Hello splus-users, I am trying to fit a regression model for an ordered
response factor. So I am using the function polr in library(MASS). My data
is a matrix of 1665 rows and 63 columns (one of the column is the dependent
variable). The code I use is polr(as.ordered(q23p)~.,data=newdatap)
but I am getting the following warning message singularity encountered in:
nlminb.1(temp, p, liv, lv, objective, gradient, bounds, scale)
I looked in the MASS help for nlminb and I found that for the function
nlminb(start, objective, gradient=NULL, hessian=NULL,
scale=1, control=NULL, lower=-Inf, upper=Inf)
when returning a warning message of singularity means that the optimization
algorithm thinks it can't make any further progress because it has too many
degrees of freedom. It usually means that the objective function is either
not differentiable, or it may not have an optimum.
So for my data an optimum can't be obtained.
Is this true?
Can I ignore this warning message since what I want to find is values for
the boundaries? Will the values for the boundaries be accurate even though
I get the warning message?
Why have you sent a message about S-PLUS to R-help, one that has already been answered on S-news? There is no function nlminb in R. On 24 Feb 2004, C. Spanou wrote:> Hello splus-users, I am trying to fit a regression model for an ordered > response factor. So I am using the function polr in library(MASS). My data > is a matrix of 1665 rows and 63 columns (one of the column is the dependent > variable). The code I use is polr(as.ordered(q23p)~.,data=newdatap) > but I am getting the following warning message singularity encountered in: > nlminb.1(temp, p, liv, lv, objective, gradient, bounds, scale) > > I looked in the MASS help for nlminb and I found that for the function > nlminb(start, objective, gradient=NULL, hessian=NULL, > scale=1, control=NULL, lower=-Inf, upper=Inf) > > > when returning a warning message of singularity means that the optimization > algorithm thinks it can't make any further progress because it has too many > degrees of freedom. It usually means that the objective function is either > not differentiable, or it may not have an optimum. > > So for my data an optimum can't be obtained. > Is this true? > > Can I ignore this warning message since what I want to find is values for > the boundaries? Will the values for the boundaries be accurate even though > I get the warning message? > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > >-- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595
I am really sorry. I was supposed to send it to the Splus users but by mistake I sent to the R-users. Sorry once again On Feb 24 2004, Prof Brian Ripley wrote:> Why have you sent a message about S-PLUS to R-help, one that has already > been answered on S-news? > > There is no function nlminb in R. > > On 24 Feb 2004, C. Spanou wrote: > > > Hello splus-users, I am trying to fit a regression model for an > > ordered response factor. So I am using the function polr in > > library(MASS). My data is a matrix of 1665 rows and 63 columns (one of > > the column is the dependent variable). The code I use is > > polr(as.ordered(q23p)~.,data=newdatap) > > but I am getting the following warning message singularity > > encountered in: nlminb.1(temp, p, liv, lv, objective, gradient, bounds, > > scale) > > > > I looked in the MASS help for nlminb and I found that for the function > > nlminb(start, objective, gradient=NULL, hessian=NULL, > > scale=1, control=NULL, lower=-Inf, upper=Inf) > > > > > > when returning a warning message of singularity means that the > > optimization algorithm thinks it can't make any further progress > > because it has too many degrees of freedom. It usually means that the > > objective function is either not differentiable, or it may not have an > > optimum. > > > > So for my data an optimum can't be obtained. > > Is this true? > > > > Can I ignore this warning message since what I want to find is values > > for the boundaries? Will the values for the boundaries be accurate even > > though I get the warning message? > > > > ______________________________________________ > > R-help at stat.math.ethz.ch mailing list > > https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read > > the posting guide! http://www.R-project.org/posting-guide.html > > > > > >