Hi Folks! Just started using the gamlss package and I tried a simple code
example (see below). Why the negative sigma?
John
y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, :
possible convergence problem: optim gave code=1 false convergence
(8)2: In MLE(ll4, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma,
:
possible convergence problem: optim gave code=1 false convergence
(8)3: In MLE(ll4, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma,
:
possible convergence problem: optim gave code=1 false convergence
(8)4: In MLE(ll4, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma,
:
possible convergence problem: optim gave code=1 false convergence
(8)5: In MLE(ll4, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma,
:
possible convergence problem: optim gave code=1 false convergence (8)> > m1
Family: c("TF", "t Family")
Fitting method: "nlminb"
Call: gamlssML(y = y, family = DIST[i])
Mu Coefficients:
[1] 0.2376
Sigma Coefficients:
[1] -0.09865
Nu Coefficients:
[1] -0.2281
Degrees of Freedom for the fit: 3 Residual Deg. of Freedom 97
Global Deviance: 545.069
AIC: 551.069
SBC: 558.885
Another draw:
y <- rt(100, df=1)> m1<-fitDist(y, type="realline")Warning messages:1: In MLE(ll3, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma, :
possible convergence problem: optim gave code=1 false convergence
(8)2: In MLE(ll4, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma,
:
possible convergence problem: optim gave code=1 false convergence
(8)3: In MLE(ll4, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma,
:
possible convergence problem: optim gave code=1 false convergence
(8)4: In MLE(ll4, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma,
:
possible convergence problem: optim gave code=1 false convergence
(8)5: In MLE(ll4, start = list(eta.mu = eta.mu, eta.sigma = eta.sigma,
:
possible convergence problem: optim gave code=1 false convergence (8)> > m1
Family: c("ST1", "Skew t (Azzalini type 1)")
Fitting method: "nlminb"
Call: gamlssML(y = y, family = DIST[i])
Mu Coefficients:
[1] -0.05249
Sigma Coefficients:
[1] -0.1545
Nu Coefficients:
[1] 0.04445
Tau Coefficients:
[1] -0.1352
Degrees of Freedom for the fit: 4 Residual Deg. of Freedom 96
Global Deviance: 505.414
AIC: 513.414
SBC: 523.835
sessionInfo()R version 2.15.1 (2012-06-22)
Platform: x86_64-pc-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United
States.1252 LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C LC_TIME=English_United
States.1252
attached base packages:
[1] splines stats graphics grDevices utils datasets
methods base
other attached packages:
[1] gamlss_4.1-8 gamlss.data_4.1-0 gamlss.dist_4.1-6 nlme_3.1-104
MASS_7.3-21
loaded via a namespace (and not attached):
[1] grid_2.15.1 lattice_0.20-10 survival_2.36-14 tools_2.15.1
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