The source code listed below (just before the system information) makes
"optim" to ignore the bounds on one of the parameters; this is
detected
by the function to be optimized (sn.dev) and the program stops. On my
computer the numeric optput which I obtain is:
sn.dev: (cp,dev)=[1] -1.3222 0.3738 0.3263 42.1824
sn.dev.gh: gradient=[1] 2.092 4.059 -4.039
sn.dev: (cp,dev)=[1] -2.2611 0.2060 0.6266 686.3262
sn.dev.gh: gradient=[1] -1208.1 -5719.7 -287.1
sn.dev: (cp,dev)=[1] -1.3632 0.3665 0.3394 42.6260
sn.dev.gh: gradient=[1] -27.758 1.067 -4.431
sn.dev: (cp,dev)=[1] -1.3279 0.3728 0.3281 42.1705
sn.dev.gh: gradient=[1] -1.976 3.989 -4.090
sn.dev: (cp,dev)=[1] -1.3279 0.3667 0.3348 42.1461
sn.dev.gh: gradient=[1] -0.4494 -4.9931 -4.0685
sn.dev: (cp,dev)=[1] -1.3280 0.3671 0.3403 42.1219
sn.dev.gh: gradient=[1] -0.5089 -4.4250 -4.0727
sn.dev: (cp,dev)=[1] -1.3282 0.3686 0.3623 42.0268
sn.dev.gh: gradient=[1] -0.7684 -2.2082 -4.0932
sn.dev: (cp,dev)=[1] -1.3290 0.3748 0.4506 41.6734
sn.dev.gh: gradient=[1] -2.128 5.823 -4.226
sn.dev: (cp,dev)=[1] -1.3341 0.3927 0.9953 25934.9488
sn.dev.gh: gradient=[1] 219013 -312643 441647332
[1] -1.3546 0.4645 3.1741
Error in fn(par, ...) : cp outside bounds
The code looks somewhat involved.. however, what it does is to set up
ingredients to run the final statement:
opt<- optim(cp, fn=sn.dev, gr=sn.dev.gh, method="L-BFGS-B",
lower=c(-rep(Inf,m),1e-10, -0.99527),
upper=c(rep(Inf,m), Inf, 0.99527),
control=list(iter.max=100, abs.tol=1e-5),
X=X, y=y, trace=TRUE, hessian=FALSE)
The above sequence stoped because the last component of vector cp
is 3.1741, which exceeds the bound 0.99527.
Notice that the problem occurs "with 0 probability", in the sense
that
1. if you change some of the data below (in vector monica2), then it is
most likely to run fine;
2. if you change the starting point, then again it works;
specifically I changed
cp <- c(qr.coef(qrX,y), s, gamma1)
to
cp <- c(-1.322168500, 0.373810085, 0.326283975)
which differ by [1] 2.666e-10 -3.154e-10 -3.033e-10
and it worked from the second starting vector;
sn.dev: (cp,dev)=[1] -1.3222 0.3738 0.3263 42.1824
sn.dev.gh: gradient=[1] 2.092 4.059 -4.039
sn.dev: (cp,dev)=[1] -2.2611 0.2060 0.6266 686.3263
sn.dev.gh: gradient=[1] -1208.1 -5719.7 -287.1
sn.dev: (cp,dev)=[1] -1.3632 0.3665 0.3394 42.6260
sn.dev.gh: gradient=[1] -27.758 1.067 -4.431
sn.dev: (cp,dev)=[1] -1.3279 0.3728 0.3281 42.1705
sn.dev.gh: gradient=[1] -1.976 3.989 -4.090
sn.dev: (cp,dev)=[1] -1.3279 0.3667 0.3348 42.1461
sn.dev.gh: gradient=[1] -0.4494 -4.9931 -4.0685
