.654 18.868 Mallows-Std: sd1 : 0.052 0.145 0.166 4.285 Mallows-Std: sigma1, nitw, nit1 : 0.774 38 7 > z2 <- Kra.Wel(x, y) rbmost: theta0 : 0.674 -0.171 -0.678 20.043 rbmost: sigma0, nitw : 0.846 20 Krasker-Welsch: theta2 : 0.68 -0.177 -0.679 19.974 Krasker-Welsch: sd2 : 0.047 0.067 0.141 3.664 Krasker-Welsch: sigma2, nitw, nit2 : 0.732 21 4 > > > # > # ---- Examples of Chapter 4: Covariance matrix of the coefficient estimates ------ > # > > # > # Read x[1:4] and then set x[,4] <- 1 > # > z <- c(80, 27, 89, 1, 80, 27, 88, 1, 75, 25, 90, 1, + 62, 24, 87, 1, 62, 22, 87, 1, 62, 23, 87, 1, + 62, 24, 93, 1, 62, 24, 93, 1, 58, 23, 87, 1, + 58, 18, 80, 1, 58, 18, 89, 1, 58, 17, 88, 1, + 58, 18, 82, 1, 58, 19, 93, 1, 50, 18, 89, 1, + 50, 18, 86, 1, 50, 19, 72, 1, 50, 19, 79, 1, + 50, 20, 80, 1, 56, 20, 82, 1, 70, 20, 91, 1) > x <- matrix(z, ncol=4, byrow=TRUE) > n <- 21; np <- 4; ncov <- np*(np+1)/2 > dfvals() NULL > # Cov. matrix of Huber-type estimates > dfrpar(x, "huber") $itypw [1] 0 $itype [1] 1 $isigma [1] 1 > s <- liepsh() > epsi2 <- s$epsi2; epsip <- s$epsip > z <- rimtrf(x) > xt <- z$x; sg <- z$sg; ip <- z$ip > zc <- kiascv(xt, fu=epsi2/epsip^2, fb=0.) > covi <- zc$cov # Can be used in ryhalg with ic=0 > zc <- kfascv(xt, covi, f=1, sg=sg, ip=ip) > covf <- zc$cov > #....................................................................... > str <- rep(" ", ncov); str[cumsum(1:np)] <- "\n" > { + cat("covf:\n") + cat(round(covf,6),sep=str) + } covf: 0.00182 -0.003653 0.013553 -0.000715 1e-06 0.002444 0.028778 -0.065222 -0.167742 14.16076 > > > # > # ---- Examples of Chapter 5: Asymptotic relative efficiency ------ > # > #----------------------------------------------------------------------- > # Huber > # > dfcomn(ipsi=1,c=1.345,d=1.345) $ipsi [1] 1 $iucv [1] -1 $iwww [1] -1 > .dFvPut(1,"ite") NULL > z <- airef0(mu=3, ialfa=1, sigmx=1) > #....................................................................... > { + cat(" airef0 : Huber\n reff, alfa, beta, nit: ") + cat(round(c(z$reff,z$alfa,z$beta,z$nit),3),sep=c(", ",", ",", ","\n")) + } airef0 : Huber reff, alfa, beta, nit: 0.95, 0, 0, 0 > #----------------------------------------------------------------------- > # Schweppe: Krasker-Welsch > # > dfcomn(ipsi=1,c=3.76,iucv=3,ckw=3.76,iwww=1) $ipsi [1] 1 $iucv [1] 3 $iwww [1] 1 > .dFvPut(3,"ite") NULL > z <- airef0(mu=3, ialfa=1, sigmx=1) > #....................................................................... > { + cat(" airef0 : Krasker-Welsch\n reff, alfa, beta, nit: ") + cat(round(c(z$reff,z$alfa,z$beta,z$nit),3),sep=c(", ",", ",", ","\n")) + } airef0 : Krasker-Welsch reff, alfa, beta, nit: 0.95, 1.091, 1.17, 6 > #----------------------------------------------------------------------- > # Mallows-Standard > # > dfcomn(ipsi=1,c=1.5,iucv=1,a2=0,b2=6.16,iww=3) $ipsi [1] 1 $iucv [1] 1 $iwww [1] 3 > .dFvPut(2,"ite") NULL > z <- airef0(mu=3, ialfa=1, sigmx=1) > #....................................................................... > { + cat(" airef0 : Mallows-Std \n reff, alfa, beta, nit: ") + cat(round(c(z$reff,z$alfa,z$beta,z$nit),3),sep=c(", ",", ",", ","\n")) + } airef0 : Mallows-Std reff, alfa, beta, nit: 0.95, 1.031, 1.09, 5 > #======================================================================= > z <- c(1, 0, 0, + 1, 0, 0, + 1, 0, 0, + 1, 0, 0, + 0, 1, 0, + 0, 1, 0, + 0, 1, 0, + 0, 1, 0, + 0, 0, 1, + 0, 0, 1, + 0, 0, 1, + 0, 0, 1) > tt <- matrix(z,ncol=3,byrow=TRUE) > n <- nrow(tt); mu <- 2 > nu <- ncol(tt) > > #----------------------------------------------------------------------- > # Huber > # > dfrpar(tt,"Huber") $itypw [1] 0 $itype [1] 1 $isigma [1] 1 > z <- airefq(tt, mu=mu, sigmx=1) > #....................................................................... > { + cat(" airefq : Huber\n reff, beta, nit: ") + cat(round(c(z$reff,z$beta,z$nit),3),sep=c(", ",", ",", ","\n")) + } airefq : Huber reff, beta, nit: 0.95, 0, 0 > #----------------------------------------------------------------------- > # Krasker-Welsch > # > dfrpar(tt,"kra-wel",upar=3.755) $itypw [1] 1 $itype [1] 3 $isigma [1] 2 > z <- airefq(tt, mu=mu, sigmx=1,init=1) Flavor: r-devel-linux-x86_64-fedora-clang