Stable package on Windows doesn't work???

Hi Jim,

Troels Ring told me to get Version 0.7 which I did.

It's probably me that doesn't understand how it works (I'm new to R) but
stablereg is supposed to find the most appropriate parameters for the stable
distribution (loc, disp, skew, tail) given some starting values right?

When I try the examples that are given with the function help file I get the
following results.

************************************************************
R : Copyright 1999, The R Development Core Team
Version 0.90.0  (November 22, 1999)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type   "?license" or "?licence" for distribution details.

R is a collaborative project with many contributors.
Type   "?contributors" for a list.

Type   "demo()" for some demos, "help()" for on-line help, or
        "help.start()" for a HTML browser interface to help.
Type    "q()" to quit R.

library(stable)

Loading required package: rmutil

help(stablereg)
## Share return over a 50 day period (see reference above)
        # shares
        y <-

c(296,296,300,302,300,304,303,299,293,294,294,293,295,287,288,297,
+
305,307,307,304,303,304,304,309,309,309,307,306,304,300,296,301,298,
+
295,295,293,292,297,294,293,306,303,301,303,308,305,302,301,297,299)

        # returns
        ret <- (y[2:50]-y[1:49])/y[1:49]
        # hist(ret, breaks=seq(-0.035,0.045,0.01))

        day <- seq(0,0.48,by=0.01) # time measured in days/100
        x <- seq(1,length(ret))-1

        # Classic stationary normal model tail=2
        print(z1 <- stablereg(y = ret, delta = 1/y[1:49],

+              loc = ~ 1, disp= ~ 1, skew = ~ 1, tail = tail.g(1.9999999),
+              iloc = 0, idisp = 0, iskew = 0, otail = F, oskew = F))

Call:
stablereg(y = ret, delta = 1/y[1:49], loc = ~1, disp = ~1, skew = ~1,
    tail = tail.g(1.9999999), iloc = 0, idisp = 0, iskew = 0,
    otail = F, oskew = F)

Warning: no convergence - error 5

-Log likelihood              292.4987
No. of obs                   49
No. of estimated parameters  2
No. of parameters            4
Degrees of freedom           47
AIC                          294.4987
Iterations                   1

Location parameters
~1
              estimate       se
(Intercept)  0.0001445  0.07433

Dispersion parameters
~1
             estimate     se
(Intercept)        -1  4.055

Correlations:
         1        2
1  1.00000 -0.01525
2 -0.01525  1.00000

        # Normal model (tail=2) with dispersion=disp.h(b0+b1*day)
        print(z2 <- stablereg(y = ret, delta = 1/y[1:49],

+              loc = ~ day, disp = ~ 1, skew = ~ 1, tail = tail.g(1.999999),
+              iloc = c(0,0), idisp = 0, iskew = 0, oskew = F, otail =F))

Call:
stablereg(y = ret, delta = 1/y[1:49], loc = ~day, disp = ~1,
    skew = ~1, tail = tail.g(1.999999), iloc = c(0, 0), idisp = 0,
    iskew = 0, oskew = F, otail = F)

Warning: no convergence - error 5

-Log likelihood              292.4987
No. of obs                   49
No. of estimated parameters  3
No. of parameters            5
Degrees of freedom           46
AIC                          295.4987
Iterations                   1

Location parameters
~day
               estimate      se
(Intercept)   1.445e-04  0.1467
day          -6.053e-06  0.5265

Dispersion parameters
~1
             estimate     se
(Intercept)        -1  4.063

Correlations:
         1        2        3
1  1.00000 -0.86207 -0.06098
2 -0.86207  1.00000  0.06159
3 -0.06098  0.06159  1.00000

        # Stable model with loc(ation)=loc.h(b0+b1*day)
        print(z3 <- stablereg(y = ret, delta = 1/y[1:49],

+              loc = ~ day, disp = ret ~ 1, skew = ~ 1, tail = ~ 1,
+              iloc = c(0.001,0), idisp = 0, iskew = 0, itail = 0))

Call:
stablereg(y = ret, delta = 1/y[1:49], loc = ~day, disp = ret ~
    1, skew = ~1, tail = ~1, iloc = c(0.001, 0), idisp = 0, iskew = 0,
    itail = 0)

Warning: no convergence - error 5

-Log likelihood              291.591
No. of obs                   49
No. of estimated parameters  5
No. of parameters            5
Degrees of freedom           44
AIC                          296.591
Iterations                   1

Location parameters
~day
               estimate       se
(Intercept)   0.0004749  0.08946
day          -0.0001862  0.43214

Dispersion parameters
ret ~ 1
             estimate     se
(Intercept)   -0.9998  3.340

Skew parameters
~1
               estimate   se
(Intercept)  -0.0002456  NaN

Tail parameters
~1
             estimate   se
(Intercept)  -0.02018  NaN

Correlations:
         1        2        3   4   5
1  1.00000 -1.15904 -0.06692 NaN NaN
2 -1.15904  1.00000  0.06058 NaN NaN
3 -0.06692  0.06058  1.00000 NaN NaN
4      NaN      NaN      NaN NaN NaN
5      NaN      NaN      NaN NaN NaN
Warning message:
NaNs produced in: sqrt(diag(cov))