sn.dev: (cp,dev)=[1] -1.3280 0.3671 0.3403 42.1219
sn.dev.gh: gradient=[1] -0.5089 -4.4250 -4.0727
sn.dev: (cp,dev)=[1] -1.3282 0.3686 0.3623 42.0268
sn.dev.gh: gradient=[1] -0.7684 -2.2082 -4.0932
sn.dev: (cp,dev)=[1] -1.3290 0.3748 0.4506 41.6734
sn.dev.gh: gradient=[1] -2.128 5.823 -4.226
sn.dev: (cp,dev)=[1] -1.3341 0.3927 0.9953 25934.8747
sn.dev.gh: gradient=[1] 219013 -312642 441646067
sn.dev: (cp,dev)=[1] -1.3307 0.3808 0.6321 40.9462
sn.dev.gh: gradient=[1] -2.806 8.579 -4.256
sn.dev: (cp,dev)=[1] -1.3324 0.3867 0.8137 40.3288
<..snip..>
sn.dev: (cp,dev)=[1] -1.3505 0.4184 0.9953 35.7271
sn.dev.gh: gradient=[1] 12.18 -13.92 -13349.90> opt
$par
[1] -1.3505 0.4184 0.9953
$value
[1] 35.73
$counts
function gradient
44 44
$convergence
[1] 0
$message
[1] "CONVERGENCE: REL_REDUCTION_OF_F <= FACTR*EPSMCH"
3. when I run the same code on my MS-windows 2000 laptop (R 1.7.0),
again it runs fine.
Hope to have provided a complete description.
With best regards,
Adelchi Azzalini <azzalini@stat.unipd.it>
Dipart.Scienze Statistiche, Università di Padova, Italia
http://azzalini.stat.unipd.it/
#--------------------------------------------
# code which produced the above output, you can just source() it
rm(list=ls())
sn.dev <- function(cp, X, y, trace=FALSE)
{ # -2*logL for centred parameters
m <- ncol(X)
if(abs(cp[length(cp)])> 0.99527) {print(cp); stop("cp outside
bounds")}
dp <- as.vector(cp.to.dp(cp))
location <- X %*% as.matrix(dp[1:m])
scale <- dp[m+1]
# AVOID: logL <- sum(log(dsn(y,location,dp[m+1],dp[m+2])))
z <- (y-location)/scale
nlogL <- (length(y)*log(2.506628274631*scale) + 0.5*sum(z^2)
- sum(zeta(0,dp[m+2]*z)))
if(trace) {cat("sn.dev: (cp,dev)="); print(c(cp,2*nlogL))}
return(2*nlogL)
}
sn.dev.gh <- function(cp, X, y, trace=FALSE, hessian=FALSE)
{
# computes gradient and hessian of dev=-2*logL for centred parameters
# (and observed information matrix);
m <- ncol(X)
n <- nrow(X)
np <- m+2
score <- rep(NA,np)
info <- matrix(NA,np,np)
beta <- cp[1:m]
sigma <- cp[m+1]
gamma1 <- cp[m+2]
lambda <- gamma1.to.lambda(gamma1)
# dp<-cp.to.dp(c(beta,sigma,gamma1))
# info.dp <- sn.info(dp,y)$info.dp
mu <- as.vector(X %*% as.matrix(beta))
d <- y-mu
r <- d/sigma
E.Z<- lambda*sqrt(2/(pi*(1+lambda^2)))
s.Z<- sqrt(1-E.Z^2)
z <- E.Z+s.Z*r
p1 <- as.vector(zeta(1,lambda*z))
p2 <- as.vector(zeta(2,lambda*z))
omega<- sigma/s.Z
w <- lambda*p1-E.Z
DE.Z <- sqrt(2/pi)/(1+lambda^2)^1.5
Ds.Z <- (-E.Z/s.Z)*DE.Z
Dz <- DE.Z + r*Ds.Z
DDE.Z<- (-3)*E.Z/(1+lambda^2)^2
DDs.Z<- -((DE.Z*s.Z-E.Z*Ds.Z)*DE.Z/s.Z^2+E.Z*DDE.Z/s.Z)
DDz <- DDE.Z + r*DDs.Z
score[1:m] <- omega^(-2)*t(X) %*% as.matrix(y-mu-omega*w)
score[m+1] <- (-n)/sigma+s.Z*sum(d*(z-p1*lambda))/sigma^2
score[m+2] <- score.l <- n*Ds.Z/s.Z-sum(z*Dz)+sum(p1*(z+lambda*Dz))
Dg.Dl <-1.5*(4-pi)*E.Z^2*(DE.Z*s.Z-E.Z*Ds.Z)/s.Z^4
R <- E.Z/s.Z
T <- sqrt(2/pi-(1-2/pi)*R^2)
Dl.Dg <- 2*(T/(T*R)^2+(1-2/pi)/T^3)/(3*(4-pi))
R. <- 2/(3*R^2 * (4-pi))
T. <- (-R)*R.*(1-2/pi)/T
DDl.Dg <- (-2/(3*(4-pi))) * (T./(R*T)^2+2*R./(T*R^3)+3*(1-2/pi)*T./T^4)
score[m+2] <- score[m+2]/Dg.Dl # convert deriv wrt lamda to gamma1
gradient <- (-2)*score
if(hessian){
info[1:m,1:m] <- omega^(-2) * t(X) %*% ((1-lambda^2*p2)*X)
info[1:m,m+1] <- info[m+1,1:m] <-
s.Z* t(X) %*% as.matrix((z-lambda*p1)+d*(1-lambda^2*p2)*
s.Z/sigma)/sigma^2
info[m+1,m+1] <- (-n)/sigma^2+2*s.Z*sum(d*(z-lambda*p1))/sigma^3 +
s.Z^2*sum(d*(1-lambda^2*p2)*d)/sigma^4
info[1:m,m+2] <- info[m+2,1:m] <-
t(X)%*%(-2*Ds.Z*d/omega+Ds.Z*w+s.Z*(p1+lambda*p2*(z+lambda*Dz)
-DE.Z))/sigma
info[m+1,m+2] <- info[m+2,m+1] <-
-sum(d*(Ds.Z*(z-lambda*p1)+s.Z*(Dz-p1-p2*lambda*(z+lambda*Dz))
))/sigma^2
info[m+2,m+2] <- (n*(-DDs.Z*s.Z+Ds.Z^2)/s.Z^2+sum(Dz^2+z*DDz)-
sum(p2*(z+lambda*Dz)^2)- sum(p1*(2*Dz+lambda*DDz)))
info[np,] <- info[np,]/Dg.Dl # convert info wrt lamda to gamma1
info[,np] <- info[,np]*Dl.Dg # an equivalent form of the above
info[np,np] <- info[np,np]-score.l*DDl.Dg
}
attr(gradient,"hessian") <- 2*info
if(trace) {cat("sn.dev.gh: gradient="); print(-2*score)}
return(gradient)
}
cp.to.dp <- function(param){
# converts centred parameters cp=(mu,sigma,gamma1)
# to direct parameters dp=(xi,omega,lambda)
# Note: mu can be m-dimensional, the other must be scalars
b <- sqrt(2/pi)
m <- length(param)-2
gamma1 <- param[m+2]
if(abs(gamma1)>0.9952719) stop("abs(gamma1)>0.9952719 ")
A <- sign(gamma1)*(abs(2*gamma1/(4-pi)))^(1/3)
delta <- A/(b*sqrt(1+A^2))
lambda <- delta/sqrt(1-delta^2)
E.Z <- b*delta
sd.Z <- sqrt(1-E.Z^2)
location <- param[1:m]
location[1] <- param[1]-param[m+1]*E.Z/sd.Z
scale <- param[m+1]/sd.Z
dp <- c(location,scale,lambda)
names(dp)[(m+1):(m+2)] <- c("scale","shape")
if(m==1) names(dp)[1] <- "location"
dp
}
zeta <- function(k,x){# k integer \in (0,4)
k <- as.integer(k)
na <- is.na(x)
x <- replace(x,na,0)
if(any(abs(x)==Inf)) stop("Inf not allowed")
# funzionerebbe per k=0 e 1, ma non per k>1
ok <- (-35<x)
if(k>2 & sum(!ok)>0) na<- (na | x < (-35))
if(k==0)
{ax <- (-x[!ok])
ay <- (-0.918938533204673)-0.5*ax^2-log(ax)
y <- rep(NA,length(x))
y[ok] <- log(2*pnorm(x[ok]))
y[!ok]<- ay
}
else {if(k==1) {y <- (-x)*(1+1/x^2)