        # Stable model with disp(ersion)=disp.h(b0+b1*day)
        print(z4 <- stablereg(y = ret, delta = 1/y[1:49],

+              loc = ret ~ 1, disp = ~ day, skew = ~ 1, tail = ~ 1,
+              iloc = 0, idisp = c(-4.5,0), iskew = -2, itail = 1))

Call:
stablereg(y = ret, delta = 1/y[1:49], loc = ret ~ 1, disp = ~day,
    skew = ~1, tail = ~1, iloc = 0, idisp = c(-4.5, 0), iskew = -2,
    itail = 1)

-Log likelihood              132.1774
No. of obs                   49
No. of estimated parameters  5
No. of parameters            5
Degrees of freedom           44
AIC                          137.1774
Iterations                   70

Location parameters
ret ~ 1
             estimate        se
(Intercept)  0.000938  0.001892

Dispersion parameters
~day
             estimate       se
(Intercept)   -4.6768  0.06621
day           -0.6556  0.61348

Skew parameters
~1
             estimate   se
(Intercept)    -10.87  NaN

Tail parameters
~1
             estimate      se
(Intercept)    0.6575  0.6067

Correlations:
        1       2       3   4       5
1  1.0000 -1.0000  0.8075 NaN -0.3810
2 -1.0000  1.0000 -0.8075 NaN  0.3810
3  0.8075 -0.8075  1.0000 NaN -0.2914
4     NaN     NaN     NaN NaN     NaN
5 -0.3810  0.3810 -0.2914 NaN  1.0000
Warning message:
NaNs produced in: sqrt(diag(cov))

        # Stable model with skew(ness)=skew.h(b0+b1*day)
        # Evaluation at fixed parameter values (since noopt is set to

TRUE)

        print(z5 <- stablereg(y = ret, delta = 1/y[1:49],

+              loc = ret ~ 1, disp = ~ 1, skew = ~ day, tail = ~ 1,
+              iloc = 5.557e-04, idisp = -4.957, iskew = c(2.811,-2.158),
+              itail = -5.261e-1, noopt=T))

Call:
stablereg(y = ret, delta = 1/y[1:49], loc = ret ~ 1, disp = ~1,
    skew = ~day, tail = ~1, iloc = 0.0005557, idisp = -4.957,
    iskew = c(2.811, -2.158), itail = -0.5261, noopt = T)

-Log likelihood              150.2681
No. of obs                   49
No. of estimated parameters  0
No. of parameters            5
Degrees of freedom           49
AIC                          150.2681

        # Stable model with tail=tail.h(b0+b1*day)
        print(z6 <- stablereg(y = ret, delta = 1/y[1:49], loc = ret ~ 1,

+              disp = ~ 1, skew = ~ 1, tail = ~ day, iloc = 0.001,
+              idisp = -5, iskew = -3, itail = c(2,-7), hessian=F))

Call:
stablereg(y = ret, delta = 1/y[1:49], loc = ret ~ 1, disp = ~1,
    skew = ~1, tail = ~day, iloc = 0.001, idisp = -5, iskew = -3,
    itail = c(2, -7), hessian = F)

-Log likelihood              129.4914
No. of obs                   49
No. of estimated parameters  5
No. of parameters            5
Degrees of freedom           44
AIC                          134.4914
Iterations                   83

Location parameters
ret ~ 1
             estimate  se
(Intercept)  0.001841  NA

Dispersion parameters
~1
             estimate  se
(Intercept)    -4.785  NA

Skew parameters
~1
             estimate  se
(Intercept)    -13.82  NA

Tail parameters
~day
             estimate  se
(Intercept)     1.926  NA
day            -6.716  NA

****************************************************************

Are those result what they're supposed to be?

If it is so, I find it confusing that there are NA's, NaN's and/or "Warning:
no convergence - error 5 "  in practically all results. I took a look at the
source code and there are 2 messages that give an hint that the result are
probably ok. It kind of looks like these example are counter example if the
results are correct that is.

Is there some documentation for the library, I mean more then the help files
provide?

Any help you can provide will be appreciated.

Thanks.

Yves Gauvreau

library(stable)

## Share return over a 50 day period (see reference above)
# shares
y <- c(296,296,300,302,300,304,303,299,293,294,294,293,295,287,288,297,

# returns
ret <- (y[2:50]-y[1:49])/y[1:49]
# hist(ret, breaks=seq(-0.035,0.045,0.01))

day <- seq(0,0.48,by=0.01) # time measured in days/100
x <- seq(1,length(ret))-1

# Classic stationary normal model tail=2
print(z1 <- stablereg(y = ret, delta = 1/y[1:49],

# Normal model (tail=2) with dispersion=disp.h(b0+b1*day)
print(z2 <- stablereg(y = ret, delta = 1/y[1:49],

# Stable model with loc(ation)=loc.h(b0+b1*day)
print(z3 <- stablereg(y = ret, delta = 1/y[1:49],

# Stable model with disp(ersion)=disp.h(b0+b1*day)
print(z4 <- stablereg(y = ret, delta = 1/y[1:49],

# Stable model with skew(ness)=skew.h(b0+b1*day)
# Evaluation at fixed parameter values (since noopt is set to TRUE)
print(z5 <- stablereg(y = ret, delta = 1/y[1:49],

# Stable model with tail=tail.h(b0+b1*day)
print(z6 <- stablereg(y = ret, delta = 1/y[1:49], loc = ret ~ 1,