y[ok]<-dnorm(x[ok])/pnorm(x[ok]) }
else { if(k==2) y<-(-zeta(1,x)*(x+zeta(1,x)))
else{ if(k==3) y<-(-zeta(2,x)*(x+zeta(1,x))-zeta(1,x)*(1+zeta(2,x)))
else{ if(k==4)
y<-(-zeta(3,x)*(x+2*zeta(1,x))-2*zeta(2,x)*(1+zeta(2,x)))
else stop("k must be integer in (0,4)") }}}}
replace(y,na,NA)
}
gamma1.to.lambda<- function(gamma1){
max.gamma1 <- 0.5*(4-pi)*(2/(pi-2))^1.5
na <- (abs(gamma1)>max.gamma1)
if(any(na)) warning("NAs generated")
gamma1<-replace(gamma1,na,NA)
a <- sign(gamma1)*(2*abs(gamma1)/(4-pi))^0.33333
delta<- sqrt(pi/2)*a/sqrt(1+a^2)
lambda<-delta/sqrt(1-delta^2)
as.vector(lambda)
}
monica2 <- structure(c(-1.13886379255452, -1.49413719201167,
-1.87841064955904,
-0.513738594928947, -1.00896950224601, -1.6532307897784, -1.30938967543405,
-1.06041025293133, -1.81471688461199, -1.33766295196981, -1.64330728995432,
-1.06469899752166, -1.00001063084463, -1.39630202884595, -0.69585045438959,
-1.69203796703315, -1.52865988854611, -1.62709073708234, -0.934436685805195,
-1.11123590657362, -1.67332502980791, -1.16134216754217, -1.27483304830845,
-1.83766242751329, -1.56429386551254, -0.70663591112117, -0.806879736670856,
-1.39706539761330, -1.48444503984765, -1.48192041745309, -0.763350185813698,
-1.42340943250413, -1.5034850496403, -0.916057843936642, -0.797650717731944,
-1.90218776624194, -1.89573584905280, -1.55031254908193, -1.55488970245028,
-1.17137569123985, -1.81490209565657, -1.82974300222698, -0.797407302025425,
-1.16514651844490, -1.31007112485982, -1.71325589588157, -1.54253707834837,
-1.28190616471927, -1.28990272862791, -0.593534374170964),
parameters = c(-1.71631422551004, 0.5, 2))
#----
# cp <- c(-1.322168500, 0.373810085, 0.326283975)
y <- monica2
X <- matrix(1, nrow=length(monica2))
n <- length(y)
m <- ncol(X)
qrX <- qr(X)
s <- sqrt(sum(qr.resid(qrX, y)^2)/n)
gamma1 <- sum(qr.resid(qrX, y)^3)/(n*s^3)
if(abs(gamma1)>0.99527) gamma1<- sign(gamma1)*0.95
cp <- c(qr.coef(qrX,y), s, gamma1)
opt<- optim(cp, fn=sn.dev, gr=sn.dev.gh, method="L-BFGS-B",
lower=c(-rep(Inf,m),1e-10, -0.99527),
upper=c(rep(Inf,m), Inf, 0.99527),
control=list(iter.max=100, abs.tol=1e-5),
X=X, y=y, trace=TRUE, hessian=FALSE)
--please do not edit the information below--
Version:
platform = i686-pc-linux-gnu
arch = i686
os = linux-gnu
system = i686, linux-gnu
status =
major = 1
minor = 7.0
year = 2003
month = 04
day = 16
language = R
Search Path:
.GlobalEnv, package:methods, package:ctest, package:mva, package:modreg,
package:nls, package:ts, file:/home/aa/SW/R-misc/local-def.save, Autoloads,
package:base